P. D. Setyorini, H. Mahmudah, Okkie Puspitorini, N. Siswandari, A. Wijayanti
{"title":"利用指数平滑提高学习向量量化(LVQ)的驾驶行为分类准确率","authors":"P. D. Setyorini, H. Mahmudah, Okkie Puspitorini, N. Siswandari, A. Wijayanti","doi":"10.1109/ICoICT49345.2020.9166370","DOIUrl":null,"url":null,"abstract":"Identification of driving activities is important to find out abnormal driving conditions such as accidents. In this work, identifying driving activities is carried out using the LVQ (Learning Vector Quantization) algorithm. This algorithm creates a prototype that is easily interpreted for experts in each application domain. The dataset for each driving activity is obtained from the accelerometer sensor and the android smart gyroscope. The exponential smoothing method is used in the sensor dataset to improve the accuracy of classification results. The best accuracy is obtained from the classification of the gyroscope sensor dataset after smoothing with an accuracy of 90.429%.","PeriodicalId":113108,"journal":{"name":"2020 8th International Conference on Information and Communication Technology (ICoICT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Accuracy Improvement on Learning Vector Quantization (LVQ) Using Exponential Smoothing for Driving Activity Classification\",\"authors\":\"P. D. Setyorini, H. Mahmudah, Okkie Puspitorini, N. Siswandari, A. Wijayanti\",\"doi\":\"10.1109/ICoICT49345.2020.9166370\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Identification of driving activities is important to find out abnormal driving conditions such as accidents. In this work, identifying driving activities is carried out using the LVQ (Learning Vector Quantization) algorithm. This algorithm creates a prototype that is easily interpreted for experts in each application domain. The dataset for each driving activity is obtained from the accelerometer sensor and the android smart gyroscope. The exponential smoothing method is used in the sensor dataset to improve the accuracy of classification results. The best accuracy is obtained from the classification of the gyroscope sensor dataset after smoothing with an accuracy of 90.429%.\",\"PeriodicalId\":113108,\"journal\":{\"name\":\"2020 8th International Conference on Information and Communication Technology (ICoICT)\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 8th International Conference on Information and Communication Technology (ICoICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICoICT49345.2020.9166370\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 8th International Conference on Information and Communication Technology (ICoICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICoICT49345.2020.9166370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Accuracy Improvement on Learning Vector Quantization (LVQ) Using Exponential Smoothing for Driving Activity Classification
Identification of driving activities is important to find out abnormal driving conditions such as accidents. In this work, identifying driving activities is carried out using the LVQ (Learning Vector Quantization) algorithm. This algorithm creates a prototype that is easily interpreted for experts in each application domain. The dataset for each driving activity is obtained from the accelerometer sensor and the android smart gyroscope. The exponential smoothing method is used in the sensor dataset to improve the accuracy of classification results. The best accuracy is obtained from the classification of the gyroscope sensor dataset after smoothing with an accuracy of 90.429%.