Oumaima Bader, Dhia Haddad, Ahmed Yahia Kallel, N. Amara, O. Kanoun
{"title":"基于s参数和k近邻算法的通信电缆识别","authors":"Oumaima Bader, Dhia Haddad, Ahmed Yahia Kallel, N. Amara, O. Kanoun","doi":"10.1109/SSD52085.2021.9429367","DOIUrl":null,"url":null,"abstract":"Cable identification has a significant role in cable maintenance and fault detection. Before replacing defective cables in a network, they must be properly identified. In this paper, a novel method for coaxial communication cables identification based on scattering parameters is proposed. The input port reflection's magnitude measured by a Nano Vector Network Analyzer at 101 frequencies for 10 coaxial communication cables are used as features for the K-Nearest Neighbors algorithm. The investigation is held on cables of various lengths, dimensions and connector types. The cable's length, type and connectors are considered as a unique class. The classification accuracy reached is 99% for a test set composed of 100 measurements.","PeriodicalId":6799,"journal":{"name":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","volume":"1 1","pages":"808-811"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of Communication Cables Based on S-Parameters and K-Nearest Neighbors Algorithm\",\"authors\":\"Oumaima Bader, Dhia Haddad, Ahmed Yahia Kallel, N. Amara, O. Kanoun\",\"doi\":\"10.1109/SSD52085.2021.9429367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cable identification has a significant role in cable maintenance and fault detection. Before replacing defective cables in a network, they must be properly identified. In this paper, a novel method for coaxial communication cables identification based on scattering parameters is proposed. The input port reflection's magnitude measured by a Nano Vector Network Analyzer at 101 frequencies for 10 coaxial communication cables are used as features for the K-Nearest Neighbors algorithm. The investigation is held on cables of various lengths, dimensions and connector types. The cable's length, type and connectors are considered as a unique class. The classification accuracy reached is 99% for a test set composed of 100 measurements.\",\"PeriodicalId\":6799,\"journal\":{\"name\":\"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)\",\"volume\":\"1 1\",\"pages\":\"808-811\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSD52085.2021.9429367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 18th International Multi-Conference on Systems, Signals & Devices (SSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSD52085.2021.9429367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of Communication Cables Based on S-Parameters and K-Nearest Neighbors Algorithm
Cable identification has a significant role in cable maintenance and fault detection. Before replacing defective cables in a network, they must be properly identified. In this paper, a novel method for coaxial communication cables identification based on scattering parameters is proposed. The input port reflection's magnitude measured by a Nano Vector Network Analyzer at 101 frequencies for 10 coaxial communication cables are used as features for the K-Nearest Neighbors algorithm. The investigation is held on cables of various lengths, dimensions and connector types. The cable's length, type and connectors are considered as a unique class. The classification accuracy reached is 99% for a test set composed of 100 measurements.