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International Conference on Information and Communication Systems (ICICS)

作者： Omar Banimelhem Baghdad Al-khateeb Department of Network Engineering and Security Jordan University of Science and Technology Irbid Jordan Department of Computer Engineering Jordan University of Science and Technology Irbid Jordan

The lack of clarity of most artificial intelligence algorithms which have many applications has led to the emergence of explainable artificial intelligence (XAI), which includes machine learning algorithms that introduce a clear explanation of learning decisions for human users. In the field of using drones which have recently been expanded in several applications, the drone designers face many challenges due to the weather and the difficulty of making a decision during the flight. Therefore, explainable artificial intelligence may play a significant role in these models of unmanned aerial vehicle. This paper presents a brief review for the recent articles that were published in the field of using XAI in drone's applications.

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Incipient Fault Detection in Power Distribution System: A Time-Frequency Embedded Deep Learning Based Approach

arXiv

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arXiv 2023年

作者： Li, Qiyue Luo, Huan Cheng, Hong Deng, Yuxing Sun, Wei Li, Weitao Liu, Zhi School of Electrical Engineering and Automation Hefei Unviersity of Techonology Hefei China Department of Computer and Network Engineering The University of Electro-Communications Japan

Incipient fault detection in power distribution systems is crucial to improve the reliability of the grid. However, the non-stationary nature and the inadequacy of the training dataset due to the self-recovery of the incipient fault signal, make the incipient fault detection in power distribution systems a great challenge. In this paper, we focus on incipient fault detection in power distribution systems and address the above challenges. In particular, we propose an ADaptive Time-Frequency Memory (AD-TFM) cell by embedding wavelet transform into the Long Short-Term Memory (LSTM), to extract features in time and frequency domain from the non-stationary incipient fault signals. We make scale parameters and translation parameters of wavelet transform learnable to adapt to the dynamic input signals. Based on the stacked AD-TFM cells, we design a recurrent neural network with ATtention mechanism, named AD-TFM-AT model, to detect incipient fault with multi-resolution and multi-dimension analysis. In addition, we propose two data augmentation methods, namely phase switching and temporal sliding, to effectively enlarge the training datasets. Experimental results on two open datasets show that our proposed AD-TFM-AT model and data augmentation methods achieve state-of-the-art (SOTA) performance of incipient fault detection in power distribution system. We also disclose one used dataset logged at State Grid Corporation of China to facilitate future research. © 2023, CC BY.

关键词： Long short-term memory

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Risk-Aware Distributed Multi-Agent Reinforcement Learning

Risk-Aware Distributed Multi-Agent Reinforcement Learning

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American Control Conference (ACC)

作者： Abdullah Al Maruf Luyao Niu Bhaskar Ramasubramanian Andrew Clark Radha Poovendran Department of Electrical and Computer Engineering California State University Los Angeles CA USA Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA USA Electrical and Computer Engineering Western Washington University Bellingham WA USA Electrical and Systems Engineering Washington University in St. Louis St. Louis MO USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Autonomous cyber and cyber-physical systems need to perform decision-making, learning, and control in unknown environments. Such decision-making can be sensitive to multiple factors, including modeling errors, changes in costs, and impacts of events in the tails of probability distributions. Although multi-agent reinforcement learning (MARL) provides a framework for learning behaviors through repeated interactions with the environment by minimizing an average cost, it is not adequate to overcome the above challenges. In this paper, we develop a distributed MARL approach to solve decision-making problems in unknown environments by learning risk-aware actions. We use the conditional value-at-risk (CVaR) to define the cost function that is being minimized, and introduce a Bellman operator to characterize the value function associated to a given state-action pair. We prove that this operator satisfies a contraction property, and that it converges to the optimal value function. We then propose a distributed MARL algorithm called the CVaR QD-Learning algorithm, and establish that value functions of individual agents reach consensus. We identify several challenges that arise in the implementation of the CVaR QD-Learning algorithm, and present solutions to overcome these. We evaluate the CVaR QD-Learning algorithm through simulations, and demonstrate the effect of a risk parameter on value functions at consensus.

关键词： Training Costs Decision making Reinforcement learning Tail Artificial neural networks Cyber-physical systems

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A 7nm-Based Decodable Self-Resetting Regfile Circuit 15

A 7nm-Based Decodable Self-Resetting Regfile Circuit

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Zhao, Wanlong Zhang, Yuejun Ren, Mingze Wen, Liang Wang, Pengjun Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China China Coast Guard Academy Department of Electronic Technology Ningbo China Wenzhou University College of Electrical and Electronic Engineering Wenzhou325035 China

ISBN: (纸本)9798350312980

This paper presents a novel design scheme for a decodable self-resetting Regfile circuit that aims to reduce the power consumption caused by massive data and high computing power. The scheme consists of three steps: first, it analyzes the working principle and the energy consumption process of the decoding circuit;second, it proposes a self-resetting decoding circuit structure that uses the clock to control the decoding state and automatically resets when the clock is low, thus avoiding the extra power consumption due to the inversion of the memory cell state during the non-working period of the decoding circuit;third, it implements a two-read, two-write, decodable self-resetting Regfile circuit using a full-custom design method under the TSMC 7nm FinFET process. The experimental results demonstrate that the circuit has an area of 5593μm2 and an energy-delay product of 0.41pJ•nS, and it can operate stably at 4GHz under various temperatures and process corner conditions. © 2023 IEEE.

关键词： 7nm process high speed low power consumption Regfile circuit

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Quantum Computing in Wireless Communications and networking: A Tutorial-cum-Survey

arXiv

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arXiv 2024年

作者： Zhao, Wei Weng, Tangjie Ruan, Yue Liu, Zhi Wu, Xuangou Zheng, Xiao Kato, Nei School of Computer Science and Technology Anhui University of Technology China The University of Electro-Communications Department of Computer and Network Engineering Japan Tohoku University Sendai Japan

Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such as transportation, pharmaceuticals, and industrial manufacturing, achieving unprecedented milestones. In modern society, wireless communication stands as an indispensable infrastructure, with its essence lying in optimization. Although artificial intelligence (AI) algorithms such as reinforcement learning (RL) and mathematical optimization have greatly enhanced the performance of wireless communication, the rapid attainment of optimal solutions for wireless communication problems remains an unresolved challenge. QC, however, presents a new alternative. This paper aims to elucidate the fundamentals of QC and explore its applications in wireless communications and networking. First, we provide a tutorial on QC, covering its basics, characteristics, and popular QC algorithms. Next, we introduce the applications of QC in communications and networking, followed by its applications in miscellaneous areas such as security and privacy, localization and tracking, and video streaming. Finally, we discuss remaining open issues before concluding. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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The Combination of Recognition Technology and Artificial Intelligence for Questioning and Clarification Mechanisms to Facilitate Meaningful EFL Writing in Authentic Contexts 6th

The Combination of Recognition Technology and Artificial Int...

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Proceedings of the 6th International Conference on Innovative Technologies and Learning, ICITL 2023

作者： Hwang, Wu-Yuin Nurtantyana, Rio Lai, Yu-Fu Chiang, I-Chin Nonie Ghenia, George Tsai, Ming-Hsiu Michelle Graduate Institute of Network Learning Technology National Central University Taoyuan City Taiwan School of Computing Telkom University Bandung Indonesia Department of Humanities National Open University New Taipei City Taiwan Department of Computer Science Brunel University London United Kingdom Department of Foreign Languages and Literature Feng Chia University Taichung City Taiwan Department of Computer Science and Information Engineering College of Science and Engineering National Dong Hwa University Hualien County Taiwan

ISBN: (纸本)9783031401121

Most studies of English as a Foreign Language (EFL) writing usually used grammar checking to help EFL learners to check writing errors. However, it is not enough since EFL learners have to learn how to create more meaningful content, particularly using their surroundings in authentic contexts. Therefore, we develop one app, Ubiquitous English (UEnglish), with recognition technology, particularly Image-to-Text Recognition (ITR) texts to provide the vocabulary and description from authentic pictures, and generative-AI that can generate meaningful questions and clarifications to trigger EFL learners to write more. In addition, EFL learners need to answer the question from generative-AI before they receive the clarification. Hence, we proposed a Smart Questioning-Answering-Clarification (QAC) mechanism to help EFL writing. A total of 35 participants were assigned into two groups, experimental groups (EG) with 19 learners and control groups (CG) with 16 learners with/without Smart QAC mechanism support, respectively. In this study, the quasi-experiment was conducted over five weeks and we used quantitative analysis methods. The results revealed that the EG with ITR-texts and Smart QAC had a significant difference with CG in the learning behaviors and post-test. Furthermore, EG could write more meaningful words in the assignments. Therefore, the Smart QAC mechanism could facilitate EFL learners to enhance their EFL writing in authentic contexts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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An Intelligent Movies Recommendation System Based Facial Attributes Using Machine Learning

An Intelligent Movies Recommendation System Based Facial Att...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mohammed Balfaqih Abdullah Altwaim Akram A. Almohammedi Mohammad Hafiz Mohd Yusof Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Electrical & Electronic Engineering Department Karabük University Turkey Faculty of Computer and Mathematical Sciences Universiti Teknologi Mara UiTM Tapah Malaysia

Movie theaters and platforms offer a wide selection of movies that require filtering to match the preferences of individual users. Recommender systems are an effective tool for this task. This study introduces a hybrid recommender system that combines collaborative filtering and content-based approaches to provide personalized movie recommendations. The proposed system considers age, gender, emotion, and genre attributes to ensure the suitability of the recommended movies. The proposed system targets the visitors of cinema theaters to try a new experience of choosing the next movie to watch by recognizing the visitor's face to determine his/her age, gender and emotion using camera. Therefore, our system is expected to achieve better results than traditional approaches. The system's performance is evaluated using standard metrics such as precision, recall, and F1-measure. The findings indicated that the proposed system outperforms the benchmark system in the most tested scenarios. However, the precision of the benchmark work is slightly higher in (8–14 and 15–24) age groups.

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Improvement of Spiking Neural network with Bit Planes and Color Models

arXiv

引用

arXiv 2024年

作者： Luu, Nhan T. Luu, Duong T. Nam, Pham Ngoc Thang, Truong Cong Department of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Information and Network Management Center Can Tho University Can Tho Viet Nam College of Engineering and Computer Science VinUniversity Hanoi Viet Nam

Spiking neural network (SNN) has emerged as a promising paradigm in computational neuroscience and artificial intelligence, offering advantages such as low energy consumption and small memory footprint. However, their practical adoption is constrained by several challenges, prominently among them being performance optimization. In this study, we present a novel approach to enhance the performance of SNN for images through a new coding method that exploits bit plane representation. Our proposed technique is designed to improve the accuracy of SNN without increasing model size. Also, we investigate the impacts of color models of the proposed coding process. Through extensive experimental validation, we demonstrate the effectiveness of our coding strategy in achieving performance gain across multiple datasets. To the best of our knowledge, this is the first research that considers bit planes and color models in the context of SNN. By leveraging the unique characteristics of bit planes, we hope to unlock new potentials in SNNs performance, potentially paving the way for more efficient and effective SNNs models in future researches and applications. © 2024, CC BY.

关键词： Neural network models

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Design and Implementation of Links Generation for Inter Domain Routing System 12

Design and Implementation of Links Generation for Inter Doma...

引用

12th International Conference on Information Science and Technology, ICIST 2022

作者： Wang, Yu Qiao, Zhi Yin, Junru Zhang, Mingliang College of Software Henan University of Engineering Zhengzhou China College of Information and Electrical Engineering China Agriculture University Beijing China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Network Research Department Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments. © 2022 IEEE.

关键词： Genetic algorithms

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Roland: Robust In-band Parallel Communication for Magnetic MIMO Wireless Power Transfer System 42

Roland: Robust In-band Parallel Communication for Magnetic M...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zhou, Wangqiu Zhou, Hao Cui, Xiang Wang, Xinyu Wang, Xiaoyan Liu, Zhi Linke Lab School of Computer Science and Technology University of Science and Technology of China China University of Science and Technology of China Cas Key Laboratory of Wireless-Optical Communications China Deqing Alpha Innovation Institute Huzhou China Ibaraki University Graduate School of Science and Engineering Ibaraki Japan The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798350334142

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn't be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%. © 2023 IEEE.

关键词： MIMO systems

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