• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.103-111
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• 2003

Kernel machines are used widely in real-world regression tasks. Kernel ridge regressions(KRR) and support vector machines(SVM) are typical kernel machines. Here, we focus on two types of KRR. One is inductive KRR. The other is transductive KRR. In this paper, we study how differently they work in the interpolation and extrapolation areas. Furthermore, we study prediction interval estimation method for KRR. This turns out to be a reliable and practical measure of prediction interval and is essential in real-world tasks.

• 제어로봇시스템학회논문지
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• 제8권7호
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• Journal of Welding and Joining
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• 제10권4호
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• pp.199-212
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• 1992

Expert system is a practical application part of the artificial intelligence and can be generally described as a computer-based system designed to simulate the knowledge and reasoning of a human expert, and to make that knowledge conveniently available to other people in a useful way. Expert systems consist of three major components, knowledge base, inference engine and user interface. In this paper, it is aimed to construct a prototype system to design the horizontal-typed pressure vessel. To do this, a representative artificial programming language, Turbo Prolog, was employed, and the knowledge representation was mainly done by the production rule such as "If(condition), than (action)" style and by the predicate logic. In the developed system, it was quite easy to represent the knowledge of "If(condition), then (action)"style and by the predicate logic. In the developed system, it was quite easy to represent the knowledge of "If(condition). then(action)" style and the various table-like data. It was also effective to represent the graphics. Though this expert system is by now small and incomplete, it is possible to expand it to a larger and refined system later.rger and refined system later.

• 동의생리병리학회지
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• 제23권4호
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• pp.745-750
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• 2009

The goal of this study is to examine on reasoning and search for construction of diagnosis ontology as a knowledge base of diagnosis expert system in oriental medicine. Expert system is a field of artificial intelligence. It is a system to acquire information with diverse reasoning methods after putting expert's knowledge in computer systematically. A typical model of expert system consists of knowledge base and reasoning & explanatory structure offering conclusion with the knowledge. To apply ontology as knowledge base to expert system practically, consideration on reasoning and search should be together. Therefore, this study compared and examined reasoning, search with diagnosis process in oriental medicine. Reasoning is divided into Rule-based reasoning and Case-based reasoning. The former is divided into Forward chaining and Backward chaining. Because of characteristics of diagnosis, sometimes Forward chaining or backward chaining are required. Therefore, there are a lot of cases that Hybrid chaining is effective. Case-based reasoning is a method to settle a problem in the present by comparing with the past cases. Therefore, it is suitable to diagnosis fields with abundant cases. Search is sorted into Breadth-first search, Depth-first search and Best-first search, which have respectively merits and demerits. To construct diagnosis ontology to be applied to practical expert system, reasoning and search to reflect diagnosis process and characteristics should be considered.

• 대한가정학회지
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• 제40권9호
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• pp.1-15
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• 2002

The multi-faceted influence of price on consumers' purchasing process of apparel products: Relationships with attitudinal and behavioral variables Price has a significant relationship to clothing products not only because of its practical, emotional and symbolic attributes but also because of its wide range and frequent changes. The purpose of this study was to identify the multi-faceted influence of price on consumers' purchasing process of clothing products. Six types of price-perceptions were related to various attitudinal and behavioral variables in a clothing purchase. A questionnaire was developed and data were collected from 720 adult women living in Seoul. Factor analysis, multiple regression, t-test and canconical correlation were employed to analyze the data. Low price consciousness was negatively related to product-oriented aspects of clothing and effected the one-price sale, visiting public markets and using interpersonal sources of price information. Value for money consciousness was positively related to product-oriented aspects of clothing and consumers' age or marriage and effected price considerations at the on-purchase and post-purchase stage. Price-quality inference was related to product-oriented and market-oriented aspects of clothing while price-prestige inference was related to visual and symbolic aspects of clothing and effected normal-price purchasing. Sale proneness was related to market-oriented aspects of clothing and effected seasonal sale price purchasing and price mavenism was related to market-oriented and visual aspects of clothing and effected price considerations at the pre-purchase stage.

• 대한조선학회논문집
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• 제57권5호
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• pp.305-311
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• 2020

This study presents a probabilistic time series forecast of ship structural response using Bayesian inference combined with Volterra linear model. The structural response of a ship exposed to irregular wave excitation was represented by a linear Volterra model and unknown uncertainties were taken care by probability distribution of time series. To achieve the goal, Volterra series of first order was expanded to a linear combination of Laguerre functions and the probability distribution of Laguerre coefficients is estimated using the prepared data by treating Laguerre coefficients as random variables. In order to check the validity of the proposed methodology, it was applied to a linear oscillator model containing damping uncertainties, and also applied to model test data obtained by segmented hull model of 400,000 DWT VLOC as a practical problem.

• Advances in Computational Design
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• 제8권2호
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• pp.147-177
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• 2023

Elliptical concrete-filled steel tubular (CFST) column is widely used in modern structures for both aesthetical appeal and structural performance benefits. The ultimate axial load is a critical factor for designing the elliptical CFST short columns. However, there are complications of geometric and material interactions, which make a difficulty in determining a simple model for predicting the ultimate axial load of elliptical CFST short columns. This study aims to propose an efficient adaptive neuro-fuzzy inference system (ANFIS) model for predicting the ultimate axial load of elliptical CFST short columns. In the proposed method, the ANFIS model is used to establish a relationship between the ultimate axial load and geometric and material properties of elliptical CFST short columns. Accordingly, a total of 188 experimental and simulation datasets of elliptical CFST short columns are used to develop the ANFIS models. The performance of the proposed ANFIS model is compared with that of existing design formulas. The results show that the proposed ANFIS model is more accurate than existing empirical and theoretical formulas. Finally, an explicit formula and a Graphical User Interface (GUI) tool are developed to apply the proposed ANFIS model for practical use.

• 대한토목학회논문집
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• 제39권1호
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• pp.135-146
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• 2019

본 논문에서는 베이지안 추론법을 이용한 교량 실제 운영단계에서의 예방적 유지관리 전략 수립 기법을 제안하였다. 제안된 기법은 모니터링의 불확실성을 고려하여 실제 대상 교량의 상태 변화를 높은 확률로 예측할 수 있다. 실제 공용 중인 교량에 제안된 기법의 적용성을 검토함과 동시에, 손상이 발현된 후 유지보수 조치계획을 수립하는 현행 유지관리체계와 비교하여 유지관리 비용 효율성 측면에서 유리함을 분석하였다. 제안된 기법을 이용하여 기존 유지관리방법의 한계를 극복하고, 공용 중인 교량의 실질적인 유지관리체계 수립을 위한 교량 유지관리 의사결정에 활용할 수 있을 것으로 기대한다.

• 논리연구
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• 제13권2호
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• pp.61-82
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• 2010

X가 참(거짓)이라는 사실이 알려지지 않았다는 무지전제로부터 X는 거짓(참)이라는 지식결론을 추론하는 논증을 일반적으로 무지로부터의 논증이라고 하는데, 코피 등을 비롯한 많은 논리학자들은 이를 오류 논증의 하나라고 설명하고 있다. 그들의 주장에 따르면, 무지로부터의 논증처럼 보이지만 오류논증이 아니고 설득력 있는 받아들일 만한 논증은 사실은 무지로부터의 논증이 아니고, 조건적 지식전제가 암암리에 포함된 논증이다. 이 논문은 그러한 주장에 반대해서 모든 무지로부터의 논증은 암암리에 조건적 지식전제가 포함된 것으로 해석될 수 있고, 또 모든 무지로부터의 논증이 다 오류는 아니라는 점을 논증한다. 무지로부터의 논증 형식을 지닌 논증 중에서 오류논증과 그렇지 않은 논증의 기준을 제시하고, 특히 실천논증의 경우, 사회적 맥락이 오류논증과 설득력 있는 논증을 가르는 중요한 기준임을 논증한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권2호
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• pp.111-118
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• 2016

Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple's technology.