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Evolutionary Multi/Many-Objective Optimisation via Bilevel Decomposition

IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1973-1986页

作者： Shouyong Jiang Jinglei Guo Yong Wang Shengxiang Yang School of Automation Central South UniversityChangsha 410083China Department of Computing Science University of AberdeenAberdeen AB243FXUK School of Computer Science Central China Normal UniversityWuhan 430079China School of Computer Science and Informatics De Montfort UniversityLeicester LE19BHUK

Decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective ***,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation *** paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective *** bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower *** subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the *** bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective *** analysis and component analysis have been also carried out to further justify the proposed algorithm.

关键词： Bilevel decomposition evolutionary algorithm many-objective optimisation multi-objective optimisation

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Efficient One-Stage Deep Learning for Text Detection in Scene Images

EEA - Electrotehnica, Electronica, Automatica

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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 65-71页

作者： Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Faculty of Exact Sciences Natural And Life Sciences University Mohamed Khider of Biskra Algeria Computer Science Department University Mohamed Khider of Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Italy

Text detection in natural images is a crucial task for extracting and recognizing valuable information, but it comes with significant challenges. Traditional image processing methods often rely on synthetic features, which may not be suitable for handling the diverse range of unstructured text scenarios encountered in real-world environments. To overcome this challenge, deep learning techniques have emerged as a powerful tool for adaptively learning and extracting features to digitize text. In this paper, we introduce a novel approach based on the YOLOv5 architecture for text detection. Our method leverages the strengths of YOLOv5, including its efficient and lightweight architecture, multi-scale prediction capability, and advanced data augmentation techniques. By employing these features, our proposed model can accurately identify regions in an image that are more likely to contain text. Through extensive experimentation on popular benchmark datasets, we have evaluated the performance of our proposed model. The results have demonstrated that our method outperforms state-of-the-art approaches in terms of both detection efficiency and accuracy. This indicates the effectiveness of our approach in handling the challenges posed by text detection in natural images. © Editura ELECTRA 2024. All rights reserved.

关键词： Deep Learning Natural scene image Text detection Yolov5

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Control Structures and Algorithms for Force Feedback Bilateral Teleoperation Systems: A Comprehensive Review

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computer Modeling in Engineering & sciences 2025年第2期142卷 973-1019页

作者： Jiawei Tian Yu Zhou Lirong Yin Salman A.AlQahtani Minyi Tang Siyu Lu Ruiyang Wang Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Force feedback bilateral teleoperation represents a pivotal advancement in control technology,finding widespread application in hazardous material transportation,perilous environments,space and deep-sea exploration,and healthcare *** paper traces the evolutionary trajectory of force feedback bilateral teleoperation from its conceptual inception to its current *** elucidates the fundamental principles underpinning interaction forces and tactile exchanges,with a specific emphasis on the crucial role of tactile *** this review,a quantitative analysis of force feedback bilateral teleoperation development trends from 2011 to 2024 has been conducted,utilizing published journal article data as the primary source of *** review accentuates classical control frameworks and algorithms,while also delving into existing research advancements and prospec-tive breakthrough ***,it explores specific practical scenarios ranging from intricate surgeries to hazardous environment exploration,underscoring the technology’s potential to revolutionize industries by augmenting human manipulation of remote *** underscores the pivotal role of force feedback bilateral teleoperation as a transformative human-machine interface,capable of shaping flexible control strategies and addressing technological *** research endeavors in force feedback bilateral teleoperation are expected to prioritize the creation of more immersive experiences,overcoming technical hurdles,fortifying human-machine collaboration,and broadening application domains,particularly within the realms of medical intervention and hazardous *** the continuous progression of technology,the integration of human intelligence and robotic capabilities is expected to produce more innovations and breakthroughs in the field of automatic control.

关键词： Force feedback bilateral teleoperation robot development history application prospect

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Selection of software requirements using TOPSIS under fuzzy environment

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International Journal of computers and Applications 2022年第6期44卷 503-512页

作者： Sadiq, Mohd. Khan, Shadab Mohammad, Chaudhary Wali Department of Computer Science and Automation Indian Institute of Science Bangalore India New Delhi India

The goal of the decision making process is to find out the most appropriate alternative(s) from the set of the alternatives based on different criteria. Software requirements selection is a multi-criteria decision making (MCDM) problem whose objective is to choose the software requirements (SRs) from the set of the SRs so that the need of the stakeholders can be implemented during the development of software. Due to the increasing complexity of the socio-economic environment, multiple decision makers are involved during SRs selection process because it is difficult for single decision maker to deal with all the SRs. During the SRs selection process, decision makers use linguistic terminologies to specify the preferences of the SRs. Therefore, this paper presents a decision making method for the selection of the SRs using fuzzy TOPSIS method in which linear scale transformation is used to construct the fuzzy decision matrix based on the triangular fuzzy numbers. Finally, the applicability of the proposed method is demonstrated with the help of an example. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fuzzy logic

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Dual RIS-aided Parallel Intelligence Surface for IoAMVSs: A Co-Design Approach for 3C Problems

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-10页

作者： Bo, Peng Tu, Wanqing Tu, Xingbin Qu, Fengzhong Wang, Fei-Yue Ocean College Zhejiang University China Department of Computer Science Durham University U.K Institute of Automation Chinese Academy of Sciences Beijing China

The internet of autonomous marine vehicle systems (IoAMVSs) requires ultra-reliable communications, ultra-real-time control, and ultra-high precision computation. Classical parallel intelligence theory is a popular method for developing IoAMVSs in the literature. However, this method has made it difficult to achieve the anticipated performance when co-designing communications, control, and computing (3C) in complex oceanic communication environments. This article explores the efficient integration of reconfigurable intelligence surface (RIS) with classical parallel intelligent theory to address these issues effectively. A novel framework is proposed in this article to implement a dual RIS-aided parallel intelligence theory for enabling large-scale cross-media 3C co-design in IoAMVSs. The framework consists of electromagnetic RIS and acoustic RIS, which form the dual RIS-aided parallel intelligence surfaces. Our dual RIS-aided parallel intelligence surfaces have the potential to efficiently achieve highly accurate position, navigation, cooperative control, and data fusion for IoAMVSs. We hope that our framework can promote the development of more efficient, energy-saving, and safer intelligent ocean transportation systems. IEEE

关键词： Computation theory

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Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 919-931页

作者： Fei Ming Wenyin Gong Ling Wang Yaochu Jin the School of Computer Science China University of GeosciencesWuhan 430074China the Department of Automation Tsinghua UniversityBeijing 100084China the Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia33619 BielefeldGermany

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable *** constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling *** performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at ***,improving operator selection is promising and necessary for *** work proposes an online operator selection framework assisted by Deep Reinforcement *** dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the *** using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic *** framework is embedded into four popular CMOEAs and assessed on 42 benchmark *** experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

关键词： Constrained multi-objective optimization deep Qlearning deep reinforcement learning(DRL) evolutionary algorithms evolutionary operator selection

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Confidentiality Preserving Data Sharing for Life Cycle Assessment in Process Industries 29

Confidentiality Preserving Data Sharing for Life Cycle Asses...

引用

29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Perera, Hansani Atmojo, Udayanto Dwi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350361230

The pulp and paper industry faces significant en-vironmental challenges, such as air pollution, greenhouse gas emissions, and wastewater discharge, requiring smarter and more sustainable operations. Regulatory bodies are imposing stringent measures to mitigate these impacts, prompting the industry to adopt sustainable practices and technologies. Life Cycle Assessment (LCA) models are crucial in this effort, pro-viding a comprehensive evaluation of environmental impacts and aiding decision making for sustainable manufacturing. However, organisations prioritise the confidentiality of their sensitive data, which can hinder collaborative efforts and LCA calculations. This paper addresses organisational requirements for improving confidentiality, tamper-proof data transfer, and ensuring data sovereignty. The ongoing proof-of-concept introduces a novel approach in LCA, employing Secure Multiparty Computation (SMPC) and data spaces to enable confidentiality-preserving LCA. Our solution ensures data sovereignty and accurate LCA calculations, promoting sustainable practices across the value chain. This paper lays the foundation for a collaborative data platform that meets the critical needs of confidentiality, security, and sustainability in the process manufacturing industry. © 2024 IEEE.

关键词： Life cycle assessment

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Towards Web Platform for Cloud-Based Virtual Commissioning of IEC 61499 Distributed automation Systems 29

Towards Web Platform for Cloud-Based Virtual Commissioning o...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Lyu, Tuojian Nguyen, Tung Rumiantsev, Roman Kolesnikov, Mikhail Perera, Hansani Atmojo, Udayanto Dwi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798350361230

In this paper, we propose a cloud-based virtual commissioning Web platform for IEC 61499 compliant distributed automation systems that support Create-Read-Update-Delete operations for SoftPLC containers from various versions and provide a remote engineering workstation with an embedded web Integrated Development Environment (IDE), Human Machine Interface (HMI), and 3D simulation models. A water tank use case demonstrates the platform's flexibility, enabling control software testing entirely via a Web browser without local installations. © 2024 IEEE.

关键词： Cloud platforms

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Framework for Faster-Than-Real-Time Testing of IEC 61499 Applications with Embedded Process Simulation 22

Framework for Faster-Than-Real-Time Testing of IEC 61499 App...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Vyatkin, Valeriy Rumiantcev, Roman Department of Electrical Engineering and Automation Aalto University Espoo Finland Department of Computer Science Computer and Space Engineering Lulea Tekniska Universitet Sweden

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527471

This paper presents a novel framework for testing automation applications compliant with the international standard IEC 61499 and including process simulation. The framework enables automation programs to be run in testing mode faster than real time, validated by an external tester program. Application of the approach is illustrated on example of a simple component-based system. © 2024 IEEE.

关键词： Automatic testing

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Optimal Function Block Distribution for IEC 61499 Distributed Control Application 3

Optimal Function Block Distribution for IEC 61499 Distribute...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Bojnurdi, Vahid Ehteshami Lyu, Tuojian Atmojo, Udayanto Dwi Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Lulea Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331540319

The efficient distribution of function blocks across programmable logic controllers (PLCs) is critical to improving the performance of distributed automation systems. This paper presents a novel randomized greedy optimization algorithm to minimize communication latency and maximize resource utilization by optimizing the allocation of function blocks across multiple PLCs in compliance with the IEC 61499 standard. Simulations demonstrate the effectiveness of the approach in achieving substantial performance improvements in distributed control applications. The results highlight the flexibility and scalability of the proposed optimization method, particularly in environments with varying PLC configurations. © 2024 IEEE.

关键词： Resource allocation

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