• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2755-2762
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• 1997

Open Network Management Systems (NMS) are not sufficient to use in a large and complex computer network environment because many-year experiences and skills are requeired for using NMS. And also, customizing NMS means a difficult programming with API(Application programming Interface) supplied by NMs. The more intelligent NMS you want, the more difficult programming you must do. In this paper, we proposed an intelligent network management system suign a structure of multiple and distributed expert systems, so as to represent expertises and knowledges of network managers into rule format, maintain the knowledgesstructurally and perform the network managmenet intelligently. expert system for amanaging computer network should understand the management protocol, analyze messages from agents, take a proper action, and report the situations by pre-defined network operation principle and strategy. A multiple expert sytem is composed of monitor expert module, fault expert module and manager expert module which are controlled by enconded knowledges.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.709-716
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• 2004

This paper describes an intelligent real-time monitoring system of a semiconductor processing equipment, which determines normal or not for a wafer in processing, using multiple time-series pattern recognition. The proposed system consists of three phases, initialization, learning and real-time prediction. The initialization phase sets the weights and tile effective steps for all parameters of a monitoring equipment. The learning phase clusters time series patterns, which are producted and fathered for processing wafers by the equipment, using LBG algorithm. Each pattern has an ACI which is measured by a tester at the end of a process The real-time prediction phase corresponds a time series entered by real-time with the clustered patterns using Dynamic Time Warping, and finds the best matched pattern. Then it calculates a predicted ACI from a combination of the ACI, the difference and the weights. Finally it determines Spec in or out for the wafer. The proposed system is tested on the data acquired from etching device. The results show that the error between the estimated ACI and the actual measurement ACI is remarkably reduced according to the number of learning increases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.683-691
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• 2000

In this paper, we proposed the cell multiplexer using Hopfield neural network and the bandwidth predictor using the backpropagation neural network in order to make an accurate call setup decision. The cell multiplexer controls heterogeneous traffic and the bandwidth predictor estimates minimum bandwidth which satisfies traffic's QoS and maximizes throughput in network. Also, a novel connection admission controller decides on connection setup using the predicted bandwidth from bandwidth predictor and available bandwidth in networks. And then, we proposed a fuzzy traffic policer, when traffic sources violate the contract, takes an appropriate action and aim proved traffic shaper, which controls burstness which is one of key characteristics in multimedia traffic. We simulated the proposed controller. Simulation results show that the proposed controller outperforms existing controller.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2007.02a
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• pp.83-86
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• 2007

비디오에서의 물체 추적은 컴퓨터비젼(computer vision)의 주요 연구 분야로 지능형 로봇, 무인 감시 체제 등의 영역의 핵심 기술로 여겨지고 있다. 본 논문에서는 다중물체추적을 통해 카메라로 부터 입력된 동영상에서 특정 장소를 지나가는 사람들을 추적함으로서, 그 지역에서의 인구의 이동 패턴을 추출하고 자 한다. 물체 추적은 블롭 추적(blob tracking) 방식을 이용하며, 이를 위해 정확한 전경물체 추출, 추출된 이미지 블롭(blob)과 기존 트랙과의 연결, 새로운 물체(사람)의 등장과 퇴장등의 작업을 수행한다. 추적된 물체들이 궤적을 통해, 시간의 변화에 따른 그 지역에서의 인구의 밀도, 주 이동 경로, 방향 등의 변화를 추출한다. 이러한 통계치는 해당 지역의 개발 정책 수립 및 시장성 조사를 위한 2차 데이타로 활용할 수 있다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.11
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• pp.1070-1077
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• 2011

This paper introduces a radar signal processing technique for intelligent rear view monitoring of an automobile. The linear frequency modulation-frequency shift keying(LFM-FSK) waveform, which is the combination of frequency modulation continuous wave(FMCW) and frequency shift keying(FSK) waveform, is employed to simultaneously estimate the range, relative aspect angle, and velocity of an automobile. Hence, it can be applied to monitor the rear view of an automobile. FMCW waveform has high range resolution capability, but it produces ghost targets under a multiple target environment. In contrast, FSK waveform can provide high velocity resolution and avoids the problem of ghost targets. However, it fails to identify multiple targets along the radar's line of sight. With LFM-FSK waveform, we can estimate the ranges and velocities of multiple targets with very high resolution, which avoids the ghost target problem of an FMCW waveform. Simulation result shows that LFM-FSK wavefrom is suitable for use in the lane change assistance system for an automobile.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.1
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• pp.69-76
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• 1999

This paper proposed a design of intelligent supervisory control systems for maintenance of highway traffic information equiprrent and processing algorithm of equiprrent fault data. The fault data of highway traffic equipment are transmitted from rerrnte supervisory controller to central supervisory system by real time, the transmitted fault data are anaIyzed the characteristic using evaluation algorithm of fault data in central supervisory system. The evaluation algorithm includes a neural network and fault knowlOOge-base for processing the multi-generated fault data. For validating the evaluation algorithm of intelligent supervisory control systems, the rrethod of analysis used to the five pattern of binary signal by transmitted real time and the opTclting user-interface constructed in central supervisory system.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.789-790
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• 2008

In this paper, we introduce the multi-target-based auto surveillance algorithm. Multi-target-based surveillance system detects intrusion objects in the specified areas. The proposed algorithm can divide into two parts: i) background generation, ii) object extraction. In this paper, one of the optical flow equation methods for estimation of gradient method used to generate the background [2]. In addition, the objects and back- ground video images that are continually entering the differential extraction.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.04a
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• pp.307-310
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• 2000

인터넷 이용자들의 증가로 인해 개방된 네트워크에서의 실시간 분산환경이 고려되고 있으며, 이들 분산된 정보 및 컴퓨터 자원들에 대한 접근 요구가 증대됨에 따라 네트워크 상호연결 요구또한 커지고 있다. 이러한 정보제공에 대한 이용자들의 요구를 충족시키기위해 다중 서버를 두고 이들 서버간에 네트워크의 성능을 효율적으로 감시하고 제어하기 위한 모니터링 서버를 제공함으로써 분산 시스템의 성능 향상은 물론 이용자 입장에서의 정보에 대한 응답시간과 반환시간을 최소화하고, 시스템의 전반적인 측면에서의 작업 처리율과 자원의 활용도를 최대화 할수 있으며, 동적인 상황에 따라 스스로 판단하고 적절하게 대응할 수 잇는 지능형 이동 에이전트 템플릿을 설계구현하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.812-820
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• 2012

In this paper, multiple pedestrians tracking system using Histogram of Oriented Gradient and occlusion detection is proposed. The proposed system is applicable to Intelligent Surveillance System. First, we detect pedestrian in a image sequence using pedestrian's feature. To get pedestrian's feature, we make block-histogram using gradient's direction histogram based on HOG(Histogram of Oriented Gradient), after that a pedestrian region is classified by using Linear-SVM(Support Vector Machine) training. Next, moving objects are tracked by using position information of the classified pedestrians. And we create motion trajectory descriptor which is used for content based event retrieval. The experimental results show that the proposed method is more fast, accurate and effective than conventional methods.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.43-53
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• 2007

e-Learning education system as the next educational trend supporting remote and multimedia education. However, the students stay mainly at remote place and it is hard to certificate whether he is really studying now or not. To solve this problem, some solutions were proposed such as instructor's supervision by real time motion picture or message exchanging. Unhappily, as you can see, it needs much cost to establish the motion exchanging system and trampling upon human rights could occasion to reduce the student's will. Accordingly, we propose the new intelligent system based on face recognition to reduce the system cost. The e-Learning system running on the web page can check the student's status by motion image, and the images transfer to the instructor. For this study, 20 students and one instructor takes part in capturing and recognizing the face images. And the result produces the prevention the leave of students from lecture and improvement of attention.