• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.76-78
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• 2014

나이브 베이스 학습은 유명하면서도, 빠르면서도 효과적인 지도 학습 방법으로, 다소 잡음을 가진 라벨이 있는 데이터집합을 다루는 데 좋은 성능을 보인다. 그러나, 나이브 베이스의 조건적 독립성 가정은 실세계 데이터를 다루는 데 필요한 특성에 다소 제약사항을 가지게 한다. 지금까지 연구자들이 이 조건적 독립성 가정을 완화시키는 방법들을 제안해 왔다. 이러한 방법들은 어트리뷰트 가중치, 커널 밀도 측정 등이 있다. 본 논문에서, 우리는 커널 밀도 측정과 어트리뷰트 가증치를 이용하여 나이브 베이스의 학습 효과를 개선하기 위한 NB Based on Attribute Weighting in Kernel Density Estimation (NBAWKDE) 이라는 새로운 접근 방법을 제안한다.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.607-617
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• 2003

This paper offers a practical comparison of efficiency between local likelihood approach and conventional kernel approach in density estimation. The local likelihood estimation procedure maximizes a kernel smoothed log-likelihood function with respect to a polynomial approximation of the log likelihood function. We use two types of data driven bandwidths for each method and compare the mean integrated squares for several densities. Numerical results reveal that local log-linear approach with simple plug-in bandwidth shows better performance comparing to the standard kernel approach in heavy tailed distribution. For normal mixture density cases, standard kernel estimator with the bandwidth in Sheather and Jones(1991) dominates the others in moderately large sample size.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 춘계학술대회
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• pp.86-88
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• 2014

나이브 베이스가 가지는 가정은 실세계 데이터를 분류함에 있어 해로운 효과를 보이곤 한다. 이러한 가정을 완화하기 위해, 우리는 Naive Bayes Mutual Information Attribute Weighting with Smooth Kernel Density Estimation (NBMIKDE) 접근 방법을 소개한다. NBMIKDE는 애트리뷰트를 위한 스무드 커널과 상호 정보량 측정값을 기반으로 하는 어트리뷰트 가중치 기법을 조합한 것이다.

• 응용통계연구
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• 제27권3호
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• pp.397-406
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• 2014

최소밀도함수승간격 추정법은 Baus 등 (1998)에 의해 처음 소개된 이후 많은 관심의 대상이 되었다. 최소밀도함수승간격 추정량은 우수한 로버스트 성질을 갖고 효율성도 최우추정량에 필적한 것으로 알려져 있다. 본 논문에서는 생물정보학에서 사용되는 노말-지수 분포에 근거한 추정량을 최소밀도함수승간격 추정법을 사용하여 구하는 방법을 다루고자 한다. 그런데 그 과정에서 간격을 적분을 통해 구하는 것이 매우 어려움으로 인해 직접적인 적분 대신 라플라스 근사를 시도할 것을 제안한다. 그 결과 추정량이 다소 효율성이 줄어들지만 로버스트 성질을 갖고 있음을 수학적 방법과 모의실험을 통하여 보였다.

• 전력전자학회논문지
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• 제23권2호
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• pp.106-111
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• 2018

In this work, we propose techniques for estimating the SOC of Li-air battery. First, we describe and explain the operation principle of the Li-air battery. Energy density of the Li-air battery was compared with that of the Li-ion battery. The capacity and impedance value of the fully discharged voltage is analyzed, and the OCV value for SOC estimation is measured through the electrochemical characterization of the Li-air battery. Estimation value is obtained by SOC modeling through extended Kaman filter and is compared with the measurement value from the Coulomb counting method. Moreover, the performance of SOC estimation circuit is evaluated.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2000년도 제2회 학술대회 논문집 일렉트렛트 및 응용기술전문연구회
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• pp.45-49
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• 2000

An ozone condenser by a selective adsorption on the silica gel surface is constructed. Ozone density is evaluated by three methods; ultraviolet absorption, thermal decomposition and Q-mass analyzing methods. Thermal decomposition method is found to be available to the density evaluation from dilute to highly condensed ozone. The highest ozone density condensed by the adsorption method is evaluated to be 97 mol%.

• Smart Structures and Systems
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• 제15권3호
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• JSTS:Journal of Semiconductor Technology and Science
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• 제11권4호
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• pp.344-350
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• 2011

Higher integrated density in 3D ICs also brings the difficulties of routing, which can cause the routing failure or re-design from beginning. Hence, precise congestion estimation at the early physical design stage such as floorplan is beneficial to reduce the total design time cost. In this paper, an effective estimation method of routing congestion is proposed for 3D ICs at floorplan stage. This method uses synthesized virtual signal nets, power/ground network and clock network to achieve the estimation. During the synthesis, the TSV location is also under consideration. The experiments indicate that our proposed method had small difference with the estimation result got at the post-placement stage. Furthermore, the comparison of congestion maps obtained with our method and global router demonstrates that our estimation method is able to predict the congestion hot spots accurately.

• Journal of Multimedia Information System
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• 제6권2호
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• pp.67-74
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• 2019

Bone density is one of the factors in the early failure of dental implants and doctors should make a preoperative assessment of jaw bone density using patient's CT data before dental implant surgery in order to find out whether the patient has osteoporosis and osteopenia. The main goal of this study was to propose a method that based on image processing techniques in order to provide accurate information about where to drill and place an abutment screw of implants in the jaw bone for doctors and reduce human activity for the estimation of the local cancellous bone density of mandible using CT data. The experiment was performed on a computed tomography data of the jaw bone of two different individuals. We assumed that the result of the estimation of jaw bone density depends on the angle of drilling and average HU (Hounsfield Unit) values were used to evaluate the quality of local cancellous bone density of mandible. As a result of this study, we have been developed a toolbox that can be used to estimate jaw bone density automatically and found a positive correlation between the angle of the drill and time complexity but a negative correlation between the diameter of the drill and time complexity.

• 대한전기학회논문지
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• 제45권2호
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• pp.325-328
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• 1996

In this paper, we proposed an estimation method of a posterior probability and PDF(Probability density function) using a feed forward neural network and code books of VQ(vector quantization). In this study, We estimates a posterior probability and probability density function, which compose a new parameter with well-known Mel cepstrum and verificate the performance for the five vowels taking from syllables by NN(neural network) and PNN(probabilistic neural network). In case of new parameter, showed the best result by probabilistic neural network and recognition rates are average 83.02%.