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3rd International Conference on Advanced Electrical engineering, ICAEE 2024

作者： Boulkamh, Selma Zeghib, Nadia Yaddaden, Yacine University of Constantine 2-Abdelhamid Mehri Lire Laboratory Constantine Algeria University of Quebec at Rimouski Department of Mathematics Computer Science and Engineering Canada

ISBN: (纸本)9798350349351

Air pollution is a global issue with profound implications to human health and environmental sustainability. Especially, PM2.5, which refers to particulate matter with a size of 2.5 microns or smaller, poses significant health risks. Therefore, accurately predicting PM2.5 levels is crucial. In this aim, various machine-learning methods are employed for PM2.5 prediction. The problem arises from the use of collected datasets at varied time intervals (5 minutes, 15 minutes, 1 hour, etc.), resulting in inconsistency across predictive approaches. While shorter time intervals seems to offer heightened sensitivity to changes, implying simpler prediction, this presumption may not be universally applicable. This paper investigates the impact of time intervals on PM2.5 prediction through a comparative analysis of datasets collected at different intervals (15 minutes, 1 hour, and 8 hours) using Long Short Term Memory Neural Networks (LSTM). By leveraging statistical analysis and predictive modeling techniques, we assess the dataset's distributions and evaluate prediction model performance. This study underscores the importance of interval selection in PM2.5 prediction and informs future efforts to enhance air quality surveillance and public health protection measures. © 2024 IEEE.

关键词： Prediction models

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Collaborative Music Performances with Humanoid Robots and Humans 11th

Collaborative Music Performances with Humanoid Robots and ...

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11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Lau, Meng Cheng Anderson, John Baltes, Jacky Gallegos, Christian Melendez Diaz, Mario Mendez Li, Borui Bharti School of Engineering and Computer Science Laurentian University SudburyONP3E2C6 Canada Department of Computer Science University of Manitoba WinnipegR3T 2N2 Canada Department of Electrical Engineering National Taiwan Normal University R.O.C Taipei10610 Taiwan

ISBN: (纸本)9783031706837

In recent years, there has been a growing interest in entertainment robotics, with researchers exploring the creation of robots capable of performing a wide range of tasks, such as magic, art drawing, dancing, and music. This paper highlights our research in creating a musical band with humanoid robots and human musicians, with the goal of achieving natural synchronization and collaboration in musical performances. We selected two of our humanoid robots namely Polaris, a mid-sized humanoid robot, serving as the drummer, and Oscar, a Robotis-OP3 humanoid robot, serving as the keyboardist. The technical implementation included key elements like visual servoing, human-robot interaction, and ROS, which facilitated seamless communication and coordination between our humanoid robots and human musicians. The successful collaboration of our robots with human musicians can be visually and audibly appreciated through the following YouTube link: https://***/pFOyt1KKCfY. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Anthropomorphic robots

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Recent Advances in In-Memory Computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

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Nano-Micro Letters 2024年第7期16卷 1-30页

作者： Hangbo Zhou Sifan Li Kah-Wee Ang Yong-Wei Zhang Institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis Way#16-16 ConnexisSingapore 138632Republic of Singapore Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117583Republic of Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(A*STAR)2 Fusionopolis WaySingapore 138634Republic of Singapore

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing *** response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these *** devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological *** these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form *** review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential ***,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential *** primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

关键词： 2D materials Memristors Memtransistors Crossbar array In-memory computing

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Multilayer Satellite Network Collaborative Mobile Edge Caching:A GCN-Based Multi-Agent Approach

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China Communications 2024年第11期21卷 56-74页

作者： Yang Jie He Jingchao Cheng Nan Yin Zhisheng Han Dairu Zhou Conghao Sun Ruijin School of Telecommunications Engineering Xidian UniversityXi’an 710071China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210000China Department of Electrical and Computer Engineering University of WaterlooWaterloo ON N2L 3G1Canada

With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has *** emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user *** with the MEC and satellite networks,the network is empowered popular content ubiquitously and *** the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this ***,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale *** the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content ***,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale ***,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching ***,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.

关键词： cache placement coded caching graph convolutional network(GCN) mobile edge caching(MEC) multilayer satellite network

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Accurate Distinction of Weak and Strong AC Grids for Emerging Hierarchical-Infeed LCC-UHVDC Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 772-777页

作者： Hao Xiao Xianzhong Duan Yi Zhang Department of Electrical and Computer Engineering University of ManitobaWinnipeg R3T 2N2ManitobaCanada State Key Laboratory of Advanced Electromagnetic Engineering and Technology Hubei Electric Power Security and High Efficiency Key LaboratorySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074HubeiChina RTDS Technologies Inc. Winnipeg R3T 2E1ManitobaCanada

Distinction of weak and strong AC grids for emerging hierarchical-infeed LCC-UHVDC systems is important for planning and operation departments. However, accuracy of earlier distinction methods is limited as they were developed by empirical reasoning without rigorous theoretical analysis. Hence in this letter, hierarchical-infeed interactive effective short-circuit ratio (HIESCR) index is first used for strength evaluation of HIDC systems with complex inter-inverter interactions considered. Boundary HIESCR (BHIESCR) is also introduced in the proposed distinction method of weak and strong AC grids. That is, weak (or strong) AC grids are, respectively, identified when HIESCR is less (or greater) than BHIESCR. Second, it is shown BHIESCR remains almost unchanged as 3.0 versus various system parameters and rated operation variables based on rigorous theoretical analysis. This salient feature makes the proposed method more accurate than earlier methods. Finally, the proposed method is validated by simulations based on the PSCAD/EMTDC program.

关键词： Hierarchical-Infeedinteractive effective short-circuit ratio hierarchical-infeed LCC-UHVDC systems inter-inverter interactions strong AC grids weak AC grids

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Machine Learning-Driven Distributed Intrusion Detection System for the Internet of Vehicles 13

Machine Learning-Driven Distributed Intrusion Detection Syst...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： AlFarra, Joud Alam, Leena Marir, Naila Sarirete, Akila Computer Science Department Effat College of Engineering Effat University Jeddah Saudi Arabia LIRE Laboratory Constantine 2-Abdelhamid Mehri University Constantine Algeria

The Internet of Vehicles (IoV), integrating vehicles with EV charging stations, faces significant vulnerabilities to cyber threats. Designing an efficient Intrusion Detection System (IDS) to detect sophisticated attacks within EV charging stations is crucial for safeguarding this interconnected network. However, creating IDS models using centralized machine learning approaches requires overcoming challenges such as scalability, time-consuming analysis, and real-time processing requirements. In this context, we propose a novel distributed system to IDS design for the IoV, leveraging distributed learning techniques to address these challenges and enhance EV charging station security. This study utilizes different machine learning models to improve the effectiveness of abnormal behavior detection. Based on the robustness and adaptability of machine learning models, the detection and classification of intrusion data are significantly enhanced. Our presented system is validated using the CICEV2023 dataset. Simulation results demonstrate that our algorithm achieves higher efficiency and accuracy compared to existing centralized schemes. © 2024 Copyright for this paper by its authors.

关键词： Network intrusion

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science Macau China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City Macau China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer Information Science Macau China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Application of long short-term memory neural networks for electric arc furnace modeling

Application of long short-term memory neural networks for el...

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作者： Klimas, Maciej Grabowski, Dariusz Department of Electrical Engineering and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The world steel industry is highly dependent on the use of electric arc furnaces (EAFs). The application of the electric arc phenomenon causes many power quality (PQ) problems, such as harmonics or voltage flickering. An adequate EAF model is useful for the design and control of EAFs and PQ improvement systems. In this paper, we propose an approach to EAF modeling based on a deterministic differential equation that is enhanced with stochastic ingredients. The identification of time series that represent equation coefficients is carried out using a genetic algorithm. The final solution includes two models of the electric arc furnace, both based on long short-term memory (LSTM) networks. They recreate the time series of the coefficients with given stochastic properties. The first model uses LSTM to generate the main component of the output signal, while the second applies LSTM to include the high frequency component. The potential of LSTM models to reflect different stages of the EAF work cycle, that is, the melting and refining stages, has been investigated. The results indicate that the LTSM model outperforms chaotic or stochastic models in both stages considered. © 2023

关键词： Long short-term memory

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Rescaling of Symbol Counts for Adaptive rANS Coding 31

Rescaling of Symbol Counts for Adaptive rANS Coding

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Strutz, Tilo Coburg University Department of Electrical Engineering and Computer Science Friedrich-Streib-Str. 2 Coburg96450 Germany

ISBN: (纸本)9789464593600

The abbreviation rANS stands for a relatively new method of arithmetic coding based on asymmetric numeral systems (ANS) which combines the advantages of arithmetic coding in terms of performance and the advantages of Huffman coding in terms of speed. Compared to conventional arithmetic coding methods, the mathematical apparatus is slightly different which has the consequence that the decoding order is reversed to the encoding order, i.e. the processing follows the last-in-first-out principle. This makes it somewhat difficult to design the coding process to adapt to changing symbol statistics, and therefore rANS coding has so far only been applied in settings with fixed statistics. In particular, the frequent rescaling of statistics required to reduce the influence of old symbols becomes a problem when the order of processing is different on the encoder and decoder sides. This paper proposes a new method that allows adaptive coding within the framework of rANS coding and additionally offers the possibility of rescaling the symbols frequencies. Investigations show that this method enables the same compression performance for rANS as for conventional arithmetic coding. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Digital arithmetic

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An Accurate Iris Recognition System Based on Deep Transfer Learning and Data Augmentation 1

An Accurate Iris Recognition System Based on Deep Transfer L...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Hattab, Abdessalam Behloul, Ali Hattab, Wassila Batna 2 University LaSTIC Laboratory Computer Science Department Batna Algeria Electricals Engineering Biskra University Laboratory of Biskra Lgeb Biskra Algeria

ISBN: (纸本)9798331541064

Iris recognition is a highly secure biometric identification method that utilizes the distinct and intricate patterns of the iris, which remain consistent throughout a person's lifetime. Remarkably, it can even distinguish between identical twins, highlighting its precision and reliability. However, despite these advantages, challenges such as reflections, occlusions, varying lighting conditions, blur, and other environmental factors can significantly degrade the performance of iris recognition systems. Deep Convolutional Neural Networks (CNNs), known for their success in image recognition, often require numerous parameters, increasing computational time and resource demands. To address these challenges, we propose an advanced iris recognition system based on Deep CNNs. Our system utilizes YOLOv4-tiny for accurate iris region detection, and for the recognition task, we introduce a deep CNNs model inspired by the pre-trained Xception, which contains fewer than three million parameters. To effectively train this deep CNN on small iris datasets, we apply Data Augmentation and Transfer Learning techniques, which improve accuracy while reducing the risk of overfitting. Through two-fold cross-validation, our system achieves impressive accuracy rates of 99.81 % and 99.64 % on the CASIA-Iris-Interval and IITD datasets, respectively. Our method demonstrates a clear advantage over existing methods in terms of accuracy and reliability, making it a promising solution for various applications such as border control, law enforcement, and access control. © 2024 IEEE.

关键词： Convolutional neural networks

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