Earticle

어휘 인식 시스템은 구성되어진 모델에서 벗어난 어휘의 입력과 유사한 어휘의 입력은 인식하지 못하거나 유사한 어휘로 인식되어 인식률 저하가 나타나며, 기존의 시스템은 벡터 값을 모델로 만들어 데이터베이스로 구성하여 어휘 인식에 사용하였다. 어휘 인식을 위한 탐색 중에 형성되는 모델은 데이터베이스로 구성되어 있지 않아 인식 할 수 없는 단점이 존재한다. 따라서 본 논문에서는 특징 벡터 모델을 기반으로 바타챠랴 거리 측정법을 이용한 베이시안 인식 모델을 구성하여 탐색 중에 형성되는 벡터 모델을 인식할 수 있도록 유도하였으며, 위너 필터를 적용하여 인식률을 향상시켰다. 2 방법을 융합하여 실험한 결과 향상된 신뢰도로 인해 높은 인식 성능을 확인하였으며, 본 논문에서 제안한 측정법을 이용하여 기존의 방법들에 비하여 평균 98.2%의 성능을 나타내었다.

The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. The vector values of the existing system ​​to the model created by configuring the database was used in the recognition vocabulary. The model to be formed during the search for the recognition vocabulary is recognizable because there is a disadvantage not configured with a database. In this paper, it induced to recognize the vector model is formed by the search and configuration using a Bayesian model recognizes the Bhattacharyya distance measurement based on the vector model, by applying the Wiener filter improves the recognition rate. The result of Convergence of two method’s are improved reliability experiments for distance measurement. Using a proposed measurement are compared to the conventional method exhibited a performance of 98.2%.

PURPOSES : The objective of this paper is to develop a pavement performance model based on the Bayesian algorithm, and compare the measured and predicted performance data. METHODS : In this paper, several pavement types such as SMA (stone mastic asphalt), PSMA (polymer-modified stone mastic asphalt), PMA (polymer-modified asphalt), SBS (styrene-butadiene-styrene) modified asphalt, and DGA (dense-graded asphalt) are modeled in terms of the performance evaluation of pavement structures, using the Bayesian algorithm. RESULTS : From case studies related to the performance model development, the statistical parameters of the mean value and standard deviation can be obtained through the Bayesian algorithm, using the initial performance data of two different pavement cases. Furthermore, an accurate performance model can be developed, based on the comparison between the measured and predicted performance data. CONCLUSIONS : Based on the results of the case studies, it is concluded that the determined coefficients of the nonlinear performance models can be used to accurately predict the long-term performance behaviors of DGA and modified asphalt concrete pavements. In addition, the developed models were evaluated through comparison studies between the initial measurement and prediction data, as well as between the final measurement and prediction data. In the model development, the initial measured data were used.

본 연구는 한우의 도체형질들에 대한 유전적 변이를 분석방법에 따라 어떠한 차이가 있는지를 알아보고자 한우 후대검정자료를 이용하여 분석하였다. 분석에 이용된 도체성적 관련 자료는 가축개량사업소와 한우개량단지에서 1996년부터 2001년까지 태어난 후대검정우 1526두로부터 조사었다. 한우에 있어서 주요 개량형질인 육량과 육질에 영향하는 도체중, 도체율, 배최장근단면적, 등지방두께, 근내지방도를 대상으로 유전력과 유전상관을 추정하였다. 유전모수 추정에 있어서 REML 분석법과 Bayesian추론방법으로써 Gibbs Sampling 분석법을 사용하였는데 각각의 모수들에 대한 Gibbs Sampler는 100,000회 실시하였고 초기 1,000회는 모수의 사후분포에 대한 부정확성으로 간주하여 모수의 사후분포특성 규명에서 제외하였다. 각각의 형질들에 대한 유전변이는 이러한 두 가지 분석방법을 상호 비교 함으로써 최적의 통계분석방법을 모색하였다.도체형질에 대한 유전력 추정은 REML 방법을 통한 추정에서는 근내지방도가 0.51로 고도의 유전력을 보였고, 도체율이 0.25로 중도의 유전력이 추정되었다. Gibbs Sampling 방법을 통한 추정에서도 근내지방도가 0.54로 고도의 유전력을 도체율에서 0.25로 중도의 유전력을 보였다. REML 분석방법과 Gibbs Sampling 분석방법에서의 유전력은 다소 큰 차이는 보이지 않았으나, 대체로 Gibbs Sampling 방법으로 분석한 유전력 추정치가 높게 추정되었다.그리고, 유전상관분석에서는 REML 방법을 통한 분석에서 도체중과 배최장근단면적에서 0.651로 높은 정의 유전상관을 나타내었고, 배최장근단면적과 등지방두께에서 -0.139로 부의 유전상관을 나타내었다. Gibbs Sampling 방법에서는 도체중과 도체율, 배최장근단면적에서 각각 0.814, 0.695으로 높은 정의 상관을 나타내었고, 배최장근단면적과 등지방두께에서 -0.126으로 부의상관을 나타내었다. Gibbs Sampling 방법을 통한 분석에서 특정 형질간 유전상관이 높게 나타났으며, 다소 큰 차이를 보이지 않았다.REML 분석방법과 Bayesian Inference를 바탕으로 한 Gibbs Sampling 분석에서 모수 추정은 큰 차이를 보이지 않았다. 따라서 방대한 현장정보를 활용하여 보다 정확한 모수 추정을 수행하기 위해서는 분석모형에 대한 계수행렬의 역행렬 계산을 통한 REML 방법의 한계성을 극복할 수 있는 방법으로써 Gibbs Sampling 분석 방법이 가능할 것으로 사료된다.

The aims of this study were to estimate genetic parameters for carcass traits on Hanwoo(Korean Native Cattle) and to compare two different statistical algorithms for estimating genetic parameters. Data obtained from 1526 steers at Hanwoo Improvement Center and Hanwoo Improvement Complex Area from 1996 to 2001 were used for the analyses. The carcass traits considered in these studies were carcass weight, dressing percent, eye muscle area, backfat thickness, and marbling score. Estimated genetic parameters using EM-REML algorithm were compared to those by Bayesian inference via Gibbs Sampling to find out statistical properties. The estimated heritabilities of carcass traits by REML method were 0.28, 0.25, 0.35, 0.39 and 0.51, respectively and those by Gibbs Sampling method were 0.29, 0.25, 0.40, 0.42 and 0.54, respectively. This estimates were not significantly different, even though the estimated heritabilities by Gibbs Sampling method were higher than ones by REML method.Since the estimated statistics by REML method and Gibbs Sampling method were not significantly different in this study, it is inferred that both mothods could be efficiently applied for the analysis of carcass traits of cattle. However, further studies are demanded to define an optimal statistical method for handling large scale performance data.

Real time accessiblity and agility in Ubiquitous-commerce is required under ubiquitous computing environment. The Research has been actively processed in e-commerce so as to improve the accuracy of recommendation. Existing Collaborative filtering (CF) can not reflect contents of the items and has the problem of the process of selection in the neighborhood user group and the problems of sparsity and scalability as well. Although a system has been practically used to improve these defects, it still does not reflect attributes of the item. In this paper, to solve this problem, We can use a implicit method which is used by customer’s data and purchase history data. We propose a new clustering method of weighted preference for customer using k-means clustering and Bayesian network in order to improve the accuracy of recommendation. To verify improved performance of the proposed system, we make experiments with dataset collected in a cosmetic internet shopping mall.

어휘 인식 시스템은 학습 모델을 구성하여 인식하므로 구성되어진 모델에서 벗어난 어휘의 입력과 유사한 어휘의 입력은 인식하지 못하거나 유사한 어휘로 인식되어 인식률 저하가 나타난다. 이런 경우 인식 모델을 확장할 수 있도록 재구성하거나 인식 모델 구성 시 확장성을 반영하므로 해결할 수 있다. 본 논문에서는 모델 구성 시 확장성을 반영할 수 있는 모수 추정을 위한 베이시안 기법을 사용하여 바타차랴 알고리즘 음성 인식 학습 모델 구성 방법을 융합하여 제안하였다. 음소가 갖는 특징을 기반으로 학습 데이터의 음소에 모수 추정을 위한 베이시안 기법을 이용하였고 유사한 학습 모델은 바타챠랴 알고리즘을 이용하여 정확한 학습 모델로 인식하도록 하였다. 바타챠랴 알고리즘 인식 모델을 구성하여 인식 성능을 평가하였다. 본 논문에서 제안한 시스템을 적용한 결과 어휘 인식률에서 97.5%의 인식률과 1.2초의 학습 시간을 나타내었다.

The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. In this case, reconstructing the system in order to add or extend a range of vocabulary is a way to solve the problem. This paper propose configured Bhattacharyya algorithm standing by speech recognition learning model using the Bayesian methods which reflect parameter estimation upon the model configuration scalability. It is recognized corrected standard model based on a characteristic of the phoneme using the Bayesian methods for parameter estimation of the phoneme's data and Bhattacharyya algorithm for a similar model. By Bhattacharyya algorithm to configure recognition model evaluates a recognition performance. The result of applying the proposed method is showed a recognition rate of 97.3% and a learning curve of 1.2 seconds.

Prisoners’ dilemma is a typical game theory issue. In this study, it was treated as an incomplete information game to establish a related machine learning model using a naive Bayesian classification method. The model established was referred to as the Bayes model. Using this model, the incomplete information game was soluble with the assistance of statistical machine learning. This study proceeded as follows: firstly, four typical models were run against the Bayes model some 10,000 times. The total incomes of the models recorded suggested that Bayes model was more advantageous than other models. Even in a multi-player prisoners’ game, Bayes model also presented the desired level of performance and accrued a higher income than other models. Further statistical analysis implied that the Bayes model and the widely accepted optimum strategy tit-for-tat (TFT) model showed a tendency to be prone to defection. Secondly, according to the games run on the natural Bayes model, as well as the natural TFT model, it was found that the Bayes model accrued more benefits than the TFT model on average. Finally, comparison of the Bayes model with the TFT model revealed that the Bayes model was better. This demonstrated the efficacy of the Bayes model constructed in this study and moreover, provided a novel idea for solving the problem of an incomplete information game.

Due to the wide application of range-based location algorithm for received signal strength, and according to the requirements of high accuracy and low power cost in the location algorithm for WSNs, in this paper, a Bayesian optimization RSSI and an indoor location algorithm for ILS were introduced by setting RRS ranging as location framework. Firstly, through analyzing the RSSI-based ranging model, an indoor location model was introduced. Secondly, in view of the influence on RSSI value caused by the indoor environment,the Bayesian probabilistic model was adopted to process the RSSI measured value and to screen out the "big probability" of RSSI value. Thirdly, Obtaining accurate measured data by estimating distance using method of minimum mean square error. Finally, Estimating the node location using least square method, and according to the TelosB node of Telos Series produced by company Crossbow, the ranging experiment can be designed and thus groups of experimental data were obtained and analyzed..The experimental results showed that the proposed location project greatly increased the location accuracy and decreased the computation complexity, and has obviously more advantage of running time over other location projects.

Due to the complexity of the algorithm own limitations and environmental parameters, makes for complex work in the process of localization is bigger, computational complexity, a larger error. In order to ensure the reliability and effectiveness of communication data, ensure that improve the localization accuracy, the proposed multi-channel data transmission mechanism, to realize the collection of real-time data information effectively. Based on the traditional localization algorithm principle and error source is analyzed, combined with Bayesian filtering probability model for RSSI localization algorithm was improved, the received signal strength indicator effectively restrain random fluctuations. Through to the node coordinates between areas corrections, make the final balance of signal strength. Experiments show that improved the reliability of the improved algorithm has higher localization accuracy, and shows the validity and the correctness of the algorithm.

Aiming at the cuckoo search algorithm (CSA) with disadvantages of slow convergence speed, getting into local extremum easily and low accuracy, we put forward cuckoo search algorithm with cauchy mutation(CCSA). For Bayesian networks(BNs) structure learning with insufficient data, we propose data completion method and Bayesian network structure learning with insufficient data based on CCSA(BNSL-ID-CCSA). In BNSL-ID-CCSA, firstly, we adopt K2 metric as evaluation measure for learning Bayesian networks from data. Secondly, we use expectation maximization(EM) algorithm and CCSA to make BNSL-ID-CCSA quickly and accurately converge to the global optimal solution. The experimental results show that BNSL-ID-CCSA has strong learning ability and good stability.

In data mining applications, there are various kinds of missing values in experimental datasets. Non-substitution or inappropriate treatment of missing values has a high probability to cause a lot of warnings or errors. Besides, many classification algorithms are very sensitive to the missing values. Because of these, handling the missing values is an important phase in many classification or data mining task. This paper introduces traditional EM algorithm and disadvantage of the EM algorithm. We propose a new method to implement the missing values based on EM algorithm, which uses Naive Bayesian to improve the accuracy. We conclude by classifying seeds dataset and vertebral columns dataset and comparing the results to those obtained by applying two other missing value handling methods: the traditional EM algorithm and the non-substitution method. The experimental results prove a stable algorithm for improving the data classification accuracy on large datasets, which contain a lot of missing values.

Bayesian network is a directed acyclic graph. Existing Bayesian network learning approaches based on search & scoring usually work with a heuristic search for finding the highest scoring structure. This paper describes a new data mining algorithm to learn Bayesian networks structures based on an immune binary particle swarm optimization (IB-PSO) method and the Minimum Description Length (MDL) principle. IBPSO is proposed by combining the immune theory in biology with particle swarm optimization (PSO). It constructs an immune operator accomplished by two steps, vaccination and immune selection. The purpose of adding immune operator is to prevent and overcome premature convergence. Experiments show that IBPSO not only improves the quality of the solutions, but also reduces the time cost.

일반적으로 확률론적 위험도 평가(Probabilistic Risk Assessment, PRA)는 주어진 초기사건이 전개될 수 있는 모든 가능한 사건시컨스(Event Sequence)를 고려하여 모델링한 사건수목(Event Tree)를 기반으로 수행된다. 이러한 PRA 방법은 과거 수십 년간 여러 분야에서 위험도를 평가하기 위하여 적용되고 발전하여 왔으나 대형사고 등 희귀사건에 대한 발생빈도에 대한 추정은 여전히 연구해야 할 주제로 남아 있다. 이유 중의 하나는, 희귀사건 빈도추정은 관측 자료가 충분하지 않기 때문이다. 본 논문은 사고선행자를 활용한 베이지안 방법을 설명하고, 가정된 여러 확률 분포를 처리할 수 있는 베이지안 구현 프로그램을 기술한다. 베이지안 방법을 적용하기 위한 수치적분 방법이 필요한 이유와 각종 확률 분포를 적용할 수 있는 프로그램을 개발한 결과 및 연산결과에 대한 검증결과를 기술한다.

In general, Probabilistic Risk Assessment(PRA) is based on the Event Tree(ET) composed of several event sequences that can occur following the initiating event. While this technique has been applied and evolved for decades for the risk assessment in many industrial area, the estimation of rate event frequency is one of the important subjects that should be still under study. One reason is that the data for such events are relatively sparse. This study illustrates the method for Bayesian estimation for rare event frequency using the accident precursor data. This study also demonstrates the computational program that is implemented using Finite Element Method (FEM) and the validation results for the proposed probability distributions.

The optimal Bayesian filter for a single target is known to provide the best tracking performance in a cluttered environment. However, its main drawback is the increase in memory size and computation quantity over time. In this paper, the inevitable predicament of the optimal Bayesian filter is resolved in a suboptimal fashion through the use of a receding horizon strategy. As a result, the problems of memory and computational requirements are diminished. As a priori information, the horizon initial state is estimated from the validated measurements on the receding horizon. Consequently, the suboptimal algorithm proposed allows for real time implementation.

In this paper, a novel method for structure learning of a Bayesian network (BN) is developed. A new genetic approach called the matrix genetic algorithm (MGA) is proposed. In this method, an individual structure is represented as a matrix chromosome and each matrix chromosome is encoded as concatenation of upper and lower triangular parts. The two triangular parts denote the connection in the BN structure. Further, new genetic operators are developed to implement the MGA. The genetic operators are closed in the set of the directed acyclic graph (DAG). Finally, the proposed scheme is applied to real world and benchmark applications, and its effectiveness is demonstrated through computer simulation.

Purpose: Based on research data from 24 listed commercial banks in the period 2012-2022, via regression using the Bayesian approach, the study provided evidence of the optimal threshold in capital structure to improve the stability of banks. Design/methodology/approach: Regarding macroeconomic factors, economic growth tends to erode the banking system's stability, while inflation has a vague impact. Furthermore, through the Bayesian approach via the Monte Carlo algorithm, the study has proposed a method to determine the optimal capital structure for each specific bank to cope with risk. Findings: The results show that the deposit-to-asset value of ACB (Asia Commercial Bank) and CTG (Vietnam Joint Stock Commercial Bank for Industry and Trade) has exceeded the optimal threshold. For non-deposit-to-asset, ACB is approximately at the optimal level; for CTG, this ratio is significantly lower than the optimal level; hence, they could increase this ratio to control risks and create more capital to finance their activities. Research limitations/implications: This research result is an essential practical contribution; it could help specific banks determine the appropriate capital structure to maintain operational stability. Research results could reflect the characteristics of the market being studied. Then, we would use this research result as prior information and combine it with data from each specific bank to estimate the posterior probability of the impact of capital on risk, thereby estimating the appropriate capital structure for the bank that needs to be researched. Originality/value: The paper provided evidence of an optimal capital structure that is associated with lower risk in Vietnamese banks. In addition, foreign capital also tends to improve the stability of the banking system, while bank size increases risks.

Super resolution using stochastic approach which based on the Bayesian approach is to easy modeling for a priori knowledge. Generally, the Bayesian estimation is used when the posterior probability density function of the original image can be established. In this paper, we introduced the improved MAP algorithm based on Bayesian which is stochastic approach in spatial domain. And we presented the observation model between the HR images and LR images applied with MAP reconstruction method which is one of the major in the SR grid construction. Its test results, which are operation speed, chip size and output high resolution image Quality. are significantly improved.

Noise reduction is an important issue in the field of image processing because image noise lowers the quality of the original pure image. The basic difficulty is that the noise and the signal are not easily distinguished. Simple smoothing is the most basic and important procedure to effectively remove the noise; however, the weakness is that the feature area is simultaneously blurred. In this research, we use ways to measure the degree of noise with respect to the degree of image features and propose a Bayesian noise reduction method based on MAP (maximum a posteriori). Simulation results show that the proposed adaptive noise reduction algorithm using Bayesian MAP provides good performance regardless of the level of noise variance.

Radioisotope identification devices(RID) play a crucial role in detection and identification of illicit trafficking of radioactive/nuclear materials in nuclear security and nuclear safeguards. These devices utilize various algorithms for automated isotope identification(ID) without the need for expert intervention. In this study, an automated algorithm for real-time isotope identification is presented. The algorithm employs a second-derivative-based peak detection and a Bayesian-statistics-peak based ID approach. To demonstrate the suitability of the developed algorithm, it was applied to the gamma-ray spectra acquired with a medium energy-resolution LaBr₃(Ce) detector. In addition to point sources ⁶⁰Co, ¹⁰⁹Cd, ²²Na, ¹³⁷Cs, ²⁴¹Am, ¹⁵²Eu, and ¹³³Ba, the algorithm was also tested on the more complex gamma-ray spectra obtained from low enriched uranium reference materials 171 (EC-NRM171), and natural uranium and thorium minerals such as BL-2, BL-3, BL-4A, BL-5, RGU and OKA-2. To evaluate the performance of the algorithm, total scoring (ST) is calculated. For identification of ²²Na, ⁶⁰Co, and ¹³⁷Cs isotopes, the posterior probabilities were estimated to be greater than 99 %. For ¹³³Ba, ¹⁵²Eu, and ²⁴¹Am, the isotopes they were also correctly identified with higher posterior probabilities ranged from 92 % to 95 %. The developed algorithm successfully identified the isotopes contained in U-Th ore samples with a 100 % total score. Additionally, the performance evaluation of the results obtained with Certified Reference Uranium Materials also demonstrated 100 % score. For automatic ID, the photopeak-based Bayesian method, combined with the Mariscotti's peak detection method has great potential for real-time ID when implemented in RID devices.

최근 스마트 폰과 같은 무선 모바일 기기 사용량의 증가 때문에 위치 기반 서비스(LBS : Location Based Service)에 대한 연구가 활발히 증가하고 있다. 실외 위치 측위는 무선 모바일 기기에 내장된 GPS를 이용할 수 있다. 하지만 일반 건물보다 규모가 큰 대형 크루즈선의 실내 같은 곳에서는 GPS 사용이 불가능하므로 실내 환경에 적합한 위치 측위 방식을 고려하여야 한다. Wi-Fi(Wireless Fidelity)는 실내의 여러 곳에 설치 되어 있어서 추가로 Wi-Fi의 설치과정을 거치지 않아도 되고, 다른 무선 센서 기기와 비교하면 비교적 싼 가격을 가진다. 본 논문에서는 Wi-Fi의 신호를 이용하여 실내에서 의미 있는 공간을 인지하는 베이지안 알고리즘을 소개한다.

As the amount of the wireless mobile products like a 'Smart phone' used increases, the studies about the Location Based Service (LBS) is highly increasing. Outdoor location determination can use the GPS which is built-in in the wireless mobile products. However, it is not possible to use GPS inside the huge cruise bigger than a normal building, it is regarded to consider Indoor location determination which is appropriate at the inside environment. Wi-Fi (Wireless Fidelity) does not need an extra installation process because it is already installed here and there inside the building. In this respect, Wi-Fi has low price competitiveness compared to other wireless sensor products. In this paper, I will introduce 'Bayesian Algorithm' which can recognize useful space with Wi-Fi signal.