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Development of Granular Fuzzy Relation Equations Based on a Subset of Data

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1416-1427页

作者： Dan Wang Xiubin Zhu Witold Pedycz Zhenhua Yu Zhiwu Li School of Computer Science and Technology Xi’an University of Science and TechnologyXi’an 710054China School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of AlbertaEdmonton T6R 2V4 ABCanada Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Institute of Systems Engineering Macao University of Science and TechnologyMacaoChina

Developing and optimizing fuzzy relation equations are of great relevance in system modeling,which involves analysis of numerous fuzzy *** each rule varies with respect to its level of influence,it is advocated that the performance of a fuzzy relation equation is strongly related to a subset of fuzzy rules obtained by removing those without significant *** this study,we establish a novel framework of developing granular fuzzy relation equations that concerns the determination of an optimal subset of fuzzy *** subset of rules is selected by maximizing their performance of the obtained *** originality of this study is conducted in the following *** with developing granular fuzzy relation equations,an interval-valued fuzzy relation is determined based on the selected subset of fuzzy rules(the subset of rules is transformed to interval-valued fuzzy sets and subsequently the interval-valued fuzzy sets are utilized to form interval-valued fuzzy relations),which can be used to represent the fuzzy relation of the entire rule base with high performance and ***,the particle swarm optimization(PSO)is implemented to solve a multi-objective optimization problem,in which not only an optimal subset of rules is selected but also a parameterεfor specifying a level of information granularity is determined.A series of experimental studies are performed to verify the feasibility of this framework and quantify its performance.A visible improvement of particle swarm optimization(about 78.56%of the encoding mechanism of particle swarm optimization,or 90.42%of particle swarm optimization with an exploration operator)is gained over the method conducted without using the particle swarm optimization algorithm.

关键词： A subset of data granular fuzzy relation equations interval-valued fuzzy relation particle swarm optimization(PSO)

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To Lead or to Follow? Adaptive Robot Task Planning in Human-Robot Collaboration

arXiv

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arXiv 2024年

作者： Noormohammadi-Asl, Ali Smith, Stephen L. Dautenhahn, Kerstin Department of Electrical and Computer Engineering Faculty of Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Systems Design Engineering Faculty of Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Adaptive task planning is fundamental to ensuring effective and seamless human-robot collaboration. This paper introduces a robot task planning framework that takes into account both human leading/following preferences and performance, specifically focusing on task allocation and scheduling in collaborative settings. We present a proactive task allocation approach with three primary objectives: enhancing team performance, incorporating human preferences, and upholding a positive human perception of the robot and the collaborative experience. Through a user study, involving an autonomous mobile manipulator robot working alongside participants in a collaborative scenario, we confirm that the task planning framework successfully attains all three intended goals, thereby contributing to the advancement of adaptive task planning in human-robot collaboration. This paper mainly focuses on the first two objectives, and we discuss the third objective, participants’ perception of the robot, tasks, and collaboration in a companion paper. © 2024, CC BY-NC-ND.

关键词： Manipulators

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A Taxonomy of Defenses against Memory Corruption Attacks 44

A Taxonomy of Defenses against Memory Corruption Attacks

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44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021

作者： Novkovic, Bojan University of Zagreb Faculty of Electrical Engineering and Computing Department of Electronics Microelectronics Computer and Intelligent Systems Zagreb Croatia

ISBN: (纸本)9789532331011

Vulnerabilities caused by memory corruption related bugs are a pervasive threat, continually undermining the security of the whole computing environment. The lack of memory safety mechanisms in indispensable systems programming languages like C or C++ leaves plenty of room for programmer-induced errors which often result in catastrophic security breaches. We analyze the root causes of these vulnerabilities, providing a concise overview of memory corruption bugs and related attacks. Although present in a number of programming languages, we use the C/C++ programming language as a basis for our analyses. We categorize existing defensive mechanisms based on the attack techniques they focused on preventing and give a brief insight into state-of-the-art defenses introduced throughout the years, with a special focus on operating system defenses. © 2021 Croatian Society MIPRO.

关键词： C++ (programming language)

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Profitability of a hybrid heating system for a single-family house in Poland based on a heat pump and photovoltaics

Profitability of a hybrid heating system for a single-family...

引用

2020 Applications of Electromagnetics in Modern engineering and Medicine, PTZE 2020

作者： Drzymala, A. Korzeniewska, E. Institute of Economics University of Lodz Lodz Poland Institute of Electrical Engineering Systems Faculty of Electrical Electronic Computer and Control Engineering Lodz University of Technology Lodz Poland

The article presents an analysis of the profitability of an investment consisting of a heat pump and a photovoltaic installation located in Poland for an exemplary single-family house. The factors that have a significant impact on the profitability and payback time of such installations operating in a dual configuration are described. A simulation of profitability taking into account the legal regulations and support systems was presented. The considered case of profitability concerns a specific technical solution in which an 8 kW air / water pump was used. The pomp cooperates with a photovoltaic installation with a power of up to 10kW, operating in a discount system with an energy consumption factor of 0.8. For the purposes of this article, the DCF (discounted cash flow) method was used. Using this method, it is possible to calculate the market value of the investment with the assumed boundary criteria. A comparison of the economic efficiency of an investment can be presented using the IRR (internal rate of return) method. © Published under licence by IOP Publishing Ltd.

关键词： Profitability

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Defect Detection for Printed Circuit Board Assembly Using Deep Learning 8

Defect Detection for Printed Circuit Board Assembly Using De...

引用

8th International Conference on Control Science and systems engineering, ICCSSE 2022

作者： Ren, Jing Gabbar, Hossam A. Huang, Xishi Saberironaghi, Alireza Ontario Tech University Dept. of Electrical and Computer Engineering OshawaON Canada Faculty of Energy Systems and Nuclear Science Engineering and Applied Science Ontario Tech University OshawaON Canada Rs Opto Tech Ltd Jiangsu Suzhou China

ISBN: (纸本)9781665484138

In many industrial applications, the number of defect samples is often insufficient for defect detection using conventional deep learning techniques. Also, the frequent change of PCBA board types on the product line introduces new defect types and adds a layer of challenge to the detection task considered in this paper. We propose a deep learning algorithm that targets learning patterns from various defect types with unbalanced training samples in the PCBA manufacturing product lines. A novel batch sampling method is proposed for the deep learning method for PCBA defect detection. We have validated the proposed algorithm using normal and defective images. The results show that the proposed deep learning method can accurately identify defects in PBCA images and achieve an overall accuracy of 98%. This deep learning technique can also be extended to detect other surface-level defects. © 2022 IEEE.

关键词： Defects

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Performance Comparison of Various Dual-Band AMC Structures Using Textile Materials

Performance Comparison of Various Dual-Band AMC Structures U...

引用

International electrical engineering Congress (iEECON)

作者： Yotrawee Hengroemyat Wanwisa Thaiwirot Prayoot Akkaraekthalin Nonchanutt Chudpooti Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents the performance comparison of various dual-band AMC structures using textile materials. The textile AMC structure includes of the inner patch and outer loop to achieve dual-band operation. The felt is used as substrate of AMC unit cell while the copper-nickel plated polyester fabric is used as conductive parts. The various geometries of AMC unit cell based on square, circle, hexagon, octagon shapes are designed to operate at 2.4/5.5 GHz for WBAN and Wi-Fi applications. Moreover, the performance of AMC unit cell structures is compared in term of size and bandwidth. It is found from simulation results that the inner square patch and outer square loop can provide compact size and wider bandwidth. The hexagon shape can achieve compact size when it is arranged in form of array.

关键词： Geometry Wireless communication Shape Simulation Dual band Bandwidth Reflector antennas Substrates Wireless fidelity Textiles

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Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning

arXiv

引用

arXiv 2025年

作者： Poudel, Bibek Wang, Xuan Li, Weizi Zhu, Lei Heaslip, Kevin Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Department of Electrical and Computer Engineering George Mason University FairfaxVA United States Department of Industrial and Systems Engineering University of North Carolina at Charlotte CharlotteNC United States Department of Civil and Environmental Engineering University of Tennessee KnoxvilleTN United States

Reinforcement learning (RL) holds significant promise for adaptive traffic signal control. While existing RL-based methods demonstrate effectiveness in reducing vehicular congestion, their predominant focus on vehicle-centric optimization leaves pedestrian mobility needs and safety challenges unaddressed. In this paper, we present a deep RL framework for adaptive control of eight traffic signals along a real-world urban corridor, jointly optimizing both pedestrian and vehicular efficiency. Our single-agent policy is trained using real-world pedestrian and vehicle demand data derived from Wi-Fi logs and video analysis. The results demonstrate significant performance improvements over traditional fixed-time signals, reducing average wait times per pedestrian and per vehicle by up to 67% and 52% respectively, while simultaneously decreasing total accumulated wait times for both groups by up to 67% and 53%. Additionally, our results demonstrate generalization capabilities across varying traffic demands, including conditions entirely unseen during training, validating RL’s potential for developing transportation systems that serve all road users. © 2025, CC BY.

关键词： Reinforcement learning

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

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

引用

International Conference on Signal Processing and Communication systems, ICSPCS

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

ISBN: (数字)9798350389630

ISBN: (纸本)9798350389647

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.

关键词： Deep learning Training Accuracy Face recognition Transfer learning Pressing Robustness Mobile applications Convolutional neural networks Currencies

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Achieving optimal complexity guarantees for a class of bilevel convex optimization problems

arXiv

引用

arXiv 2023年

作者： Samadi, Sepideh Burbano, Daniel Yousefian, Farzad Department of Industrial and Systems Engineering Rutgers University PiscatawayNJ08854 United States Department of Electrical and Computer Engineering Rutgers University PiscatawayNJ08854 United States

We design and analyze a novel accelerated gradient-based algorithm for a class of bilevel optimization problems. These problems have various applications arising from machine learning and image processing, where optimal solutions of the two levels are interdependent. That is, achieving the optimal solution of an upper-level problem depends on the solution set of a lower-level optimization problem. We significantly improve existing iteration complexity to O(ϵ−0.5) for both suboptimality and infeasibility error metrics, where ϵ > 0 denotes an arbitrary scalar. In addition, contrary to existing methods that require solving the optimization problem sequentially (initially solving an optimization problem to approximate the solution of the lower-level problem followed by a second algorithm), our algorithm concurrently solves the optimization problem. To the best of our knowledge, the proposed algorithm has the fastest known iteration complexity, which matches the optimal complexity for single-level optimization. We conduct numerical experiments on sparse linear regression problems to demonstrate the efficacy of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Modulation and Estimation with a Helper

arXiv

引用

arXiv 2023年

作者： Khina, Anatoly Merhav, Neri The Department of Electrical Engineering–Systems Tel Aviv University Tel Aviv6997801 Israel The Viterby Faculty of Electrical and Computer Engineering Technion–Israel Institute of Technology Haifa3200004 Israel

The problem of transmitting a parameter value over an additive white Gaussian noise (AWGN) channel is considered, where, in addition to the transmitter and the receiver, there is a helper that observes the noise non-causally and provides a description of limited rate Rh to the transmitter and/or the receiver. We derive upper and lower bounds on the optimal achievable α-th moment of the estimation error and show that they coincide for small values of α and for high values of Rh. The upper bound relies on a recently proposed channel-coding scheme that effectively conveys Rh bits essentially error-free and the rest of the rate—over the same AWGN channel without help, with the error-free bits being allocated to the most significant bits of the quantized parameter. We then concentrate on the setting with a total transmit energy constraint, for which we derive achievability results for both channel coding and parameter modulation for several scenarios: when the helper assists only the transmitter or only the receiver and knows the noise, and when the helper assists the transmitter and/or the receiver and knows both the noise and the message. In particular, for the message-informed helper that assists both the receiver and the transmitter, it is shown that the error probability in the channel-coding task decays doubly exponentially. Finally, we translate these results to those for continuous-time power-limited AWGN channels with unconstrained bandwidth. As a byproduct, we show that the capacity with a message-informed helper that is available only at the transmitter can exceed the sum of the capacity without help and the help rate Rh, when the helper knows only the noise but not the message. © 2023, CC BY-SA.

关键词： Transmitters

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