Wifi-based localisation datasets for No-GPS open areas using smart bins

Wed 29 Sep 2021 17:39:17 AEST

In recent years, Wifi-based localisation systems have gained significant interest because of the lack of Global Positioning System (GPS) signal in indoor and certain open areas. Over the past decade, many datasets have been introduced to enable researchers to compare different localisation techniques. Existing datasets, however, have failed to cover open areas such as parks in cases where GPS is still unavailable, and there is a lack of Wifi access points. Also, the existing datasets only focus on getting Wifi fingerprint collected and labelled by users. To the best of our knowledge, no dataset provides Received Signal Strengths (RSS) collected by Wireless Access Points (APs). In this work, we offer two datasets publicly. The first is the Fingerprint dataset in which four users generated 16,032 accurate and consistently labelled WiFi fingerprints for all available Reference Points (RPs) in a central and busy area of Murdoch University, known as Bush Court. The second is the APs dataset that includes 2,450,865 auto-generated records received from 1000 users' devices, including the four users, associated with Wifi signal strengths. To overcome the Wifi coverage problem for the Bush Court, we attached our previously designed Wireless Sensor Nodes (WSNs) to existing garbage bins, enabling them to provide real-time environmental sensing and act as soft APs that sense MAC addresses and Wifi signals from surrounding devices.

The knowledge management functions of corporate university and their evolution: case studies of two Chinese corporate universities

Wed 17 Aug 2022 12:24:26 AEST

Purpose The purpose of this paper is to investigate the knowledge management functions of corporate universities and their evolution. Design/methodology/approach Two Chinese corporate universities in the ICT industry were selected for the case studies. Data were collected by interviews and consulting the documents of the two corporate universities. Grounded theory was used for data analysis. Findings The research found that the knowledge management functions of the corporate universities encompass knowledge transfer, knowledge creation and knowledge services for intrapreneurship. The knowledge management functions of the corporate universities are enhancing with the development of the corporate universities. The knowledge management functions mutually reinforce each other. The knowledge network of the corporate universities is expanding and the scope of knowledge managed is broadening. Originality/value Analyzing the knowledge management functions of corporate universities and their evolution from the perspective of knowledge network enriches research on knowledge management of corporate universities.

Associations between comorbid stress and internet gaming disorder symptoms : are there cultural and gender variations?

Wed 07 Apr 2021 14:02:33 AEST

Introduction: The American Psychiatric Association has requested additional studies examine risk, protective, and cultural factors in relation to Internet Gaming Disorder (IGD). The present study aimed to explore the association between stress as a potential IGD risk effect, the possible exacerbating role of cultural orientation (vertical individualism [VI]), and how this may vary between genders. Methods: The sample included adult gamers from the USA, UK, and Australia. Analyses were conducted via linear regression, moderation, and moderated moderation. Results: The results suggested that higher stress symptoms act to increase IGD risk. Gender and VI also influenced this association. Discussion: Males presenting with higher levels of stress and VI were at greater risk of IGD compared to females who exhibited a reduction in IGD-related behaviors. This demonstrates a need for more research to determine how culture and gender can act to mitigate or worsen the risks associated with excessive gaming. © 2020 John Wiley & Sons Australia, Ltd

Mapping the cumulative impacts of long-term mining disturbance and progressive rehabilitation on ecosystem services

Wed 07 Apr 2021 14:02:27 AEST

Open-cut coal mining can seriously disturb and reshape natural landscapes which results in a range of impacts on local ecosystems and the services they provide. To address the negative impacts of disturbance, progressive rehabilitation is commonly advocated. However, there is little research focusing on how these impacts affect ecosystem services within mine sites and changes over time. The aim of this study was to assess the cumulative impacts of mining disturbance and rehabilitation on ecosystem services through mapping and quantifying changes at multiple spatial and temporal scales. Four ecosystem services including carbon sequestration, air quality regulation, soil conservation and water yield were assessed in 1989, 1997, 2005 and 2013. Disturbance and rehabilitation was mapped using LandTrendr algorithm with Landsat. We mapped spatial patterns and pixel values for each ecosystem service with corresponding model and the landscape changes were analyzed with landscape metrics. In addition, we assessed synergies and trade-offs using Spearman's correlation coefficient for different landscape classes and scales. The results showed that carbon sequestration, air quality regulation and water yield services were both positively and negatively affected by vegetation cover changes due to mined land disturbance and rehabilitation, while soil conservation service were mainly influenced by topographic changes. There were strong interactions between carbon sequestration, air quality regulation and water yield, which were steady among different spatial scales and landscape types. Soil conservation correlations were weak and changed substantially due to differences of spatial scales and landscape types. Although there are limitations associated with data accessibility, this study provides a new research method for mapping impacts of mining on ecosystem services, which offer spatially explicit information for decision-makers and environmental regulators to carry out feasible policies, balancing mining development with ecosystem services provision. © 2020 Elsevier B.V.

Adversarial heterogeneous network embedding with metapath attention mechanism

Wed 07 Apr 2021 14:02:20 AEST

Heterogeneous information network (HIN)-structured data provide an effective model for practical purposes in real world. Network embedding is fundamental for supporting the network-based analysis and prediction tasks. Methods of network embedding that are currently popular normally fail to effectively preserve the semantics of HIN. In this study, we propose AGA2Vec, a generative adversarial model for HIN embedding that uses attention mechanisms and meta-paths. To capture the semantic information from multi-typed entities and relations in HIN, we develop a weighted meta-path strategy to preserve the proximity of HIN. We then use an autoencoder and a generative adversarial model to obtain robust representations of HIN. The results of experiments on several real-world datasets show that the proposed approach outperforms state-of-the-art approaches for HIN embedding. © 2019, Springer Science+Business Media, LLC & Science Press, China.

The effects of electrical and thermal boundary condition on the simulation of radiofrequency ablation of liver cancer for tumours located near to the liver boundary

Wed 07 Apr 2021 14:02:05 AEST

Effects of different boundary conditions prescribed across the boundaries of radiofrequency ablation (RFA) models of liver cancer are investigated for the case where the tumour is at the liver boundary. Ground and Robin-type conditions (electrical field) and body temperature and thermal insulation (thermal field) conditions are examined. 3D models of the human liver based on publicly-available CT images of the liver are developed. An artificial tumour is placed inside the liver at the boundary. Simulations are carried out using the finite element method. The numerical results indicated that different electrical and thermal boundary conditions led to different predictions of the electrical potential, temperature and thermal coagulation distributions. Ground and body temperature conditions presented an unnatural physical conditions around the ablation site, which results in more intense Joule heating and excessive heat loss from the tissue. This led to thermal damage volumes that are smaller than the cases when the Robin type or the thermal insulation conditions are prescribed. The present study suggests that RFA simulations in the future must take into consideration the choice of the type of electrical and thermal boundary conditions to be prescribed in the case where the tumour is located near to the liver boundary.

Detection of power transformer winding deformation using improved FRA based on binary morphology and extreme point variation

Wed 07 Apr 2021 14:01:46 AEST

IEEE Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types.

Time-delay analysis of wide area voltage control considering smart grid contingences in real-time environment

Wed 07 Apr 2021 14:01:46 AEST

IEEE This paper addresses the time delay effects of the wide area monitoring and control systems (WAMCS) in smart power grids which may critically impact system stability. The main purpose is to conduct a detailed delay analysis of the WAMCS in case of grid contingences. This analysis is performed via an advanced WAMCS testbed where a wide area controller (WAC) for a flexible AC transmission system (FACTS) device is implemented. The real-time measurements for the WAC are collected using phasor measurements units (PMU). The testbed is resulted from an interface of four main segments known as the WAC, the actual FACTS device, the local area controller, and the power grid system along with the PMUs are simulated via real time digital simulator (RTDS). To mimic the real case scenario both hardware-in-the-loop (HIL) and software-in-the-loop (SIL) schemes are adopted in the experimental testbed, considering time delay effects. The results obtained clarify the effect of delay in WAMCS in case of smart grid contingences.

The role of interoperable data standards in precision livestock farming in extensive livestock systems : A review

Wed 07 Apr 2021 14:01:38 AEST

Livestock industries are increasingly embracing precision farming and decision support tools. As a result, sensors, weather stations, individual animal tracking, feed monitoring and other sources create large data volumes, much of which is used only for a single purpose. There are unrealised potential benefits of making on farm data interoperable and accessible and federating it with public data sources. We reviewed recent literature on precision livestock farming (PLF) technologies in relation to the use of public data, open standards and interoperability. Livestock farms produce rising volumes of disparate private datasets, reflecting a variety of information needs and technological opportunities, but typically lacking interoperable formats and metadata. These as well as large amounts of accessible public datasets are currently underutilised in decision support tools. Tools that demonstrate the use of interoperable standards and bring together public and private data for decision support can enhance the value proposition and help lower barriers to the sharing and re-use of data. This review of interoperable standards in extensive livestock farming systems concludes that there is a need for not only a new type of decision support tool, but also a consensus on data exchange standards to prove the value of shared data at farm scale (commercial benefit) and a regional scale (public good). © 2018

Development of flexible haptic forceps based on the electrohydraulic transmission system

Wed 07 Apr 2021 14:01:37 AEST

Minimally invasive surgery (MIS) has many benefits, e.g., unnecessary trauma and blood loss can be minimized by opening tiny surgery scars, recovery process of patients can be lessened, and the possibilities of complications can be decreased. The natural orifice transluminal surgery (NOTES) is one of the MIS surgical methodologies. NOTES surgery is performed using an endoscope. This methodology does not rely on making holes to patients' body to insert the medical instruments. Although some medical robots for NOTES have been proposed, current robots mainly suffer from the absence/limitation of haptic feedback to the surgeon's hands. In order to overcome this problem, this paper proposes electro hydraulic transmission system (EHTS) based forceps. The EHTS is a remote actuation system using fluid energy. By using the EHTS, the classical position and force transmission losses will not be influenced by the tube's shape. The validity of the proposal and its advantages have been experimentally verified and presented in this paper.

Flow on the Internet : A longitudinal study of Internet addiction symptoms during adolescence

Wed 07 Apr 2021 14:01:16 AEST

Internet Addiction (IA) constitutes an excessive Internet use behavior with a significant impact on the user’s well-being. Online flow describes the users’ level of being absorbed by their online activity. The present study investigated age-related, gender, and flow effects on IA in adolescence. The sample comprised 648 adolescents who were assessed twice at age 16 and 18 years. IA was assessed using the Internet Addiction Test and online flow was assessed using the Online Flow Questionnaire. A three-level hierarchical model estimated age-related, gender, and online flow effects on IA symptoms and controlled for clustered random effects. IA symptoms decreased over time (for both genders) with a slower rate in males. Online flow was associated with IA symptoms and this remained consistent over time. Findings expand upon the available literature suggesting that IA symptoms could function as a development-related manifestation at the age of 16 years, while IA-related gender differences gradually increase between 16 and 18 years. Finally, the association between online flow and IA symptoms remained stable independent of age-related effects. The study highlights individual differences and provides directions for more targeted prevention and intervention initiatives for IA. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Redesigning the assessment of an entrepreneurship course in an information technology degree program : Embedding assessment for learning practices

Wed 07 Apr 2021 14:01:03 AEST

Entrepreneurship is a novel course in the curriculum for students in the Information Technology (IT) degree program at La Trobe University, Bundoora, Australia. In comparison to other IT-related courses, the Entrepreneurship course seeks to develop business management knowledge and skills; its learning design is thus different to that of other courses in the IT program. The concept of constructive alignment for curriculum renewal suggests that there are several components of good course design. In this paper, we use the principles of constructive alignment to analyze and redesign several components of the Entrepreneurship course. The focus is on reviewing and aligning the assessment tasks to ensure an effective evaluation and the achievement of student learning outcomes. Since assessment drives student learning, we describe the innovative assessment tasks that were implemented to enhance student learning, provide the rationale for the design of these tasks as supported by the current literature, and reflect on possible future improvements. The course redesign process and the constructive alignment and innovative assessment can be applied to other courses in the field, and more broadly to curriculum, teaching, and learning in higher education.

Feelings of the future

Wed 07 Apr 2021 14:01:02 AEST

Affective forecasting refers to the capacity to predict future feelings. Humans have been found to exhibit systematic affective forecasting biases that involve overestimation of the intensity and duration of future feelings. Although recent research has elucidated the proximate mechanisms underlying our ability to predict future feelings, explanations concerning the potential adaptive significance of these biases have attracted little attention. Here we consider the function of affective forecasts as signals of biological value, drivers of goal pursuit, and tools for eliciting collaboration. Although affective forecasting biases can have significant costs, for instance in terms of one's pursuit of happiness, they may ultimately serve adaptive functions.

Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis

Wed 07 Apr 2021 14:00:56 AEST

Glycated haemoglobin (HbA1c) is being more commonly used as an alternative test for the identification of type 2 diabetes mellitus (T2DM) or to add to fasting blood glucose level and oral glucose tolerance test results, because it is easily obtained using point-of-care technology and represents long-term blood sugar levels. HbA1c cut-off values of 6.5% or above have been recommended for clinical use based on the presence of diabetic comorbidities from population studies. However, outcomes of large trials with a HbA1c of 6.5% as a cut-off have been inconsistent for a diagnosis of T2DM. This suggests that a HbA1c cut-off of 6.5% as a single marker may not be sensitive enough or be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied on a large clinical dataset to identify an optimal cut-off value for HbA1c and to identify whether additional biomarkers can be used together with HbA1c to enhance diagnostic accuracy of T2DM. T2DM classification accuracy increased if 8-hydroxy-2-deoxyguanosine (8-OhdG), an oxidative stress marker, was included in the algorithm from 78.71% for HbA1c at 6.5% to 86.64%. A similar result was obtained when interleukin-6 (IL-6) was included (accuracy=85.63%) but with a lower optimal HbA1c range between 5.73 and 6.22%. The application of data analytics to medical records from the Diabetes Screening programme demonstrates that data analytics, combined with large clinical datasets can be used to identify clinically appropriate cut-off values and identify novel biomarkers that when included improve the accuracy of T2DM diagnosis even when HbA1c levels are below or equal to the current cut-off of 6.5%. © 2016 Elsevier Ltd.

Redesigning the assessment of an entrepreneurship course in an information technology degree program : Embedding assessment for learning practices

Wed 07 Apr 2021 14:00:54 AEST

A new modified FMEA model for ranking the risk of maintenance waste considering hierarchy of root causes and effects

Wed 07 Apr 2021 14:00:32 AEST

Implementing sustainable manufacturing practice requires efficiency of the resource utilisation and activities which add value to the operations. From this point of view, development of an improved methodology to access the criticality of non- Added value (waste) is important and believed to support the realisation of sustainable manufacturing operation. While previous studies on improving methodology to support sustainable operation from product and process design are abundantly available in the references, the contribution from maintenance field is in contrary. The goal of this study is to modify the quality improvement tool, failure mode and effect analysis (FMEA) to access the criticality of waste in maintenance operation. In an attempt to realise the above goal, an empirical study to propose the theoretical and actual maintenance waste from industrial practice is undertaken. In order to assist maintenance 218 A. Sutrisno et al. decision maker to quantify criticality of maintenance waste occurrence, a new model to rank waste maintenance mode, called the waste priority number (WPN) is given. Illustrative on using model for practical purpose is given.. © 2015 Inderscience Enterprises Ltd.

A modified immune network optimization algorithm

Wed 07 Apr 2021 14:00:30 AEST

This study proposes a modified artificial immune network algorithm for function optimization problems based on idiotypic immune network theory. A hyper-cubic mutation operator was introduced to reduce the heavy computational cost of the traditional opt-AINet algorithm. Moreover, the new symmetrical mutation can effectively improve local search. To maintain population diversity, we also devised an immune selection mechanism based on density and fitness. The global convergence of the algorithm was deduced through the method of pure probability and iterative formula. Simulation results of benchmark function optimization show that the modified algorithm converges more effectively than other immune network algorithms.

Improving authorship attribution in twitter through topic-based sampling

Wed 07 Apr 2021 13:57:13 AEST

Aliases are used as a means of anonymity on the Internet in environments such as IRC (internet relay chat), forums and micro-blogging websites such as Twitter. While there are genuine reasons for the use of aliases, such as journalists operating in politically oppressive countries, they are increasingly being used by cybercriminals and extremist organisations. In recent years, we have seen increased research on authorship attribution of Twitter messages, including authorship analysis of aliases. Previous studies have shown that anti-aliasing of randomly generated sub-aliases yields high accuracies when linking the sub-aliases, but become much less accurate when topic-based sub-aliases are used. N-gram methods have previously been demonstrated to perform better than other methods in this situation. This paper investigates the effect of topic-based sampling on authorship attribution accuracy for the popular micro-blogging website Twitter. Features are extracted using character n-grams, which accurately capture differences in authorship style. These features are analysed using support vector machines using a one-versus-all classifier. The predictive performance of the algorithm is then evaluated using two different sampling methodologies - authors that were sampled through a context-sensitive topic-based search and authors that were sampled randomly. Topic-based sampling of authors is found to produce more accurate authorship predictions. This paper presents several theories as to why this might be the case. © Springer International Publishing AG 2017.

A taxonomy of griefer type by motivation in massively multiplayer online role-playing games

Wed 07 Apr 2021 13:57:02 AEST

There is an anti-social phenomenon known as griefing that occurs in online games. Griefing refers to the act of one player intentionally disrupting another player’s game experience for personal pleasure and possibly potential gain. Achterbosch [2015. “Causes, Magnitude and Implications of Griefing in Massively Multiplayer Online Role-Playing Games.” PhD thesis, Faculty of Science and Technology, Federation University Australia] carried out a substantial two-phase mixed method investigation into the behaviour and experiences of both griefers and griefed players in massively multiplayer online role-playing games. The first phase consisted of a survey that attracted 1188 participants of a representative player population. The second phase consisted of interviews with 15 participants to expand the findings with more personalised data. The data were analysed from the perspectives of different demographics and different associations to griefing. One of the most unique findings is the factors that motivated a player to cause grief to another player. This paper analyses these factors to propose a taxonomy of ‘Griefer’ types (griefer being the individual who imposes upon others). The taxonomy consisted of eight types of griefers, based on their motivation for griefing. Some types related to previous studies, although new types of griefers were discovered such as the retaliator and elitist and these are discussed in detail in the article. © 2017 Informa UK Limited, trading as Taylor & Francis Group.

Network decomposition based large-scale reverse engineering of gene regulatory network

Wed 07 Apr 2021 13:56:50 AEST

A Gene Regulatory Network (GRN) is the functional circuitry of a living organism that exhibits the regulatory relationships among genes of a cellular system at the gene level. In real-life biological networks, the number of genes present are very large exhibiting both, the instantaneous and time-delayed regulations. While our recent technique [1] addresses the modeling of time-delays occurring in genetic interactions, the issue of large-scale GRN modeling still remains. In this paper, we propose a novel methodology for large-scale modeling of GRNs by decomposing the GRN into two independent sub-networks utilizing its biological traits. Using the time-delayed S-system model [1], these two sub-networks are learnt separately and then combined to get the entire GRN. To speed up the inference mechanism, a cardinality-based fitness function, especially developed for inferring large-scale GRNs is proposed to allow incorporation of knowledge of maximum in-degree. A novel local-search method is also proposed to further facilitate the incorporation of biological knowledge by gene clustering and gene ranking. Experimental studies demonstrate that the proposed approach is successful in learning large genetic networks, currently not achievable with existing S-system based modeling approaches.

An analytical approach for voice capacity estimation over WiFi network using ITU-T E-model

Wed 07 Apr 2021 13:56:47 AEST

To ensure customer satisfaction and greater market acceptance, voice over Wi-Fi networks must ensure voice quality under various network parameters, configurations and traffic conditions, and other practical effects, e.g., channel noise, and capturing effects. An accurate voice capacity estimation model considering these factors can greatly assist network designers. In the current work, we propose an analytical model to estimate voice over Internet Protocol (VoIP) capacity over Wi-Fi networks addressing these issues. We employ widely used ITU-T E-model to assess voice quality and VoIP call capacity is presented in the form of an optimization problem with voice quality requirement as a constraint. In particular, we analyze delay and loss in channel access and queue, and their impacts on voice quality. The proposed capacity model is first developed for a single hop wireless local area network (WLAN) and then extended for multihop scenarios. To model real network scenario closely, we also consider channel noise and capture effect, and analyze the impacts of transmission range, interference range, and WLAN radius. In absence of any existing call capacity model that considers all the above factors concomitantly, our proposed model will be extremely useful to network designers and voice capacity planners.

Dependable large scale behavioral patterns mining from sensor data using Hadoop platform

Wed 07 Apr 2021 13:56:29 AEST

Wireless sensor networks (WSNs) will be an integral part of the future Internet of Things (loT) environment and generate large volumes of data. However, these data would only be of benefit if useful knowledge can be mined from them. A data mining framework for WSNs includes data extraction, storage and mining techniques, and must be efficient and dependable. In this paper, we propose a new type of behavioral pattern mining technique from sensor data called regularly frequent sensor patterns (RFSPs). RFSPs can identify a set of temporally correlated sensors which can reveal significant knowledge from the monitored data. A distributed data extraction model to prepare the data required for mining RFSPs is proposed, as the distributed scheme ensures higher availability through greater redundancy. The tree structure for RFSP is compact requires less memory and can be constructed using only a single scan through the dataset, and the mining technique is efficient with low runtime. Current mining techniques in the literature on sensor data employ a single memory-based sequential approach and hence are not efficient. Moreover, usage of the. MapReduce model for the distributed solution has not been explored extensively. Since MapReduce is becoming the de facto model for computation on large data, we also propose a parallel implementation of the RFSP mining algorithm, called RFSP on Hadoop (RFSP-H), which uses a MapReduce-based framework to gain further efficiency. Experiments conducted to evaluate the compactness and performance of the data extraction model, RFSP-tree and RFSP-H mining show improved results. (C) 2016 Elsevier Inc. All rights reserved.

Exploiting temporal genetic correlations for enhancing regulatory network optimization

Wed 07 Apr 2021 13:56:22 AEST

Inferring gene regulatory networks (GRN) from microarray gene expression data is a highly challenging problem in computational and systems biology. To make GRN reconstruction process more accurate and faster, in this paper, we develop a technique to identify the gene having maximum in-degree in the network using the temporal correlation of gene expression profiles. The in-degree of the identified gene is estimated applying evolutionary optimization algorithm on a decoupled S-system GRN model. The value of in-degree thus obtained is set as the maximum in-degree for inference of the regulations in other genes. The simulations are carried out on in silico networks of small and medium sizes. The results show that both the prediction accuracy in terms of well known performance metrics and the computational time of the optimization process have been improved when compared with the traditional S-system model based inference. © Springer International Publishing AG 2016.

Action-02MCF : A robust space-time correlation filter for action recognition in clutter and adverse lighting conditions

Wed 07 Apr 2021 13:56:22 AEST

Human actions are spatio-temporal visual events and recognizing human actions in different conditions is still a challenging computer vision problem. In this paper, we introduce a robust feature based space-time correlation filter, called Action-02MCF (0’zero-aliasing’ 2M’ Maximum Margin’) for recognizing human actions in video sequences. This filter combines (i) the sparsity of spatio-temporal feature space, (ii) generalization of maximum margin criteria, (iii) enhanced aliasing free localization performance of correlation filtering using (iv) rich context of maximally stable space-time interest points into a single classifier. Its rich multi-objective function provides robustness, generalization and recognition as a single package. Action-02MCF can simultaneously localize and classify actions of interest even in clutter and adverse imaging conditions. We evaluate the performance of our proposed filter for challenging human action datasets. Experimental results verify the performance potential of our action-filter compared to other correlation filtering based action recognition approaches. © Springer International Publishing AG 2016.

Convergence of elitist clonal selection algorithm based on martingale theory

Wed 07 Apr 2021 13:56:04 AEST

In recent years, progress has been made in the analysis of global convergence of clonal selection algorithms (CSA), but most analyses are based on the theory of Markov chain, which depend on the description of the transition matrix and eigenvalues. However, such analyses are very complicated, especially when the population size is large, and are presented for particular implementations of CSA. In this paper, instead of the traditional Markov chain theory, we introduce martingale theory to prove the convergence of a class of CSA, called elitist clonal selection algorithm (ECSA). Using the submartingale convergence theorem, the best individual affinity evolutionary sequence is described as a submartingale, and the almost everywhere convergence of ECSA is derived. Particularly, the algorithm is proved convergent with probability 1 in finite steps when the state space of population is finite. This new proof of global convergence analysis of ECSA is more simplified and effective, and not implementation specific.

Reinforcement learning of pareto-optimal multiobjective policies using steering

Wed 07 Apr 2021 13:55:50 AEST

There has been little research into multiobjective reinforcement learning (MORL) algorithms using stochastic or non-stationary policies, even though such policies may Pareto-dominate deterministic stationary policies. One approach is steering which forms a nonstationary combination of deterministic stationary base policies. This paper presents two new steering algorithms designed for the task of learning Pareto-optimal policies. The first algorithm (w-steering) is a direct adaptation of previous approaches to steering, and therefore requires prior knowledge of recurrent states which are guaranteed to be revisited. The second algorithm (Q-steering) eliminates this requirement. Empirical results show that both algorithms perform well when given knowledge of recurrent states, but that Q-steering provides substantial performance improvements over w-steering when this knowledge is not available. © Springer International Publishing Switzerland 2015.

A mapreduce based technique for mining behavioral patterns from sensor data

Wed 07 Apr 2021 13:55:50 AEST

WSNs generate a large amount of data in the form of streams, and temporal regularity in occurrence behavior is considered as an important measure for assessing the importance of patterns in WSN data. A frequent sensor pattern that occurs after regular intervals in WSNs is called regularly frequent sensor patterns (RFSPs). Existing RFSPs techniques assume that the data structure of the mining task is small enough to fit in the main memory of a processor. However, given the emergence of the Internet of Things (IoT), WSNs in future will generate huge volume of data, which means such an assumption does not hold any longer. To overcome this, a distributed solution using MapReduce model has not yet been explored extensively. Since MapReduce is becoming the de-facto model for computation on large data, an efficient RFSPs mining algorithm on this model is likely to provide a highly effective solution. In this work, we propose a regularly frequent sensor patterns mining algorithm called RFSP-H which uses MapReduce based framework. Extensive performance analyses show that our technique is significantly time efficient in finding regularly frequent sensor patterns. © Springer International Publishing Switzerland 2015.

Frequency decomposition based gene clustering

Wed 07 Apr 2021 13:55:50 AEST

Gene expressions have been commonly applied to understand the inherent underlying mechanism of known biological processes. Although the microarray gene expressions usually appear aperiodic, with proper signal processing techniques, its periodic components can be easily obtained. Thus, if expressions of interconnected (regulatory and regulated) genes are decomposed, at least one common frequency component will appear in these genes. Exploiting this novel concept, we propose a frequency decomposition approach for gene clustering to better understand the gene interconnection topology. This method, based on Hilbert Huang Transform (HHT) enables us to segregate every periodic component of the gene expressions. Next, a multilevel clustering is performed based on these frequency components. Unlike existing clustering algorithms, the proposed method assimilates a meaningful knowledge of the gene interactions topology. The information related to underlying gene interactions is vital and can prove useful in many existing evolutionary optimisation algorithms for genetic network reconstruction. We validate the entire approach by its application to a 15-gene synthetic network. © Springer International Publishing Switzerland 2015.

Dynamical analysis of neural networks with time-varying delays using the LMI approach

Wed 07 Apr 2021 13:55:49 AEST

This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions. © Springer International Publishing Switzerland 2015.

Weighted ANN input layer for adaptive features selection for robust fault classification

Wed 07 Apr 2021 13:55:47 AEST

Model based feature selection for identification of diverse faults in rotary machines can significantly cost time and money and it is nearly impossible to model all faults under different operating environments. In this paper, feedforward ANN input-layer-weights have been used for the adaptive selection of the least number of features, without fault model information, reducing the computations significantly but assuring the required accuracy by mitigating the noise. In the proposed approach, under the assumption that presented features should be translation invariant, ANN uses entire set of spectral features from raw input vibration signal for training. Dominant features are then selected using input-layer-weights relative to a threshold value vector. Different instances of ANN are then trained and tested to calculate F1_score with the reduced dominant features at different SNRs for each threshold value. Trained ANN with best average classification accuracy among all ANN instances gives us required number of dominant features. © Springer International Publishing Switzerland 2015.

A novel motion classification based intermode selection strategy for HEVC performance improvement

Wed 07 Apr 2021 13:55:42 AEST

High Efficiency Video Coding (HEVC) standard adopts several new approaches to achieve higher coding efficiency (approximately 50% bit-rate reduction) compared to its predecessor H.264/AVC with same perceptual image quality. Huge computational time has also increased due to the algorithmic complexity of HEVC compared to H.264/AVC. However, it is really a demanding task to reduce the encoding time while preserving the similar quality of the video sequences. In this paper, we propose a novel efficient intermode selection technique and incorporate into HEVC framework to predict motion estimation and motion compensation modes between current and reference blocks and perform faster inter mode selection based on three dissimilar motion types in divergent video sequences. Instead of exploring and traversing all the modes exhaustively, we merely select a subset of candidate modes and the final mode from the selected subset is determined based on their lowest Lagrangian cost function. The experimental results reveal that average encoding time can be downscaled by 40% with similar rate-distortion performance compared to the exhaustive mode selection strategy in HEVC.

How do I save it? Usability evaluation of a systems theory-based incident reporting software prototype by novice end users

Wed 07 Apr 2021 13:55:38 AEST

The level of usability achieved by software tools is a key factor that determines their success and indeed uptake by end users. This paper describes a study that was undertaken to evaluate the usability of a prototype incident reporting software tool. The study involved novice end users completing a series of tasks using the software tool and then completing Ravden and Johnsonâ€™s Human Computer Interaction (HCI) checklist. The findings identify aspects of the system that pose particular challenges for participants. Participants appeared to lack a clear understanding of the relationship between the information required from them, and the underpinning accident analysis method of the software tool. This is perhaps unsurprising, considering that most incident reporting systems do not include these functions. The findings indicate that the tool requires better levels of intuitiveness to assist users in complex tasks so the focus is on awareness of accident causation methods rather than task instructions. The implications for the design of incident reporting software tools are discussed. © Springer International Publishing Switzerland 2015.

Bridging the research-practice gap: Validity of a software tool designed to support systemic accident analysis by risk managers

Wed 07 Apr 2021 13:55:38 AEST

Despite the proposed advantages of systems accident analysis (SAA) methods for understanding incident-causation, these approaches have not been widely adopted by practitioners. This represents a significant gap between research and practice in accident analysis. The Understanding and Preventing Led Outdoor Accidents Data System (UPLOADS) provides a series of tools to address this gap. The aim of this study was to evaluate the validity of UPLOADS by comparing analyses generated by risk managers and researchers experienced in SAA. Twenty-three risk managers used UPLOADS to collect and analyse incident data from their organization over a three month period. The reports were then analyzed by two researchers experienced in SAA, and compared to those generated by participants. Participants identified half the number of factors identified by researchers, and tended to focus on only one or two factors as the causes of each incident. The potential consequences for practitioners' understanding of incident-causation and countermeasure development are discussed, as well as ways of improving the system. © Springer International Publishing Switzerland 2015.

Modular implementation of artificial neural network in predicting in-flight particle characteristics of an atmospheric plasma spray process

Wed 07 Apr 2021 13:55:34 AEST

This paper presents a modular implementation of an artificial neural network to model the atmospheric plasma spray process in predicting the in-flight particle characteristics from the input processing parameters. The in-flight particle characteristics influence the structure and properties of the thermal spray coating and, thus, are considered important parameters to comprehend, simulate and predict the manufacturing process. The modular implementation allows simplification of the optimized model structure with enhanced ability to generalise the network. As well, the underlying relationship between each of the output in-flight characteristics with respect to the input processing parameters is explored. Smaller networks are constructed that achieves better, or in some cases, similar results. The training process is found to be more robust and stable along with fewer fluctuations in the values of the network parameters. The networks also respond to the variations of the number of hidden layer neurons with some definite trend. The predictable trend enhances reliability of the application of the artificial neural network in modelling the atmospheric plasma spray process and overcomes the variability and non-linearity associated with the process. © 2015 Elsevier Ltd. All rights reserved.

Notes on feasibility and optimality conditions of small-scale multifunction robotic cell scheduling problems with pickup restrictions

Wed 07 Apr 2021 13:55:26 AEST

Optimization of robotic workcells is a growing concern in automated manufacturing systems. This study develops a methodology to maximize the production rate of a multifunction robot (MFR) operating within a rotationally arranged robotic cell. An MFR is able to perform additional special operations while in transit between transferring parts from adjacent processing stages. Considering the free-pickup scenario, the cycle time formulas are initially developed for small-scale cells where an MFR interacts with either two or three machines. A methodology for finding the optimality regions of all possible permutations is presented. The results are then extended to the no-wait pickup scenario in which all parts must be processed from the input hopper to the output hopper, without any interruption either on or between machines. This analysis enables insightful evaluation of the productivity improvements of MFRs in real-life robotized workcells. ©2014 IEEE.

Global optimal solutions to general sensor network localization problem

Wed 07 Apr 2021 13:54:52 AEST

Sensor network localization problem is to determine the position of the sensor nodes in a network given pairwise distance measurements. Such problem can be formulated as a quartic polynomial minimization via the least squares method. This paper presents a canonical duality theory for solving this challenging problem. It is shown that the nonconvex minimization problem can be reformulated as a concave maximization dual problem over a convex set in a symmetrical matrix space, and hence can be solved efficiently by combining a general (linear or quadratic) perturbation technique with existing optimization techniques. Applications are illustrated by solving some relatively large-scale problems. Our results show that the general sensor network localization problem is not NP-hard unless its canonical dual problem has no solution in its positive definite domain. Fundamental ideas for solving general NP-hard problems are discussed. (C) 2014 Elsevier B.V. All rights reserved.

Vibration spectrum imaging : A novel bearing fault classification approach

Wed 07 Apr 2021 13:54:50 AEST

Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier.

Hybrid metaheuristic approaches to the expectation maximization for estimation of the hidden markov model for signal modeling

Wed 07 Apr 2021 13:47:08 AEST

The expectation maximization (EM) is the standard training algorithm for hidden Markov model (HMM). However, EM faces a local convergence problem in HMM estimation. This paper attempts to overcome this problem of EM and proposes hybrid metaheuristic approaches to EM for HMM. In our earlier research, a hybrid of a constraint-based evolutionary learning approach to EM (CEL-EM) improved HMM estimation. In this paper, we propose a hybrid simulated annealing stochastic version of EM (SASEM) that combines simulated annealing (SA) with EM. The novelty of our approach is that we develop a mathematical reformulation of HMM estimation by introducing a stochastic step between the EM steps and combine SA with EM to provide better control over the acceptance of stochastic and EM steps for better HMM estimation. We also extend our earlier work [1] and propose a second hybrid which is a combination of an EA and the proposed SASEM, (EA-SASEM). The proposed EA-SASEM uses the best constraint-based EA strategies from CEL-EM and stochastic reformulation of HMM. The complementary properties of EA and SA and stochastic reformulation of HMM of SASEM provide EA-SASEM with sufficient potential to find better estimation for HMM. To the best of our knowledge, this type of hybridization and mathematical reformulation have not been explored in the context of EM and HMM training. The proposed approaches have been evaluated through comprehensive experiments to justify their effectiveness in signal modeling using the speech corpus: TIMIT. Experimental results show that proposed approaches obtain higher recognition accuracies than the EM algorithm and CEL-EM as well. © 2014 IEEE.

Dynamic texture synthesis using motion distribution statistics

Wed 07 Apr 2021 13:46:35 AEST

n this paper we propose a motion based approach for synthesizing dynamic textures. Dynamic textures are natural phenomenon characterized by their distinctive motion patterns. Synthesis of these textures is thus considered as the regeneration of a motion pattern that has identical motion distribution of a source texture. In this paper we propose a synthesis technique where new textures are generated by computing their movement pattern from a known motion distribution followed by the generation of image frames. Experimental results demonstrate the ability of the proposed technique by producing visually promising dynamic textures.

An adaptive borrow-and-return model for broadcasting videos

Wed 07 Apr 2021 13:46:35 AEST

Yang proposed the concept of borrow-and-return (BR) to leverage the unused server bandwidth when a group of popular videos being broadcast with the FSFC (first segment on the first channel) broadcasting schemes in order to improve the mean waiting time (MWT) of the viewers with the help of additional receiving bandwidth available at the high-end clients. The BR model borrows the bandwidth of the videos with no new-coming viewers during a timeslot to speed up the transmission of the first segments of some of the remaining videos. In this paper, we first address the relative advantage issue among various possible BR schemes by developing a parametric generic BR (GBR) scheme controlled externally by independent borrow parameters. Later, we propose a new BR (NBR) model by incorporating an efficient transmission strategy to reduce the MWT further. Finally, an optimal NBR scheme is developed by augmenting with the optimal borrow parameters, which significantly outperforms the existing and new BR schemes in terms of overall MWT.

Modeling of secured cloud network: - The case of an educational institute

Wed 07 Apr 2021 13:46:26 AEST

A fast scalable implementation of the two-dimensional triangular discrete element Method on a GPU platform

Wed 07 Apr 2021 13:46:19 AEST

Real-time solution of the Discrete Element Method is a computational challenge that is hardly achievable on standard PCs, especially when a large number of triangular shaped particles are involved. This paper presents a scalable architecture, including a domain decomposition technique, of a GPU accelerator for the two-dimensional Discrete Element Method for triangular shaped particles. This approach achieved a speed up of about 140 times as a single core and about 80 after domain decomposition on a consumer level GPU compared to a similar algorithm run on a fast desktop PC.

A HMM-based adaptive fuzzy inference system for stock market forecasting

Wed 07 Apr 2021 13:46:10 AEST

In this paper, we propose a new type of adaptive fuzzy inference system with a view to achieve improved performance for forecasting nonlinear time series data by dynamically adapting the fuzzy rules with arrival of new data. The structure of the fuzzy model utilized in the proposed system is developed based on the log-likelihood value of each data vector generated by a trained Hidden Markov Model. As part of its adaptation process, our system checks and computes the parameter values and generates new fuzzy rules as required, in response to new observations for obtaining better performance. In addition, it can also identify the most appropriate fuzzy rule in the system that covers the new data; and thus requires to adapt the parameters of the corresponding rule only, while keeping the rest of the model unchanged. This intelligent adaptive behavior enables our adaptive fuzzy inference system (FIS) to outperform standard FISs. We evaluate the performance of the proposed approach for forecasting stock price indices. The experimental results demonstrate that our approach can predict a number of stock indices, e.g., Dow Jones Industrial (DJI) index, NASDAQ index, Standard and Poor500 (S&P500) index and few other indices from UK (FTSE100), Germany (DAX) , Australia (AORD) and Japan (NIKKEI) stock markets, accurately compared with other existing computational and statistical methods.

Flexible autonomous behaviors of kinesin and muscle myosin bio-nanorobots

Wed 07 Apr 2021 13:46:08 AEST

Kinesin and muscle myosin are considered as physical bio-nanoagents able to sense their cells through their sensors, make decision internally, and perform actions through their actuators. This paper has investigated and compared the flexible (reactive, pro-active, and interactive) autonomous behaviors of kinesin and muscle myosin bio-nanorobots. Using an automata algorithm, the agent-based deterministic finite automaton models of the internal decision making processes of the bio-nanorobots (as their reactive and pro-active capabilities) were converted to their respective computational regular languages (as their interactive capabilities). The resulted computational languages could represent the flexible autonomous behaviors of the bio-nanorobots. The proposed regular languages also reflected the degree of the autonomy and intelligence of internal decision-making processes of the bio-nanorobots in response to their environments. The comparison of flexible autonomous behaviors of kinesin and muscle myosin bio-nanorobots indicated that both bio-nanorobots employed regular languages to interact with their environments through two sensors and one actuator. Moreover, the results showed that kinesin bio-nanorobot used a more complex regular language to interact with its environment compared with muscle myosin bio-nanorobot. Therefore, our results have revealed that the flexible autonomous behavior of kinesin bio-nanorobot was more complicated than the flexible autonomous behavior of muscle myosin bio-nanorobot.

Virtual machine consolidation in cloud data centers using ACO metaheuristic C3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Wed 07 Apr 2021 13:46:04 AEST

In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.

A survey of audio-based music classification and annotation

Wed 07 Apr 2021 13:45:57 AEST

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.

Sliding-window designs for vertex-based shape coding

Wed 07 Apr 2021 13:45:50 AEST

Traditionally the sliding window (SW) has been employed in vertex-based operational rate distortion (ORD) optimal shape coding algorithms to ensure consistent distortion (quality) measurement and improve computational efficiency. It also regulates the memory requirements for an encoder design enabling regular, symmetrical hardware implementations. This paper presents a series of new enhancements to existing techniques for determining the best SW-length within a rate-distortion (RD) framework, and analyses the nexus between SW-length and storage for ORD hardware realizations. In addition, it presents an efficient bit-allocation strategy for managing multiple shapes together with a generalized adaptive SW scheme which integrates localized curvature information (cornerity) on contour points with a bi-directional spatial distance, to afford a superior and more pragmatic SW design compared with existing adaptive SW solutions which are based on only cornerity values. Experimental results consistently corroborate the effectiveness of these new strategies.

Geometric distortion measurement for shape coding: a contemporary review

Wed 07 Apr 2021 13:45:49 AEST

Geometric distortion measurement and the associated metrics involved are integral to the Rate Distortion (RD) shape coding framework, with importantly the efficacy of the metrics being strongly influenced by the underlying measurement strategy. This has been the catalyst for many different techniques with this article presenting a comprehensive review of geometric distortion measurement, the diverse metrics applied, and their impact on shape coding. The respective performance of these measuring strategies is analyzed from both a RD and complexity perspective, with a recent distortion measurement technique based on arc-length-parameterization being comparatively evaluated. Some contemporary research challenges are also investigated, including schemes to effectively quantify shape deformation.

A hybrid of multiobjective evolutionary algorithm and HMM-Fuzzy model for time series prediction

Wed 07 Apr 2021 13:45:48 AEST

In this paper, we introduce a new hybrid of Hidden Markov Model (HMM), Fuzzy Logic and multiobjective Evolutionary Algorithm (EA) for building a fuzzy model to predict non-linear time series data. In this hybrid approach, the HMM's log-likelihood score for each data pattern is used to rank the data and fuzzy rules are generated using the ranked data. We use multiobjective EA to find a range of trade-off solutions between the number of fuzzy rules and the prediction accuracy. The model is tested on a number of benchmark and more recent financial time series data. The experimental results clearly demonstrate that our model is able to generate a reduced number of fuzzy rules with similar (and in some cases better) performance compared with typical data driven fuzzy models reported in the literature.

Combining segmental semi-Markov models with neural networks for protein secondary structure prediction

Wed 07 Apr 2021 13:45:48 AEST

Predicting the secondary structure of proteins from a primary sequence alone has been variously approached from either a classification or a generative model perspective. The most prominent classification methods have used neural networks, which involves mappings from a local window of residues in the sequence to the structural state of the central residue in the window, thus capturing the local interactions effectively. However, they fail to capture distant interactions among residues. The generative models based on Bayesian segmentation capture sequence structure relationships using generalized hidden Markov models with explicit state duration. They capture non-local interactions through a joint sequence-structure probability distribution based on structural segments. In this paper, we investigate a combined architecture of Bayesian segmentation at the first stage and neural network at the second stage which captures both local and non-local correlation, to increase the single sequence prediction accuracy. The combined architecture is further enhanced by using neural network optimization and ensemble techniques.

Predicting protein protein interfaces as clusters of optimal docking area points

Wed 07 Apr 2021 13:45:47 AEST

Abstract: Desolvation property is used here to predict protein-protein binding sites exploiting the fact that lower-valued 'optimal docking area' ODA (Fernandez-Recio et al., 2005) points form cluster at the interface. The proposed method involves two steps; clustering the ODA points and representing ODA points by average ODA values. On 51 nonredundant proteins, results show the success rate improved considerably. Considering only significant ODA, the previous ODA method has obtained a success rate of 65% with overall success rate of 39%. The proposed method improved the overall success rate to 61%. Further, comparable results were found for X-ray and NMR structures.

New algorithms for navigating a gantry tractor comprising a 'chorus line' of synchronized modules

Wed 07 Apr 2021 13:45:46 AEST

This paper presents two new algorithms for real-time calculation of the wheel angles and speeds of gantry tractor modules. In transport mode, the gantry tractor is, in a sense, a snakelike robot with passive joints and active wheels, with each module having autonomous four-wheel drive and four-wheel steering. The algorithms determine the wheel angles and speeds of each module with the prescription that the four wheels will have the same center of curvature, wheel speeds provide cooperative redundancy, and all hitching points follow the same path, thereby eliminating scuffing and minimizing off-tracking. Details of the analytical algorithm for a predetermined path were presented at the 2009 IEEE International Conference on Industrial Technology, together with a simulation for a single module. In this paper, we also present the results of a newly developed numerical algorithm which enables the gantry tractor to be steered online by an operator. We also show, by simulation, that this new numerical algorithm gives a good approximation to analytical solutions. The numerical algorithm is then used to calculate wheel angles and speeds for a three-module tractor with the results depicted graphically as functions of time.

A new methodology of mobile robot navigation : The agoraphilic algorithm

Wed 07 Apr 2021 13:45:46 AEST

The Agoraphilic algorithm is an optimistic approach to reactive path planning for mobile robot platforms. The technique uses virtual, attractive forces derived from the surrounding free space. Fuzzy logic is utilised to limit the ‘free-space’ force so as to promote the movement towards the goal. The algorithm was designed to be a robust technique for reactive navigation that could be implemented without the fuss of tuning the sensitive parameters required for other classical navigation routines. Several simulations plus some preliminary experimental results are presented here to demonstrate the algorithm's potential.

Using an instructional design model to evaluate a blended learning subject in a pre-service teacher education degree

Wed 07 Apr 2021 13:45:46 AEST

Over 2007-2008, a pedagogy subject in a pre-service teacher education degree was (re)designed to help students develop their understandings and skills and a wider, more critical appreciation of the work of teachers and approaches to curriculum. The rationale for designing and including the online modules in the subject was to develop information and communication technology (ICT) skills, and to deliver a blended learning approach, argued by some to be more effective, that is, have more advantages than traditional approaches. In this paper, the face-to-face teaching alongside the eLearning that occurred in the blended learning approach is analysed using Tom Reeves and John Hedberg's model (2003) for evaluating interactive learning systems. Arguably, this evaluation model can be usefully applied to higher education teaching that is not fully online, and can help to comprise an integral part of an action research approach. This paper is a 'proof of concept' piece, demonstrating the applicability of the model to a blended learning course. Demonstrating the application of Reeves and Hedberg's model fills a knowledge void on the literature surrounding blended learning. [ABSTRACT FROM AUTHOR]

A Markov-blanket-based model for gene regulatory network inference

Wed 07 Apr 2021 13:45:32 AEST

An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1) discovery of a gene's Markov Blanket (MB), 2) formulation of a flexible measure to determine the network's quality, 3) efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions. Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.

GlobalMIT: learning globally optimal dynamic Bayesian network with the mutual information test criterion

Wed 07 Apr 2021 13:45:31 AEST

Extended HP model for protein structure prediction

Wed 07 Apr 2021 13:45:31 AEST

This paper presents the impact of twins and the measures for their removal from the population of genetic algorithm (GA) when applied to effective conformational searching. It is conclusively shown that a twin removal strategy for a GA provides considerably enhanced performance when investigating solutions to complex ab initio protein structure prediction (PSP) problems in low-resolution model. Without twin removal, GA crossover and mutation operations can become ineffectual as generations lose their ability to produce significant differences, which can lead to the solution stalling. The paper relaxes the definition of chromosomal twins in the removal strategy to not only encompass identical, but also highly correlated chromosomes within the GA population, with empirical results consistently exhibiting significant improvements solving PSP problems.

Twin removal in genetic algorithms for protein structure prediction using low-resolution model

Wed 07 Apr 2021 13:45:31 AEST

Canonical dual solutions to nonconvex radial basis neural network optimization problem

Wed 07 Apr 2021 13:45:22 AEST

Radial Basis Functions Neural Networks (RBFNNs) are tools widely used in regression problems. One of their principal drawbacks is that the formulation corresponding to the training with the supervision of both the centers and the weights is a highly non-convex optimization problem, which leads to some fundamental difficulties for the traditional optimization theory and methods. This paper presents a generalized canonical duality theory for solving this challenging problem. We demonstrate that by using sequential canonical dual transformations, the nonconvex optimization problem of the RBFNN can be reformulated as a canonical dual problem (without duality gap). Both global optimal solution and local extrema can be classified. Several applications to one of the most used Radial Basis Functions, the Gaussian function, are illustrated. Our results show that even for a one-dimensional case, the global minimizer of the nonconvex problem may not be the best solution to the RBFNNs, and the canonical dual theory is a promising tool for solving general neural networks training problems. © 2014 Elsevier B.V.

Studies on the structural stability of rabbit prion probed by molecular dyanamics simulations of its wild-type and mutants

Wed 07 Apr 2021 13:44:50 AEST

Prion diseases are invariabiably fatal and highly infectious neurodegenerative diseases that affect humans and animals. Rabbits are the only mammalian species reported to be resistant to infection from prion diseases isolated from other species (Vorber et.al., 2003). Fortunately, the NMR structure of rabbit prion (124-228) (PDB entry 2FJ3), the NMR structure of rabbit prion protein mutation s173N (PDB entry 2JOH) and the NMR structure of rabbit prion protein I214V [PDB entry 2JOM} were released recently. This paper studies these NMR structures by molecular dyanmaics simulations. Simulation results confirm the structural ability of wild-type rabbit prion, and show that the salt bridge between D177 and R163 greatly contributes to the structural stability of rabbity prion. Crown Copyright Published by Elsevier.

A quasisecant method for solving a system of nonsmooth equations

Wed 07 Apr 2021 13:44:45 AEST

In this paper, the solution of nonsmooth equations is studied. We first transform the problem into an equivalent nonsmooth optimization problem and then the quasisecant method is introduced to solve it. Some nonsmooth equations that have arisen from bilevel programming problems are solved by our proposed method. The numerical results show the effectiveness and efficiency of our proposed method. Â© 2013 Elsevier Ltd. All rights reserved.

Min-max optimal control of linear systems with uncertainty and terminal state constraints

Wed 07 Apr 2021 13:44:34 AEST

In this paper, a class of min-max optimal control problems with continuous dynamical systems and quadratic terminal constraints is studied. The main contribution is that the original terminal state constraint in which the disturbance is involved is transformed into an equivalent linear matrix inequality without disturbance under certain conditions. Then, the original min-max optimal control problem is solved via solving a sequence of semi-definite programming problems. An example is presented to illustrate the proposed method. © 2013 Elsevier Ltd. All rights reserved.

A direct optimization method for low group delay FIR filter design

Wed 07 Apr 2021 13:44:34 AEST

This paper studies the design of FIR filter with low group delay, where the desired phase response is not being approximated. It is formulated as a constrained optimization problem, which is then solved globally. Numerical experiments show that our design method can produce a filter with smaller group delay than that obtained by the existing convex optimization method used in conjunction with a minimum phase spectral factorization method under the same design criteria. Furthermore, our formulation offers us the flexibility for the trade-off between the group delay and the magnitude response directly. It also allows the feasibility of imposing constraints on the group delay. © 2013 Elsevier B.V.

K-complex detection using a hybrid-synergic machine learning method

Wed 07 Apr 2021 13:44:26 AEST

Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection. © 1998-2012 IEEE.

Novel weighting in single hidden layer feedforward neural networks for data classification

Wed 07 Apr 2021 13:44:15 AEST

We propose a binary classifier based on the single hidden layer feedforward neural network (SLFN) using radial basis functions (RBFs) and sigmoid functions in the hidden layer. We use a modified attribute-class correlation measure to determine the weights of attributes in the networks. Moreover, we propose new weights called as influence weights to utilize in the weights connecting the input layer and the hidden layer nodes (hidden weights) of the network with sigmoid hidden nodes. These weights are calculated as the sum of conditional probabilities of attribute values given class labels. Our learning procedure of the networks is based on the extreme learning machines; in which the parameters of the hidden nodes are first calculated and then the weights connecting the hidden nodes and output nodes (output weights) are found. The results of the networks with the proposed weights on some benchmark data sets show improvements over those of the conventional networks. © 2012 Elsevier Ltd. All rights reserved.

New benchmarks for large-scale networks with given maximum degree and diameter

Wed 07 Apr 2021 13:34:31 AEST

Large-scale networks have become ubiquitous elements of our society. Modern social networks, supported by communication and travel technology, have grown in size and complexity to unprecedented scales. Computer networks, such as the Internet, have a fundamental impact on commerce, politics and culture. The study of networks is also central in biology, chemistry and other natural sciences. Unifying aspects of these networks are a small maximum degree and a small diameter, which are also shared by many network models, such as small-world networks. Graph theoretical methodologies can be instrumental in the challenging task of predicting, constructing and studying the properties of large-scale networks. This task is now necessitated by the vulnerability of large networks to phenomena such as cross-continental spread of disease and botnets (networks of malware). In this article, we produce the new largest known networks of maximum degree 17 ≤ ∆ ≤ 20 and diameter 2 ≤ D ≤ 10, using a wide range of techniques and concepts, such as graph compounding, vertex duplication, Kronecker product, polarity graphs and voltage graphs. In this way, we provide new benchmarks for networks with given maximum degree and diameter, and a complete overview of state-of-the-art methodology that can be used to construct such networks.

Editorial

Wed 07 Apr 2021 13:34:21 AEST

An argumentation shell for supporting the development and drafting of legal arguments

Wed 07 Apr 2021 13:31:57 AEST

This article describes an argumentation shell to support the formulation, representation and drafting of legal arguments. The shell can be used to capture generic arguments in many legal domains as well as to assist decision-makers in constructing their own actual arguments . The shell demonstrates that knowledge represented using the generic/actual argument model (GAAM) (a variant of Toulmin's argument structure) can be used to: (a) support the development of complex arguments, (b) add context and increase specificity for the retrieval of relevant documents, (c) incorporate background knowledge, (d) assist in the drafting of documents that represent arguments made, and (e) provide a structure for complex inferences requiring a range of mechanisms. The shell can be used to support decision making in a range of legal domains, including discretionary domains.

Managing privacy, trust, security, and context relationships using weighted graph representations

Wed 07 Apr 2021 13:31:46 AEST

Determining who has access to personal data is an ongoing problem facing information system entities. The establishment of trust and its representation for known and unknown entities within the system further complicates access control rights allocation. One unique solution is through the application of graph representation to aid in the identification and management of privacy, trust and security requirements. Graphs provide a much better mental map than would textual information. In this paper we use graphs to represent informational relations concerning trust levels between entities for privacy and security requirements.

Knowledge based regulation of statistical databases

Wed 07 Apr 2021 13:31:34 AEST

A statistical database system is a system that contains information about individuals, companies or organisations that enables authorized users to retrieve aggregate statistics such as mean and count. The regulation of a statistical database involves limiting the use of the database so that no sequence of queries is sufficient to infer protected information about an individual. The database is said to be compromised when individual confidential information is obtained as a result of a statistical query. Devices to protect against compromise include adding noise to the data or restricting a query. While effective, these techniques are sometimes too strong in that legitimate compromises for reasons of public safety are always blocked. Further, a statistical database can be often be compromised with some knowledge about the database attributes (working knowledge), the real world (supplementary knowledge) or the legal system (legal knowledge). In this paper we illustrate that a knowledge based system that represents working, supplementary and legal knowledge can contribute to the regulation of a statistical database.

Editors' cut : Managing scholarly journals in mathematics and IT

Wed 07 Apr 2021 13:31:19 AEST

The first version of this essay was jointly delivered by the authors as a colloquium lecture at the University of Ballarat on 24 November, 2004. A second, expanded and illustrated version was published in German in the Mitteilungen der Deutschen Mathematikervereinigung early in 2005. Because of the very positive feedback, the authors decided it would be useful to publish a version in English in a computing journal. The purpose of the essay is to provide advice and information to authors of articles about publishing in scholarly journals from an editor's perspective. Of particular importance are remarks about etiquette.

Hybridization of neural learning algorithms using evolutionary and discrete gradient approaches

Wed 07 Apr 2021 13:31:13 AEST

In this study we investigated a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this study we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.

An intelligent offline handwriting recognition system using evolutionary neural learning algorithm and rule based over segmented data points

Wed 07 Apr 2021 13:31:12 AEST

In this paper we propose a novel technique of using a hybrid evolutionary method, which uses a combination of genetic algorithm and matrix based solution methods such as QR factorization. The training of the model is based on a layer based hierarchical structure for the architecture and the weights for the Artificial Neural Network classifier. The architecture for the classifier is found using a binary search type procedure. The hierarchical structured algorithm (EALS-BT) is also a hybrid, because it combines the Genetic Algorithm based method with the Matrix based solution method for finding weights. A heuristic segmentation algorithm is initially used to over segment each word. Then the segmentation points are passed through the rule-based module to discard the incorrect segmentation points and include any missing segmentation points. Following the segmentation the contour is extracted between two correct segmentation points. The contour is passed through the feature extraction module that extracts the angular features, after which the EALS-BT algorithm finds the architecture and the weights for the classifier network. These recognized characters are grouped into words and passed to a variable length lexicon that retrieves words that have the highest confidence value.

Antimagic valuations for the special class of plane graphs

Wed 07 Apr 2021 13:30:51 AEST

We deal with the problem of labeling the vertices, edges and faces of a special class of plane graphs with 3-sided internal faces in such a way that the label of a face and the labels of the vertices and edges surrounding that face all together add up to the weight of that face. These face weights then form an arithmetic progression with common difference d.

Global optimization in the summarization of text documents

Wed 07 Apr 2021 13:30:50 AEST

In order to ensure minimal redundancy in the summary of a document and the greatest possible coverage of its content a method for the construction of summaries (summarization) based on the clustering of sentences is proposed in the article. Clustering of sentences reduces to a determination of cluster centroids the mathematical realization of which relies on a problem of global optimization. A determination of the number of clusters is one of the complex problems in the clustering procedure. Therefore, an algorithm of stepwise determination of the number of clusters is also proposed in the present study. © 2006 by Allerton Press, Inc.

Massively multiplayer online role-playing games : The past, present, and future

Wed 07 Apr 2021 13:30:49 AEST

Massively multiplayer online role-playing games (MMORPGs) are emerging in the computer game industry as a very popular genre. These games have existed since the late 1990s, but in the last few years the market has become increasingly strong. This relatively new genre is attracting a widespread audience, bringing together those who previously enjoyed both pen and paper and computer role-playing games, as well as those who enjoy socializing with other players in a virtual environment. Game developers see MMORPGs as a potentially profitable business due to its widespread appeal, but the reality is that only a small percentage of MMORPGs that are released become a success [Kosak 2006]. This article attempts to determine the many aspects that make a successful MMORPG; it also attempts to ascertain what new and innovative features are expected by the users from the next generation of MMORPGs. This is achieved by looking at and discussing past literature and surveying the MMORPG community's perception of previous and current MMORPGs, as well as their expectations of the next generation. An online survey attracted 122 participants to provide their perceptions of current and past MMORPGs. This article determines and outlines the respondents' preferences in the MMORPG genre, discussing what implications these could have on its future. The survey also gave insight into the respondents' expectations of the future of MMORPGs. We conclude this article with a discussion of aspects of current MMORPGs that the participants would like improved, as well as new features they would like incorporated into the next generation of games. © 2008 ACM.

Electric vehicle participated electricity market model considering flexible ramping product provisions

Wed 07 Apr 2021 13:26:25 AEST

This article studies electric vehicle (EV) potential to participate in the energy market and provide flexible ramping products (FRPs). EV traffic flows are predicted by the deep belief network, and the availability of flexible EVs is estimated based on the predicted EV traffic flows. Then, a novel market mechanism in distribution system is proposed to encourage the dispatchable EV demand to react to economic signals and provide ramping services. The designed market model is based on locational marginal pricing of energy and marginal pricing of FRPs. System ramping capacity constraints and EV operation constraints are incorporated in the proposed model to achieve the balance between the system social cost minimization and the EV traveling convenience. Moreover, typical uncertainties are considered by the scenario-based approach. Finally, simulations are conducted to verify the effectiveness of the established model and demonstrate the contributions of EVs to the system reliability and flexibility. © 1972-2012 IEEE.

Bipolar radiofrequency ablation treatment of liver cancer employing monopolar needles : a comprehensive investigation on the efficacy of time-based switching

Tue 29 Jun 2021 17:30:47 AEST

Radiofrequency ablation (RFA) is a thermal ablative treatment method that is commonly used to treat liver cancer. However, the thermal coagulation zone generated using the conventional RFA system can only successfully treat tumours up to 3 cm in diameter. Switching bipolar RFA has been proposed as a way to increase the thermal coagulation zone. Presently, the understanding of the underlying thermal processes that takes place during switching bipolar RFA remains limited. Hence, the objective of this study is to provide a comprehensive understanding on the thermal ablative effects of time-based switching bipolar RFA on liver tissue. Five switch intervals, namely 50, 100, 150, 200 and 300 s were investigated using a two-compartment 3D finite element model. The study was performed using two pairs of RF electrodes in a four-probe configuration, where the electrodes were alternated based on their respective switch interval. The physics employed in the present study were verified against experimental data from the literature. Results obtained show that using a shorter switch interval can improve the homogeneity of temperature distribution within the tissue and increase the rate of temperature rise by delaying the occurrence of roll-off. The coagulation volume obtained was the largest using switch interval of 50 s, followed by 100, 150, 200 and 300 s. The present study demonstrated that the transient thermal response of switching bipolar RFA can be improved by using shorter switch intervals. © 2021 Elsevier Ltd

An improved memetic approach for protein structure prediction incorporating maximal hydrophobic core estimation concept

Tue 03 Aug 2021 10:38:58 AEST

Protein Structure Prediction (PSP) from the primary amino acid sequence, even using a simplified Hydrophobic-Polar (HP) lattice model, continues to be extremely challenging. Finding an optimal conformation, even for a small sequence, by any of the currently known evolutionary approaches is computationally extensive and time consuming. Although Memetic Algorithms (MAs) have shown success in finding the optimal solution for PSP, no significant work on the incorporation of domain or problem specific knowledge into the search process to significantly improve their performance is reported. In this paper, we present an approach to incorporate such knowledge into the initial population to enhance the effectiveness of MA for PSP. The domain knowledge we propose to use is based on the concept of maximal ‘core’ formation by exploiting the fundamental property of the H residues to be at the core of the minimum energy optimal protein structure. A generic technique is proposed for estimating the maximal Hydrophobic core (H-core) in a protein sequence for 2D Square, 3D Cubic and a more complex and realistic 3D FCC (Face Centered Cubic) lattice models. Subsequently, the knowledge of this estimated core is incorporated in an MA. The experiments conducted using HP benchmark sequences for 2D Square, 3D Cubic and 3D FCC lattice models show that the proposed MA with the new core-based population initialization technique has superior performance to the existing methods in terms of convergence speed as well as minimal energy. © 2018 Elsevier B.V.

New gene selection algorithm using hypeboxes to improve performance of classifiers

Tue 01 Nov 2022 13:31:24 AEDT

The use of DNA microarray technology allows to measure the expression levels of thousands of genes in one single experiment which makes possible to apply classification techniques to classify tumours. However, the large number of genes and relatively small number of tumours in gene expression datasets may (and in some cases significantly) diminish the accuracy of many classifiers. Therefore, efficient gene selection algorithms are required to identify most informative genes or groups of genes to improve the performance of classifiers. In this paper, a new gene selection algorithm is developed using marginal hyberboxes of genes or groups of genes for each tumour type. Informative genes are defined using overlaps between hyberboxes. The results on six gene expression datasets demonstrate that the proposed algorithm is able to considerably reduce the number of genes and significantly improve the performance of classifiers. © 2020 Inderscience Enterprises Ltd.

Application of adaptive phase-field scaled boundary finite element method for functionally graded materials

Tue 01 Jun 2021 13:04:38 AEST

In this paper, an adaptive phase-field scaled boundary finite element method for fracture in functionally graded material (FGM) is presented. The model accounts for spatial variation in the material and fracture properties. The quadtree decomposition is adopted for refinement, and the refinement is based on an error indicator evaluated directly from the solutions of the scaled boundary finite element method. This combination makes it a suitable choice to study fracture using the phase field method, as it reduces the mesh burden. A few standard benchmark numerical examples are solved to demonstrate the improvement in computational efficiency in terms of the number of degrees of freedom. © 2021 World Scientific Publishing Company.

A distributed and anonymous data collection framework based on multilevel edge computing architecture

Thu 29 Sep 2022 16:59:22 AEST

Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests. © 2005-2012 IEEE.

Missing value imputation via clusterwise linear regression

Thu 24 Nov 2022 14:08:28 AEDT

In this paper a new method of preprocessing incomplete data is introduced. The method is based on clusterwise linear regression and it combines two well-known approaches for missing value imputation: linear regression and clustering. The idea is to approximate missing values using only those data points that are somewhat similar to the incomplete data point. A similar idea is used also in clustering based imputation methods. Nevertheless, here the linear regression approach is used within each cluster to accurately predict the missing values, and this is done simultaneously to clustering. The proposed method is tested using some synthetic and real-world data sets and compared with other algorithms for missing value imputations. Numerical results demonstrate that the proposed method produces the most accurate imputations in MCAR and MAR data sets with a clear structure and the percentages of missing data no more than 25%

Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment

Thu 20 Oct 2022 14:27:37 AEDT

As one of the cyber-physical-social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial-temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users' context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones. © 2005-2012 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**

A unified model predictive voltage and current control for microgrids with distributed fuzzy cooperative secondary control

Thu 18 May 2023 15:07:42 AEST

A microgrid formed by a cluster of parallel distributed generation (DG) units is capable of operating in either islanded mode or grid-connected mode. Traditionally, by using model predictive control algorithms, these two operation modes can be achieved with two separate and different cost functions, which brings in control complexity and hence, compromises system reliability. In this article, a unified model predictive voltage and current control strategy is proposed for both islanded and grid-connected operations and their smooth transition. The cost function is kept unified with voltage and current taken into account without altering the control architecture. It can be used for high-quality voltage supply at the primary control level and for bidirectional power flow at the tertiary control level. In addition, by only using DGs' own and neighboring information, a distributed fuzzy cooperative algorithm is developed at the secondary layer to mitigate the voltage and frequency deviations inherent from the power droop. The fuzzy controller can optimize the secondary control coefficients for further voltage quality improvement. Comprehensive tests under various scenarios demonstrate the merits of the proposed control strategy over traditional methods.

Unidirectional ablation minimizes unwanted thermal damage and promotes better thermal ablation efficacy in time-based switching bipolar radiofrequency ablation

Thu 17 Nov 2022 15:08:09 AEDT

Switching bipolar radiofrequency ablation (bRFA) is a thermal treatment modality used for liver cancer treatment that is capable of producing larger, more confluent and more regular thermal coagulation. When implemented in the no-touch mode, switching bRFA can prevent tumour track seeding; a medical phenomenon defined by the deposition of cancer cells along the insertion track. Nevertheless, the no-touch mode was found to yield significant unwanted thermal damage as a result of the electrodes’ position outside the tumour. It is postulated that the unwanted thermal damage can be minimized if ablation can be directed such that it focuses only within the tumour domain. As it turns out, this can be achieved by partially insulating the active tip of the RF electrodes such that electric current flows in and out of the tissue only through the non-insulated section of the electrode. This concept is known as unidirectional ablation and has been shown to produce the desired effect in monopolar RFA. In this paper, computational models based on a well-established mathematical framework for modelling RFA was developed to investigate if unidirectional ablation can minimize unwanted thermal damage during time-based switching bRFA. From the numerical results, unidirectional ablation was shown to produce treatment efficacy of nearly 100%, while at the same time, minimizing the amount of unwanted thermal damage. Nevertheless, this effect was observed only when the switch interval of the time-based protocol was set to 50 s. An extended switch interval negated the benefits of unidirectional ablation. © 2021 Elsevier Ltd

An efficient force-feedback hand exoskeleton for haptic applications

Thu 17 Nov 2022 14:53:56 AEDT

In this paper, a study on wearable exoskeleton devices which are capable of delivering sensation to the fingers while interacting with the virtual objects in virtual reality environment has been conducted. A force-controllable hand exoskeleton system is proposed, which is able to apply force feedback to the fingertip while allowing natural finger motions. The linkage structure was inspired by the skeletal structure of a human finger. Moreover, a series elastic actuator (SEA) mechanism, which consisted of a linear motor, a spring and a potentiometer, was presented as an actuating system. The force transmission through linkage has been investigated to ensure the force feedback ability at the fingertip. By using a PID controller, the proposed actuator module could generate the desired force in two different modes: free mode and interaction mode. The experimental results show that the proposed system could effectively deliver forces to the fingertips. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

Fuzzy logic and gradient descent-based optimal adaptive robust controller with inverted pendulum verification

Thu 17 Nov 2022 09:21:40 AEDT

This paper develops an adaptive robust combination of feedback linearization (FL) and sliding mode controller (SMC) based on fuzzy rules and gradient descent laws. The new suggested control algorithm is tested to stabilize a fourth-order under-actuated nonlinear inverted pendulum system. More precisely, the reliable feedback linearization approach and the robust SMC controller are combined to design a stable control effort. In order to enhance the performance of the suggested controller, an adaptation technique as long as fuzzy rules are applied to update the control gains and the boundary layer parameter. Then, a novel evolutionary algorithm termed multi-objective ant lion optimizer (MOALO) is implemented to determine the control coefficients. The analysis and results conducted on the inverted pendulum system demonstrate the desired performance of the proposed control scheme by providing an optimal smooth control input, suitable tracking performance, and proper time responses. © 2021 Elsevier Ltd

Adaptive droop control using adaptive virtual impedance for microgrids with variable PV outputs and load demands

Thu 17 Nov 2022 09:18:53 AEDT

In microgrids, intermittency of renewable energy sources (RES) and uncertain state-of-charge (SoC) of energy storage systems (ESS) can cause power deficiency to some distributed generation units (DGs). In this case, DGs with power deficiency may not meet the power demand, resulting in voltage collapse or frequency divergence. Unfortunately, this is seldom considered in inverter control design in existing literature. Thus, in-depth investigation into the microgrid performance under renewable energy resource fluctuations and appropriate control methods are urgently needed. In this article, an adaptive droop and adaptive virtual impedance control strategy is proposed. Unlike conventional droop control where the droop coefficients are fixed by assuming the DGs can always meet the load demand, the droop coefficients here are adjusted according to actual solar PV power output. In this way, proper power sharing among DGs can be achieved under renewable energy variation. Furthermore, the impact of varying DG capacities on system stability is mathematically investigated. An adaptive virtual impedance is then incorporated into the adaptive droop method to deal with the system instability caused by renewable energy variations. The proposed strategy is analyzed theoretically and validated in MATLAB/Simulink simulation and laboratory experiments. The results demonstrate the advantages of the proposed method over conventional approaches under various scenarios. © 1982-2012 IEEE.

Comparisons between impedance-based and time-based switching bipolar radiofrequency ablation for the treatment of liver cancer

Mon 18 Oct 2021 16:35:19 AEDT

Switching bipolar radiofrequency ablation (bRFA) is a cancer treatment technique that activates multiple pairs of electrodes alternately based on a predefined criterion. Various criteria can be used to trigger the switch, such as time (ablation duration) and tissue impedance. In a recent study on time-based switching bRFA, it was determined that a shorter switch interval could produce better treatment outcome than when a longer switch interval was used, which reduces tissue charring and roll-off induced cooling. In this study, it was hypothesized that a more efficacious bRFA treatment can be attained by employing impedance-based switching. This is because ablation per pair can be maximized since there will be no interruption to RF energy delivery until roll-off occurs. This was investigated using a two-compartment 3D computational model. Results showed that impedance-based switching bRFA outperformed time-based switching when the switch interval of the latter is 100 s or higher. When compared to the time-based switching with switch interval of 50 s, the impedance-based model is inferior. It remains to be investigated whether the impedance-based protocol is better than the time-based protocol for a switch interval of 50 s due to the inverse relationship between ablation and treatment efficacies. It was suggested that the choice of impedance-based or time-based switching could ultimately be patient-dependent. © 2021 Elsevier Ltd

Trustworthiness of self-driving vehicles for intelligent transportation systems in industry applications

Fri 19 Feb 2021 16:09:48 AEDT

To enhance industrial production and automation, rapid and faster transportation of raw materials and finished products to and from distributed factories, warehouses and outlets are essential. To reduce cost with increased efficiency, this will increasingly see the use of connected and self-driving commercial vehicles fitted with industrial grade sensors on roads, shared with normal and self-driving passenger vehicles. For its wide adoption, the trustworthiness of self-driving vehicles in the intelligent transportation system (ITS) is pivotal. In this article, we introduce a novel model to measure the overall trustworthiness of a self-driving vehicle considering on-Board unit (OBU) components, GPS data and safety messages. In calculating the trustworthiness of individual OBU components, CertainLogic and beta distribution function (BDF) are used. Those trust values are fused using both the dempster-Shafer Theory (DST) and a logical operator of CertainLogic. Results of our simulation show that our proposed method can effectively determine the trust of self-driving vehicles. © 2005-2012 IEEE.

Development of flexible haptic forceps based on the electrohydraulic transmission system

Fri 15 Sep 2023 16:59:49 AEST