• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1596-1603
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• 2007

In this paper, we experimented by three kind of method for feature parameter, training method and recognition algorithm of most suitable for speech recognition system and considered. We decided speech recognition system of most suitable through two kind of experiment after we make speech recognizer. First, we did an experiment about three kind of feature parameter to evaluate recognition performance of it in speech recognizer using existent MFCC and MFCC new feature parameter that change characteristic space using PCA and ICA. Second, we experimented recognition performance or HMM, SVM and RVM by studying data number. By an experiment until now, feature parameter by ICA showed performance improvement of average 1.5% than MFCC by high linear discrimination from characteristic space. RVM showed performance improvement of maximum 3.25% than HMM in an experiment by decrease of studying data. As such result, effective method for speech recognition system to propose in this paper derives feature parameters using ICA and un recognition using RVM.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.400-401
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• 2012

• Proceedings of the KIEE Conference
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• 2005.07e
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• pp.79-81
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• 2005

Chemical methods for moisture contents detection of insulation system in transformer must dismount equipment for sampling, and there is shortcoming such as acquiring partial data for measuring. Also the samples can't immediately analyze in field. The Recovery Voltage Method (RVM) will be able to measure a moisture contents at low voltage without dismounting equipment. Therefore, in advanced countries RVM would be used to measure the moisture contents which permeates to the insulation system without weighting additive degradation or mechanical damage. In this paper we have investigated for overcoming these shortcomings using the RVM, and we have measured the moisture contents of transformer insulation with temperature.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• The Korean Journal of Pain
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• v.35 no.3
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• pp.261-270
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• 2022

Background: The rostral ventromedial medulla (RVM) is a critical region for the management of nociception. The RVM is also involved in learning and memory processes due to its relationship with the hippocampus. The purpose of the present study was to investigate the molecular mechanisms behind orexin-A signaling in the RVM and hippocampus's effects on capsaicin-induced pulpal nociception and cognitive impairments in rats. Methods: Capsaicin (100 g) was applied intradentally to male Wistar rats to induce inflammatory pulpal nociception. Orexin-A and an orexin-1 receptor antagonist (SB-334867) were then microinjected into the RVM. Immunoblotting and immunofluorescence staining were used to check the levels of cyclooxygenase-2 (COX-2) and brain-derived neurotrophic factor (BDNF) in the RVM and hippocampus. Results: Interdental capsaicin treatment resulted in nociceptive responses as well as a reduction in spatial learning and memory. Additionally, it resulted in decreased BDNF and increased COX-2 expression levels. Orexin-A administration (50 pmol/1 µL/rat) could reverse such molecular changes. SB-334867 microinjection (80 nM/1 µL/rat) suppressed orexin's effects. Conclusions: Orexin-A signaling in the RVM and hippocampus modulates capsaicin-induced pulpal nociception in male rats by increasing BDNF expression and decreasing COX-2 expression.

• Proceedings of the KIEE Conference
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• 2005.07e
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• pp.76-78
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• 2005

Moisture contents measurement is frequently used as one of parameters for degradation diagnosis of transformer insulation. In general Karl-Fisher method is mainly used for moisture contents till now. But this method is inconvenient because of dismounting transformer for sampling oil or paper, and also partial sampling. At latest Recovery Voltage Method(RVM) is noticed for complement of this method. RVM can directly estimate moisture contents of transformer insulations in field without dismounting transformer. In this paper the accelerated aging process of oil-paper samples have been investigated at a temperature up to 140$^{\circ}C$ for 500 hours. The oil-paper insulation samples have been measured at intervals of 100 hours. Next to, we have estimated moisture contents using both Karl-Fisher Titration Method and RVM. And we have compared with Karl-Fisher Titration Method and RVM for estimating moisture contents. At last we have verified reliability of RVM which is new measurement method.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Geomechanics and Engineering
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• v.1 no.3
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• pp.259-262
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• 2009

This paper examines the potential of relevance vector machine (RVM) in prediction of pullout capacity of small ground anchors. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM showed good performance and is proven to be better than ANN model. It also estimates the prediction variance. The plausibility of RVM technique is shown by its superior performance in forecasting pullout capacity of small ground anchors providing exogenous knowledge.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.733-741
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• 2018

Capacity estimation is indispensable to ensure the safety and reliability of lithium-ion batteries in electric vehicles (EVs). Therefore it's quite necessary to develop an effective on-board capacity estimation technique. Based on experiment, it's found constant current charge time (CCCT) and the capacity have a strong linear correlation when the capacity is more than 80% of its rated value, during which the battery is considered healthy. Thus this paper employs CCCT as the health indicator for on-board capacity estimation by means of relevance vector machine (RVM). As the ambient temperature (AT) dramatically influences the capacity fading, it is added to RVM input to improve the estimation accuracy. The estimations are compared with that via back-propagation neural network (BPNN). The experiments demonstrate that CCCT with AT is highly qualified for on-board capacity estimation of lithium-ion batteries via RVM as the results are more precise and reliable than that calculated by BPNN.

• Proceedings of the KIEE Conference
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• 2005.07e
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• pp.73-75
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• 2005

In present measurement of moisture contents is used as one of method for estimating degradation of transformer. Most of people use Karl-Fischer titration method for detection moisture contents but this measurement is inconvenient method because we must analyze transformer oil-paper after dismounting transformer and sampling in field. Therefore we don't directly investigate them in field. In this paper we will introduce Recovery Voltage Method(RVM) that is new method for estimating measurement in field though dismounting facility. For measure of moisture contents using RVM in accordance with accelerated thermal aging we have made experimental test cell and aged at a temperature up to 140$^{\circ}C$ for 300 hours. And we have been measured at intervals of 100 hours using RVM 5462 made in fetter company.