Earticle

일반적인 어휘 인식 시스템은 백색 잡음과 음성을 인식하는 환경에서 여러 음성의 혼재되어 정확한 음성 을 인식하지 못하고 있다. 따라서 본 논문은 효율적인 음성 인식을 위해 잡음 음성으로 부터 원하는 음성만 선택적 으로 추출하기 위한 방법과 베이시안 기법을 융합 방법을 제안한다. 음성의 선택적 추출을 위해 필터 뱅크 주파수 응답 계수를 사용한다. 하며, 이를 위해 모든 가능한 두 관측치의 조합에 대해 변수 관측치를 사용하며, 음성 신호 정보를 가지고 선택적 음성 특징 추출을 위해 잡음은 출력에 대한 에너지 비율을 구한다. 이것은 음성 특징을 추출 하는 방법을 제안하며, 이를 베이시안 기법의 어휘 인식을 융합하여 잡음을 제거하고 인식률을 향상시켰다. 본 논문에서 기존의 HMM과 CHMM 방법과 비교한 결과 잡음 환경에서의 인식률이 2.3% 향상됨을 확인하였다

Voice recognition systems which use a white noise and voice recognition environment are not correct voice recognition with variable voice mixture. Therefore in this paper, we propose a method using the convergence of Bayesian technique and selecting voice for effective voice recognition. we make use of bank frequency response coefficient for selective voice extraction, Using variables observed for the combination of all the possible two observations for this purpose, and has an voice signal noise information to the speech characteristic extraction selectively is obtained by the energy ratio on the output. It provide a noise elimination and recognition rates are improved with combine voice recognition of bayesian methode. The result which we confirmed that the recognition rate of 2.3% is higher than HMM and CHMM methods in vocabulary recognition, respectively.

본 연구는 수위-유량 관계곡선을 이용한 하천 유량 산정방법의 불확실성을 감소시키는 것을 목적으로 하였다. 하천 유량 자 료는 수문해석과 수자원 관리를 하는데 있어서 필수적으로 요구되는 자료이기 때문에 정량적으로 정확한 산정 방법을 고찰 할 필요가 있다. 이를 위해 Bayesian 및 Bootstrap 방법을 이용한 수위-유량 관계식의 매개변수와 기존의 매개변수를 비교하 였으며, 불확실성을 평가하기 위해서 표준오차법에 -분포를 적용한 추정치 결과의 신뢰구간을 비교하였다. 그 결과, 본 연 구를 통해 개발된 회귀분석에 의한 추정값은 약 1~5 %미만의 차이가 보이며, 각 지점에서 수위에 따라 기존보다 더 적용성 이 우수한 결과를 보이는 부분도 존재함을 확인하였다. 따라서 본 연구에서 제시한 방법별로 하천의 특성 및 수위에 맞게 적용한다면 보다 더 신뢰성 있는 유량 자료를 확보할 수 있을 것으로 생각된다.

The objective of this study is to reduce the uncertainty of the river discharge estimation method using the stage-discharge relation curve. It is necessary to consider the quantitative and accurate estimation method because the river discharge data is essential data for hydrological interpretation and water resource management. For this purpose, the parameters estimated by Bayesian and Bootstrap methods are compared with the ones obtained by stage-discharge relation curve. In addition, the Bayesian and Bootstrap methods are applied to assess uncertainty and then those are compared with the confidence intervals of the results from standard error method which has t-distribution. From the results of this study, The estimated value of the regression analysis developed through this study is less than 1 ~ 5%. Also It is confirmed that there are some areas where the applicability is better than the existing one according to the water level at each point. Therefore, if we use more suitable method according to the river characteristics, we could obtain more reliable discharge with less uncertainty.

본 연구는 불특정 다수인이 이용하고 공간내 밀집도가 높아 화재발생시 인명 및 재산피해 가능성이 큰 다중이용시설을 대상으로 베이지안 마코브체인 몬테카를로 기법을 적용하여 실제 화재경험이 반영된 소방대 화재진압 활동을 정량적 화재위험평가에 반영할 수 있는 최적 확률모형을 개발하였다. 지난 5년간 전국에서 발생한 실제 화재경험 데이터를 중심으로 소방대 화재진압 확률모형을 도출한 결과, 전국 17개 시도는 3개의 시도 그룹으로 분류가 가능하며, 각 시도 그룹에 대해 각각 Log-normal 기반 확률모형(μ=2.3630, σ=0.6905), Log-normal 기반 확률모형(μ=2.6820, σ=0.9742), Gamma 기반 확률모형 (=0.6151, =0.0147)이 최적의 소방대 화재진압 확률모형으로 개발되었다. 본 연구를 통해 도출된 각 시도 별 소방대 화재진압 확률모형은 정량적 화재위험평가 적용시 대형화재로의 화재확산 방지를 위한 자동 소화설비와 함께 분석 현실성 확보 및 화재안전 관리 대책 수립의 신뢰성 향상에 더욱 기여할 수 있을 것이다.

This study developed the probabilistic fire brigade suppression model for assembly occupancies whose risk is significant in terms of life and property damage due to unspecified majority of people and the high density in the space when fire occurs using Bayesian Markove Chain Monte Carlo method. As a result of deriving a fire brigade suppression probability model based on actual fire experience data over the past five years, 17 cities and provinces were able to be grouped into 3 for which, Log-normal or Gamma based probability models are developed. The probabilistic fire brigade suppression models for 3 groups drawn through this study are expected to contribute to secure the realism of quantitative fire risk assessment and to enhance reliability of the fire safety management measures through support of risk based decision by reflecting the real fire event experiences.

정확한 장단기 주택가격 예측은 효율적인 부동산 정책수립과 운용, 부동산 투자및 은행의 담보대출 위험관리 등에 필수적임에도 불구하고 잠재적인 예측변수의수가 너무 많아 예측을 실시하는 데 기술적 어려움이 존재한다. 본 연구는 이러한예측변수와 모형 불확실성 문제를 고려함으로써 보다 정확한 장단기 서울 아파트매매가격지수 예측분포를 도출하고자 기존의 베이지안 변수선택 기법을 보완 및확장한 새로운 변수선택 알고리즘을 제안한다. 본 연구에서 제시된 변수선택기법은 각 예측변수의 선택여부 뿐만 아니라 종속변수에 미치는 영향의 방향까지 식별한다는 점에서 기존 기법과 차별화된다. 표본 외 예측결과, 중장기 예측에서 본연구의 베이지안 변수선택 기법을 적용한 모형이 다른 모든 경쟁모형들보다 예측력에서 우월한 것으로 나타났다. 반면 서울 아파트매매가격지수의 높은 지속성으로 인해 단기 예측에서는 p차 자기회귀모형의 예측력이 가장 우수했다.

Accurate house price forecasts are essential for efficient policy-making, investment, and risk management of mortgage loan. Nevertheless, there are few empirical studies on the Korean house price prediction. This seems to be because of the large number of variables. In this study, we provide a new Bayesian variable selection method for the Seoul apartment price index forecasting, considering the uncertainty of the variables. To do this, we extend the standard Bayesian variable selection by using a more flexible and interpretable spike-and-slab prior. This method consists of two stages: variable selection and predictive density simulation. According to our out-of-sample forecasting experiment, the proposed model outperforms the standard variable selection method and first-order auto-regressive (AR(p) model in the medium- and long-term horizons. Meanwhile, the AR(p) is found to be the best for the short-term forecasting due to the high persistence of the price index growth.

HMM(Hidden Markov Model)을 이용한 어휘 인식에서 인식 어휘의 모델들의 대한 인식 확률이 이산적인 분포를 나타내며 인식을 위한 계산량이 적은 장점이 있지만 인식률을 계산했을 때 상대적으로 낮은 단점이 있다. 이 를 개선하기 위하여 HMM(Hidden Markov Model) 모델 최적화를 이용한 베이시안 기법 인식률 향상을 제안한다. 본 논문은 HMM 어휘 인식에서 인식을 위한 모델 구성을 가우시안 믹스쳐 모델로 최적화한 인식 모델을 생성하였으며 베이시안 기법인 사전확률과 사후확률을 이용한 인식률을 향상시켰다. 본 논문에서 제안한 방법을 적용한 결과 어휘 인식률에서 97.9%의 인식률을 나타내었다.

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. Improve them with a HMM model is proposed for the optimization of the Bayesian methods. In this paper is posterior distribution and prior distribution in recognition Gaussian mixtures model provides a model to optimize of the Bayesian methods vocabulary recognition. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

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.

인식 모델을 구성할 때 정의되지 않은 모델, 인식 모델 구성 후에 추가되어진 모델, 모델이 부족하여 하나의 모델 클러스터링으로 모델링하여 생성된 인식 모델들은 인식률 저하의 원인이 된다. 이러한 원인을 개선하기 위하여 Bayesian 기법의 모수 추정을 이용한 결정트리 상태 공유 모델링 방법을 제안하였다. 제안 방법은 Bayesian 기법의 파라미터 추정을 통하여 탐색된 결과로부터 결정트리 기반 상태 공유 모델링의 최대 확률 기법에 따라 인식 모델을 결정한다. 본 논문에서 제안하여 시뮬레이션 데이터를 이용한 실험 결과에서 제안한 군집화 방식을 비교하여 1.29%의 음성인식 오류감소율을 보였으며, 기존 군집화 방식에 비해 개선된 성능을 보였다.

Recognition model is not defined when you configure a model, Been added to the model after model building awareness, Model a model of the clustering due to lack of recognition models are generated by modeling is causes the degradation of the recognition rate. In order to improve decision tree state tying modeling using parameter estimation of Bayesian method. The parameter estimation method is proposed Bayesian method to navigate through the model from the results of the decision tree based on the tying state according to the maximum probability method to determine the recognition model. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method error rate reduction of 1.29% compared with baseline model, which is slightly better performance than the existing approach.

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.

T Bayesian network methodology taking place of fault tree analysis used for reliability assessment has gotten lots of attention in recent years. On basis of the current Bayesian network method used for calculating structural importance degree, a new Bayesian network method is raised. This new method can avoid repeat modification of the parameters in the conditional probability tables, and hence the intermediate results computed during inference process can be shared to decrease calculating complexity. The new method is proved to be correct in a mathematic way and a corresponding algorithm named SID_Jointree for realizing this new method is designed, which guarantees the new method can be realized in computer. Finally, the correctness and efficiency of the new method is validated by using two fault tree cases.

Local feature matching is an essential component of many image and object retrieval algorithms. Feature similarities between object model and scene graph are complemented with a regularization term that measures differences of the relational structure. In this paper, we present a novel approach to the optimal feature matching using new Bayesian graph theory. First, we will discuss properties of various local invariant feature detectors and descriptors for scale, affine transformation and illumination changes. Second, we propose an efficient features corresponding method using local invariant features and new graph matching algorithm. Main theoretical background of our algorithm is that it can be based on the Bayes theorem and an iterative convex successive projection algorithm used to obtain the global optimum solution for feature matching problem. Finally, we have conducted the comparative experiments between proposed method and existing method on various real images. Experimental results show that our method outperforms clearly rather than the existing algorithms about feature correspondence in two images with rotation or scale transformation and illumination changes.

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 the context of mass customization, to address the issues about method based on Constraint Satisfaction Problem (CSP) in the field of product configuration, a combined product configuration method based on the CSP and Bayesian Network (BN) is proposed in this paper. On the basis of CSP and BN theory, a product configuration model established based on CSP and BN, and the specific method for solving was given, Including the reasoning on posterior probability by establishing the Bayesian Network model and the reasoning on Constraint Satisfaction Problem. Finally, to assemble the computer as an example, a configuration system of assembly computer product is developed so as to verify the feasibility and effectiveness.

In this paper Satisfaction Curve has been applied to estimate the material safety by using Bayesian Method based on given parameters which are obtained from experimental results of other researchers. From the results, Bayesian Method is proven to be an available method for safety estimation of material.

Background: With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and Methods: A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results: The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions: The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.

This paper develops a Bayesian method to derive the optimal sequential preventive maintenance(PM) policy by determining the PM schedules which minimize the mean cost rate. Such PM schedules are derived based on a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) and may have unequal length of PM intervals. To apply the Bayesian approach in this problem, we assume that the failure times follow a Weibull distribution and consider some appropriate prior distributions for the scale and shape parameters of the Weibull model. The solution is proved to be finite and unique under some mild conditions. Numerical examples for the proposed optimal sequential PM policy are presented for illustrative purposes.

In this paper, we propose a Bayesian approach to determine the adaptive preventive maintenance(PM) policy for a general sequential imperfect PM model proposed by Lin, Zuo and Yam(2000) that PM not only reduces the effective age of the system but also changes the hazard rate function. Assuming that the failure times follow Weibull distribution, we adopt a Bayesian approach to update unknown parameters and determine the Bayesian optimal sequential PM policies. Finally, numerical examples of the optimal adaptive PM policy are presented for illustrative purposes.

The problem of information related to I poission experiments, each having a distinct failure rate $\theta$i I=1,2,…,I, is considered. Instead of using a standard exchangeable prior for $\theta$=($\theta$1,$\theta$2,…,$\theta$I), we consider a partition of the experiments and take the $\theta$i's belonging to the same partition subgroup to be exchangeable and the $\theta$i's belonging to distinct subgroups to be independent. And we perform Gibbs sampling approach for Bayesian inference on $\theta$ conditional on a partition. Numerical study using real data is provided.