• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.1
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• pp.77-82
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• 2002

IDS has been studied mainly in the field of the detection decision and collecting of audit data. The detection decision should decide whether successive behaviors are intrusions or not , the collecting of audit data needs ability that collects precisely data for intrusion decision. Artificial methods such as rule based system and neural network are recently introduced in order to solve this problem. However, these methods have simple host structures and defects that can't detect changed new intrusion patterns. So, we propose the method using data mining that can retrieve and estimate the patterns and retrieval of user's behavior in the distributed different hosts.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1939-1943
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• 1991

Path planning is an important task for optimal motion of a robot in structured or unstructured environment. The goal of this paper is to plan the shortest collision-free path in 3D, when a robot is navigated to pick up some tools or to repair some parts from various locations. To accomplish the goal of this paper, the Path Coordinator is proposed to have the capabilities of an obstacle avoidance strategy[3] and a traveling salesman problem strategy(TSP)[23]. The obstacle avoidance strategy is to plan the shortest collision-free path between each pair of n locations in 2D or in 3D. The TSP strategy is to compute a minimal system cost of a tour that is defined as a closed path navigating each location exactly once. The TSP strategy can be implemented by the Neural Network. The obstacle avoidance strategy in 2D can be implemented by the VGraph Algorithm. However, the VGraph Algorithm is not useful in 3D, because it can't compute the global optimality in 3D. Thus, the Path Coordinator is proposed to solve this problem, having the capabilities of selecting the optimal edges by the modified Genetic Algorithm[21] and computing the optimal nodes along the optimal edges by the Recursive Compensation Algorithm[5].

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.279-286
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• 2005

The objective of this study is to suggest a credit scoring model of a capital company's customers by integration of multiple classifiers using genetic algorithm. For this purpose , an integrated model is derived in two phases. In first phase, three types of classifiers MLP (Multi-Layered Perceptron), RBF (Radial Basis Function) and linear models - are trained, in which each type has three ones respectively so htat we have nine classifiers totally. In second phase, genetic algorithm is applied twice for integration of classifiers. That is, after htree models are derived from each group, a final one is from these three, In result, our suggested model shows a superior accuracy to any single ones.

• Composites Research
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• v.13 no.1
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• pp.40-49
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• 2000

The objective of this research is to develop the impact monitoring techniques providing impact identification and damage diagnostics of smart composite laminates susceptible to impacts. This can be implemented simultaneously by using the acoustic waves by the impact loads and the acoustic emission waves from damage. In the previous research, we have discussed the impact location detection process in which impact generated acoustic waves are detected by PZT using the improved neural network paradigm. This paper describes the implementation of time-frequency analysis such as the Short-Time Fourier Transform (STFT) and the Wavelet Transform (WT) on the determination of the occurrence and the estimation of damage.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.427-432
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• 2003

EEG is an electrical signal, which occurs during information processing in the brain. These EEG signals have been used clinically, but nowadays we ate mainly studying Brain-Computer Interface (BCI) such as interfacing with a computer through the EEG, controlling the machine through the EEG. The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. This research makes the controlling system of directions with the artifact that are generated from the subject s will, for the purpose of controlling the machine correctly and reliably We made the system like this. First, we select the particular artifact among the EEG mixed with artifact, then, recognize and classify the signals pattern, then, change the signals to general signals that can be used by the controlling system of directions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.51-59
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• 2005

To improve the quality of bus service, providing bus ravel time information to passenger through station screen. Generally, bus travel time information predict by using previous bus data such as neural network, Kalman filtering, and moving average algorithms. However, when they got a difficulty about bus travel time information because of the missing previous bus data, they use pattern data. Generally, nevertheless the difference of range is big. Hence in this research to calculate the bus travel time information when the bus information is missed, use queue detector's data which set up in link. The application of several factors which influence in bus link travel time, we used CORSIM Version 5.1 simulation package.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.307-310
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• 2003

In the maritime container terminal, LMTT(Linear Motor-based Transfer Technology) is horizontal transfer system for the yard automation, which has been proposed to take the place of AGV(Automated Guided Vehicle). The system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car (mover). Because of large variant of mover's weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's trouble etc., LMCPS (Linear Motor Conveyance Positioning System) is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the soft-computing method of a multi-step prediction control for LMCPS using DR-FNN (Dynamically-constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi-step prediction. Consequently, the system has an ability to adapt for external disturbance, cogging force, force ripple, and sudden changes of itself.

• Journal of the Korean GEO-environmental Society
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• v.15 no.12
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• pp.79-85
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• 2014

To analyze the landslide susceptibility in Baekdu mountain area in china, we get two susceptibility maps using AcrView software through weighted overlay GIS (Geographic Information System) method in this paper. To assess the landslide susceptibility, five factors which affect the landslide occurrence were selected as: slope, aspect, soil type, geological type, and land use. The weight value and rating value of each factor were calculated by the two different methods of AHP (Analytic Hierarchy Process) and ANN (Artificial Neural Network). Then, the weight and rating value was used to obtain the susceptibility maps. Finally, the susceptibility map shows that the very dangerous areas (0.9 or higher) were mainly distributed in the mountainous areas around JiAnShi, LinJiangShi, and HeLongShi near the china-north Korea border and in the mountainous area between the WangQingXian and AnTuXian. From the contrast two susceptibility map, we also Knew that The accuracy of landslide susceptibility map drew by ANN method was better than AHP method.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.91-96
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• 2008

증강현실(Augmented Reality)을 위한 효과적인 비전 기반 인터페이스 개발은 꾸준히 진행되어 왔으나, 대부분 환경적 제약을 받거나, 특수한 장비 혹은 복잡한 모델을 요구한다. 예를 들어, 마커를 이용하면 구현 상의 편의성과 정확성을 보장하지만, 일반적으로 마커는 환경과 대비되는 모양을 가지기 때문에, 사용자에게 거부감을 줄 수 있으며 무엇보다 복잡한 인터랙션에는 적용되기 힘들다. 한편, 손동작을 이용할 경우, 자연스럽고 다양한 인터랙션을 수행할 수 있지만, 색을 이용한 손동작 인식은 복잡한 환경에서 인식률이 크게 저하되고, 3 차원 모델 기반의 손동작 인식은 많은 연산량을 필요로 한다는 문제점을 가진다. 이로 인해 지금까지 제안된 방법을 증강현실 시스템에 적용하는 데는 한계가 있다. 본 논문에서는 기본적으로 손동작을 이용한 인터페이스를 제안하는데, 손동작 인식을 위한 알고리즘을 효율적으로 개선함으로써, 복잡한 환경에서 적은 연산량으로 자연스러운 인터랙션을 제공하고자 한다. 제안방법은 손목에 컬러 밴드를 착용하고, 색 정보를 이용하여 손을 포함하는 최소 영역을 용이하게 검출함으로써, 손 동작 인식률이 좋아지도록 하였다. 제안된 인터페이스는 손의 자연스러운 움직임을 감지해서 손의 모양과 동작에 따라서 가상의 물체를 자연스럽게 제어할 수 있도록 해 준다. 예를 들어, 손이 지정한 위치에 가상의 물체를 나타내고, 가상의 물체를 잡고 다양한 조작을 하는 등의 제어를 할 수 있다. 다양한 환경에서의 실험 및 사용자 평가를 통해 제안된 인터페이스의 유용성을 검증하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.