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19th IET Conference on Developments in Power System Protection (DPSP Europe 2025)

作者： Prasoon Arya Sarasij Das Tarlochan Sidhu Department of Electrical Engineering Indian Institute of Science Bangalore India Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9781837243167

Static relays are reliable, robust and fast compared to digital relays. However, they are bulky and hard to configure. The discovery of microprocessor-based relays overcame these limitations, although they are prone to cyber-attacks as they are digital computers. It is tough to detect malicious code and malware in digital computers; conversely, analog circuits are immune to these attacks. Before microprocessor-based relays, static relays were used in power systems protection; they are still considered reliable, robust, fast and immune to cyber threats as they didn’t have any digital components. With the developments in analog technology, such as Field Programmable analog Array (FPAA), there is now an opportunity for analog relays to rebound with reduced size, ease of design and reconfigurability. This work proposes an FPAA-based new generation analog mho relay demonstrating a fully analog implementation of mho distance characteristics on FPAA. The line-ground (LG) and line-line (LL) elements are modelled in the AN231K04-QUAD4 kit and tested on the IEEE 9-Bus system. The results are promising. Relay shows dependability over the designed range of its operation. Besides, this work also proposes using FPAA-based static relays along with the existing digital relays to enhance the cyber security of the protection schemes.

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A Case Study on ChatGPT Question Generation

A Case Study on ChatGPT Question Generation

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Chan, Winston An, Aijun Davoudi, Heidar York University Dept. of Electrical Engineering and Computer Science Toronto Canada Ontario Tech University Faculty of Science OshawaON Canada

ISBN: (纸本)9798350324457

The advent of transformers and the subsequent development of Large Language Models (LLMs) based on these technologies has revolutionized the field of Natural Language Processing (NLP). These models are able to understand and generate coherent natural language and hold conversations with humans continuously. Meanwhile, ChatGPT has become famous among many LLMs for its general-purpose characteristics and versatility. With that in mind, we investigate the capabilities of ChatGPT, which is very successful in many downstream NLP tasks on the task of Question Generation (QG). In particular, our experiments show that appropriate context through our designed prompts makes ChatGPT an appropriate tool for accurately performing the QG task. We compare ChatGPT's question generation results with the state-of-the-art models, particularly on the SQuAD and car manual datasets. The results show that ChatGPT is able to compete with or even outperform some of the baseline models. Furthermore, we illustrate that we may improve ChatGPT through additional fine-tuning of the prompts. Finally, we also investigate the use of ChatGPT to evaluate QG models. While the use of ChatGPT for such purposes is still in its early stages, our results demonstrate that ChatGPT can potentially be a strong QG accuracy evaluator comparable to human evaluators. © 2023 IEEE.

关键词： large language model natural language processing question generation

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House price prediction using clustering and genetic programming along with conducting a comparative study 21

House price prediction using clustering and genetic programm...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Azimlu, Fateme Rahnamayan, Shahryar Makrehchi, Masoud Ontario Tech University Electrical Computer and Software Engineering Department Oshawa Canada

ISBN: (纸本)9781450383516

One of the most important tasks in machine learning is prediction. Data scientists use different regression methods to find the most appropriate and accurate model for each type of datasets. This study proposes a method to improve accuracy in regression and prediction. In common methods, different models are applied to the whole data to find the best model with higher accuracy. In our proposed approach, first, we cluster data using different methods such as K-means, DBSCAN, and agglomerative hierarchical clustering algorithms. Then, for each clustering method and for each generated cluster we apply various regression models including linear and polynomial regressions, SVR, neural network, and symbolic regression in order to find the most accurate model and study the genetic programming potential in improving the prediction accuracy. This model is a combination of clustering and regression. After clustering, the number of samples in each created cluster, compared to the number of samples in the whole dataset is reduced, and consequently by decreasing the number of samples in each group, we lose accuracy. On the other hand, specifying data and setting similar samples in one group enhances the accuracy and decreases the computational cost. As a case study, we used real estate data with 20 features to improve house price estimation;however, this approach is applicable to other large datasets. © 2021 ACM.

关键词： Genetic programming

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IMPLEMENTATION OF MHO CHARACTERISTICS IN FPAA-BASED RECONFIGURABLE NEW GENERATION STATIC RELAY 19

IMPLEMENTATION OF MHO CHARACTERISTICS IN FPAA-BASED RECONFIG...

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19th IET Conference on Developments in Power System Protection, DPSP Europe 2025

作者： Arya, Prasoon Das, Sarasij Sidhu, Tarlochan Department of Electrical Engineering Indian Institute of Science Bangalore India Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

Static relays are reliable, robust and fast compared to digital relays. However, they are bulky and hard to configure. The discovery of microprocessor-based relays overcame these limitations, although they are prone to cyber-attacks as they are digital computers. It is tough to detect malicious code and malware in digital computers;conversely, analog circuits are immune to these attacks. Before microprocessor-based relays, static relays were used in power systems protection;they are still considered reliable, robust, fast and immune to cyber threats as they didn't have any digital components. With the developments in analog technology, such as Field Programmable analog Array (FPAA), there is now an opportunity for analog relays to rebound with reduced size, ease of design and reconfigurability. This work proposes an FPAA-based new generation analog mho relay demonstrating a fully analog implementation of mho distance characteristics on FPAA. The line-ground (LG) and line-line (LL) elements are modelled in the AN231K04-QUAD4 kit and tested on the IEEE 9-Bus system. The results are promising. Relay shows dependability over the designed range of its operation. Besides, this work also proposes using FPAA-based static relays along with the existing digital relays to enhance the cyber security of the protection schemes. © 2025 The Institution of engineering & technology.

关键词： computer viruses

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Log Anomaly Detection by Leveraging LLM-Based Parsing and Embedding with Attention Mechanism

Log Anomaly Detection by Leveraging LLM-Based Parsing and Em...

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Asma Fariha Vida Gharavian Masoud Makrehchi Shahryar Rahnamayan Sanaa Alwidian Akramul Azim Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada Department of Engineering Brock University St. Cathartines Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

During the software operation phase, automated log analysis is crucial for the early detection of anomalies to prevent critical incidents, like system failure. Learning-based anomaly detection techniques have shown the potential for real-time anomaly detection from trace logs through learning the execution patterns. However, extracting features from raw text format log files of diversified structures has been challenging and tackled in different ways. With the recent advancements in large language models (LLM), several LLM-based parsing methods have been proposed, where most of these methods struggled with uncertain output from LLM or manual rules set requirements for the parsing. To address these challenges, we have proposed a hybrid framework leveraging LLM in parsing and embedding. Our proposed approach uses the LLM to generate Regular expressions (REGEX) for the parser, along with parsing and event embedding (EM) using a pre-trained LLM model. Then, this framework leverages the reconstructive capacity of the autoencoder with attention mechanism (AM) for unsupervised learning of log patterns. The experimental case study shows the model’s effectiveness in anomaly detection using a public dataset with 96% accuracy. This framework will provide flexibility to pre-process different text-based log structures without human involvement in parsing.

关键词： Attention mechanisms Accuracy Computational modeling Large language models Manuals Feature extraction software

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Effects of Regenerative Braking on Hybrid Battery Balancing

Effects of Regenerative Braking on Hybrid Battery Balancing

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Alvin Huynh Akash Samanta Sheldon Williamson Faculty of Electrical and Software Engineering Ontario Tech University Oshawa Canada

Few studies have been conducted on the interaction between regenerative braking and cell balancers with dynamic cycling. High transient dynamics can potentially damage the Lithium-ion batteries by charging cells over 5 mV the maximum voltage or current spikes over the allowable current limitations. This paper will conduct an analysis of an active balancer dynamics with and without regenerative braking utilizing a reduced vehicle model based on the ontario tech racing electric vehicle. The result was 1.5% larger state of charge difference and higher minimum SOC compared to the system without regenerative braking.

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Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning

arXiv

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arXiv 2024年

作者： Zhang, Chong Dong, Min Liang, Ben Afana, Ali Ahmed, Yahia Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

We propose an uplink over-the-air aggregation (OAA) method for wireless federated learning (FL) that simultaneously trains multiple models. To maximize the multi-model training convergence rate, we derive an upper bound on the optimality gap of the global model update, and then, formulate an uplink joint transmit-receive beamforming optimization problem to minimize this upper bound. We solve this problem using the block coordinate descent approach, which admits low-complexity closed-form updates. Simulation results show that our proposed multi-model FL with fast OAA substantially outperforms sequentially training multiple models under the conventional single-model approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning

Uplink Over-the-Air Aggregation for Multi-Model Wireless Fed...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Chong Zhang Min Dong Ben Liang Ali Afana Yahia Ahmed Dept. of Electrical and Computer Engineering University of Toronto Canada Ericsson Canada Canada Dept. of Electrical Computer and Software Engineering Ontario Tech University Canada

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

关键词： Training Upper bound Accuracy Array signal processing Federated learning Wireless networks Simulation Uplink Optimization Convergence

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Beamforming and Power Control for Wireless Network Virtualization in Uplink MIMO Systems

Beamforming and Power Control for Wireless Network Virtualiz...

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IEEE International Conference on Communications (ICC)

作者： Ahmed F. Almehdhar Ben Liang Min Dong Gary Boudreau Yahia Ahmed Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical Computer and Software Engineering Ontario Tech University Canada Ericsson Canada Inc. Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We consider wireless network virtualization (WNV) in an uplink multiple-input multiple-output system, where multiple service providers (SPs) operate in virtually isolated networks managed by an infrastructure provider (InP) that owns the communication equipment. Service isolation is achieved at the physical layer by exploiting a large number of antennas at the base stations. We formulate this WNV as a non-convex optimization problem for the InP, jointly considering the uplink receive beamforming at the BS and the transmit power of the SPs' subscribing user devices. We decompose the problem into two subproblems and derive closed-form solutions to both. We then adopt an alternating optimization approach to combine the closed-form solutions to solve the original problem. Our simulation results show that the proposed method provides strong service isolation among the SPs while retaining efficiency similar to or better than centralized beamforming without virtualization, and it substantially outperforms traditional WNV with strict resource separation.

关键词： Closed-form solutions Array signal processing Wireless networks Simulation Power control Virtualization Uplink

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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

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computers, Materials & Continua 2022年第12期73卷 4763-4777页

作者： Mir Mushhood Afsar Shizza Saqib Yazeed Yasin Ghadi Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Virtual reality is an emerging field in the whole *** problem faced by people today is that they are more indulged in indoor technology rather than outdoor ***,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person *** system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the *** track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the ***,the sensor data is sent to a machine learning model,which detects the game played by the *** detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human *** main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting *** proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational *** experimentation proved the validity of the proposed system.

关键词： Artificial neural networks bluetooth connection inertial sensors machine learning virtual reality exergaming

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