• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.743-755
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• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.263-266
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• 2007

This paper discusses the Bayesian statistical inference. This paper discusses the Bayesian inference, MCMC (Markov Chain Monte Carlo) integration, MCMC method, Metropolis-Hastings algorithm, Gibbs sampling, Maximum likelihood estimation, Expectation Maximization algorithm, missing data processing, and BMA (Bayesian Model Averaging). The Bayesian statistical inference is used to process a large amount of data in the areas of biology, medicine, bioengineering, science and engineering, and general data analysis and processing, and provides the important method to draw the optimal inference result. Lastly, this paper discusses the method of principal component analysis. The PCA method is also used for data analysis and inference.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.121-130
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• 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
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• v.63 no.1
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• pp.47-53
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• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.205-214
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• 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
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• v.17 no.4
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• pp.37.1-37.8
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• 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
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• v.33 no.5
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• pp.483-497
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• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.215-222
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• 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.

• The Journal of Korean Institute for Practical Engineering Education
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• v.5 no.1
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• pp.1-13
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• 2013

For a basic engineering education the confirmation and verification of the deductive Inference was studied and the principle of probability inference was applied. The background of introduction of deductive Inference and its test method was mentioned, and historic arguments on the compatibility of deductive statistical inference was summarized and analyzed. Philosophical arguments on the deductive confirmation for engineering experiments was introduced. Premise, procedure, and control of the experiments are studied. As an example of the deductive probability inference three groups of experimental data were used in order to find successful inferences respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.6 no.1
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• pp.35-41
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• 1988

In the three dimensional deformation analysis by terrestrial photographs, iterative reweighted similarity transformation method is used for more accurate displacement computation. Also, Bayesian Inference method is used in the detection of unstable points and the analytical method for displacement patterns analysis is proposed in this study. In results, the accuracy of displacement estimation was improved by applying the weights of least absolute method ($\Sigma$｜d｜⇒min) and more accurate detection of displaced points could be achieved by Bayesian Inference. The analytical method in the patterns of displacement proposed in this study could be adapted to the movement analysis of objects wholly or partly.