• 한국정밀공학회지
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• 제15권3호
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• pp.18-23
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• 1998

This paper describes a research work of developing a computer-aided design of blanking and piercing for irregular-shaped sheet metal products. An approach to the CAD system is based on the knowledge-based rules. Knowledge for the CAD system is formulated from plasticity theories, experimental results and the empirical knowledge of field experts. The system has been written in AutoLISP on the AutoCAD with a personal computer and is composed of four main modules, which are input and shape treatment, flat pattern layout, production feasibility check, and strip layout module. Based on knowledge-based rules, the system is designed by considering several factors, such as radius and angle of bend. material and thickness of product, complexities of blank geometry and punch profile, and availability of press. This system is capable of unfolding a formed sheet metal part to give flat pattern and automatically account for the adjustment of bend allowances to match tooling requirements by checking the dimensions and relationships of parts of the folded product. Also this system can carry out a process planning which is obtained from results of irregular shape of product that was successful in production feasibility check module according to flat pattern layout and generate strip layout drawing in graphic forms. The developed system provides its efficiency for flat pattern layout, and strip layout for the irregularly shaped sheet metal products.

• 디자인학연구
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• 제15권4호
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• pp.337-346
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• 2002

디자인 지식의 습득, 저장, 검색 및 응용과 같은 컴퓨터를 이용한 디자인 과정에 있어서, 창조적이며 디자인 요구에 적당한 결과물을 생산하는데 필요한 디자인 지식을 인지하고 습득하는 과정은 매우 중요하다 하겠다. 특히 인간의 인지능력과 유사한 기능을 같고 중요한 형태 디자인 지식을 습득하는 것은 필수적이다. 형태의 물리적인 속성에 의하여 인지되는 1차원적인 형태 지식이 아닌, 이들로부터 형성되는 2차원 또는 그 이상의 차원에서 인지되는 형태 디자인 지식을 인지해야만 한다. 지식의 인지 및 습득은 기억 장치에 저장되어 있는 지식과 인지되는 지식을 비교하여 동일하거나 유사한 경우 그 디자인 지식이 습득된다. 이때 1차원적인 디자인 지식은 형판 매칭과 속성 매칭에 의하여 그 유사성이 쉽게 인지되지만, 2차원 이상의 디자인 지식에 대해서는 인간은 쉽게 인지하나 컴퓨터를 이용한 인지에는 어려움이 많다. 본 연구는 컴퓨터에 이러한 능력을 부여하기 위하여 형태패턴 표현을 이용한 형태의 유사성을 판별하는 방법에 대하여 설명하였다.

• 대한전기학회논문지:전력기술부문A
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• 제53권12호
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• pp.637-644
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• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• 부모자녀건강학회지
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• 제15권1호
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• pp.20-32
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• 2012

Purpose: The purpose of this study was to identify clinical decision making pattern of pediatric nurses and analyze how it shows the differences in types of decision making pattern by nurses characters. Methods: A self-administered questionnaire was used to pediatric nurses of 4 general hospitals in Seoul from February 2004 to April 2004. The data of 251 nurses was analyzed by varimax rotation factor analysis, t-test, and ANOVA. Results: 6 decision making patterns were identified: Individual Patient-oriented, Pattern-oriented Intuitive, Typical Nursing Knowledge-oriented, Nursing Model-oriented, Medical Knowledge-oriented, and Patient-Family-Nurse Collaborative. Individual Patient-oriented, Pattern-oriented Intuitive, Typical Nursing Knowledge-oriented, and Nursing Model-oriented decision making pattern got meaningful differences in age, marital status, total number of years in nursing practice, and number of years in pediatric nursing practice. Conclusion: We expect the result of this study can be applied for promotion of understanding the decision making of nurses that occurs in pediatric nursing practice and also can be used as foundation data for development and expansion of pediatric nursing practice.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• International Journal of Reliability and Applications
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• 제10권1호
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• pp.1-15
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• 2009

Even though the impact of manufacturing quality to reliability is not considered much as well as that of design area, a major cause of an early failure of the product is known as manufacturing problem. This research applies two different types of neural network algorithms, the Back propagation (BP) algorithm and Learning Vector Quantization (LVQ) algorithm, to identify and classify the nonrandom variation pattern on the control chart based on knowledge-based diagnosis of dimensional variation. The performance and efficiency of both algorithms are evaluated to choose the better pattern recognition system for auto body assembly process. To analyze hundred percent of the data obtained by Optical Coordinate Measurement Machine (OCMM), this research considers an application in which individual observations rather than subsample means are used. A case study for analysis of OCMM data in underbody assembly process is presented to demonstrate the proposed knowledge-based pattern recognition system.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• 정보처리학회논문지D
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• 제17D권4호
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• pp.253-258
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• 2010

지금까지의 빈발 패턴(Frequent Pattern) 마이닝에서는 각 항목들의 중요도(Weight)는 모든 같은 값으로 다루어 왔으나 실 환경에서는 각 항목들의 중요도가 다르게 적용되는 경우가 많이 있고 또 같은 항목이라도 시간에 따라 다른 중요도 값으로 다루어져야 할 경우가 있다. 비즈니스 데이터 분석 환경이나 웹 클릭 데이터 분석 환경과 같은 응용에서도 동적으로 변하는 중요도를 고려하여야 한다. 지금까지 항목의 중요도를 고려하는 여러 패턴 마이닝 기법들이 제안되고 있으나 동적으로 변하는 항목의 중요도를 고려하는 연구는 발표되지 않고 있다. 본 논문에서는 처음으로 동적인 항목들의 중요도(혹은 가중치)를 고려하는 빈발 패턴 마이닝 알고리즘을 제안한다. 제안하는 기법은 단 한번의 데이터베이스 스캔으로 처리되므로 스트림 데이터를 분석할 수 있다. 여러 실험을 통하여 제안하는 기법은 매우 효과적이며 확장성이 좋은 것임을 보인다.

• 가정과삶의질연구
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• 제7권2호
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• pp.31-43
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• 1989

The purposes of this study were to identify the overall levels of consumer problem, consumer competencies and purchase pattern of urban low-income consumers and to examine the factors affecting the consumer problem and the subareas-market environment problem(MEP) and transaction relation problem(TRP). The related factors, that is, independent variables were competencies-related factors(consumption-oriented attitude, attitude on consumerism, consumer knowledge), purchase pattern-related factors (search pattern, credit pattern, peddler pattern) and socio-demorgraphic factors(age, educational level, family size). For this purpose, a survey was conducted by interview using questionaires on 198 homemakers that lived in the poor areas of Seoul. Statistics used for data analysis were Frequency Distribution, Percentile, Mean, Pearson's Correlation, One-way ANOVA, Scheffe-test, Breakdown and Multiple Classification Analysis. Major findings were as follows: 1) In the level of consum r problem were in the middle level and the level of MEP were higher than that of TRP. The attitude on consumption-orientation was so negative, while attitude on consumerism was positive. The level of consumer knowledge was in the middle level. The urban low-income consumers searched a little and depended on credit and peddler in the low level. 2) Consumer problem perceived by urban low-income consumers differed significantly according to attitude on consumerism, credit pattern, monthly charge of peddler purchase. The MEP depended on attitude on consumerism and monthly charge of peddler purchase, and the TRP was affected by credit pattern and attitude on consumerism. Resulting from MCA, the most influencial variable was attitude on consumerism and credit pattern in the consumer problem, and attitude on consumerism in the MEP, and credit pattenr in the TRP.

• 대한전기학회논문지:전력기술부문A
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• 제55권9호
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• pp.351-358
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• 2006

In this paper, DSPOCS(Digital Switchgear-Panel Operation and Control Solution) is designed, which is the intelligent inference based operation and control solution to obtain the safety and reliability of electric power supply in substation based on IED. DSPOCS is designed as a scheduled monitoring and control task and a real-time alarm inference task, and is interlinked with BRES(Bus Reconfiguration Expert System) in the required case. The intelligent alarm inference task consists of the alarm knowledge generation part and the real-time pattern matching part. The alarm knowledge generation part generates automatically alarm knowledge from DB saves it in alarm knowledge base. On the other hand, the pattern matching part inferences the real-time event by comparing the real-time event information furnished from IEDs of substation with the patterns of the saved alarm knowledge base.; Especially, alarm knowledge base includes the knowledge patterns related with fault alarm, the overload alarm and the diagnosis alarm. In order to design the database independently in substation structure, busbar is represented as a connectivity node which makes the more generalized graph theory possible. Finally, DSPOCS is implemented in MS Visual $C^{++}$, MFC, the effectiveness and accuracy of the design is verified by simulation study to the typical distribution substation.