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Genetic Algorithm-Based Task Assignment for Fleet of Unmanned Surface Vehicles in Dynamically Changing Environment 基于遗传算法的动态变化环境下无人水面车队任务分配
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-07-27 DOI: 10.1080/01969722.2023.2240645
Miroslav Dvorak, P. Doležel, Dominik Stursa, Mohamed Chouai
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引用次数: 0
A Fuzzy Analysis Approach to Green-Resilient Supplier Selection in Electronic Manufacturing Systems 电子制造系统绿色弹性供应商选择的模糊分析方法
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-07-04 DOI: 10.1080/01969722.2022.2067633
Mansour Abedian, A. Saghafinia, Maryam Hejazi
Abstract Adopting environmentally sound practices and utilizing resilient issues to face disruptive events and risks are new important agendas for enterprises, governments, and societies. The issue of supplier selection and evaluation in accordance with green and resilient principles has received little attention in the electronic industry. So, the present study aims to consider both criteria of resilience and greenness in a new supplier selection model. This study presents an integrated decision-making method based on the fuzzy set theory and data envelopment analysis (DEA) approach for supplier selection in a supply chain network in an electronic manufacturing company. The fuzzy set theory was used for analyzing suppliers' indicator weights, the suppliers' performance, and rating them with respect to subcriteria. Then, the obtained data were aggregated to classify and prepare them for use in a version assurance region of the DEA method with one dummy input and three output variables to rank the suppliers. The performance of the proposed DEA-based model has been verified through the sensitivity analysis and it has been validated by the existing FIS-based model in the literature. Furthermore, the proposed DEA-based model can be designed with any number of suppliers and criteria and subcriteria.
摘要采取无害环境的做法,利用有弹性的问题来应对破坏性事件和风险,是企业、政府和社会新的重要议程。根据绿色和弹性原则选择和评估供应商的问题在电子行业很少受到关注。因此,本研究旨在在一个新的供应商选择模型中同时考虑弹性和绿色性标准。本文提出了一种基于模糊集理论和数据包络分析(DEA)方法的电子制造企业供应链网络供应商选择综合决策方法。模糊集理论用于分析供应商的指标权重、供应商的绩效,并根据次级标准对其进行评级。然后,将获得的数据进行汇总,对其进行分类,并准备在DEA方法的版本保证区域中使用,该方法具有一个虚拟输入和三个输出变量,以对供应商进行排名。通过敏感性分析验证了所提出的基于DEA的模型的性能,并通过文献中现有的基于FIS的模型进行了验证。此外,所提出的基于DEA的模型可以用任何数量的供应商、标准和次标准来设计。
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引用次数: 3
Performance Investigation of EMFO-Based Perpetual Online Tuned Variable SR-Controller in Simulated Real Environment 仿真真实环境下基于emfo的永续在线调谐变sr控制器性能研究
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-07-04 DOI: 10.1080/01969722.2022.2073703
Vishal Vishnoi, S. Tiwari, R. Singla
Abstract This study uses the enhanced moth flame optimization (EMFO) algorithm with an online tuning approach to optimize the parameters of a variable range split range PID (SRPID) controller. Before implementing the developed EMFO algorithm in the actual plant, it is necessary to investigate the performance of the same in the simulated real environment. Therefore, in the present work, an electrical analogous model of the practical environment is simulated for investigation by considering several effects, namely imperfect insulation, density, viscosity, and compressibility. Further, to check the effectiveness of the proposed algorithm, the controller performance using the EMFO algorithm is compared with the performance using the original MFO algorithm. The validation results show a substantial improvement in the case of EMFO-based controller with an online tuning method in comparison to MFO-based controller. EMFO algorithm exhibits superior performance as it combines the benefits of three modifications (change the spiral path, opposition learning-based initialization, and change in flames selection) in the original MFO algorithm. Furthermore, the system is also investigated for the effect of system dynamics and process disturbance. It is concluded that the developed EMFO algorithm gives superior performance in a simulated real environment paving the way for possible implementation in practical situations.
摘要采用基于在线整定的增强型飞蛾火焰优化(EMFO)算法对变量程分割PID (SRPID)控制器进行参数优化。在将所开发的EMFO算法应用于实际工厂之前,有必要对其在模拟真实环境中的性能进行研究。因此,在本工作中,通过考虑几种影响,即不完全绝缘、密度、粘度和可压缩性,模拟了实际环境的电气模拟模型进行研究。进一步,为了验证所提算法的有效性,将采用EMFO算法的控制器性能与采用原始MFO算法的控制器性能进行了比较。验证结果表明,采用在线整定方法的基于emfo的控制器与基于mfo的控制器相比有很大的改进。EMFO算法结合了原MFO算法的三种修改(改变螺旋路径、基于对立学习的初始化和改变火焰选择)的优点,表现出优越的性能。此外,还研究了系统动力学和过程扰动对系统的影响。结果表明,所提出的EMFO算法在模拟真实环境中具有较好的性能,为实际应用奠定了基础。
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引用次数: 0
Attack Detection in Wireless Sensor Network: A Big Data Perspective 无线传感器网络攻击检测:大数据视角
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-05-17 DOI: 10.1080/01969722.2022.2157615
Kulkarni A. V., Mithra V., Radhika Menon
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引用次数: 0
An Efficient Cloud Storage Model for GOP-Level Video Deduplication using Adaptive GOP Structure 基于自适应GOP结构的GOP级视频重复数据删除高效云存储模型
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-04-09 DOI: 10.1080/01969722.2023.2176665
G. Sujatha, A. Devipriya, D. Brindha, G. Premalatha
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引用次数: 0
Efficient Energy Management Strategy for an Electric Vehicle Powered by a Hybrid Energy Storage System Based on Hybrid GBDT-RSA Approach 基于混合GBDT-RSA方法的混合储能系统电动汽车高效能量管理策略
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-03-31 DOI: 10.1080/01969722.2023.2176588
K. Kandaswamy, A. Jagadeeshwaran, R. Anand
{"title":"Efficient Energy Management Strategy for an Electric Vehicle Powered by a Hybrid Energy Storage System Based on Hybrid GBDT-RSA Approach","authors":"K. Kandaswamy, A. Jagadeeshwaran, R. Anand","doi":"10.1080/01969722.2023.2176588","DOIUrl":"https://doi.org/10.1080/01969722.2023.2176588","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45345634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Efficient DDoS Attack Detection Using Chaos Henry Gas Solubility Optimization Weight Initialization Based Rectified Linear Unit 基于混沌亨利气体溶解度优化权初始化的整流线性单元DDoS攻击检测
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-03-17 DOI: 10.1080/01969722.2023.2175140
Selvam Lakshmanan, Uma Maheswari Gnaniyan Ponnusamy, Senthilkumar Andi
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引用次数: 0
Design of UPQC with Solar PV and Battery Storage Systems for Power Quality Improvement 基于太阳能光伏和电池储能系统的UPQC设计以提高电能质量
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-03-15 DOI: 10.1080/01969722.2023.2175144
Koganti Srilakshmi, K. Krishna Jyothi, G. Kalyani, Y. Sai Prakash Goud
{"title":"Design of UPQC with Solar PV and Battery Storage Systems for Power Quality Improvement","authors":"Koganti Srilakshmi, K. Krishna Jyothi, G. Kalyani, Y. Sai Prakash Goud","doi":"10.1080/01969722.2023.2175144","DOIUrl":"https://doi.org/10.1080/01969722.2023.2175144","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48234749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Recognition of Kannada Character Scripts Using Hybrid Feature Extraction and Ensemble Learning Approaches 使用混合特征提取和集成学习方法识别卡纳达文字
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-03-09 DOI: 10.1080/01969722.2023.2175145
Supreetha Patel Tiptur Parashivamurthy, Sannangi Viswaradhya Rajashekaradhya
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引用次数: 1
Leukemia Detection Using Invariant Structural Cascade Segmentation Based on Deep Vectorized Scaling Neural Network 基于深度矢量化尺度神经网络的不变结构级联分割白血病检测
IF 1.7 4区 计算机科学 Q2 Computer Science Pub Date : 2023-03-01 DOI: 10.1080/01969722.2023.2177798
A. Arthi, V. Vennila, U. Arun Kumar
{"title":"Leukemia Detection Using Invariant Structural Cascade Segmentation Based on Deep Vectorized Scaling Neural Network","authors":"A. Arthi, V. Vennila, U. Arun Kumar","doi":"10.1080/01969722.2023.2177798","DOIUrl":"https://doi.org/10.1080/01969722.2023.2177798","url":null,"abstract":"","PeriodicalId":55188,"journal":{"name":"Cybernetics and Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48391108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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