Modeling neurocognitive reaction time with gamma distribution
- Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Aryal, Sunil , Klein, Britt
- Date: 2018
- Type: Text , Conference proceedings
- Relation: ACSW'18 . Proceedings of the Australasian Computer Science Week Multiconference; Brisbane, QLD; January 2018; Article 28 p. 1-10
- Full Text: false
- Reviewed:
- Description: As a broader effort to build a holistic biopsychosocial health metric, reaction time data obtained from participants undertaking neurocognitive tests have been examined using Exploratory Data Analysis (EDA) for assessing its distribution. Many of the known existing methods assume, that the reaction time data follows a Gaussian distribution and thus commonly use statistical measures such as Analysis of Variance (ANOVA) for analysis. However, it is not mandatory for the reaction time data, to necessarily follow Gaussian distribution and in many instances, it can be better modeled by other representations such as Gamma distribution. Unlike Gaussian distribution which is defined using mean and variance, the Gamma distribution is defined using shape and scale parameters which also considers higher order moments of data such as skewness and kurtosis. Generalized Linear Models (GLM), based on the family exponential distributions such as Gamma distribution, which have been used to model reaction time in other domains, have not been fully explored for modeling reaction time data in psychology domain. While limited use of Gamma distribution have been reported [5, 17, 21], for analyzing response times, their application has been somewhat ad-hoc rather than systematic. For this proposed research, we use a real life biopsychosocial dataset, generated from the 'digital health' intervention programs conducted by the Faculty of Health, Federation University, Australia. The two digital intervention programs were the 'Mindfulness' program and 'Physical Activity' program. The neurocognitive tests were carried out as part of the 'Mindfulness' program. In this paper, we investigate the participants' reaction time distributions in neurocognitive tests such as the Psychology Experiment Building Language (PEBL) Go/No-Go test [19], which is a subset of the larger biopsychosocial data set. PEBL is an open source software system for designing and running psychological experiments. Analysis of participants' reaction time in the PEBL Go/No-Go test, shows that the reaction time data are more compatible with a Gamma distribution and clearly demonstrate that these can be better modeled by Gamma distribution.
Monitoring oxy-coal flame stability
- Authors: Valliappan, Palaniappan , Wilcox, Steven , Spliethoff, Hartmut , Diego Garcia, Ruth
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 847-853
- Full Text: false
- Reviewed:
- Description: This paper presents a novel approach to monitoring the stability of oxy-coal flames. Oxy-coal combustion has the potential to generate high concentrations of carbon dioxide in the exhaust gas stream. This could increase the efficiency of the removal of carbon dioxide emissions from a coal fired utility boiler. In order to convert an existing boiler high levels of flue gas need to be recycled to reduce the combustion zone temperatures, but this can lead to combustion instability. This paper presents an approach using three broadband photodiodes to monitor the infra-red, visible and ultra-violet emissions from an individual flame and then, by using the Wigner-Ville transform, detect unstable flames.
- Description: IEEE International Symposium on Industrial Electronics
Near field wireless power transfer for multiple receivers by using a novel magnetic core structure
- Authors: Chen, Manxin , Cheng, Eric Ka-Wai , Hu, Jiefeng
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 IEEE Energy Conversion Congress and Exposition (ECCE);Portland, OR, USA; 09-2018; p. 1190-1195
- Full Text: false
- Reviewed:
- Description: A Wireless Power Transfer (WPT) method is proposed by using a novel magnetic core structure. Similar to those transformers, magnetic cores are utilized as main object that conducts magnetic flux in the proposed cell. Power is transferred through near magnetic field and the distance between the receiver and the transmitter is thus relatively short. Every basic cell has only one primary side, where the magnetic cores of different permeabilities are combined to build a multi-transmitter structure for multiple receivers. By arranging and guiding the flux via a specific magnetic path, it offers some space of freedom for the locations of the multiple receivers. The usage of the magnetic core also increases the coupling coefficients between the primary-side transmitter and secondary-side receivers compared to core-less WPT using spiral coils. Analysis of the three working modes of the proposed WPT cell is presented. Experimental results show that the proposed basic cell with three receivers achieves 85% efficiency at 100W.
Neural network with added inertia for linear complementarity problem
- Authors: He, Xing , Huang, Junjian , Li, Chaojie
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 15th International Conference on Control, Automation, Robotics and Vision; Singapore, Singapore; 18th-21st November 2018 p. 135-139
- Full Text: false
- Reviewed:
- Description: In this brief, considering the inertial term into first order neural networks(NNs), an inertial NN(INN) modeled by means of a differential inclusion is proposed for solving linear complementarity problem with P-0 matrix. Compared with existing NNs, the presence of the inertial term allows us to overcome some drawbacks of many NNs, which are constructed based on the steepest descent method, and this model is more convenient for exploring different optimal solution. It is proved that the proposed NN is stable in the sense of Lyapunov and any equilibrium of our NN is the optimal solution of LCP with P-0 matrix. Simulation results on two numerical examples show the effectiveness and performance of the proposed neural network.
Organisational learning with SaaS CRM – A case study of higher education
- Authors: Oseni, Taiwo , Chadhar, Mehmood , Ivkovic, Sasha , Firmin, Sally
- Date: 2018
- Type: Text , Conference proceedings
- Relation: Australasian Conference on Information Systems ; Sydney ; 2018 published in Australasian Conference on Information Systems 2018.
- Relation: http://creativecommons.org/licenses/by-nc-nd/4.0
- Full Text:
- Reviewed:
- Description: Customer Relationship Management (CRM) generally has a reputation as a technology that does not live up to its over-inflated expectations. Yet, implementations in higher education remain on the rise. Higher Education institutions (HEIs) are embracing cloud-based CRM systems to upsurge performance, encourage better management practices, and enhance their relationship with staff and students. CRM success however relies heavily on an adaptive organisational learning (OL) process upon which proactive decisions can be made. This paper emphasises that committed learning in post-implementation use is paramount to attaining further understanding of the capabilities, features and functionality of the CRM. Investigating how SaaS CRM usage reflect an organisation’s learning in a Higher Education context, the paper presents theoretical and practical contributions in a framework for effective SaaS CRM utilisation, and recommends a continuous cycle of exploration-exploitation-exploration. Yet the reality is that organisations explore, exploit, and then stop exploring.
Passive detection of splicing and copy-move attacks in image forgery
- Authors: Islam, Mohammad , Kamruzzaman, Joarder , Karmakar, Gour , Murshed, Manzur , Kahandawa, Gayan
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th International Conference on Neural Information Processing, ICONIP 2018; Siem Reap, Cambodia; 13th-16th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11304 LNCS, p. 555-567
- Full Text:
- Reviewed:
- Description: Internet of Things (IoT) image sensors for surveillance and monitoring, digital cameras, smart phones and social media generate huge volume of digital images every day. Image splicing and copy-move attacks are the most common types of image forgery that can be done very easily using modern photo editing software. Recently, digital forensics has drawn much attention to detect such tampering on images. In this paper, we introduce a novel feature extraction technique, namely Sum of Relevant Inter-Cell Values (SRIV) using which we propose a passive (blind) image forgery detection method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP). First, the input image is divided into non-overlapping blocks and 2D block DCT is applied to capture the changes of a tampered image in the frequency domain. Then LBP operator is applied to enhance the local changes among the neighbouring DCT coefficients, magnifying the changes in high frequency components resulting from splicing and copy-move attacks. The resulting LBP image is again divided into non-overlapping blocks. Finally, SRIV is applied on the LBP image blocks to extract features which are then fed into a Support Vector Machine (SVM) classifier to identify forged images from authentic ones. Extensive experiment on four well-known benchmark datasets of tampered images reveal the superiority of our method over recent state-of-the-art methods.
Performance evaluation of two interconnected high voltage utility substations using PRP topology based on IEC 62439-3
- Authors: Kumar, Shantanu , Das, Narottam , Islam, Syed
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2017 Australasian Universities Power Engineering Conference, AUPEC 2017; Melbourne, Australia; 19th-22nd November 2017 Vol. 2017, p. 1-5
- Full Text: false
- Reviewed:
- Description: In a modern power system network having multiple interconnected High Voltage (HV) substations, communication amongst Intelligent Electronic Devices (IED) becomes an important feature in an automation system. Time critical information, such as feeder faults, overcurrent, under frequency messages between multiple geographically isolated substations have opened up number of issues related to reliability in protection, automation and control. There are problems related to latency, data loss and transfer of Sampled Value (SV) packets transmitted to these inter-connected substations on radio wireless and Ethernet mode linked up in a Wide Area Network (WAN) configuration. Few other key area of concerns in a digital protection scheme are packet losses, errors in packet arrival at destination, latency in transmission, End to End delay in packet transfer due to flooding of the network, out of sequence packet arrival etc. Error prone packets could seriously compromise the protection scheme and endanger the safety of primary plant assets in a transmission substation. This paper investigates performance of SV packet transmission between two interconnected substations]. Practical simulations were performed to evaluate the performance of SV packets in a laboratory set up to assess the performance of digital protection scheme based on Parallel Redundancy Protocol (PRP) topology involving digital equipment such as Merger Units (MUs), switches, IEDs and Redundant boxes (Red box), etc.
Permit-to-work systems as a health and safety risk control strategy in mining : A prospective study in resilience engineering
- Authors: Pillay, Manikam , Tuck, Michael
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: AHFE 2017 International Conference on Human Error, Reliability, Resilience, and Performance, 2017 : Advances in Human Error, Reliability, Resilience, and Performance; Los Angeles, USA; 17th-21st July 2017; published in Advances in Intelligent Systems and Computing Vol. 589, p. 145-154
- Full Text:
- Reviewed:
- Description: Mining is an important contributor to the social and economic fabric of our society. However, it is also considered to be one of the most dangerous industries. Compared to manufacturing, mining is generally regarded as a more complex industry to work in, creating additional challenges for policy makers, researchers and practitioners. This paper first discusses the state of mining health and safety in Australia, followed by an examination of some of the complexities that characterizes the industry. Next one contemporary approach, permit-to-work systems (PTW), is introduced, followed by a review of the literature relating to its use as a health and safety risk control strategy. This is followed by a discussion of Resilience engineering (RE) as an innovation in health and safety management, and a case made for investigating RE as a safety management strategy using PTW systems. The paper concludes by suggesting a pragmatism research framework and two organizational theories upon which such research can be advanced. © Her Majesty the Queen in Right of United Kingdom 2018.
Prediction of clogging in stormwater filters using artificial neural network
- Authors: Lin, Junlin , Kandra, Harpreet , Choudhury, Tanveer , Barton, Andrew
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 771-776
- Full Text: false
- Reviewed:
- Description: Stormwater filtration technologies play a significant role in improving water quality and making treated water available for non-potable uses. However, during treatment processes, contaminants such as suspended solids would lead to clogging in storm water filters, especially those with high infiltration rates. There are several parameters that affect clogging of filters, and a major challenge is to understand the parameter interdependencies, correlations and their individual effects. A robust methodology is, thus, required to accurately predict clogging which would contribute to the development of filtration technologies and in predictive maintenance. This research employs the use of Artificial Neural Network (ANN) model to predict clogging performance of stormwater filters under different operational conditions using experimental data from previous work. A single hidden layer ANN model with 19 hidden layer neurons was developed in this preliminary work.
- Description: IEEE International Symposium on Industrial Electronics
Prediction of index properties of different rocks using non-destructive testing
- Authors: Khandelwal, Manoj
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 52nd U.S. Rock Mechanics/Geomechanics Symposium; Seattle, Washington; 17th-20th June 2018 p. 1-6
- Full Text: false
- Reviewed:
- Description: Index properties of rocks are vital in the planning and design of geo-mining structures. It is a time consuming and laborious job to determine index properties in laboratory and also involves expensive equipment and proficiency, but the determination of P-wave velocity in laboratory is an easy, dependable, and less difficult task. So, in this paper, an attempt has been made to correlate Impact strength index, Schmidt hammer rebound number, Slake durability index and Protodyakonov strength index of different rocks with the P-wave velocity. A simple linear regression analysis was performed and a strong correlation was established between the P-wave velocity and different index properties of various rock types with very higher coefficient of determination. Student’s t-test were performed to confirm the validity of the proposed linear relations.
- Description: 52nd U.S. Rock Mechanics/Geomechanics Symposium
Protein complexes detection based on global network representation learning
- Authors: Xu, Bo , Li, Kun , Liu, Xiaoxia , Liu, Delong , Zhang, Yijia , Lin, Hongfei , Yang, Zhihao , Wang, Jian , Xia, Feng
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); Madrid, Spain; 3-6 Dec. 2018 p. 210-213
- Full Text: false
- Reviewed:
- Description: Detecting protein complexes from protein-protein interaction (PPI) networks allows biologists reveal the principle of cellular organization and functions. Existing computational methods try to incorporate biological evidence to enhance the quality of predicted complexes. However, it is still a challenge to integrate biological information into complexes discovery process under a unified framework. Recently, network embedding methods showed their effectiveness in graph data analysis tasks. It provides a framework for incorporating both network structure and additional node attribute information. This salient feature is particularly desirable in the context of protein complexes identification. However, none of the existing network embedding methods take node attribute proximity and high-order structure proximity into account at the same time. In this paper, we propose a novel global network embedding method, which preserves global network structure and biological information. We utilize this global representation learning method to learn vector representation for proteins. Then, we use a seed-extension clustering method to discover overlapping protein complexes with the embedding results. This novel protein complexes detection method we called GLONE. Evaluated on five real yeast PPI networks, our method outperforms the competing algorithms in terms of different evaluation metrics.
Rapid anomaly detection using integrated prudence analysis (IPA)
- Authors: Maruatona, Omaru , Vamplew, Peter , Dazeley, Richard , Watters, Paul
- Date: 2018
- Type: Text , Conference proceedings
- Relation: PAKDD 2018.Trends and Applications in Knowledge Discovery and Data Mining. p. 137-141
- Full Text: false
- Reviewed:
- Description: Integrated Prudence Analysis has been proposed as a method to maximize the accuracy of rule based systems. The paper presents evaluation results of the three Prudence methods on public datasets which demonstrate that combining attribute-based and structural Prudence produces a net improvement in Prudence Accuracy.
Relevance of frequency of heart-rate peaks as indicator of ‘Biological’ Stress level
- Authors: Santhanagopalan, Meena , Chetty, Madhu , Foale, Cameron , Aryal, Sunil , Klein, Britt
- Date: 2018
- Type: Text , Conference proceedings
- Relation: ICONIP 2018 International on Neural Information Processing; Siem Reap, Cambodia; 13th-16th December, 2018 p. 598-609
- Full Text: false
- Reviewed:
- Description: The biopsychosocial (BPS) model proposes that health is best understood as a combination of bio-physiological, psychological and social determinants, and thus advocates for a far more comprehensive investigation of the relationships between ‘mind-body’ health. For this holistic analysis, we need a suitable measure to indicate participants’ ‘biological’ stress. With the advent of wearable sensor devices, health monitoring is becoming easier. In this study, we focus on bio-physiological indicators of stress, from wearable devices using the heart-rate data. The analysis of such heart-rate data presents a set of practical challenges. We review various measures currently in use for stress measurement and their relevance and significance with the wearables’ heart-rate data. In this paper, we propose to use the novel ‘peak heart-rate count’ metric to quantify level of ‘biological’ stress. Real life biometric data obtained from digital health intervention program was considered for the study. Our study indicates the significance of using frequency of ‘peak heart-rate count’ as a ‘biological’ stress measure.
Secure passive keyless entry and start system using machine learning
- Authors: Ahmad, Usman , Song, Hong , Bilal, Awais , Alazab, Mamoun , Jolfaei, Alireza
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2018; Melbourne, Australia; 11th-13th December 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11342 LNCS, p. 304-313
- Full Text: false
- Reviewed:
- Description: Despite the benefits of the passive keyless entry and start (PKES) system in improving the locking and starting capabilities, it is vulnerable to relay attacks even though the communication is protected using strong cryptographic techniques. In this paper, we propose a data-intensive solution based on machine learning to mitigate relay attacks on PKES Systems. The main contribution of the paper, beyond the novelty of the solution in using machine learning, is in (1) the use of a set of security features that accurately profiles the PKES system, (2) identifying abnormalities in PKES regular behavior, and (3) proposing a countermeasure that guarantees a desired probability of detection with a fixed false alarm rate by trading off the training time and accuracy. We evaluated our method using the last three months log of a PKES system using the Decision Tree, SVM, KNN and ANN and provide the comparative analysis of the relay attack detection results. Our proposed framework leverages the accuracy of supervised learning on known classes with the adaptability of k-fold cross-validation technique for identifying malicious and suspicious activities. Our test results confirm the effectiveness of the proposed solution in distinguishing relayed messages from legitimate transactions.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Shedding light on our practices: Four assumption hunters on a quest
- Authors: Garbutt, Dawn , Brandenburg, Robyn , Thomas, Lynn , Ovens, Alan
- Date: 2018
- Type: Text , Conference proceedings
- Relation: Pushing boundaries and crossing borders; Self-Study of Teacher Education Practices, Herstmonceux, UK: 2018 p. 409-415
- Full Text:
- Reviewed:
- Description: We are an international collaborative of self-study researchers who have begun work together to identify and challenge assumptions that underpinned our practice as teacher educators. Assumptions are the underlying biases that define how pedagogy is enacted. Seeking out and challenging assumptions helps to discover the unconscious biases that define and mediate a practitioner’s actions in the classroom. Assumptions by themselves are neither good nor bad things, but rather are the tacit beliefs that guide a teacher’s decision making. As Brookfield (1995) states, “informed actions…are based on assumptions that have been carefully and critically investigated” (p.80). By using this as the starting point for self-study, the objective is then not to eliminate assumptions from our teaching practice, but to better understand and analyse those assumptions through a process of rigorous self-inquiry. Such inquiry empowers us to assess the impact of our assumptions on our professional practice.
Solving minimax problems : Local smoothing versus global smoothing
- Authors: Bagirov, Adil , Sultanova, Nargiz , Al Nuaimat, Alia , Taheri, Sona
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 4th International Conference on Numerical Analysis and Optimization, NAO-IV 2017; Muscat, Oman; 2nd-5th January 2017; published in Numerical Analysis and Optimization NAO-IV (part of the Springer Proceedings in Mathematics and Statistics book series PROMS, volume 235) Vol. 235, p. 23-43
- Full Text: false
- Reviewed:
- Description: The aim of this chapter is to compare different smoothing techniques for solving finite minimax problems. We consider the local smoothing technique which approximates the function in some neighborhood of a point of nondifferentiability and also global smoothing techniques such as the exponential and hyperbolic smoothing which approximate the function in the whole domain. Computational results on the collection of academic test problems are used to compare different smoothing techniques. Results show the superiority of the local smoothing technique for convex problems and global smoothing techniques for nonconvex problems. © 2018, Springer International Publishing AG, part of Springer Nature.
- Description: Springer Proceedings in Mathematics and Statistics
Target learning : A novel framework to mine significant dependencies for unlabeled data
- Authors: Wang, Limin , Chen, Shenglei , Mammadov, Musa
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018; Melbourne, Australia; 3rd-6th June 2018; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10937 LNAI, p. 106-117
- Full Text: false
- Reviewed:
- Description: To mine significant dependencies among predictiveattributes, much work has been carried out to learn Bayesian netwrok classifiers (BNC T s) from labeled training data set T. However, if BNC T does not capture the “right” dependencies that would be most relevant to unlabeled testing instance, that will result in performance degradation. To address this issue we propose a novel framework, called target learning, that takes each unlabeled testing instance as a target and builds an “unstable” Bayesian model BNC P for it. To make BNC P and BNC T complementary to each other and work efficiently in combination, the same learning strategy is applied to build them. Experimental comparison on 32 large data sets from UCI machine learning repository shows that, for BNCs with different degrees of dependence target learning always helps improve the generalization performance with minimal additional computation.
- Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Texture based vein biometrics for human identification : A comparative study
- Authors: Bashar, Khayrul , Murshed, Manzur
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018; Tokyo, Japan; 23rd-27th July 2018 Vol. 2, p. 571-576
- Full Text:
- Reviewed:
- Description: Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin micro-textures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.
- Description: Proceedings - International Computer Software and Applications Conference
Tire size identification using extreme learning machine algorithm
- Authors: Kahandawa, Gayan , Choudhury, Tanveer , Ibrahim, Yousef
- Date: 2018
- Type: Text , Conference proceedings
- Relation: 27th IEEE International Symposium on Industrial Electronics, ISIE 2018; Cairns, Australia; 13th-15th June 2018 Vol. 2018-June, p. 571-576
- Full Text: false
- Reviewed:
- Description: Precise tire size identification is needed to increase the efficiency and the reliability of tire inflators and to minimize the inflation cycle time. On the other hand the correct inflation pressure improve the road safety and tire life as well. A single hidden layer feed forward neural network (SLFN) is used in this study to precisely identify a tire size to enhance the tire inflation cycle. The training times of traditional back propagation algorithms, mostly used to model such tire identification processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. It is found that networks trained with ELM have relatively good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The result represents robustness of the trained networks and enhance reliability of the mode. Together with short training time, the algorithm has valuable application in tire identification process.
- Description: IEEE International Symposium on Industrial Electronics
Understanding deep aquifer responses to interseam materials of brown coal mines
- Authors: Rastogi, Sid , Barton, Andrew , Mackay, Rae , Kandra, Harpreet , Tolooiyan, Ali
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 Hydrology and Water Resources Symposium: Water and Communities, HWRS 2018; Melbourne, Australia; 3rd-6th December 2018 p. 711-722
- Full Text: false
- Reviewed:
- Description: Brown coal deposits in the Latrobe Valley form part of the tertiary coal system of the Gippsland Basin, which is one of three major tertiary basins in Victoria, Australia. There are currently two operating brown coal mines in the Latrobe Valley (Yallourn and Loy Yang Mines) where coal is mined for power generation, with a third mine (Hazelwood) having recently ceased operations. An ongoing challenge in the mines is the management of geotechnical stability of the open pit batters. This includes the management of significant issues such as instability due to floor heave which is directly related to groundwater pressures of the underlying confined aquifers. The time dependent pressure distributions in the interseam layers are complex due to the complex heterogeneous stratigraphy of these layers. A model of the fine scale stratigraphy using Minescape has been developed to explore how pressure redistribution occurs and how the groundwater flow systems impact the interseam pore pressures due to pumping activity, leading to potential impacts on the mine batter movements. The objective of the preliminary groundwater modelling presented in this paper is to examine the hydraulic connectivity between the lower pumped aquifer layers and the upper sandy layers. The goal is to assess whether the connections are solely through vertical flows through the interbedded aquitard layers or whether there are lateral connections of the sandy layers that govern the vertical connections. A one-dimensional vertical flow model has been used for this purpose in conjunction with high quality groundwater head data from multiple depths in vertically sealed bores. The results suggest that the pressure redistributions vertically cannot be explained by vertical flows alone and that lateral exchange between layers is also occurring. This work will inform the next stage of modelling that will use the detailed stratigraphic modelling in three dimensions.