• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.41-58
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• 1998

본 연구는 여러 가지 Data Mining 기법들로부터 도출된 지식과 AHP를 이용하여 도출된 전문가의 지식을 사용된 정보의 특성에 따라 조사하고, 이러한 각각의 지식들을 중심으로 부도예측 모형을 설계한 후, 각 모형의 특성 및 부도예측력에 대한 실증적 비교연구에 그 목적을 두고 있다. 사용된 Data Mining 기법들은 통계적 다중판별분석 모형, ID3 모형, 인공신경망 모형이며, 전문가 지식의 추출은 AHP를 사용하여 45명의 전문가로부터 부도와 관련하여 인터뷰 및 설문조사를 실시하였다. 특히 부도예측에 사용된 변수의 특성을 정량적 재무정보와 정성적 비재무정보로 나누어서 각 모형의 특성을 비교연구하였다. 연구결과 부도예측시 정성적정보의 중요성을 확인하였으며, 전문가의 지식을 기반으로한 AHP 모형이 위험예측모형으로 사용될 수 있음을 실증적으로 보여주었다.

• The Journal of Information Systems
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• v.15 no.4
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• pp.211-224
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• 2006

This study investigates the application of data mining techniques such as artificial neural networks, rough sets, and induction teaming to the intrusion detection systems. To maximize the effectiveness of data mining for intrusion detection systems, we introduced the asymmetric costs with false positive errors and false negative errors. And we present a method for intrusion detection systems to utilize the asymmetric costs of errors in data mining. The results of our empirical experiment show our intrusion detection model provides high accuracy in intrusion detection. In addition the approach using the asymmetric costs of errors in rough sets and neural networks is effective according to the change of threshold value. We found the threshold has most important role of intrusion detection model for decreasing the costs, which result from false negative errors.

• The Journal of Society for e-Business Studies
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• v.5 no.2
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• pp.49-60
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• 2000

A major topic in the field of network and telecommunications is doing business on the Word Wide Web(WWW), which is called Electronic Commerce(EC). Another major topic is blending Artificial Intelligent techniques with the WWW. To provide customer with the information of goods in suit with a customer liking, we propose multi agent system which is consist of customer agent and search agent etc. Also we use case-based reasoning for customer liking searching the information of goods and training through the reuse. This reuse make efficient management of information and a process of operation. In the relation between customer and goods, if there are some goods which is not search from case-base reasoning, we calculate satisfaction function for customer purchase goods. And to provide customer with the information of goods in the first of satisfaction function, This EC system can always provide the information of goods which is satisfied to customer.

• Nuclear Engineering and Technology
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• v.25 no.1
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• pp.1-7
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• 1993

This paper describes a prototype expert system to aid operation in rod consolidation process. The knowledgebase is composed of three database groups and 60 rules with production, and object oriented techniques that correlates database groups. The expert system is designed to track the transitions of nuclear materials through the operation areas of the rod consolidation process, to diagnose current status in any operating conditions, normal and off-normal, and to advise operators to properly recover off-normality. The expert system can give efficient management of nuclear material accountability and process operation in the rod consolidation.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.759-766
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• 2002

In the paper the authors describe the development of ITIS(Intelligent Tunneling Information System) for the Purpose of applying the 3D visualization technique, GIS, AI(Artificial Intelligence) to tunnel design and construction. VR(Virtual Reality) and 3D visualization techniques are applied in order to develope the 3D model of characteristics and structures of ground and rock mass. Database for all the materials related to site investigation and tunnel construction is developed using GIS technique. AI technique such as fuzzy theory and neural network is applied to predict ground settlement, decide tunnel support method and estimate ground and rock mass properties according to tunnel excavation steps. ITIS can help to inform various necessary tunnel information to engineers quickly and manage tunnel using acquired information based on D/B.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.131-140
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• 1994

Dynamics of AUV has heavy nonlinearities and many unknown parameters due to its bluff shape and low cruising speed. Intelligent algorithms, therefore, are required to overcome these nonlinearities and unknown system dynamics. Several identification techniques have been suggested for the application of control of underwater vehicles during last decade. This paper applies the neural network to identification and motion control problem of AUVs. Nonlinear dynamic systems of an AUV are identified using feedforward neural network. Simulation results show that the learned neural network can generate the motion of AUV. This paper, also, suggest an adaptive control scheme up-dates the controller weights with reference model and feedforward neural network using error back propagation.

• IE interfaces
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• v.12 no.2
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• pp.294-304
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• 1999

This paper describes the theoretical background and detailed structure of Navi-HEGS (Navigation system Human factors Evaluation and Guideline System) which has been developed for the human factors and HMI(Human-Machine Interface) researches for a CNS (Car Navigation System) and a digital map. Navi-HEGS is and integrated system that consists of a digital map UIMS(User Interface Management System), a CNS simulator, various evaluation tools, and a design guideline system. If Navi-HEGS is properly applied and utilized, it is possible to extract the substantial users requirements and preferences of a CNS and a digital map and then, these requirements can be simulated and evaluated with various human factors evaluation techniques. Applications of Navi-HEGS can improve the CNS usability, drivers safety and performance that directly affect the success of ITS(Intelligent Transport System). Also, results can be used as the basic data to establish the standards and design guidelines for the driver-centered CNS design.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1102-1104
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• 2003

The human beings have increased concern about energy saving and alternative energy. The Power demand has increased the growth of industry and the improvement of lift. We have to explore alternate energy sources and utilize effectively domestic resources. The lighting equipments developed energy saving by using an electric ballast. The load installation should be promoted to rational power management according to the network, intelligent and high-function. Therefore, this paper has studied the method of energy saving and consulting.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.535-544
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• 2017

In-flight aircraft engine performance estimation is one of the key techniques for advanced intelligent engine control and in-flight fault detection, isolation and accommodation. This paper detailed the current performance degradation estimation methods, and an improved hybrid Kalman filter via velocity-based LPV (VLPV) framework for these needs is proposed in this paper. Composed of a nonlinear on-board model (NOBM) and VLPV, the filter shows a hybrid architecture. The outputs of NOBM are used for the baseline of the VLPV Kalman filter, while the system performance degradation factors on-line estimated by the measured real system output deviations are fed back to the NOBM for its updating. In addition, the setting of the process and measurement noise covariance matrices' values are also discussed. By applying it to a commercial turbofan engine, simulation results show the efficiency.