{"title":"利用随机森林分类器检测畸变正弦信号中的过零点","authors":"Venkataramana Veeramsetty, Pravallika Jadhav, Eslavath Ramesh, Srividya Srinivasula","doi":"10.1007/s13198-024-02484-8","DOIUrl":null,"url":null,"abstract":"<p>The identification of zero-crossing points in a sinusoidal signal is critical in a variety of electrical applications, including protection of power system components and designing of controllers. In this article, 96 datasets are generated from a deformed sinusoidal waveforms using MATLAB. MATLAB generates deformed sinusoidal waves with varying amounts of noise and harmonics. In this study, a random forest model is utilized to estimate the zero crossing point in a deformed waveform using input characteristics such as the slope, intercept, correlation, and RMSE. The random forest model was developed and evaluated in the Google Colab platform. According to simulation data, the model based on random forest predicts the zero-crossing point more accurately than other models such as logistic regression and decision tree classifier.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Zero crossing point detection in a distorted sinusoidal signal using random forest classifier\",\"authors\":\"Venkataramana Veeramsetty, Pravallika Jadhav, Eslavath Ramesh, Srividya Srinivasula\",\"doi\":\"10.1007/s13198-024-02484-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The identification of zero-crossing points in a sinusoidal signal is critical in a variety of electrical applications, including protection of power system components and designing of controllers. In this article, 96 datasets are generated from a deformed sinusoidal waveforms using MATLAB. MATLAB generates deformed sinusoidal waves with varying amounts of noise and harmonics. In this study, a random forest model is utilized to estimate the zero crossing point in a deformed waveform using input characteristics such as the slope, intercept, correlation, and RMSE. The random forest model was developed and evaluated in the Google Colab platform. According to simulation data, the model based on random forest predicts the zero-crossing point more accurately than other models such as logistic regression and decision tree classifier.</p>\",\"PeriodicalId\":14463,\"journal\":{\"name\":\"International Journal of System Assurance Engineering and Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of System Assurance Engineering and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s13198-024-02484-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02484-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Zero crossing point detection in a distorted sinusoidal signal using random forest classifier
The identification of zero-crossing points in a sinusoidal signal is critical in a variety of electrical applications, including protection of power system components and designing of controllers. In this article, 96 datasets are generated from a deformed sinusoidal waveforms using MATLAB. MATLAB generates deformed sinusoidal waves with varying amounts of noise and harmonics. In this study, a random forest model is utilized to estimate the zero crossing point in a deformed waveform using input characteristics such as the slope, intercept, correlation, and RMSE. The random forest model was developed and evaluated in the Google Colab platform. According to simulation data, the model based on random forest predicts the zero-crossing point more accurately than other models such as logistic regression and decision tree classifier.
期刊介绍:
This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems.
Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.