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arXiv 2024年

作者： Li, Jinhao Zhang, Ruichang Wang, Hao Liu, Zhi Lai, Hongyang Zhang, Yanru Chengdu611731 China the Department of Data Science and AI Faculty of Information Technology Monash University MelbourneVIC3800 Australia the Department of Computer Science University of Manchester United Kingdom affiliated with the Monash Energy Institute the Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan the School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China Chengdu611731 China

Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertainties to DNs, frequently resulting in voltage fluctuations that threaten system security and hamper the further adoption of RERs. To incentivize more RER penetration, we propose a deep reinforcement learning (DRL)-based strategy to dynamically balance the trade-off between voltage fluctuation control and renewable accommodation. To further extract multi-time-scale spatial-temporal (ST) graphical information of a DN, our strategy draws on a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN), consisting of ST attention mechanism and ST convolution to explore the node correlations in the spatial and temporal views. The continuous decision-making process of balancing such a trade-off can be modeled as a Markov decision process optimized by the deep deterministic policy gradient (DDPG) algorithm with the help of the derived ST information. We validate our strategy on the modified IEEE 33, 69, and 118-bus radial distribution systems, with outcomes significantly outperforming the optimization-based benchmarks. Simulations also reveal that our developed MG-ASTGCN can to a great extent accelerate the convergence speed of DDPG and improve its performance in stabilizing node voltage in an RER-rich DN. Moreover, our method improves the DN’s robustness in the presence of generator failures. © 2024, CC BY.

关键词： Reinforcement learning

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KA$^{2}$2SE: Key-Aggregation Authorized Searchable Encryption Scheme for Data Sharing in Wireless Sensor Networks

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IEEE Transactions on Services Computing 2024年第1期18卷 226-238页

作者： Haijiang Wang Jianting Ning Wei Wu Chao Lin Kai Zhang School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China School of Cyber Science and Engineering Wuhan University Wuhan China Faculty of Data Science City University of Macau Macau China College of Education Sciences Hong Kong University of Science and Technology Guangzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou China Guangdong Key Laboratory of Blockchain Security Guangzhou University Guangzhou China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China

As a promising technology, key-aggregation searchable encryption with constant computation overhead is especially suitable for sensor nodes with limited computation resources in wireless sensor networks. However, in most of the existing key-aggregation searchable encryption schemes, the authorized aggregation key is generated in a deterministic way. As a result, these schemes suffer from “ Key Forge Attack ” and “ Trapdoor Forge Attack ” that we proposed and hence fail to support the security property as they claimed (which is an important goal to be achieved in key-aggregation searchable encryption schemes). To fix these flaws, in this paper, we identify the security challenges related to key-aggregation searchable encryption and propose a lightweight key-aggregation authorized searchable encryption scheme based on attribute-based encryption, called KA $^{2}$ SE. It enables a data owner to share encrypted data with an authorized query user by issuing only a single authorized aggregation key, and the authorized query user only needs to submit a single trapdoor to the cloud server to perform keyword search. We formulate the security definitions for KA $^{2}$ SE and prove its security. Finally, empirical evaluations demonstrate that KA $^{2}$ SE is computationally efficient in comparison with existing schemes.

关键词： Encryption Cryptography Keyword search Wireless sensor networks Servers Privacy Cloud computing Security Probabilistic logic Authorization

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Mobile Phone Detector in Restricted Area

Mobile Phone Detector in Restricted Area

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IEEE Global Conference on Consumer electronics (GCCE)

作者： Abdurrahman Ismail Asmarashid Ponniran Chew Chang Choon Chessda Uttraphan A-L Eh Kan Mohd Anuaruddin Ahmadon Shingo Yamaguchi Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Malaysia Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Graduate School of Sciences and Technology for Innovation Yamaguchi University Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

The proliferation of smartphones has heightened concerns regarding their unauthorized use in restricted areas, posing significant security risks and compromising data integrity. This paper addresses the challenge of detecting unauthorized mobile phone activity in sensitive locations, focusing on examination rooms as a critical case study. It proposes a mobile phone detector system using RF signal detection and an IoT platform for real-time alerts. The system, built with Arduino and an antenna, uses an AD8318 RF power detector to analyse signals from mobile phones. The system successfully detects 4G and 5G signals within a voltage range of 1.2 V to 0.7 V and an effective range of up to 30 cm. It also features real-time alerts via a buzzer and data transmission to the Blynk IoT platform, enhancing security. Tests confirmed the system's accuracy and reliability, though it has limitations such as inconsistent response times and limited detection range. Future improvements will focus on extending the detection range and refining the alert mechanisms for better performance in various environments.

关键词： 5G mobile communication Detectors Voltage Mobile handsets Real-time systems Security Reliability Time factors Usability Smart phones

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Focused X-ray Beams from Van der Waals Heterostructures

Focused X-ray Beams from Van der Waals Heterostructures

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Shi, Xihang Kurman, Yaniv Shentcis, Michael Wong, Liang Jie de Abajo, F. Javier García Kaminer, Ido Solid State Institute Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa32000 Israel School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Spain ICFO-Institució Catalana de Recerca i Estudis Avançats Passeig Lluís Companys 23 Barcelona08010 Spain

We present the concept of customized van der Waals heterostructures with periodicity tailored to generate pre-designed X-ray patterns from free electrons. As an example, we demonstrate focused X-ray beams. © Opti... 详细信息

ISBN: (纸本)9781557528209

关键词： Van der Waals forces

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Triangle Topology Enhancement for Multi-View Graph Clustering

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4338-4348页

作者： Wu, Danyang Wang, Penglei Lu, Jitao Hu, Zhanxuan Zhang, Hongming Nie, Feiping Northwest A&F University College of Information Engineering Yangling712100 China South China University of Technology School of Software Engineering Guangzhou510641 China Xi’an Jiaotong University School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an710049 China Yunnan Normal University School of Information Science and Technology Kunming650092 China Northwestern Polytechnical University School of Computer Science School of Artificial Intelligence OPtics and ElectroNics Xi’an710072 China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi’an710072 China

Most existing multi-view graph clustering models focus on integrating the topological structure of different views directly, which cannot efficiently stimulate the collaboration between multiple views. To alleviate this problem, this paper proposes a Triangle Topology Enhancement (T2E) module, which expands two topological structures based on the raw topology of each view, including the self-triangle enhanced topology that highlights the local view information and the cross-view triangle enhanced topology containing the global-local view information. Afterward, this paper designs a novel multi-view graph clustering model, named MGC-T2E, to integrate both the raw and derived topological structures and directly induce consistent clustering indicators based on a self-supervised clustering module. In the simulation, the experimental results demonstrate that MGC-T2E achieves state-of-the-art performances compared with a mass of current competitors. © 1989-2012 IEEE.

关键词： Topology

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The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things

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computer Methods and Programs in Biomedicine 2023年第1期241卷 107745-107745页

作者： Aminizadeh, Sarina Heidari, Arash Toumaj, Shiva Darbandi, Mehdi Navimipour, Nima Jafari Rezaei, Mahsa Talebi, Samira Azad, Poupak Unal, Mehmet Medical Faculty of Islamic Azad University of Tabriz Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul Turkey Urmia University of Medical Sciences Urmia Iran Department of Electrical and Electronic Engineering Eastern Mediterranean University Gazimagusa99628 Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Tabriz University of Medical Sciences Faculty of Surgery Tabriz Iran Department of Computer Science University of Texas at San Antonio TX United States Department of Computer Science University of Manitoba Winnipeg Canada Department of Computer Engineering Nisantasi University Istanbul Turkey

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL

关键词： Deep learning

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Harmonics Analysis and Evaluation for Active LC-LPF Attached PMSM Drive by IRM-ILQ Control Through Experiments

Harmonics Analysis and Evaluation for Active LC-LPF Attached...

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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Kazuki Abe Yoshiki Sasaki Hiroshi Takami Fuminori Ishibashi Koji Shibasaki Maruwa Takumi Ebara Graduate School of Electrical Eng. and Computer Science Shibaura Institute of Technology Koto-ku Tokyo Japan Dept. of Electrical Eng. and Robotics Course College of Engineering Shibaura Institute of Technology Koto-ku Tokyo Japan Dept. of Electrical Eng. Faculty of Engineering Shibaura Institute of Technology Koto-ku Tokyo Japan Electronic Inc. Kashiwa-shi Chiba Japan Maruwa Electronic Inc. Kashiwa-shi Chiba Japan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

This paper describes analysis and evaluation of harmonics reduction for Active LC Low Pass Filter (LC-LPF) attached Surface Permanent Magnet Synchronous Motor (SPMSM) drive operated by IRM-ILQ (Inverse Reference Model - Inverse Linear Quadratic) current control. Harmonics due to high-speed switching of inverter should be reduced since it brings not only low efficiency motor drive such as torque ripple, also cause electromagnetic interference (EMI) problems. Those problems will be solved by appending LC-LPF though, LC resonant phenomena against inverter's carrier frequency and motor's operating frequency should be considered. The IRM-ILQ control is classified into modern control methods and simultaneously transacts multiple variables. Owing to it, stable motor drive can be realized even though LC-LPF is attached. In experiments, degrees of the harmonics reduction by Active LC-LPF are evaluated via Fast Fourier Transform (FFT) analysis and Total Harmonic Distortion (THD) and confirmed 2 % of voltage distortion on average in rated speed region.

关键词： Current control Motor drives Total harmonic distortion Fast Fourier transforms Electromagnetic interference Switches Harmonic analysis Distortion Voltage control Power harmonic filters

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Free-electron-driven X-ray caustics from strained van der Waals materials

Free-electron-driven X-ray caustics from strained van der Wa...

引用

CLEO: QELS_Fundamental Science, QELS 2022

作者： Shi, Xihang Kurman, Yaniv Shentcis, Michael Wong, Liang Jie de Abajo, F. Javier García Kaminer, Ido Solid State Institute Faculty of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa32000 Israel School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Spain ICREA-Institució Catalana de Recerca i Estudis Avançats Passeig Lluís Companys 23 Barcelona08010 Spain

ISBN: (纸本)9781557528209

关键词： Van der Waals forces

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Generative technology for Human Emotion Recognition: A Scope Review

arXiv

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arXiv 2024年

作者： Ma, Fei Yuan, Yucheng Xie, Yifan Ren, Hongwei Liu, Ivan He, Ying Ren, Fuji Yu, Fei Richard Ni, Shiguang Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Guangzhou China Department of Psychology Faculty of Arts and Sciences Beijing Normal University at Zhuhai Zhuhai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify and interpret human emotional states from different modalities, such as speech, facial images, text, and physiological signals. In recent years, important progress has been made in generative models, including Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model. These models, with their powerful data generation capabilities, emerge as pivotal tools in advancing emotion recognition. However, up to now, there remains a paucity of systematic efforts that review generative technology for emotion recognition. This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 320 research papers until June 2024. Specifically, this survey will firstly introduce the mathematical principles of different generative models and the commonly used datasets. Subsequently, through a taxonomy, it will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities in several aspects, including data augmentation, feature extraction, semi-supervised learning, cross-domain, etc. Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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All-fiber orbital-angular-momentum mode encoding system based on helical fiber gratings

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Photonics Research 2025年第4期 960-967页

作者： YU CHEN MING GAO YUTAO WANG XINYUE HUANG HUI HAO XIAOQI NI HUALI LU HUA ZHAO PENG WANG XIN WANG HONGPU LI School of Computer and Electronic Information Nanjing Normal University Intelligent Optoelectronic Sensing Technology Innovation Center Nanjing Normal University College of Electronic Engineering Nanjing Xiaozhuang University Key Laboratory of Optoelectronic Materials and Devices Chinese Academy of Sciences Faculty of the Engineering Shizuoka University

In this study, we propose and demonstrate an all-fiber orbital-angular-momentum (OAM) mode encoding system, where through helical fiber gratings (HFGs), binary symbols are encoded to or decoded from two OAM modes with topological charges (TCs) of ?1 and +1, respectively. We experimentally validate that the OAM mode generated by a clockwise-helix HFG (cHFG) can be converted back into fundamental mode by using an HFG with a helix orientation opposite to that of the c HFG, i.e., ccHFG.

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