• Journal of Mechanical Science and Technology
• /
• 제19권3호
• /
• pp.743-755
• /
• 2005

Human Sensibility Engineering System (HSES) has been applied to product development for customer's satisfaction based on ergonomic technology. The system is composed of three parts such as human sensibility analysis, inference mechanism, and presentation technologies. Inference mechanism translating human sensibility into design elements plays an important role in the HSES. In this paper, we propose a rule-based inference model for HSES. The rule-based inference model is composed of five rules and two inference approaches. Each of these rules reasons the design elements for selected human sensibility words with the decision variables from regression analysis in terms of forward inference. These results are evaluated by means of backward inference. By comparing the evaluation results, the inference model decides on product design elements which are closer to the customer's feeling and emotion. Finally, simulation results are tested statistically in order to ascertain the validity of the model.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (C)
• /
• pp.263-266
• /
• 2007

본 논문은 베이지안 통계 추론에 대하여 논의한다. 논문은 베이지안 추론, Markov Chain과 Monte Carlo 적분, MCMC(Markov Chain Monte Carlo) 기법, Metropolis-Hastings 알고리즘, Gibbs 샘플링, Maximum Likelihood Estimation, EM 알고리즘, 상실된 데이터 보완 기법, BMA(Bayesian Model Averaging) 순서로 논의를 진행한다. 이러한 통계적 기법들은 대용량의 데이터를 처리하는 생물학, 의학, 생명 공학, 과학과 공학, 그리고 일반 데이터 조사와 처리 등에 사용되고 있으며, 최적의 추론 결과를 이끌어 내는데 중요한 방법을 제공하고 있다. 그리고 마지막으로 PC(Principal Component) 분석 기법에 대하여 논의한다. PC 분석 기법도 데이터 분석과 연구에 많이 활용된다.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
• /
• pp.121-130
• /
• 1999

The objective of this paper is to apply Extended AND-OR Graph (EAOG)-related techniques to extract knowledge from a specific problem-domain and perform analysis in complicated decision making area. Expert systems use expertise about a specific domain as their primary source of solving problems belonging to that domain. However, such expertise is complicated as well as uncertain, because most knowledge is expressed in causal relationships between concepts or variables. Therefore, if expert systems can be used effectively to provide more intelligent support for decision making in complicated specific problems, it should be equipped with real-time inference mechanism. We develop two kinds of EAOG-driven inference mechanisms(1) EAOG-based forward chaining and (2) EAOG-based backward chaining. and The EAOG method processes the following three characteristics. 1. Real-time inference : The EAOG inference mechanism is suitable for the real-time inference because its computational mechanism is based on matrix computation. 2. Matrix operation : All the subjective knowledge is delineated in a matrix form, so that inference process can proceed based on the matrix operation which is computationally efficient. 3. Bi-directional inference : Traditional inference method of expert systems is based on either forward chaining or backward chaining which is mutually exclusive in terms of logical process and computational efficiency. However, the proposed EAOG inference mechanism is generically bi-directional without loss of both speed and efficiency.

• Structural Engineering and Mechanics
• /
• 제63권1호
• /
• pp.47-53
• /
• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• 대한임베디드공학회논문지
• /
• 제13권4호
• /
• pp.205-214
• /
• 2018

The reliability test is a crucial step for ensuring robustness of high-cost and complex weapon systems. In this paper, we present a set of quantitative criteria to select critical parts or components in weapon systems for the reliability test, and implement a fuzzy inference system by applying developed criteria to fuzzy theory. We classify the selection criteria of critical parts or components into four fuzzy sets and membership functions. A fuzzy inference rule is proposed based on the AHP (Analytic Hierarchy Process) analysis technique so as to derive a convincing reliability test. The credibility of the fuzzy inference system is confirmed through a case study using actual equipment data exacted from an existent weapon system.

• Genomics & Informatics
• /
• 제17권4호
• /
• pp.37.1-37.8
• /
• 2019

Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST, and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.

• Nuclear Engineering and Technology
• /
• 제33권5호
• /
• pp.483-497
• /
• 2001

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2001년도 춘계정기학술대회
• /
• pp.215-222
• /
• 2001

The task for chromosome analysis and diagnosis by experienced cytogenetists are being concerned as repetitive, time consuming job and expensive. FOr that reason, chromosome analysis system based on knowledge base for CAI had been established to be able to analyze chromosomes and obtain necessary advises from the knowledge base instead of human experts. That s to say, knowledge base by IF THEN production rule was implemented to a knowledge domain with normal and abnormal chromosomes, and then the inference results by knowledge base could enter the inference data into the database. Experimental data were composed of normal chromosome of 2,736 patients'cases and abnormal chromosomes of 259 patients'cases that have been obtained from GTG-banding metaphase peripheral blood and amniotic fluid samples. The complete system provides variously morphological information by analysis of normal or abnormal chromosomes and it also has the advantage of being able to consult with user on chromosome analysis and diagnosis.

• 한국실천공학교육학회논문지
• /
• 제5권1호
• /
• pp.1-13
• /
• 2013

이 연구의 본론에서 공학 교육에 적용되는 귀납법적 확증(confirmation)과 연역법적 검증(verification)을 다루고 이어서 귀납법 추리의 원리를 모형도를 통해 알아보았다. 그리고 이어서 공학교육에서 널리 쓰이는 확률론적 추론의 도입 배경과 보편적 명제에 대한 확률적 검정(test) )을 논의하였고 또한 실험에 대한 귀납법의 인정여부를 가지고 역사적으로 학계에서 끊임없이 논의 되어온 귀납법적 추론에 대한 정당성을 비교 분석하였다. 공학 교육에서 흔히 쓰이는 실험에 대한 철학적 명제를 가지고 실험에 대한 설명으로 선택된 귀납법의 승리와 반전, 그리고 확증에 대해 알아보았다. 이어서 실험에서의 전제, 절차, 및 통제에 대하여 논의 되어졌다. 마지막으로 귀납법적 추론 예제로써 Elliptical Trainer 실험 결과를 가지고 확률론적 추론이 어떻게 가능한지 보여 주었다. 그 결과 82%의 참 확률을 가지고 3개의 추론을 하였는데 이 연구에서는 보통 공학연구와 달리 추론(결론 법칙)에 대한 참 확률을 표기하여 공학에서 주로 적용하는 귀납법적 방법 자체가 확률추론임을 알린다.

• 한국측량학회지
• /
• 제6권1호
• /
• pp.35-41
• /
• 1988

지상사진에 의한 삼차원변형해석을 하는데 있어서 변위양계산의 정확도를 향상시키기 위해 반복경증률 상사변환법이 사용되었으며, 변위점검출에서는 Bayesian Inference가 적용되었고, 변위형태해석을 위해 변위방정식을 이용하는 방법을 제시하였다. 그 결과 변위양계산에서는 최소절대법($\Sigma$$\mid$d$\mid$⇒min)에 의한 경중률조건이 정확도를 향상시켰으며, 또한 Bayesian Inference을 적용하므로써 정확한 변위점검출을 할 수 있었다. 변위형태해석에서는 최적변위방정식을 이용하여 대상들의 전체 또는 부분적인 움직임을 해석할 수 있었다.