• Journal of Electrical Engineering and Technology
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• 제13권2호
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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.

• Ocean Systems Engineering
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• 제5권1호
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• pp.41-54
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• 2015

This study presents the development of predictive models for uplift capacity of suction caisson in clay using an artificial intelligence technique, extreme learning machine (ELM). Other artificial intelligence models like artificial neural network (ANN), support vector machine (SVM), relevance vector machine (RVM) models are also developed to compare the ELM model with above models and available numerical models in terms of different statistical criteria. A ranking system is presented to evaluate present models in identifying the 'best' model. Sensitivity analyses are made to identify important inputs contributing to the developed models.

• 한국정보통신학회논문지
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• 제11권8호
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• pp.1596-1603
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• 2007

본 논문에서는 음성인식 시스템을 구현함에 있어 중요한 특징 파라미터와 학습, 인식 알고리즘의 선택을 위한 제안을 하기 위하여 각각 세 가지의 방법을 조합하여 인식 실험을 수행하고 검토하였다. 두 종류의 실험을 통하여 하드웨어 장치로 구현할 경우 보다 효과적인 음성 인식 시스템을 제안한다. 첫 번째로는 특징 파라미터의 성능을 평가하기 위하여 기존의 MFCC와 MFCC를 PCA와 ICA를 이용하여 특징 공간을 변화시킨 새로운 특징 파라미터를 제안하여 총 3종류의 특징파라미터에 대한 인식 실험을 수행하였으며, 두 번째로는 학습데이터 수에 따른 HMM, SVM, RVM의 인식 성능을 실험하였다. 이상의 실험에 의하여 ICA에 의한 특징 파라미터가 특징 공간상에서의 높은 선형 분별성에 의해 MFCC와 비교하여 평균 1.5%의 성능향상을 확인할 수 있었으며 학습데이터의 감소에 따른 인식실험에서는 HMM과 비교하여 RVM에서 최고 3.25%의 성능향상을 확인하였다. 이에 근거하여 TI사의 DSP(TMS320C32)를 사용하여 음성 인식기를 구현하여 실시간으로 실험하여 시뮬레이션과 비교하였다. 이와 같은 결과로서 본 논문에서 제안하는 음성인식시스템을 위한 효과적인 방법은 ICA를 이용한 특징 파라미터를 추출하고 RVM을 이용하여 인식을 수행하는 것이라 판단한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 춘계학술발표대회
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• pp.86-88
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• 2019

In a complex geographical environment, communication quality of communication equipment is being seriously challenged. Secondary Users(SUs) must make the best possible use the idle spectrums that Primary Users(PUs) do not use and change spectrum frequently. Using the relevance vector machine(RVM) to establish a signal noise Ratio(SNR) Model for interference information and bit error rate(BER). Through the model and real-time interference information, the minimum channel SNR meeting the BER requirements of communication equipment can be predicted, and we can also calculate the minimum transmitted power. According to the simulation results, this method has better performance for selecting available channel and restraining interference.

• Structural Engineering and Mechanics
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• 제66권3호
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• pp.399-409
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• 2018

This paper demonstrates the potential application of machine learning algorithms for approximate prediction of the load and deflection capacities of the novel type of Laced Steel Concrete-Composite (LSCC) beams proposed by Anandavalli et al. (Engineering Structures 2012). Initially, global and local responses measured on LSCC beam specimen in an experiment are used to validate nonlinear FE model of the LSCC beams. The data for the machine learning algorithms is then generated using validated FE model for a range of values of the identified sensitive parameters. The performance of four well-known machine learning algorithms, viz., Support Vector Regression (SVR), Minimax Probability Machine Regression (MPMR), Relevance Vector Machine (RVM) and Multigene Genetic Programing (MGGP) for the approximate estimation of the load and deflection capacities are compared in terms of well-defined error indices. Through relative comparison of the estimated values, it is demonstrated that the algorithms explored in the present study provide a good alternative to expensive experimental testing and sophisticated numerical simulation of the response of LSCC beams. The load carrying and displacement capacity of the LSCC was predicted well by MGGP and MPMR, respectively.

• Computers and Concrete
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• 제24권1호
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• pp.7-17
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• 2019

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.