• 한국재난정보학회 논문집
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• 제20권1호
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• pp.60-68
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• 2024

연구목적: 화학물질 취급, 사용 등의 플랜트 및 사업장 설계 시 국내 안전관리 관련 법령을 준수하여 설계가 이루어졌는지 자동으로 판단할 수 있는 체계를 마련하여 검토 시간의 단축과 정확성을 높이고자 한다. 연구방법: 화학물질 취급, 사용 사업장에 대한 안전 설계 기준을 조사하고, 조문을 의미단위로 구분하여 자동으로 설계오류를 판단할 수 있는 유형과 체계를 도출하였다. 연구결과: 건물 설계 시 수행하던 자동 설계 검토 방법을 화학물질 취급 사업장을 설계 시 요구되는 안전설계 사항을 Rule Checker 개발을 통해 검토할 수 있는 체계를 제안하였다. 법 적용 대상 여부의 확인 후 전처리를 통해 의미단위로 안전설계 Rule을 구분한다. 구분된 결과는 4가지 유형으로 분류할 수 있고, 특정 장치 및 설비에 대한 사양, 공간, 조건, 상황에 대한 부분과 안전성을 강화하기위한 특정 장치 및 설비에 대한 사항이 대표적인 유형으로 분석되었다. 각각의 안전설계 Rule에 대한 Diagram을 작성하고, 이를 Rule checker 프로그램을 통해 검토할 수 있도록 하는 체계를 제안한다.

• 한국의류산업학회지
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• 제11권5호
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• pp.771-778
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• 2009

There may be some problems in size fitness for ready-made-clothes purchasers with fatty body shape especially for them with abdominal obesity. The purpose of this study was to improve the wearing comfort of Middle-aged women slacks by setting grading points and developing grading rules proper to the abdominal obesity. The subjects in this study was over $25kg/m^2$ of the BMI, over 85cm of waist, and over 0.8 of WHR. A total of 176 Middle-aged women satisfyingthem were enrolled. The results of this study are as follows: (1) The majority segment is 85-94 size accounting for the 11.36% of subjects. (2) The measurements on 11 size from three groups after drawing slacks pattern on them revealed that the changed values of slacks front and back on the grading point marked are differ, indicating that it is needed to apply body shape and age specific grading rule. (3) The evaluation on the appearance for wearing slacks showed relatively high score, indicating that the grading between sizes within each group was effective. The studies on the body size distribution on each obesity pattern and on the setting size system reflecting characteristics of each body shape should be performed continuously.

• 한국정보시스템학회지:정보시스템연구
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• 제12권2호
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• pp.1-20
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• 2003

This paper deals with the application of the genetic algorithm to the technical trading rule of the stock market. MACD(Moving Average Convergence & Divergence) and the Stochastic techniques are widely used technical trading rules in the financial markets. But, it is necessary to determine the parameters of these trading rules in order to use the trading rules. We use the genetic algorithm to obtain the appropriate values of the parameters. We use the daily KOSPI data of eight years during January 1995 and October 2002 as the experimental data. We divide the total experimental period into learning period and testing period. The genetic algorithm determines the values of parameters for the trading rules during the teaming period and we test the performance of the algorithm during the testing period with the determined parameters. Also, we compare the return of the genetic algorithm with the returns of buy-hold strategy and risk-free asset. From the experiment, we can see that the genetic algorithm outperforms the other strategies. Thus, we can conclude that genetic algorithm can be used successfully to the technical trading rule.

• 품질경영학회지
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• 제44권3호
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• pp.649-660
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• 2016

Purpose: The purpose of this study is to find empirical studies the causal relationship among positive defining representation, negative defining representation, surface acting, deep acting, dissatisfaction of job, and intention of job transfer. Methods: The collected data through the survey by customer satisfaction team of "A" company call center. Results: The result of this study is that emotion and behavior norm influence on deep act of expressing both positive impacts of the employees. Conclusion: Feelings of corporate representation norm is psychologically of employees that because it affects even also must facilitate the surface acts and internal acts of employees and job dissatisfaction at the same time due to emotional dissonance and emotional exhaustion and turnover are hidden on the back emotion norms in the contact department, it should be aware of the need to address the emotional problems. Companies need to develop a variety of programs with attention to addressing the emotional difficulties experienced by workers.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2003년도 춘계학술대회
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• pp.319-328
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• 2003

With the proliferation of WWW, providing more intelligence to Web sites has become a major concern in e-business industry. In recent days, this trend is more accelerated by prosperity of CRM (Customer Relationship Management) in terms of various aspects such as product recommendation, self after service, etc. To accomplish this goal, many e-companies are eager to embed web enabled rule-based system, that is, expert systems into their Web sites and several well-known commercial tools are already available in the market. Most of those tools are developed based on CGI so far but CGI based systems inherently suffer over-burden problem when there are too many service demands at the same time due to the nature of CGI. To overcome this limitation of the existing CGI based expert systems, we propose a new form of Web-enabled expert system using hyperlink-based inference mechanism. In terms of burden to Web server, our approach is proven to outperform CGI based approach theoretically and also empirically. For practical purpose, our this approach is implemented in a software system, WeBIS and a graphic rule editing methodology, Expert Diagram is incorporated into the system to facilitates rule generation and maintenance. WeBIS is now successfully operated for financial consulting in the web site of a leading financial consulting company in Korea.

• Asia pacific journal of information systems
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• 제34권2호
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• pp.565-584
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• 2024

More than 1 million automobile insurance repairs occur per year globally, and the related repair costs add up to astronomical amounts. Insurance companies and repair shops are spending a great deal of money on manpower every year to claim reasonable insurance repair costs. For this reason, promptly predicting insurance claims for vehicles in accidents can help reduce social costs related to auto insurance. Several recent studies have been conducted in auto insurance repair prediction using variables such as photos of vehicle damage. We propose a new model that reflects auto insurance repair characteristics to predict auto insurance repair claims through an association rule method that combines gradient descent and location information. This method searches for the appropriate number of rules by applying the gradient descent method to results generated by association rules and eventually extracting main rules with a distance filter that reflects automobile part location information to find items suitable for insurance repair claims. According to our results, predictive performance could be improved by applying the rule set extracted by the proposed method. Therefore, a model combining the gradient descent method and a location-based association rule method is suitable for predicting auto insurance repair claims.

• 지능정보연구
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• 제17권4호
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• pp.109-130
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• 2011

연속발생 데이터는 데이터의 원천으로부터 데이터 저장소로 연속적으로 축적이 되는 데이터를 말한다. 이렇게 축적된 데이터의 크기는 시간이 지남에 따라 점점 커진다. 또한 이러한 대용량 데이터에서 정보를 추출하기 위해서는 저장공간, 시간, 그리고 많은 자원이 필요하다. 이러한 연속발생 데이터의 특성은 시간이 지남에 따라 축적된 대용량 데이터의 이용을 어렵고 고비용이 되게 한다. 만약 정보나 패턴을 추출할 때 누적된 전체 발생 데이터 중에서 최근의 일부만 사용 한다면 적은 일부 표본의 사용의 문제로 인하여 전체 데이터 사용에서 발견될 수 있는 유용한 정보의 유실이 있을 수 있다. 이러한 문제점을 해결하기 위해서 본 연구는 연속발생 데이터를 발생 시점에서 계속 모으기 보다 이러한 발생되는 데이터에서 규칙을 추출하여 효율적으로 지식을 관리하고자 한다. 이 방법은 기존의 방법에 비하여 적은 양의 데이터 저장공간을 필요로 한다. 또한 이렇게 축적된 규칙집합은 미래에 예측을 위해서 언제든 실시간 예측을 할 수 있게 준비가 된다. 여러 예측 모델을 결합시키는 방법인 앙상블 이론에 의하면 본 연구가 제시하는 데로 체계적으로 규칙집합을 시간에 따라 융합시킬 경우 더 나은 예측 성과가 가능하다. 본 연구는 주식시장의 변동성을 예측하기 위하여 주식시장 데이터를 사용하였다. 본 연구는 이 데이터를 이용해 본 연구가 제시하는 방법과 기존의 방법의 예측 정확도를 비교 하였다.

• Journal of information and communication convergence engineering
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• 제1권3호
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• pp.150-156
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• 2003

This paper explored the use of an algorithm for the data mining and method in handling missing data which had generated enhanced association patterns observed using the data illustrated here. The evaluations showed that more association patterns are generated in the second analysis which suggests more meaningful rules than in the first situation. It showed that the model offer more precise and important association rules that is more valuable when applied for business decision making. With the discovery of accurate association rules or business patterns, strategies could be efficiently planned out and implemented to improve marketing schemes. This investigation gives rise to a number of interesting issues that could be explored further like the effect of outliers and missing data for detecting fraud and devious database entries.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.192-197
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• 2001

A recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly nowadays so the concerns about various recommendation procedures are increasing. We introduce a recommendation methodology by which e-commerce sites suggest new products of services to their customers. The suggested methodology is based on web log analysis, product taxonomy, and association rule mining. A product recommendation system is developed based on our suggested methodology and applied to a Korean internet shopping mall. The validity of our recommendation system is discussed with the analysis of a real internet shopping mall case.

• Management Science and Financial Engineering
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• 제2권1호
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• pp.147-176
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• 1996

The main objective of this study is to uncover the differences in the programming behavior between methodology creators and methodology users. We conducted an experiment with methodology creators who have invented one of the major object-oriented methodologies and with professional programmers who have used the same methodology for their software-development projects. In order to explain the difference between the two groups, we propose a theoretical framework that views programming as search in four problem spaces: representation, rule, instance and paradigm spaces. The main problem spaces in programming are the representation and rule spaces, while the paradigm and instance spaces are the supporting spaces. The results of the experiment showed that the methodology creators mostly adopted the paradigm space as their supporting space, while the methodology users chose the instance space as their supporting space. This difference in terms of the supporting space leads to different search behaviors in the main problem spaces, which in turn resulted in different final programs and performance.