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A3Graph : adversarial attributed autoencoder for graph representation learning

- Hou, Mingliang, Wang, Lei, Liu, Jiaying, Kong, Xiangjie, Xia, Feng

  • Authors: Hou, Mingliang , Wang, Lei , Liu, Jiaying , Kong, Xiangjie , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 36th Annual ACM Symposium on Applied Computing, SAC 2021 p. 1697-1704
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  • Description: Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph. © 2021 ACM.

Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots

- Van Nguyen, Thien, Le, Hai, Tran, Hoang, Nguyen, Duc, Nguyen, Minh, Nguyen, Linh

  • Authors: Van Nguyen, Thien , Le, Hai , Tran, Hoang , Nguyen, Duc , Nguyen, Minh , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper , Conference Paper
  • Relation: 2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021, 28 April 2021 through 29 April 2021 p. 111-116
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  • Description: The paper addresses the problem of effectively controlling a two-wheel robot given its inherent non-linearity and parameter uncertainties. In order to deal with the unknown and uncertain dynamics of the robot, it is proposed to employ the adaptive dynamic programming, a reinforcement learning based technique, to develop an optimal control law. It is interesting that the proposed algorithm does not require kinematic parameters while finding the optimal state controller is guaranteed. Moreover, convergence of the optimal control scheme is theoretically proved. The proposed approach was implemented in a synthetic two-wheel robot where the obtained results demonstrate its effectiveness. © 2021 IEEE.

An efficient approach for SIMO systems using adaptive fuzzy hierarchical sliding mode control

- Van Nguyen, Thien, Le, Hai, Tran, Hoang, Nguyen, Duc, Nguyen, Minh, Nguyen, Linh

  • Authors: Van Nguyen, Thien , Le, Hai , Tran, Hoang , Nguyen, Duc , Nguyen, Minh , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper , Conference Paper
  • Relation: 2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021 p. 85-90
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  • Description: The paper addresses the problem of efficiently controlling a class of single input multiple output (SIMO) under-Actuated robotic systems such as a two dimensional inverted pendulum cart or a two dimensional overhead crane. It is first proposed to employ the hierarchical sliding mode control approach to design a control law, which guarantees stability and anti-swing of the vehicle when it is driven on a predefined trajectory. More importantly, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by the proposed fuzzy logic mechanism, which results in the efficient operation of the SIMO under-Actuated system in real time. The proposed algorithm was then implemented in the synthetic environment, where the obtained results demonstrate its effectiveness. © 2021 IEEE.

An Exploratory Study on the Employers' Perceptions of ICT Graduate work-readiness

- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew

  • Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: Pacific Asia Conference on Information Systems (PACIS 2021) 12th to 14th July 2021
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  • Description: Drawing on information gathered from scoping interviews with graduate recruiters and industry experts in Australia, this study extends our understanding of how employers, rather than researchers, describe the desired work-ready skills for graduate/entry level roles in the Australian information and communication technology (ICT) industry. Contrary to the developing literature on work-readiness, the findings showed that the skills which contribute to work-readiness should not be limited to field-specific knowledge, skills and cognitive skills, but that they should be extended to include affective skills or personal attributes and behaviors, such as selfefficacy, willingness to learn, disposition, tolerance and integrity. Results have practical implications for developing academic programs aimed at enhancing cognitive and affective skills among IT graduates for employment potential and successful transition into work.

ANSWER : generating information dissemination network on campus

- Qing, Qing, Guo, Teng, Zhang, Dongyu, Xia, Feng

  • Authors: Qing, Qing , Guo, Teng , Zhang, Dongyu , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 32nd Australasian Database Conference, ADC 2021 Vol. 12610 LNCS, p. 74-86
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  • Description: Information dissemination matters, both on an individual and group level. For college students who are physically and mentally immature, they are more sensitive and susceptible to unnormal information like rumors. However, current researches focus on large-scale online message sharing networks like Facebook and Twitter, rather than profile the information dissemination on campus, which fail to provide any references for daily campus management. Against this background, we propose a framework to generate the information dissemination network on campus, named ANSWER (cAmpus iNformation diSsemination netWork gEneRation), based on multimodal data including behavior data, appearance data, and psychological data. The construction of the ANSWER is listed as four steps. First, we use a convolutional autoencoder to extract the students’ facial features. Second, we process the behavior data to construct a friendship network. Third, heterogeneous information is embedded in the low-dimensional vector space by using network representation learning to obtain embedding vectors. Fourth, we use the deep learning model to predict. The experiment results show that ANSWER outperforms other methods in multiple feature fusion and prediction of information dissemination relationship performance. © 2021, Springer Nature Switzerland AG.

Inspection of open-pit mine drainage characteristics with a horizontal borehole camera

- Perdigao, Cristhiana, Dyson, Ashley, Yaghoubi, Mohammadjavad, Baumgartl, Thomas

  • Authors: Perdigao, Cristhiana , Dyson, Ashley , Yaghoubi, Mohammadjavad , Baumgartl, Thomas
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 14th Baltic Sea Region Geotechnical Conference, BSGC 2020 Vol. 727
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  • Description: Horizontal bores and drains are crucial infrastructures for maintaining the stability of large open-pit mines. Induced deformations as the result of mining activities and the infiltration of water from large surface catchments during heavy rain events can cause the build-up of pore water pressures in brown coal batters. This can potentially lead to catastrophic slope failures. Horizontal boreholes and drains are commonly installed at shallow inclines and typically range in length from 150 to 400 metres. Due to complexities in surveying lengthy horizontal bores, the long-term internal properties of these structures are poorly understood. In this research, a specialised horizontal borehole camera was developed to observe a range of factors influencing borehole performance including the identification of fractured or jointed material, borehole geometry and features, and locationally dependent water outflow and drainage paths. Investigations were undertaken at an operational brown coal mine in the Latrobe Valley, located in Victoria, Australia. Features observed on the profile of horizontal bores are discussed, with an emphasis on providing in-situ material characterisation and for the purposes of maintaining stable mine batters. © Published under licence by IOP Publishing Ltd.

Non-invasive smartphone use monitoring to assess cognitive impairment

- Thang, Nguyen, Oatley, Giles, Stranieri, Andrew, Walker, Darren

  • Authors: Thang, Nguyen , Oatley, Giles , Stranieri, Andrew , Walker, Darren
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 13th International Conference on Computer and Automation Engineering, ICCAE 2021 p. 64-67
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  • Description: Background: There are many tests for the early detection of Mild Cognitive Impairment (MCI) to prevent or delay the development of dementia, particularly amongst the elderly. However, many tests are complex and are required to be performed repeatedly. Cognitive assessment apps for a smartphone have emerged, but like other tests, they require the user to perform complex tasks like drawing time on a clock. Few studies have explored non-invasive ways of tracking and assessing MCI without having the user perform specific tests. Objective: This research ultimately aims to develop an app that runs in the background and collects smartphone usage data that correlates well with MCI test results. The focus of this preliminary study was to develop an app that collects usage data and common MCI questionnaires to see if usage data between people varied, and to establish associations between phone usage and cognitive tests results. Method: An android application was developed to gather data over three weeks by three volunteers (authors). Usage data collected included: SMS and call log, accelerometer, location, app usage, self-report. Cognitive tests implemented were Stroop, Go/No Go tests and absent-mindedness questionnaires. Due to the small sample size and Covid-19 restrictions (October 2020), location data was not reliable. SMS text was not collected for privacy reasons. Results: App categories can differentiate people, but the app usage cannot be used to distinguish people. © 2021 IEEE.

The implementation of Blockchain framework in MOOCs to support a freedom of learning in Indonesia

- Febrinanto, Falih, Dafik, Nisviasari, R.

  • Authors: Febrinanto, Falih , Dafik , Nisviasari, R.
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 4th International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2020 Vol. 1836
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  • Description: A freedom of learning program has been released by the Indonesian Ministry and Culture this year 2020. There are three ways for students to earn their credits, namely take the subject course in face-to-face based class, virtual based class or under Massive Open Online Courses (MOOCs). MOOCs is a model that is developed to help people to learn about certain skills through the online platform, without any limitation in the audience. MOOCs aim to enhance broad collaboration between individuals in creating learning environments that have high scalability and can be accessed by anyone and anywhere. The complexity arises when students undertake a subject course through MOOCs, how to certify the completion of their program in which the certification can be gained easily, and the last how secure the obtained certificate? Blockchain technology can help to improve the quality of MOOCs by providing control of academic records as evidence that someone has completed a learning process on MOOCs. Academic records generated will be stored in one place forever and safely stored in the Blockchain environment. This article will explore how the possible to implement the Blockchain framework in MOOCs to support a freedom of learning in Indonesia. © 2021 Published under licence by IOP Publishing Ltd.

A dynamic surface controller based on adaptive neural network for dual arm robots

- Le, Hai, Nguyen, Linh, Thiyagarajan, Karthick

  • Authors: Le, Hai , Nguyen, Linh , Thiyagarajan, Karthick
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 p. 555-560
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  • Description: The paper introduces an adaptive controller to efficiently manipulate the dual arms of a robot (DAR) under uncertainties including actuator nonlinearities, system parameter variations and external disturbances. It is proposed that by the use of the dynamic surface control (DSC) method, the control strategy is first established, which enables the robot arms to robustly operate on the desired trajectories. Nevertheless, the dynamic model parameters of the DAR system are unknown and impractically estimated due to its uncertain nonlinearities and unexpected external factors. Hence, it is then proposed to employ the radial basis function network (RBFN) to adaptively estimate the uncertain system parameters. The Lyapunov theory is theoretically utilized to derive the adaptation mechanism so that the stability of the closed-loop control system is guaranteed. The proposed RBFN-DSC approach was validated in a synthetic environment with the promising results. © 2020 IEEE.

A patient centric agent assisted private blockchain on hyperledger fabric for managing remote patient monitoring

- Wadud, Md Anwar, Amir-Ul-Haque Bhuiyan, T., Uddin, Md Ashraf, Rahman, Md Motiur

  • Authors: Wadud, Md Anwar , Amir-Ul-Haque Bhuiyan, T. , Uddin, Md Ashraf , Rahman, Md Motiur
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 11th International Conference on Electrical and Computer Engineering, ICECE 2020 p. 194-197
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  • Description: Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor's care and check their health status through their mobile phone call, Skype, Facebook Messenger, or other online resources. There is, however, a major concern about the privacy of patients when using online resources. Patients usually choose to keep their information confidential, which should be only accessible to authorized individuals. The most current remote patient monitoring system is organization-centric and patient's privacy and security rely on healthcare providers' mercy. Blockchain technologies have attracted the attention of researchers for designing eHealth applications to provide patients with secure and privacy-preserving health services. Blockchain researchers have recently proposed some models for remote patient monitoring systems. However, most of those researchers have applied public blockchains where health data is available to all participants with the property of data tamper-proof. In this paper, we propose a novel remote patient monitoring model using a decentralized private blockchain to protect patient's privacy and increase the system's efficiency. The private blockchain will be implemented on Hyperledger Fabric where a Patient-centric Agents (PCA) manage patient's data and coordinate authorization to form a secure channel to transmit data to the private blockchain. A hybrid consensus by combining Proof of Integrity (PoI) and Proof of Validity (PoV) is used to protect data privacy and integrity when retrieving data from a blockchain-based cloud database. Finally, the Merkle Tree algorithm was used for data processing and authentication when collecting data and uploading it to a cloud database. © 2020 IEEE.

A weighted overlook graph representation of eeg data for absence epilepsy detection

- Wang, Jialin, Liang, Shen, Wang, Ye, Zhang, Yanchun, Ma, Jiangang

  • Authors: Wang, Jialin , Liang, Shen , Wang, Ye , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 20th IEEE International Conference on Data Mining, ICDM 2020 Vol. 2020-November, p. 581-590
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  • Description: Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances. © 2020 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate "Jiangang Ma“ is provided in this record**

Active model selection for positive unlabeled time series classification

- Liang, Shen, Zhang, Yanchun, Ma, Jiangang

  • Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 36th IEEE International Conference on Data Engineering, ICDE 2020 Vol. 2020-April, p. 361-372
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  • Description: Positive unlabeled time series classification (PUTSC) refers to classifying time series with a set PL of positive labeled examples and a set U of unlabeled ones. Model selection for PUTSC is a largely untouched topic. In this paper, we look into PUTSC model selection, which as far as we know is the first systematic study in this topic. Focusing on the widely adopted self-training one-nearest-neighbor (ST-1NN) paradigm, we propose a model selection framework based on active learning (AL). We present the novel concepts of self-training label propagation, pseudo label calibration principles and ultimately influence to fully exploit the mechanism of ST-1NN. Based on them, we develop an effective model performance evaluation strategy and three AL sampling strategies. Experiments on over 120 datasets and a case study in arrhythmia detection show that our methods can yield top performance in interactive environments, and can achieve near optimal results by querying very limited numbers of labels from the AL oracle. © 2020 IEEE.
  • Description: E1

An approach to map geography mark-up language data to resource description framework schema

- Faqir, Ammara, Mahmood, Aqsa, Qazi, Kiran, Malik, Saleem

  • Authors: Faqir, Ammara , Mahmood, Aqsa , Qazi, Kiran , Malik, Saleem
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2nd International Conference on Intelligent Technologies and Applications, INTAP 2019 Vol. 1198, p. 343-354
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  • Description: GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS. © 2020, Springer Nature Singapore Pte Ltd.
  • Description: E1

An Enhanced Local Texture Descriptor for Image Segmentation

- Tania, Sheikh, Murshed, Manzur, Teng, Shyh, Karmakar, Gour

  • Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Image Processing, ICIP 2020 Vol. 2020-October, p. 1526-1530
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  • Description: Texture is an indispensable property to develop many vision based autonomous applications. Compared to colour, feature dimension in a local texture descriptor is quite large as dense texture features need to represent the distribution of pixel intensities in the neighbourhood of each pixel. Large dimensional features require additional time for further processing that often restrict real-time applications. In this paper, a robust local texture descriptor is enhanced by reducing feature dimension by three folds without compromising the accuracy in region-based image segmentation applications. Reduction in feature dimension is achieved by exploiting the mean of neighbourhood pixel intensities radially along lines across a certain radius, which eliminates the need for sampling intensity distribution at three scales. Both the results of benchmark metrics and computational time are promising when the enhanced texture feature is used in a region-based hierarchical segmentation algorithm, a recent state-of-the-art technique. © 2020 IEEE.

CRI : measuring city infection risk amid COVID-19

- Liu, Mingliang, Yu, Shuo, Chu, Xinbei, Xia, Feng

  • Authors: Liu, Mingliang , Yu, Shuo , Chu, Xinbei , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
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  • Description: The outbreak of COVID-19 has brought incalculable economy and life losses. Accurately assessing the risk of a certain city can help formulate effective measures to prevent and control COVID-19 in time. It will be of great significance for us to measure city risk in infection amid epidemics. City risk in infection is related to many factors. To address this problem, this paper proposes city risk index (CRI) to measure city risk in infection, considering the following four perspectives: Economy (i.e., GDP and FCI), technology (i.e., education and innovation), population, and geographical position (i.e., latitude and longitude). The experimental results show that CRI can be effectively employed to measure city risk in infection amid COVID-19 as well as other similar epidemics. The proposed CRI can be used to guide policymakers for better emergency management policies making when coping with COVID-19. © 2020 IEEE.

Cyberbullying detection on social networks using machine learning approaches

- Islam, Md Manowarul, Uddin, Md Ashraf, Islam, Linta, Akter, Arnisha, Sharmin, Selina, Acharjee, Uzzal

  • Authors: Islam, Md Manowarul , Uddin, Md Ashraf , Islam, Linta , Akter, Arnisha , Sharmin, Selina , Acharjee, Uzzal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
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  • Description: The use of social media has grown exponentially over time with the growth of the Internet and has become the most influential networking platform in the 21st century. However, the enhancement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment cyberbullying, cybercrime and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), are used to analyse the accuracy level of four distinct machine learning algorithms. © 2020 IEEE.

DEFINE: friendship detection based on node enhancement

- Pan, Hanxiao, Guo, Teng, Bedru, Hayat, Qing, Qing, Zhang, Dongyu, Xia, Feng

  • Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
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  • Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
  • Description: E1

Discovery of small group interactions and performance from project emails

- Ivkovic, Sasha, Oseni, Taiwo, Chadhar, Mehmood, Firmin, Sally

  • Authors: Ivkovic, Sasha , Oseni, Taiwo , Chadhar, Mehmood , Firmin, Sally
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020
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  • Description: Despite latest advances in small group research, discovery of group interactions and performance from analysis of small group communication, such as project emails, is still minimally represented. This paper presents a novel approach of studying small groups through analysis of the participants' emails sent to the project manager. We examined 1,105 email messages from managers' email in-boxes across five distinct ICT projects from the personal, social, collaborative, and engaging perspective of the email senders and link the findings to group performance. The study provides theoretical evidence that analysis of incoming communication from project managers' email in-box can be used to measure a group's success. For project managers the approach has the potential to be highly beneficial for monitoring of indicators for the state of project health. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved.

Discovery of small group interactions and performance from project emails

- Ivkovic, Sasha, Oseni, Taiwo, Chadhar, Mehmood, Firmin, Sally

  • Authors: Ivkovic, Sasha , Oseni, Taiwo , Chadhar, Mehmood , Firmin, Sally
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020
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  • Description: Despite latest advances in small group research, discovery of group interactions and performance from analysis of small group communication, such as project emails, is still minimally represented. This paper presents a novel approach of studying small groups through analysis of the participants' emails sent to the project manager. We examined 1,105 email messages from managers' email in-boxes across five distinct ICT projects from the personal, social, collaborative, and engaging perspective of the email senders and link the findings to group performance. The study provides theoretical evidence that analysis of incoming communication from project managers' email in-box can be used to measure a group's success. For project managers the approach has the potential to be highly beneficial for monitoring of indicators for the state of project health. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved.

Dynamic derivative-droop control for supercapacitor synthetic inertial support

- Akram, Umer, Mithulananthan, N, Shah, Rakibuzzaman, Islam, Rabiul

  • Authors: Akram, Umer , Mithulananthan, N , Shah, Rakibuzzaman , Islam, Rabiul
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2020 IEEE Industry Applications Society Annual Meeting, IAS 2020 Vol. 2020-January
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  • Description: Energy storage is recognized as a potential solution to alleviate the impacts of inertia reduction and intermittency due to the integration of inverter based renewable energy sources (RES) in power systems. Out of various rapid responsive energy storage technologies, supercapacitor energy storage (SCES) is the most promising technology for synthetic inertia support. Because the SCES has high power density, very small response time, and large cycle life. In this paper, a dynamic derivative-droop control strategy is developed for SCES to provide the synthetic inertia in low inertia power system. The proposed strategy overcomes the limitations of separately applied derivative and droop controls. In addition, the use of time varying gains (referred as dynamic) instead of fixed gains improves the performance compared to derivative-droop coordinated control. Different types of events are created at different penetration levels of RES to test the robustness of the proposed control. A comparison, based on RoCoF and frequency nadir, between the derivative, droop, derivative-droop coordinated and the proposed controls is presented to show the effectiveness of the proposed control approach. © 2020 IEEE.