Jun Jiang, Yiwen. Chen, Othman Lakhal, R. Merzouki
{"title":"基于人工智能的SoS弹性反应性能分类","authors":"Jun Jiang, Yiwen. Chen, Othman Lakhal, R. Merzouki","doi":"10.1109/SoSE59841.2023.10178103","DOIUrl":null,"url":null,"abstract":"This works provides a comprehensive performance evaluation approach for system-of-systems (SoS). Various attributes are extracted from different level and various aspects, such as graph-based communication quality, dynamic response of physical component systems (PCSs) and mission-oriented features of management component systems (MCSs). AI-based classifier is employed thereafter based on the performance feature construction, to classify SoS performance into 5 status, Normal, Robust, Fault-tolerance, Resilience and Breakdown, which shows the necessity of a resilience SoS reaction. The effectiveness of the performance feature construction is verified by the high accuracy of training set in the simulation.","PeriodicalId":181642,"journal":{"name":"2023 18th Annual System of Systems Engineering Conference (SoSe)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-based SoS performance classification for resilience reaction\",\"authors\":\"Jun Jiang, Yiwen. Chen, Othman Lakhal, R. Merzouki\",\"doi\":\"10.1109/SoSE59841.2023.10178103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This works provides a comprehensive performance evaluation approach for system-of-systems (SoS). Various attributes are extracted from different level and various aspects, such as graph-based communication quality, dynamic response of physical component systems (PCSs) and mission-oriented features of management component systems (MCSs). AI-based classifier is employed thereafter based on the performance feature construction, to classify SoS performance into 5 status, Normal, Robust, Fault-tolerance, Resilience and Breakdown, which shows the necessity of a resilience SoS reaction. The effectiveness of the performance feature construction is verified by the high accuracy of training set in the simulation.\",\"PeriodicalId\":181642,\"journal\":{\"name\":\"2023 18th Annual System of Systems Engineering Conference (SoSe)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 18th Annual System of Systems Engineering Conference (SoSe)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SoSE59841.2023.10178103\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 18th Annual System of Systems Engineering Conference (SoSe)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SoSE59841.2023.10178103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AI-based SoS performance classification for resilience reaction
This works provides a comprehensive performance evaluation approach for system-of-systems (SoS). Various attributes are extracted from different level and various aspects, such as graph-based communication quality, dynamic response of physical component systems (PCSs) and mission-oriented features of management component systems (MCSs). AI-based classifier is employed thereafter based on the performance feature construction, to classify SoS performance into 5 status, Normal, Robust, Fault-tolerance, Resilience and Breakdown, which shows the necessity of a resilience SoS reaction. The effectiveness of the performance feature construction is verified by the high accuracy of training set in the simulation.