Identification of Power Quality Disturbances Using S-Transform and Multi-Class Support Vector Machine

Abstract: An essential issue in power quality disturbances is identifying and classifying power quality disturbances from anywhere and at any time. This article proposed a new approach to identify and classify power quality disturbances over the web using S-transform, Multi-Class Support vector machine (SVM), and Matlab framework. S-Transform is used as an extraction feature to obtain the temporal frequency characteristics of power quality events. The development of the multi-class SVM classifier, in which the system classifies various power quality disturbances. Finally, the Matlab framework integrated the graphical and computational processes with remote access via the web. The test result indicated the suggested method's effectiveness and robustness for identifying and classifying power quality disturbances through the web.Abstrak: Masalah penting dalam gangguan kualitas daya adalah mengidentifikasi dan mengklasifikasikan gangguan kualitas daya dari mana saja dan kapan saja. Artikel ini mengusulkan pendekatan baru untuk mengidentifikasi dan mengklasifikasikan gangguan kualitas daya melalui web menggunakan S-transform, Multi-Class Support vector machine (SVM), dan Matlab. S-Transform digunakan sebagai fitur ekstraksi untuk mendapatkan karakteristik frekuensi temporal dari peristiwa kualitas daya. Multi class SVM classifier dikembangkan dimana sistem mengklasifikasikan berbagai gangguan kualitas daya. Akhirnya, Matlab framework mengintegrasikan proses grafis dan komputasi sehingga dapat diakses jarak jauh melalui web. Hasil pengujian menunjukkan efektivitas dan robustnes metode yang usulkan untuk mengidentifikasi dan mengklasifikasikan gangguan kualitas daya melalui web

IDENTIFICATION OF POWER QUALITY DISTURBANCES USING S- TRANSFORM AND MULTI-CLASS SUPPORT VECTOR MACHINE

An essential issue in power quality disturbances is identifying and classifying power quality disturbances from anywhere and at any time. This article proposed a new approach to identify and classify power quality disturbances over the web using S-transform, Multi-Class Support vector machine (SVM), and Matlab framework. S-Transform is used as an extraction feature to obtain the temporal frequency characteristics of power quality events. The development of the multi-class SVM classifier, in which the system classifies various power quality disturbances. Finally, the Matlab framework integrated the graphical and computational processes with remote access via the web. The test result indicated the suggested method's effectiveness and robustness for identifying and classifying power quality disturbances through the web

Machine Learning Systems for Multimodal Affect Recognition [electronic resource] /

Markus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons. Contents Classification and Regression Approaches Applications and Affective Corpora Modalities and Feature Extraction Machine Learning for the Estimation of Affective Dimensions Adaptation and Personalization of Classifiers Experimental Validation Target Groups Lecturers and students of neuroinformatics, artificial intelligence, machine learning, human-machine interaction/affective computing Practitioners in the field of artificial intelligence and human-machine interaction The Author Dr. Markus Kächele is managing partner of Ikara Vision Systems, a spin-off of the German Research Center for Artificial Intelligence (DFKI). He focuses on bridging the gap between research and industrial applications in the fields of deep learning and computer vision.Classification and Regression Approaches -- Applications and Affective Corpora -- Modalities and Feature Extraction -- Machine Learning for the Estimation of Affective Dimensions -- Adaptation and Personalization of Classifiers -- Experimental Validation.Markus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons. Contents Classification and Regression Approaches Applications and Affective Corpora Modalities and Feature Extraction Machine Learning for the Estimation of Affective Dimensions Adaptation and Personalization of Classifiers Experimental Validation Target Groups Lecturers and students of neuroinformatics, artificial intelligence, machine learning, human-machine interaction/affective computing Practitioners in the field of artificial intelligence and human-machine interaction The Author Dr. Markus Kächele is managing partner of Ikara Vision Systems, a spin-off of the German Research Center for Artificial Intelligence (DFKI). He focuses on bridging the gap between research and industrial applications in the fields of deep learning and computer vision