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IEEE Cloud Summit

作者： Chengyu Tang Sanjeev Baskiyar Dept. of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798350370065

ISBN: (纸本)9798350370072

As urban traffic becomes increasingly complex, conventional actuated traffic signal control methods are showing their limitations in mitigating congestion, and Deep Reinforcement Learning (DRL) is considered a promising solution. Existing studies mainly focus on using distributed agents, each intersection being controlled by a separate agent that can communicate with its neighbors to facilitate local coordination, which subsequently increases network complexity and usage. This article presents a DRL-based intelligent traffic signal control framework that leverages the fog computing paradigm. Traffic signals are divided into clusters, each controlled by a shared DRL agent in a fog node based on the collective information of the regional traffic situation. We have conducted simulation experiments in multiple scenarios, and the experimental results show that the proposed method yields lower average waiting times compared to existing methods.

关键词： Deep reinforcement learning Complexity theory Edge computing

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Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks using Reinforcement Learning

Optimal Slot Utilization in IEEE 802.15.4e TSCH Networks usi...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tarana Ara Ramiro Liscano Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

This paper presents an innovative MAC scheduling algorithm to achieve energy savings in IEEE 802.15.4e TSCH sensor networks leveraging reinforcement learning to determine an optimal number of slots to keep active in order to maintain a specific packet delivery ratio for the network. The goal is to turn off slots in the 802.15.4e TSCH frame that are not highly utilized. Each node determines the slots that should be deactivated based on a threshold $\mathbf{Q}$ value. This scheduling strategy allows the nodes to conserve energy effectively by finding the optimal active timeslots for both transmission and reception. This algorithm we name it as RAST Reinforcement-based Slot utilization Technique) algorithm. Through extensive simulations and evaluations across various network configurations, the RAST algorithm reveals a significant packet delivery ratio improvement as compared to Orchestra utilizing less active slots.

关键词： IEEE 802.15 Standard Scheduling algorithms Energy conservation Reinforcement learning Land mobile radio

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Using ChatGPT in Undergraduate computer Science and software engineering Courses: A Students' Perspective

Using ChatGPT in Undergraduate Computer Science and Software...

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Frontiers in Education (FIE) Conference

作者： Noah Andersen-Kiel Panagiotis Panos Linos Computer Science and Software Engineering Dept. Butler University Indianapolis Indiana USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer Science and software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer Science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Surveys Codes Scalability computer bugs Project management Chatbots Object oriented programming Programming profession software engineering Python

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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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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms

Unsupervised Insider Threat Detection Using Multi-Head Self-...

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Intelligent Cybersecurity Conference (ICSC)

作者： Simon Bertrand Pascal Germain Nadia Tawbi dept. of Computer Science & Software Engineering Laval University Québec Canada

ISBN: (数字)9798350354775

ISBN: (纸本)9798350354782

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%.

关键词： Neural networks Manuals Feature extraction Vectors Threat assessment Data models Real-time systems Bayes methods Optimization Tuning

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Optimization-Based Comparative System Evaluation of Single and Dual Traction Inverters with Focus on Partial Load Efficiency and Chip Area

Optimization-Based Comparative System Evaluation of Single a...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Sachs, Christoph Stamer, Fabian Allgeier, Jan Thrimawithana, Duleepa Neuburger, Martin Esslingen University Faculty of Mobility and Technology Germany University of Stuttgart Institute for System Dynamics Germany Robert Bosch GmbH Germany The University of Auckland Dept. of Electrical Computer and Software Engineering New Zealand

ISBN: (纸本)9798350376067

The transition to electric transportation demands efficient and cost-effective powertrains. Optimizing energy use is crucial for extending range and reducing expenses. However, comparing inverter and motor efficiency based on inverter topologies is challenging due to biased methodologies that favor certain designs over others. This document introduces a novel optimization-based approach for enhancing partial load efficiency and minimizing chip area of single and dual traction inverters, indicating potential energy savings and cost reduction. Recent publications of both industry [1], [2] and academia [3] underscore the importance of these design goals achieved by either novel inverter topologies or enhanced control methods. Two promising topologies with the inherent capability of partial load optimization are evaluated regarding chip area and system efficiency to find the most suitable concept for future electric vehicle power trains. © 2024 IEEE.

关键词： Energy utilization

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Automated Hematological Analysis:Advances in Segmentation Approaches and Future Directions

Automated Hematological Analysis:Advances in Segmentation Ap...

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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Saleha Eram Muhammad Usman Akram Maham Nayab Dept. Computer & Software Engineering National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Hematological analysis is crucial for diagnosing blood disorders such as leukemia and anemia, a process that has traditionally depended on manual microscopy, and is susceptible to variability and human error. The quest for more reliable and efficient diagnostic methods has led to the development of automated systems. This review examines the methods used for automated blood cell segmentation, with a particular emphasis on deep learning approaches such as semantic and instance segmentation. Advanced models like Mask R-CNN and DeepLabv3+ were evaluated for their effectiveness in accurately segmenting and identifying blood components in complex histological images. The findings revealed that Mask R-CNN achieved a segmentation accuracy of 92%, while DeepLabv3+ came in close behind at 94.5%. Both models significantly surpassed traditional methods, which generally provide accuracy rates of 75–80 %. Despite these advancements, the review highlights ongoing challenges related to the robustness and scalability of these techniques in clinical settings. The paper concludes by emphasizing the transformative potential of deep learning in hematological diagnostics and calls for further research to address current limitations and improve patient outcomes.

关键词： Deep learning Instance segmentation Accuracy Scalability Computational modeling Semantic segmentation Semantics Robustness Blood Systematic literature review

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DC Fault Detection Using Time Derivative of High-Pass Filter Response in MTDC Systems

DC Fault Detection Using Time Derivative of High-Pass Filter...

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Annual Conference of Industrial Electronics Society

作者： Saad Ahmed Khan Abhisek Ukil Nirmal-Kumar C. Nair Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

In the DC fault event, DC link capacitors immediately discharge a colossal current of high frequency. These large currents of high frequencies can instantly damage the components attached to the DC transmission system. Therefore, a rapid and robust DC fault detection topology is necessary, facilitating protection coordination in forthcoming Multi-Terminal Direct Current (MTDC) systems. This study introduces a novel fault detection technique using the time derivative of the High Pass Filter (HPF) response to identify fault currents in just a few microseconds (µs). This topology enables protection coordination for modern MTDC systems. Furthermore, this technique can identify different types of DC faults and remains unaffected by AC faults and load variation.

关键词： Fault diagnosis Accuracy Fault detection Load management Electrical fault detection Topology Power systems High frequency Passband Protection

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A Novel Dual Channel Isolated Current Source Gate Driver for Fast Switching MOSFETs

A Novel Dual Channel Isolated Current Source Gate Driver for...

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Annual Conference of Industrial Electronics Society

作者： Asjad Elahi Mohamed Youssef Dept. of Electrical Computer and Software Engineering University of Ontario Institute of Technology Oshawa

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

A new dual-channel discontinuous mode isolated current source gate driver (ICSGD) is proposed in this paper. The proposed gate driver can provide two isolated gate drive signals for two power MOSFETs in circuits where these switches are in sync. The proposed gate driver can achieve very fast turn on and turn off transitions, due to its current mode nature. In addition, this technique can achieve partial gate energy recovery by recovering a fraction of the RMS (CV2) energy, which is typically dissipated over the gate drive resistors in voltage source gate drive (VSGD) techniques. Thus, provides better efficiency. The proposed circuit consists of six transistors and a small pulse transformer to allow swift operation over a wide range of duty cycles. Current through the transformer is discontinuous to avoid conduction losses due to circulating currents such as in some resonant gate drivers (RGD). Consequently, the proposed ICSGD provides superior performance over its VSGD counterparts, especially in high-frequency (HF) operation. The design is implemented in both PSIM and Typhoon HIL (hardware in the loop) platforms. The model is verified with a match of results from PSIM and Typhoon. The PSIM & HIL results show that the ICSGD achieves a minimum of 20% efficiency improvement.

关键词： MOSFET Tropical cyclones Circuits Switches Logic gates Drives Gate drivers Pulse transformers Transistors Synchronization

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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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