Multi-agent based autonomous control of microgrid
- Authors: Shawon, Mohammad Hasanuzzaman , Ghosh, Arimdam , Muyeen, S. , Baptista, Murilo , Islam, Syed
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020, 15-18 Sept. 2020, Bangkok, Thailand p. 333-338
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- Description: Microgrid (MG), a revolutionary concept in the energy infrastructure, plays an important role for the establishment of a resilient grid infrastructure. Since its emergence, it has evolved around a number of cutting edge technologies for its smooth operation and control. Among them multi-agent system (MAS) provides an intelligent and decentralized platform for the control of microgrid. This paper highlights the application of a MAS in an AC microgrid, including a detailed structure of microgrid, the communication interface between microgrid and multi-agent platform. A detailed small scale microgrid model has been simulated in MATLAB/SIMULINK environment, whereas the agent platform has been implemented in JADE (Java Agent Development Framework) platform. The MAS autonomously detects main grid outage and facilitates seamless transition from grid-connected mode to islanding mode; thus ensures overall smooth operation of the power network. Simulation results are presented to verify the effectiveness of the MAS based control system. © 2020 IEEE.
OFFER: A Motif Dimensional Framework for Network Representation Learning
- Authors: Yu, Shuo , Xia, Feng , Xu, Jin , Chen, Zhikui , Lee, Ivan
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 p. 3349-3352
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- Description: Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency. © 2020 ACM.
Partial undersampling of imbalanced data for cyber threats detection
- Authors: Moniruzzaman, Md , Bagirov, Adil , Gondal, Iqbal
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
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- Description: Real-time detection of cyber threats is a challenging task in cyber security. With the advancement of technology and ease of access to the internet, more and more individuals and organizations are becoming the target for various cyber attacks such as malware, ransomware, spyware. The target of these attacks is to steal money or valuable information from the victims. Signature-based detection methods fail to keep up with the constantly evolving new threats. Machine learning based detection has drawn more attention of researchers due to its capability of detecting new and modified attacks based on previous attack's behaviour. The number of malicious activities in a certain domain is significantly low compared to the number of normal activities. Therefore, cyber threats detection data sets are imbalanced. In this paper, we proposed a partial undersampling method to deal with imbalanced data for detecting cyber threats. © 2020 ACM.
- Description: E1
Piracy on the internet: Publisher-side analysis on file hosting services
- Authors: Chan, Marcus , Gong, Mingwei , Naha, Ranesh , Mahanti, Aniket
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 International Symposium on Networks, Computers and Communications (ISNCC); Montreal, QC, Canada; 20-22 October 2020 p. 1-7
- Full Text: false
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- Description: In the file sharing ecosystem, One-Click File Hosting Services (FHS) such as Rapidgator and Uploaded, the previously Rapidshare and Megaupload, provide a platform for users to share copyrighted content. We present a publisher-side analysis of FHS file sharing dynamics through data collected from active measurement by crawling Warez-BB. The website is essentially a forum where publishers can share links to content they have uploaded on file hosting services. Consumers can use the website to gain access to content shared on the website, often free of charge. We primarily analyse various characteristics of file sharing with respect to view count as the evaluation metric.
Pre-trained language models with limited data for intent classification
- Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Karmakar, Gour , Walls, Darren
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: 2020 International Joint Conference on Neural Networks, IJCNN 2020
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- Description: Intent analysis is capturing the attention of both the industry and academia due to its commercial and noncommercial significance. The rapid growth of unstructured data of micro-blogging platforms, such as Twitter and Facebook, are amongst the important sources for intent analysis. However, the social media data are often noisy and diverse, thus making the task very challenging. Further, the intent analysis frequently suffers from lack of sufficient data because the labeled datasets are often manually annotated. Recently, BERT (Bidirectional Encoder Representation from Transformers), a state-of-the-art language representation model, has attracted attention for accurate language modelling. In this paper, we investigate the application of BERT for its suitability for intent analysis. We study the fine-tuning of the BERT model through inductive transfer learning and investigate methods to overcome the challenges due to limited data availability by proposing a novel semantic data augmentation approach. This technique generates synthetic sentences while preserving the label-compatibility using the semantic meaning of the sentences, to improve the intent classification accuracy. Thus, based on the considerations for finetuning and data augmentation, a systematic and novel step-bystep methodology is presented for applying the linguistic model BERT for intent classification with limited data available. Our results show that the pre-trained language can be effectively used with noisy social media data to achieve state-of-the-art accuracy in intent analysis under low labeled-data regime. Moreover, our results also confirm that the proposed text augmentation technique is effective in eliminating noisy synthetic sentences, thereby achieving further performance improvements. © 2020 IEEE.
THCluster: herb supplements categorization for precision traditional Chinese medicine
- Authors: Ruan, Chunyang , Wang, Ye , Zhang, Yanchun , Ma, Jiangang , Chen, Huijuan , Aickelin, Uwe , Zhu, Shanfeng , Zhang, Ting
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);Kansas City, MO, USA; 13-16 Nov. 2017 p. 417-424
- Full Text: false
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- Description: There has been a continuing demand for traditional and complementary medicine worldwide. A fundamental and important topic in Traditional Chinese Medicine (TCM) is to optimize the prescription and to detect herb regularities from TCM data. In this paper, we propose a novel clustering model to solve this general problem of herb categorization, a pivotal task of prescription optimization and herb regularities. The model utilizes Random Walks method, Bayesian rules and Expectation Maximization(EM) models to complete a clustering analysis effectively on a heterogeneous information network. We performed extensive experiments on the real-world datasets and compared our method with other algorithms and experts. Experimental results have demonstrated the effectiveness of the proposed model for discovering useful categorization of herbs and its potential clinical manifestations.
Towards smart online dispute resolution for medical disputes
- Authors: Bellucci, Emilia , Stranieri, Andrew , Venkatraman, Sitalakshmi
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2020); Melbourne, Australia; 3rd-7th February 2020. p. 1-5
- Full Text: false
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- Description: With the advancements in technologies, digitization of health records in the healthcare industry is undertaking a rapid revolution. This is further fueled with the entrance of Internet of Things (IoT), where mobile health devices have resulted in an explosion of health data and increased accessibility via wireless communications and sensor networks. With the introduction of an Electronic Health Record (EHR) system as an important venture for the general health and wellbeing of a country's citizens, privacy issues and medical disputes are expected to rise. In addition to critical health information being documented and shared electronically, integrating data from diverse smart medical IoT devices are leading towards increasingly more complex disputes that require immense time and effort to resolve. Online dispute resolution (ODR) programs have been successfully applied to cost-effectively help disputants resolve commercial, insurance and other legal disputes, but as yet have not been applied to healthcare. This paper takes a modest step in this direction, firstly to identify the drivers of medical disputes that include patient empowerment and technology advancements and trends. Secondly, we explore dispute resolution models and identify the status and limitations of current ODR systems.
- Description: This work was funded by the University of Ballarat Deakin University Collaborative Fund. 160134
3D-CNN for glaucoma detection using optical coherence tomography
- Authors: George, Yasmeen , Antony, Bhavna , Ishikawa, Hiroshi , Wollstein, Gadi , Schuman, Joel , Garnavi, Rahil
- Date: 2019
- Type: Text , Conference proceedings
- Relation: Ophthalmic Medical Image Analysis 6th International workshop, OMIA; Shenzen, China; October 17, 2019 in Lecture Notes in Computer Science (LNCS, volume 11855) p. 52-59
- Full Text: false
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- Description: The large size of raw 3D optical coherence tomography (OCT) volumes poses challenges for deep learning methods as it cannot be accommodated on a single GPU in its original resolution. The direct analysis of these volumes however, provides advantages such as circumventing the need for the segmentation of retinal structures. Previously, a deep learning (DL) approach was proposed for the detection of glaucoma directly from 3D OCT volumes, where the volumes were significantly downsampled first. In this paper, we propose an end-to-end DL model for the detection of glaucoma that doubles the number of input voxels of the previously proposed method, and also boasts an improved AUC = 0.973 over the results obtained using the previously proposed approach of AUC = 0.946. Furthermore, this paper also includes a quantitative analysis of the regions of the volume highlighted by grad-CAM visualization. Occlusion of these highlighted regions resulted in a drop in performance by 40%, indicating that the regions highlighted by gradient-weighted class activation maps (grad-CAM) are indeed crucial to the performance of the model.
A Comprehensive protection method for securing the organization's network against cyberattacks
- Authors: Kbar, Ghassan , Alazab, Ammar
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 Cybersecurity and Cyberforensics Conference (CCC); Melbourne, VIC, Australia; 8-9 May 2019 p. 118-122
- Full Text: false
- Reviewed:
- Description: The advance in technologies helped in providing efficient system that connect people worldwide such as the use of internet. At the same time cyber attackers exploited the vulnerabilities existed in these technologies to conduct large variety of attack activities against the information and systems. Researchers and solution's providers implemented different countermeasure mechanisms to protect the system against attacks and saved the discovered type of attack in attack database for future analysis and decision. Intrusion Detection (ID) system is an example for protecting the system against attacks by monitoring the network activities and updating the attack database for future analysis and protection decision. In addition to IDs, firewall, intrusion prevention, encryption, authorization and authentication are used to protect the system. Furthermore, a supplementary configurations honeypot systems can be used to strengthen the system security.
- Description: The advance in technologies helped in providing efficient system that connect people worldwide such as the use of internet. At the same time cyber attackers exploited the vulnerabilities existed in these technologies to conduct large variety of attack activities against the information and systems. Researchers and solution's providers implemented different countermeasure mechanisms to protect the system against at-tacks and saved the discovered type of attack in attack database for future analysis and decision. Intrusion Detection (ID) system is an example for protecting the system against attacks by monitoring the network activities and updating the attack data-base for future analysis and protection decision. In addition to IDs, firewall, intrusion prevention, encryption, authorization and authentication are used to protect the sys-tem. Furthermore, a supplementary configurations honeypot systems can be used to strengthen the system security.
A Decentralized Patient Agent Controlled Blockchain for Remote Patient Monitoring
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 15th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2019 Vol. 2019-October, p. 207-214
- Full Text: false
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- Description: Blockchain emerging for healthcare provides a secure, decentralized and patient driven record management system. However, the storage of data generated from IoT devices in remote patient management applications requires a fast consensus mechanism. In this paper, we propose a lightweight consensus mechanism and a decentralized patient software agent to control a remote patient monitoring (RPM) system. The decentralized RPM architecture includes devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) software replicated on the Smartphone, Fog and Cloud servers processes medical data to ensure reliable, secure and private communication. Performance analysis has been conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled remote patient monitoring system. © 2019 IEEE.
- Description: E1
A Magnetic linked modular cascaded multilevel converter for medium voltage grid applications
- Authors: Hasan,Md Mubashwar , Islam, Syed , Abu-Siada, Ahmed , Islam, Rabiul Md
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 29th Australasian Universities Power Engineering Conference (AUPEC); Nadi, Fiji; 26-29 November 2019
- Full Text: false
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- Description: One of the key advantages of cascaded multilevel inverters (CMLI) is their ability to generate medium voltage output by using low voltage rated circuit components. For this reason, CMLI has been given much attention in renewable and industrial applications. However, in spite CMLI advantages, balanced input dc voltage management at the cascaded cells is still considered one of the main drawbacks, which limits its straightforward applications. Moreover, galvanic isolation between the input dc supply and the inverter output voltage is essential for grid-connected application. In such case, a step-up transformer is utilized between the inverter output terminals and the grid. This solution incurs additional cost, increases implementation size, weight and maintenance. In this paper, a CMLI is proposed for medium voltage applications by utilizing high frequency magnetic link to ensure galvanic isolation without the need to a conventional step-up transformer as per the current practice. 3 rd harmonic-injected sine pulse width modulation strategy is adopted as a switching controller for the proposed cascaded inverter that is implemented and tested. Experimental results attest the simulation results and confirm the feasibility of the proposed inverter
A Reinforcement learning based algorithm towards energy efficient 5G Multi-tier network
- Authors: Islam, Nahina , Alazab, Ammar , Alazab, Mamoun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 Cybersecurity and Cyberforensics Conference (CCC); Melbourne, Vic; 8th-9th May, 2019 p. 96-101
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- Description: Energy efficiency is a key factor in the next generation wireless communication systems. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for improving the energy efficiency. In this paper, we propose a novel reinforcement learning based decision making algorithm to implement sleep mode in the base stations (BSs) used in multi-tier 5G networks. We propose a Markovian Decision process (MDP) based algorithm to switch between three different power consumption modes of a BS for improving the energy efficiency of the 5G network. The MDP based approach intelligently switches between the states of the BS based on the offered traffic whilst maintaining a prescribed minimum channel rate per user. Our results show that there is a significant gain in the energy efficiency when using our proposed MDP algorithm together with the three-state BSs. We have also shown the energy-delay tradeoff in order to design a delay aware network.
A Rotation invariant HOG descriptor for tire pattern image classification
- Authors: Liu, Ying , Ge, Yuxiang , Wang, Fuping , Liu, Qiqi , Lei, Yanbo , Zhang, Dengsheng , Lu, Guojun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Brighton, UK, 12-17 May 2019. p. 2412-2416
- Full Text: false
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- Description: Texture feature is important in describing tire pattern image which provides useful clue in solving crime cases and traffic accidents. In this paper, we propose a novel texture feature extraction method based on HOG (Histogram of Oriented Gradient) and dominant gradient (DG) in tire pattern images, named HOG-DG. The proposed HOG-DG is not only robust to illumination and scale changes but also is rotation-invariant. In the proposed HOG-DG, HOG features are first computed from circular local cells, and HOG features from an image are concatenated and normalized using the DG to construct the HOG-DG feature. HOG-DG is used to train a support-vector-machine (SVM) classifier for tire pattern classification. Experimental results demonstrate its outstanding performance for tire pattern description.
A small study of big issues in apprenticeship: Companies’ apprenticeship management practices in Australia
- Authors: Smith, Erica
- Date: 2019
- Type: Text , Conference proceedings
- Relation: Contemporary Apprenticeship Reforms and Reconfigurations, 8th International INAP Conference; Kontanz, Germany March 21st-22nd
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Acoustic sensor networks and mobile robotics for sound source localization
- Authors: Nguyen, Linh , Miro, Jaime Valls
- Date: 2019
- Type: Text , Conference proceedings
- Relation: IEEE 15th International Conference on Control and Automation (ICCA);Edinburgh, UK; 16-19 July 2019 p. 1453-1458
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- Description: Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising.
Adaptive low-power wireless sensor network architecture for smart street furniture-based crowd and environmental measurements
- Authors: Nassar, Mohammed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis , IEEE
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE 20th International Symposium on a World of Wireless, Mobile and Multimedia Networks
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- Description: Street furniture such as bins, seats and bus shelters can become "smart" with the inclusion of wireless sensor nodes, which consist of environmental sensors, wireless modules, processors and microcontrollers. One of the most crucial challenges for smart street furniture is how to manage power consumption efficiently without affecting data freshness. In this work, we propose a novel Wireless Sensor Network (WSN) architecture for smart street furniture. Unlike existing WSNs which are based on a one-way communication model between wireless sensor nodes and the server, the proposed architecture employs a two-way communication model and a dynamic adaptation of the time interval of measurements to balance between power consumption and data updates. Our approach also provides a real-time low-power design for wireless sensor nodes which efficiently communicate the updated data instead of sending the same data on a regular basis. To the best of our knowledge, this is the first work in the relevant literature which extends the functionality of the wireless module in wireless sensor nodes to act not only as a station sending environmental data but also as soft Access Point (AP), sensing MAC addresses and WiFi signal strengths from surrounding WiFi-enabled devices. We have conducted experiments on the Murdoch University campus and our results show that our proposal improves lifetime of wireless sensor nodes up to 293% compared to static architectures similar to the ones that have been proposed in the literature. Moreover, network bandwidth is improved up to 38% without affecting data freshness. Finally, storage space for the database at the server is reduced up to 99%.
- Description: E1
Agoraphilic navigation algorithm in dynamic environment with and without prediction of moving objects location
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
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- Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
- Description: E1
An efficient selective miner consensus protocol in blockchain oriented iot smart monitoring
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne; Australia; 13th-15th February 2019 Vol. 2019-February, p. 1135-1142
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- Description: Blockchains have been widely used in Internet of Things(IoT) applications including smart cities, smart home and smart governance to provide high levels of security and privacy. In this article, we advance a Blockchain based decentralized architecture for the storage of IoT data produced from smart home/cities. The architecture includes a secure communication protocol using a sign-encryption technique between power constrained IoT devices and a Gateway. The sign encryption also preserves privacy. We propose that a Software Agent executing on the Gateway selects a Miner node using performance parameters of Miners. Simulations demonstrate that the recommended Miner selection outperforms Proof of Works selection used in Bitcoin and Random Miner Selection.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
Assessing transformer oil quality using deep convolutional networks
- Authors: Alam, Mohammad , Karmakar, Gour , Islam, Syed , Kamruzzaman, Joarder , Chetty, Madhu , Lim, Suryani , Appuhamillage, Gayan , Chattopadhyay, Gopi , Wilcox, Steve , Verheyen, Vincent
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 29th Australasian Universities Power Engineering Conference, AUPEC 2019
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- Description: Electrical power grids comprise a significantly large number of transformers that interconnect power generation, transmission and distribution. These transformers having different MVA ratings are critical assets that require proper maintenance to provide long and uninterrupted electrical service. The mineral oil, an essential component of any transformer, not only provides cooling but also acts as an insulating medium within the transformer. The quality and the key dissolved properties of insulating mineral oil for the transformer are critical with its proper and reliable operation. However, traditional chemical diagnostic methods are expensive and time-consuming. A transformer oil image analysis approach, based on the entropy value of oil, which is inexpensive, effective and quick. However, the inability of entropy to estimate the vital transformer oil properties such as equivalent age, Neutralization Number (NN), dissipation factor (tanδ) and power factor (PF); and many intuitively derived constants usage limit its estimation accuracy. To address this issue, in this paper, we introduce an innovative transformer oil analysis using two deep convolutional learning techniques such as Convolutional Neural Network (ConvNet) and Residual Neural Network (ResNet). These two deep neural networks are chosen for this project as they have superior performance in computer vision. After estimating the equivalent aging year of transformer oil from its image by our proposed method, NN, tanδ and PF are computed using that estimated age. Our deep learning based techniques can accurately predict the transformer oil equivalent age, leading to calculate NN, tanδ and PF more accurately. The root means square error of estimated equivalent age produced by entropy, ConvNet and ResNet based methods are 0.718, 0.122 and 0.065, respectively. ConvNet and ResNet based methods have reduced the error of the oil age estimation by 83% and 91%, respectively compared to that of the entropy method. Our proposed oil image analysis can calculate the equivalent age that is very close to the actual age for all images used in the experiment. © 2019 IEEE.
- Description: E1
Bayesian analysis of random finite element method slip surfaces for slope stability
- Authors: Dyson, Ashley , Tolooiyan, Ali
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 5th ISRM Young Scholars' Symposium on Rock Mechanics and International Symposium on Rock Engineering for Innovative Future, YSRM 2019 p. 118-123
- Full Text: false
- Reviewed:
- Description: The Random Finite Element Method (RFEM) is a powerful technique for incorporating spatially variable shear strength parameters with slope stability numerical simulations. In this research, two-dimensional probabilistic analyses of a large open-cut brown coal mine are presented with particular consideration given to slope Factors of Safety (FoS), when faced with highly anisotropic cohesion and friction angle shear strength parameters. Bayesian methods are implemented to determine updated shear strength parameters based on Factors of Safety and Representative Slip Surfaces (RSS) categorizations. By this method, the impact of observed slip surface depths and safety factors is further investigated. Monte Carlo simulation is implemented in the Finite Element environment Abaqus, with an optimised Strength Reduction Method to determine Factors of Safety. Comparisons of conditional shear strength distributions are made for associated slope safety factors and shallow slip surfaces from a cross-section of the Yallourn open-cut brown coal mine, in Victoria, Australia. The updated shear strength distributions provide a greater understanding of the necessary conditions of particular slope failure mechanisms, contributing further understanding of the stability of Victorian brown coal mines. ©2019 Japanese Society for Rock Mechanics