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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Jing, Pengxiang Zhang, Xibeng Ukil, Abhisek Swain, Akshya The University of Auckland Department of Electrical Computer and Software Engineering New Zealand Henan University School of Artificial Intelligence China

ISBN: (纸本)9798350327748

This paper introduces a Sliding Mode Observer (SMO) based Finite Control Set Model Predictive Control (FCSMPC) strategy. A digital model of the system, implemented in Matlab/Simulink, is utilized to verify the effectiveness of the proposed approach. The simulation results, encompassing both steady-state and dynamic analyses, are discussed and benchmarked against traditional FCS-MPC. The results highlight improved total harmonic distortion (THD) values, superior dynamic response, and more stable performance when integrating SMO into the FCS-MPC methodology. © 2023 IEEE.

关键词： Simulink

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Optimizing Parameters in Soft-hard BPGD for Lossy Source Coding 12

Optimizing Parameters in Soft-hard BPGD for Lossy Source Cod...

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12th International Symposium on Topics in Coding, ISTC 2023

作者： Alinia, Masoumeh Mitchell, David G. M. New Mexico State University Klipsch School of Electrical and Computer Engineering Las CrucesNM88003 United States

ISBN: (纸本)9798350326116

In this paper, we investigate lossy source coding based on a soft-decision based belief propagation guided decimation (BPGD) algorithm for low-density generator matrix (LDGM) codes, called soft-hard BPGD. For this algorithm, the optimal performance can only be achieved by carefully selecting the softness parameters which are conventionally found by exhaustive empirical search. To address this issue, we have introduced a framework that leverages the cavity method to predict the values of softness parameters at which phase transition occurs. This framework can significantly reduce the need for time-consuming empirical searches to determine the best parameter values for soft-hard BPGD. Our approach has been found to deliver superior rate-distortion performance compared to the hard-decimated BPGD algorithm and faster implementation than the soft-decimated algorithm. © 2023 IEEE.

关键词： Electric distortion

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Physics-Informed Neural networks for Quantum Wavefunctions

Physics-Informed Neural Networks for Quantum Wavefunctions

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2024 International Conference on computer and Applications, ICCA 2024

作者： Mahmud, Istiak Asthana, Ayush Hoffmann, Mark Abdelhadi, Ahmeb School of Electrical Engineering & Computer Science University of North Dakota ND United States University of North Dakota Department of Chemistry ND United States

ISBN: (纸本)9798350367560

The wave functions and permitted energy levels of quantum systems, which characterize the probability distributions and particle behaviors, are able to be determined via the time-independent Schrodinger equation. Our research study introduces an experimental method of solving the time-independent Schrodinger equation by utilizing Physics-Informed Neural networks (PINNs) with a customized loss function designed for output waveshape prediction. The technique makes use of PINNs' capacity to directly integrate physical rules into the learning process, assuring that the solutions follow the guiding principles of quantum mechanics. We forecast the associated wavefunctions for different energy levels (n states) and effectively solve the Schrodinger equation for distinct quantum states by using PINNs and a customized loss function. Our findings show that PINNs are a reliable and accurate way to represent the fine features of quantum wavefunctions, and they present a viable substitute for more conventional numerical techniques. This methodology not only improves computer performance but also offers more profound understanding of the behavior of systems in quantum mechanics, which may have implications for material science, nanotechnology, and quantum computing. © 2024 IEEE.

关键词： Schrodinger equation

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Novel DER and OLTC Management Scheme for Coordinated TSO-DSO Flexibility Services Provision

Novel DER and OLTC Management Scheme for Coordinated TSO-DSO...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Laaksonen, Hannu Khajeh, Hosna Hatziargyriou, Nikos School of Technology and Innovations University of Vaasa Vaasa Finland School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9798350396782

In the future, increasing amount of flexibility is needed to fulfill the management needs of renewables and electric vehicles (EV) -based power systems. This paper presents a novel adaptive and coordinated management scheme for distributed energy resources (DER) and on-load-tap-changers (OLTCs) that enables coordinated and prioritized flexibility services provision by distribution network (DN) connected DER for the transmission and distribution system operators (TSOs and DSOs) by considering frequency deviation severity i.e. frequency level. Also the location and type of DER in the DN is taken into account in its active control methods and droop settings. The aim of the proposed scheme is to increase DER hosting capacity of the DN and the availability of the DN connected DER for the flexibility services provision during smaller frequency deviations as well as prioritize and maximize support for the TSO needs during larger frequency deviations. The simulation studies are done with HV/MV/LV network PSCAD model including active DER units at MV and LV networks as well as OLTCs at HV/MV and MV/LV substations. © 2023 IEEE.

关键词： Active network management Distributed energy resources Flexibility services Frequency control Voltage control

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Cyberbullying and Cyberviolence Detection:A Triangular User-Activity-Content View

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1384-1405页

作者： Shuwen Wang Xingquan Zhu Weiping Ding Amir Alipour Yengejeh Department of Electrical Engineering and Computer Science Florida Atlantic UniversityBoca RatonFL 33431 USA School of Information Science and Technology Nantong UniversityNantong 226019China

Recent years have witnessed the increasing popularity of mobile and networking devices,as well as social networking sites,where users engage in a variety of activities in the cyberspace on a daily and real-time *** such systems provide tremendous convenience and enjoyment for users,malicious usages,such as bullying,cruelty,extremism,and toxicity behaviors,also grow noticeably,and impose significant threats to individuals and *** this paper,we review computational approaches for cyberbullying and cyberviolence detection,in order to understand two major factors:1)What are the defining features of online bullying users,and 2)How to detect cyberbullying and *** achieve the goal,we propose a user-activities-content(UAC)triangular view,which defines that users in the cyberspace are centered around the UAC triangle to carry out activities and generate ***,we categorize cyberbully features into three main categories:1)User centered features,2)Content centered features,and 3)Activity centered *** that,we review methods for cyberbully detection,by taking supervised,unsupervised,transfer learning,and deep learning,etc.,into *** UAC centered view provides a coherent and complete summary about features and characteristics of online users(their activities),approaches to detect bullying users(and malicious content),and helps defend cyberspace from bullying and toxicity.

关键词： Classification clustering cyberbullying natural language processing social network

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A Deep Learning Approach to Recognizing New Thai Currency Using Transfer Learning for Visually Impaired Individuals 17

A Deep Learning Approach to Recognizing New Thai Currency Us...

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17th International Conference on Signal Processing and Communication Systems, ICSPCS 2024

作者： Ahmad, Irfan Sharif, Abubakar Azmat, Aneeka Jamil, Muhammad Usman Maynooth University Maynooth International Engineering College Faculty of Science and Engineering Ireland Maynooth University Maynooth International Engineering College Faculty of Science and Engineering Fuzhou China University of Glasgow James Watt School of Engineering United Kingdom National Tsinghua University College of Electrical Engineering and Computer Science Institute of Information Systems and Application Hsinchu Taiwan Chuzhou University School of Mechanical and Electrical Engineering Chuzhou China

ISBN: (纸本)9798350389630

Assistive mobile applications play a pivotal role for visually impaired individuals worldwide. These applications often face challenges in currency recognition due to varying perspectives, inconsistent illumination, and background clutter. This issue is especially pressing in developing countries like Thailand, where there is a notable gap in robust currency recognition systems, particularly for the new Thai currency notes. This study employs a deep learning approach using a convolutional neural network (CNN) to automate the recognition of the new Thai currency notes. Using transfer learning, we fine-tuned the CNN using the Xception model, renowned for its depth-wise separable convolution. The network trained on a meticulously curated dataset comprising 3600 images (without data augmentation) of five different denominations of the new Thai currency (20, 50, 100, 500, and 1000 baht) notes, captured under various conditions. The resulting model achieved an average training accuracy of 99.5% and a validation accuracy of 99.8%. Given its robustness and high accuracy, the model can be integrated into an Android application. Such an application would offer a user-friendly and reliable tool for visually impaired individuals to effortlessly identify the new Thai currency notes in their everyday transactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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Testing for the Bradley-Terry-Luce Model

Testing for the Bradley-Terry-Luce Model

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Makur, Anuran Singh, Japneet Purdue University Department of Computer Science West LafayetteIN47907 United States Purdue University School of Electrical and Computer Engineering West LafayetteIN47907 United States

ISBN: (纸本)9781665475549

The Bradley-Terry-Luce (BTL) model is one of the most widely used models for ranking a set of items given data about pairwise comparisons among them. While several studies in the literature have attempted to empirically test how accurately a BTL model can model some given pairwise comparison data, this work aims to develop a formal, computationally efficient hypothesis test to determine whether the BTL model accurately represents the data. Specifically, we first propose such a formal hypothesis test, establish an upper bound on the critical radius of the proposed test, and then provide a complementary lower bound on the critical radius. Our bounds prove the minimax optimality of the scaling of the critical radius with respect to the number of items (up to constant factors). Finally, we also take the first step towards characterizing the stability of rankings under the BTL model when there is a small model mismatch. © 2023 IEEE.

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A Classifier-Ensemble-Based Surrogate-Assisted Evolutionary Algorithm for Distributed Data-Driven Optimization

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 711-725页

作者： Guo, Xiao-Qi Wei, Feng-Feng Zhang, Jun Chen, Wei-Neng South China University of Technology School of Computer Science and Engineering Guangzhou510006 China South China University of Technology State Key Laboratory of Subtropical Building of Big Data and Computational Intelligence Guangzhou510006 China Hanyang University ERICA School of Electrical and Engineering Ansan15588 Korea Republic of Nankai University College of Artificial Intelligence Tianjin300071 China

Surrogate-assisted evolutionary algorithms (SAEAs) have achieved effective performance in solving complex data-driven optimization problems. In the Internet of Things environment, the data of many problems are collected and processed in distributed network nodes and cannot be transmitted. As each local node can only access and build surrogate models based on partial data, local models are usually not accurate and even conflicting. To address these challenges, this article proposes a classifier-ensemble-based surrogate-assisted evolutionary algorithm (CESAEA) with the following features. First, the local nodes in CESAEA train classifiers as surrogate models based on their own data to classify candidates into several levels according to their fitness quality. The classifiers are less sensitive to the partial and biased data than regression models in local nodes. Second, the central node in CESAEA ensembles the local surrogates to form a global classifier with a relaxation condition to guide the evolutionary optimizer to generate promising candidates. The relaxation condition helps to overcome the problem of local model inconsistency. Overall, CESAEA is composed of local classifier construction, global classifier ensemble, classifier-assisted evolutionary optimization and local regression-assisted selection. As only classifiers are allowed to transmit from local nodes to the central node, the mapping relationship between decision vector and objective is hidden and thus data privacy is protected. The experimental results on benchmark functions as well as distributed feature selection problems verify the effectiveness of CESAEA compared to several state-of-the-art approaches. © 1997-2012 IEEE.

关键词： Linear programming

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Adversarial Example Attacks in Internet of Things (IoT) 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Gu, Yuzhe Jiang, Na Chen, Yanjiao Gong, Xueluan School of Cyber Science and Engineering Wuhan University Wuhan China College of Electrical Engineering Zhejiang University Hangzhou China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Recently, the Internet of Things (IoT) technology has made tremendous progress, and it is beginning to enter many areas of social life, such as autonomous driving, medical care, etc. Due to the massive data in IoT, deep neural networks (DNN) are often involved in helping process and analyzing data, but DNNs still face many security threats. Adversarial example attack is a common attack against DNN models, which interferes with model decisions through processed samples. It will undoubtedly threaten DNN-based IoT systems. This paper presents the possible attack scenarios of adversarial example attacks in IoT systems and extensively studies the defense methods of adversarial example attacks in IoT systems. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

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