جستجوی مقالات مرتبط با کلیدواژه « ماشین بردار پشتیبان چند کلاسه » در نشریات گروه « فنی و مهندسی »

Intelligent fault detection diagnosis methods are one of the common topics in recently investigations. In this paper, a new hybrid technique is presented based on discrete wavelet transform (DWT) and multi – class support vector machine (Multi-SVM). The considered vibrational signals are collected in three conditions: normal, chipped tooth and worn teeth. These signals are decomposed using DWT methods with different wavelet base functions and the most appropriate level of decomposition are selected by the cross - correlation concept. The feature vector for each sample is extracted using different time domain statistical functions. «One – against - one» support vector machine (SVM-OAO) is utilized for detecting the gearbox conditions. The condition recognition of a gearbox is depended on the extracted features type and setting the SVM parameters. Therefore, in this study, particle swarm optimization (PSO) is used for identifying the most sensitive features to the defect and its type and determining the optimal parameters of SVM method. The obtained results show that the identification accuracy of the gearbox conditions is significantly increased with improving the feature matrix and the SVM classifier method.

The most relevant method to detect epileptic seizures is the electroencephalogram (EEG) based signal processing method which, due to the need for installing some electrodes on different places of the person's head, causes many movement problems. The aim of this research is to automatically and intelligently detect grand-mal epileptic seizures and also to recognize normal activities of a person suffering from the disease by video surveillance. In this paper we have used the combination of machine vision and machine learning techniques to automatically detect grand-mal epileptic seizure when the person is lying on the ground or on the bed. After subtracting the background from video frame sequences and extracting the image silhouette, appropriate geometrical features have been extracted and fed to the multi-class support vector machine as the input for automatically classifying the videos and assigning proper activity label. All the implementations have been done on MATLAB R2011a. In this intelligent system the accuracy of detecting and recognizing activities is 90.21%. Using this system in addition to reducing the number of human observers is very helpful for the on time and constant detection of the condition. The need for just a conventional video camera and a computer system makes it affordable for people with different incomes. Because it needs not to be in contact with the person's body, there is no movement problem too. High accuracy verifies the optimal performance of the system.