• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.128-132
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• 2001

In this paper, we evaluate and contrast four neural network rule extraction approaches for credit scoring. Experiments are carried our on three real life credit scoring data sets. Both the continuous and the discretised versions of all data sets are analysed The rule extraction algorithms, Neurolonear, Neurorule. Trepan and Nefclass, have different characteristics, with respect to their perception of the neural network and their way of representing the generated rules or knowledge. It is shown that Neurolinear, Neurorule and Trepan are able to extract very concise rule sets or trees with a high predictive accuracy when compared to classical decision tree(rule) induction algorithms like C4.5(rules). Especially Neurorule extracted easy to understand and powerful propositional if -then rules for all discretised data sets. Hence, the Neurorule algorithm may offer a viable alternative for rule generation and knowledge discovery in the domain of credit scoring.

• 전기학회논문지
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• 제57권11호
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• pp.2108-2116
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• 2008

The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2000년도 추계학술대회 논문집
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• pp.132-135
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• 2000

A process design expert system for rotationally symmetric deep drawing products has been developed The application of the expert system to non-axisymmetric components, however, has not been reported yet. Thus, in this present study, the expert system for non-axisymmetric deep drawing products with elliptical shape was constructed by using process sequence design. The system developed in this work consists of four modules. The first one is a recognition of shape module to recognize non-axisymmetric products. The second one is three dimensional (3-D) modeling module to calculate the surface area for non-axisymmetric products. The third one is a blank design module to create an oval-shaped blank with the identical surface area. The forth one is a process planning module based on the production rules that play the best important role in an expert system for manufacturing. The production rules are generated and upgraded by interviewing with field engineers.

• 통상정보연구
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• 제12권4호
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• pp.429-453
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• 2010

The purpose of this paper is to examine the significances of choosing the place of arbitration, the principles of fixing the place, which the major international arbitration institutions(including the ICC, LCIA, AAA, CIETAC and so on) have in their arbitration rules, and the methods of drafting the place of arbitration in arbitration agreements. When the contract parties have agreed on the place of the arbitration, the institutions have no role regarding the selection of the place of arbitration. But the parties have not agreed on the place of arbitration, it is fixed by the rules of selected institution, by considering the lists of criteria including local laws, N.Y. Convention, neutrality, convenience and so on. This author suggested four alternatives on how to designate the place of arbitration, and advantages and disadvantages of each one: the place of claimant, the place of respondent, the place agreed on in advance in Bilateral Agreement between two Arbitration Institutions established in two countries or the third country. In conclusion, the decision of all elements in the international contract is greatly influenced by the power of negotiation, and the place of arbitration in arbitration agreement has a lot of influential significances on both parties when resolving the disputes. So it is advisable for the parties to fix the place according to the global standard(the place of respondent), the arbitration rules of major international arbitration institutes and the result of the negotiation between parties.

• 대한가정학회지
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• 제34권4호
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• pp.49-62
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• 1996

This paper is to examine the development of Korean young children's judgement on moral and conventional rules. The subjects are 120 children, 30 each at four age levels; age 3(2.8-3.5), age 4(3.7-4.4), age 5(4.8-5.5), and age 6(5.7-6.5) in a day care center in Taegu. Results are summarized as follows: 1. In terms of nonpermissibility, there was a significant difference in regard to the moral and the conventional rule transgression between the group of age 3 years and three groups of ages 4, 5, and 6. 2. In terms of seriousness, there was a significant difference according to domain. Three groups ages 4, 5 and 6 years evaluate moral transgressions to be more serious than conventional transgressions. 3. In terms of rule contingency and generalizability, there was a significant difference in regard to the moral and conventional transgression between the group of age 3 years and three groups of ages 4, 5 and 6. 4. In terms of punishment, there was significant difference according to domain. Three groups of age 4, 5, and 6 years evaluate moral transgressions to be more punishable than conventional transgressions. 5. Children of age 5 with institutional experience do not make a distinction between moral and conventional rules on punishment criterion.

• 한국정밀공학회지
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• 제16권11호
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• pp.174-182
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• 1999

This paper describes a research work of developing a computer-aided design of product with bending constraints and piercing for progressive working. 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 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, bending sequence, availability of press. Strip layout drawing generated by piercing with punch profiles divide into automatically for external area is shown into graphic forms, including bending sequences for the product with piercing and bending constraints. Results obtained using the modules enable the designer and manufacturer of piercing and bending dies to be more efficient in this field.

• 한국항해항만학회지
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• 제32권7호
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• pp.529-535
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• 2008

As a method to reduce collision accidents of ships at sea, this paper suggests an expert system for collision avoidance and navigation (hereafter "ESCAN"). The ESCAN is designed and developed by using the theory and technology of expert system and based on the information provided by AIS and RADAR/ARPA system. In this paper the ESCAN is composed of four(4) components; Facts/Data Base in charge of preserving data from navigational equipment, Knowledge Base storing production rules of the ESCAN, Inference Engine deciding which rules are satisfied by facts or objects, User System Interface for communication between users and ESCAN. The ESCAN has the function of real--time analysis and judgment of various encountering situations between own ship and targets, and is to provide navigators with appropriate plans of collision avoidance and additional advice and recommendation This paper, as a basic study, is to introduce the basic design and function of ESCAN.

• 인지과학
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• 제4권1호
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• pp.5-23
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• 1993

홀랜드 등(1986)은 인공 지능 시스템 내에서 경쟁한는 여러 규칙들 사이에 그 결과를 결정할 수 있는 주요 요소로서 대응성,강도,명세성,지원도의 네 가지를 든 바 있다.이러한 요소들은 그 시스템이 주어진 환경내에서 어떤 대상들을 인지함에 있어 일정한 규칙을 선호할 수 있는 기준으로 작용 할 수도 있다. 본 논문의 목적은 이러한 기준 중 특히 강도의 개념을 통계학에서의 우도의 개념으로써 해명하는 일이다.강도의 개념 자체나 그 활용 방식들이 우도의 개념으로써 좀더 충분히 이해될수 있다고 생각한다.학문사의 개념들은 그것을 여타 개념들과 연결하면 연결할수록 좀더 풍부해질수 있다.

• 대한전기학회논문지
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• 제43권7호
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• pp.1193-1205
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• 1994

This paper presents a fuzzy system that adaptively classifies subimages to four classes according to image activity distribution. In adaptive transform image coding, subimage classification improves the compression performance by assigning different bit maps to different classes. A conventional classification method sorts subimages by their AC energy and divides them to classes with equal number of subimages. The fuzzy system provides more flexible classification to natural images with various distribution of image details than does the conventional method. Clustering of training data in the input-output product space generated the fuzzy rules for subimage classification. The fuzzy system of small number of fuzzy rules successfully classified subimages to improve the compression performance of the transform image coding without sorting of AC energies.