Classification of silent speech using support vector machine and relevance vector machine

Matsumoto, Mariko; Hori, Jun‐ichi

doi:10.1016/j.asoc.2013.10.023

Cited by 86 publications

(44 citation statements)

References 11 publications

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“…Traditional multilayer neural networks have some limitations: (i) many inputs due to need of diversity for inputs, (ii) requirement of crucial features for inputs, (iii) trial and error for number of neurons in the hidden layer, and (iv) multimodal with many local minimums. Avoiding the above demerits, SVM is a supervised artificial neural network designed for solving classification problems [26,27]. In essence, SVM maximizes the margin between the training data and the decision boundary, which can be formulated as a quadratic optimization problem.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform,k-Means, Genetic Algorithms, and Support Vector Machine

Hong

Huang

2015

Mathematical Problems in Engineering

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High-impedance faults (HIFs) caused by downed conductors in electric power systems are in general difficult to be detected using traditional protection relays due to small fault currents. The energized downed conductor can pose a safety risk to the public and cause a fire hazard. This paper presents a new method for locating the line (feeder) section of the HIF with the help of limited measurements in electric power systems. The discrete wavelet transform is used to extract the features of transients caused by HIFs. A modifiedk-means algorithm associated with genetic algorithms is then utilized to determine the placement of measurement facilities. The signal energies attained by wavelet coefficients serve as inputs to the support vector machine for locating the HIF line section. The simulation results obtained from an 18-busbar distribution system show the applicability of the proposed method.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform,k-Means, Genetic Algorithms, and Support Vector Machine

Hong

Huang

2015

Mathematical Problems in Engineering

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“…The RVM is a sparse kernel model based on Bayesian probability, and the functional form of the RVM is identical to that of the SVM [18,19]. The RVM method uses maximum likelihood estimation to train the model weight under the total probability framework.…”

Section: : Rvm Prediction Model By Introducing Time Series Datamentioning

confidence: 99%

Time Series Extended Finite‐State Machine‐Based Relevance Vector Machine Multi‐Fault Prediction

Zhou

Zhu

2017

Chem Eng & Technol

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Fault prediction means to detect faults that can occur in the future. While most studies focus on predicting one fault at a time, multi-fault prediction is more practical for industrial processes as multiple faults can cause much more damage than a single one. A time series extended finite-state machine (TS-EFSM)-based relevance vector machine (RVM) approach is proposed for multi-fault prediction. Time lags and correlation coefficients between the process variables and process states are determined. Then, a variable and a state dependence diagram based on the correlation coefficients is established with the EFSM. Furthermore, the RVM is applied to identify parameters for the sake of better prediction accuracy and shorter testing times. With the prediction parameters, faults can be predicted using the aforementioned TS-EFSM state transitions.

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“…The key feature of RVM is that as well as offering excellent performance of prediction and generalization. It improves the inadequacy of SVM [9,10]. Therefore, this approach has been successfully applied in many fields, such as: speech In this paper, Section 2 describes the structure and common faults of SEVA pneumatic actuator.…”

Section: Figure 1 Traditional and Self-validating Pneumatic Actuatormentioning

confidence: 99%

Self-validating Pneumatic Actuator Fault Diagnosis Based on Relevance Vector Machine

Zhang¹,

Ru²

2014

IJCA

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Classification of silent speech using support vector machine and relevance vector machine

Cited by 86 publications

References 11 publications

Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform,k-Means, Genetic Algorithms, and Support Vector Machine

Locating High-Impedance Fault Section in Electric Power Systems Using Wavelet Transform,k-Means, Genetic Algorithms, and Support Vector Machine

Time Series Extended Finite‐State Machine‐Based Relevance Vector Machine Multi‐Fault Prediction

Self-validating Pneumatic Actuator Fault Diagnosis Based on Relevance Vector Machine

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