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12th International Conference on Renewable Power Generation, RPG 2023

作者： Nasser Danapour, S. Mohammadi, Fazel Department of Electrical and Computer Engineering University of Tabriz Tabriz51666-67541 Iran Electrical and Computer Engineering and Computer Science Department University of New Haven West HavenCT06516 United States

The integrated wind turbine-power transmission line systems integrate wind turbines into high-voltage power transmission lines by directly interconnecting wind turbines to high-voltage power transmission lines without using high-voltage power transformers. In such systems, by considering double-circuit power transmission lines, creating a specific phase difference between two power transmission lines can lead to creating an appropriate voltage difference vector between them for injecting the power of wind turbines into power transmission lines without using high-ratio step-up power transformers. Then, a number of power transformers, which are termed receiving-end transformers, absorb the injected power of wind turbines. Such transformers increase the complexity and overall cost of the system. In this paper, an improved method for creating a voltage difference vector between two power transmission lines in an integrated wind turbine-power transmission line system is proposed. In addition, a new approach for injecting the power of wind turbines into power transmission lines and delivering to the load center without the need for receiving-end transformers is presented. The proposed method and its operation are analyzed in MATLAB/Simulink. © The Institution of engineering & Technology 2023.

关键词： Electric power transmission

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TURNING THE CURSE OF HETEROGENEITY IN FEDERATED LEARNING INTO A BLESSING FOR OUT-OF-DISTRIBUTION DETECTION 11

TURNING THE CURSE OF HETEROGENEITY IN FEDERATED LEARNING INT...

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11th International Conference on Learning Representations, ICLR 2023

作者： Yu, Shuyang Hong, Junyuan Wang, Haotao Wang, Zhangyang Zhou, Jiayu Department of Computer Science and Engineering Michigan State University United States Department of Electrical and Computer Engineering University of Texas Austin United States

Deep neural networks have witnessed huge successes in many challenging prediction tasks and yet they often suffer from out-of-distribution (OoD) samples, misclassifying them with high confidence. Recent advances show promising OoD detection performance for centralized training, and however, OoD detection in federated learning (FL) is largely overlooked, even though many security sensitive applications such as autonomous driving and voice recognition authorization are commonly trained using FL for data privacy concerns. The main challenge that prevents previous state-of-the-art OoD detection methods from being incorporated to FL is that they require large amount of real OoD samples. However, in real-world scenarios, such large-scale OoD training data can be costly or even infeasible to obtain, especially for resource-limited local devices. On the other hand, a notorious challenge in FL is data heterogeneity where each client collects non-identically and independently distributed (non-iid) data. We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL. The key is that for each client, non-iid data from other clients (unseen external classes) can serve as an alternative to real OoD samples. Specifically, we propose a novel Federated Out-of-Distribution Synthesizer (FOSTER), which learns a class-conditional generator to synthesize virtual external-class OoD samples, and maintains data confidentiality and communication efficiency required by FL. Experimental results show that our method outperforms the state-of-the-art for OoD tasks by 2.49%, 2.88%, 1.42% AUROC, and 0.01%, 0.89%, 1.74% ID accuracy, on CIFAR-10, CIFAR-100, and STL10, respectively. Codes are available: https://***/illidanlab/FOSTER. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Data privacy

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections

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computer Modeling in engineering & sciences 2024年第2期138卷 1301-1322页

作者： Nure Alam Chowdhury Lulu Wang Md Shazzadul Islam Linxia Gu Mehmet Kaya Department of Biomedical Engineering and Science Florida Institute of TechnologyMelbourneFL 32901USA Biomedical Device Innovation Center Shenzhen Technology UniversityShenzhen518118China Department of Electrical and Computer Engineering International Islamic University MalaysiaJalan GombakSelangor53100Malaysia

This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human *** size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast *** operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of *** the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor *** simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.

关键词： Antenna microwave wideband cancer breast phantom tumor detection

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Overnight Charging Mechanism of Battery Electric Buses for a Transit Network in Smart Cities 5

Overnight Charging Mechanism of Battery Electric Buses for a...

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5th Technology Innovation Management and engineering science International Conference, TIMES-iCON 2024

作者： Sobhani, Ehsan Yassine, Abdulsalam Ameli, Amir Riahinia, Shahin Department of Computer Science Lakehead University Thunder BayON Canada Department of Software Engineering Lakehead University Thunder BayON Canada Department of Electrical and Computer Engineering Lakehead University Thunder BayON Canada

ISBN: (纸本)9798350362602

—Battery electric buses (BEBs) are known for being eco-friendly transportation in smart cities. They are cost-effective compared to their diesel counterpart if BEBs are charged efficiently. There are two main charging approaches for BEBs, namely, off-service (overnight) and on-route (opportunity) charging. The overnight electricity rate is typically lower than the daytime rate. Consequently, overnight charging is considered the most affordable approach to meet the daily energy needs of the electrified transit network. This paper introduces a new intelligent overnight charging mechanism—i.e., Priority Charging Mechanism (PCM)—that charges BEBs simultaneously using the optimum number of chargers to minimize infrastructure costs. The model is evaluated using the actual transit network data of the city of Thunder Bay. ©2024 IEEE.

关键词： Charging stations

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On interoperability of Datacentre and the Energy market in the Nordic Region 32

On interoperability of Datacentre and the Energy market in t...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Yang, Chen-Wei Galkin, Nikolai Vyatkin, Valeriy Luleå Tekniska Universitet Computer and Space Engineering Department of Computer Science Sweden Aalto University Department of Electrical Engineering and Automation Espoo Finland

ISBN: (纸本)9798350399714

Data centres are emerging as the essential backbone infrastructure for the booming information age and are becoming a sizable consumer of the energy system. Green and modular data centre are a new class of data centres which could participate in the reserve energy market due to their renewable energy flexibility. This paper investigates the interoperability of the data centre (in IEC 61850) with the energy market (in EDIEL) by means of a protocol translating aggregator node. Interoperability is demonstrated in a case study where EDIEL documents (specifically the reserve allocation document) are sent to the aggregator node prototyped on a Beaglebone microdevice with SOAP web services for IEC 61850 translation. © 2023 IEEE.

关键词： Smart power grids

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Joint Access Selection and Computation Offloading in LEO Ubiquitous Edge Computing Networks: An Alternating DRL-Based Approach

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1870-1886页

作者： Yang, Junyi Zhang, Yuanjun Xiao, Zhenyu Han, Zhu Beihang University School of Electronic and Information Engineering Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the increase in users' service diversity and demand for quality of experience (QoE), the utilization of low earth orbit (LEO) satellite networks for assisted or independent offloading of computation tasks has become a promising trend. However, due to the high mobility and uneven resource distribution of LEO satellites, it is difficult to meet the requirements of low delay and low overhead by using traditional optimization algorithms or common reinforcement learning (RL) algorithms. Therefore, in this paper, we consider a scenario in which terrestrial users offload delay-sensitive (DS) computation tasks to a LEO satellite network in order to study the joint access selection and computation offloading problem. First, we analyze the characteristics of the scenario and the computation tasks, and establish a generic mathematical model. Then, based on the block descent coordinate (BCD) principle, we propose a novel algorithm of alternating Dueling DQN (ADDQN) for the joint decision-making problem, where access selection and computation offloading are performed with corresponding independent agent respectively. Comprehensive simulations show that compared with other benchmark algorithms, the proposed method not only has better convergence, but also can maximize the number of successfully completed sub-tasks and the optimization objective value meanwhile reducing the unnecessary access handovers. © 2024 IEEE.

关键词： Satellites Computational modeling Decision making Low earth orbit satellites Handover Benchmark testing Mathematical models Quality of experience Optimization Convergence

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Persuasive AI Feedback: Enhancing Student Emotions and Engagement in Higher Education 19

Persuasive AI Feedback: Enhancing Student Emotions and Engag...

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19th International Conference on Persuasive Technology Adjunct, PERSUASIVE-ADJ 2024

作者： Alsaiari, Omar Baghaei, Nilufar School of Electrical Engineering and Computer Science The University of Queensland St Lucia4072 Australia Department of Computer Science College of Science and Arts Najran University Sharurah68341 Saudi Arabia

This study focuses on the employment of persuasive technology with artificial intelligence (AI) to enhance student’s emotions and engagement in educational environments. By integrating persuasive elements such as praise, personalization, reminders, and emojis into AI-driven feedback, we aim to bridge the gap in understanding their effects on students’ emotions and engagement levels. Anchored in the control and value theory of achievement emotions, our mixed-methods research will assess these impacts using the Achievement Emotions Questionnaire-Short (AEQ-S) and engagement data from the RiPPLE platform. We hypothesize that such AI feedback strategies will significantly improve students’ emotional experiences and engagement with learning tasks. This inquiry contributes to educational technology by offering insights into designing emotionally intelligent AI feedback systems, potentially enriching the learning experience for university students. Expected outcomes include practical guidance for leveraging AI in creating more engaging and supportive educational settings. © 2024 Copyright for this paper by its authors.

关键词： Students

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Designing Optimal Parameters for Single-Axis Solar-Tracker through Analysis of Sun-Path and Tracking Vector Dynamics 6

Designing Optimal Parameters for Single-Axis Solar-Tracker t...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Akhtaruzzaman, M. Islam, Md Shofiqul Shahryer, Fahim Arif, Muammer Din Mollik, Md. Sazib Rahman, Muhammad Towfiqur Rahman, Md. Mahbubur Hasan, Mohammad Kamrul Ahmed, Shugata Ashrafuzzaman, Md. Department of Computer Science and Engineering Dhaka1216 Bangladesh Department of Mechanical Engineering Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Faculty of Information Science and Technology Bangi Malaysia Department of Robotics and Mechatronics Engineering Bangladesh Sdn. Bhd. Cyberjaya Malaysia

ISBN: (纸本)9798350385779

The persistent challenges faced by sun-tracking systems include inefficient power production, wastage of energy, ineffective control, and high costs. Most of the existing systems are either static or dual-axis tracking which lead to inefficient output or increasing energy wastage and cost overheads. Though, single-axis tracking system has some benefits over the static or dual-axis tracking, most of the systems do not analyze the selection of optimal sun-path which again leads to inefficient and inadequate absorption of sunlight. Addressing the issues, this article presents a design of optimal parameters for single-axis solar-tracking by analyzing the sun-path and tracking vector dynamics. The study mainly focuses on analyzing solar-tracking dynamics, variations in sunray angles, Azimuth and Elevation dynamics throughout the day for the twelve months, Sun-vector calculation for local coordinates, joint rotational-axis, and joint motion control using a Proportional-Derivative controller. Additionally, the article outlines a conceptual architecture for automated control, monitoring, and power distribution for the potential development of a sustainable smart city. The proposed strategy addresses the issues of the optimal design of a single-axis solar-tracker, thus ensuring the reduction of cost overhead and energy wastage. © 2024 IEEE.

关键词： Dynamics

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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