• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.597-609
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• 2008

Due to the recursive updating nature of background model, previous background modeling methods are often perturbed by crowd scenes where foreground pixels occurs more frequently than background pixels. To resolve this problem, an adaptive background modeling method, which is based on the well-known Gaussian mixture background model, is proposed. In the proposed method, the learning rate of background model is adaptively adjusted with respect to the crowdedness of the scene. Consequently, the learning process is suppressed in crowded scene to maintain proper background model. Experiments on real dataset revealed that the proposed method could perform background subtraction effectively even in crowd situation while the performance is almost the same to the previous method in normal scenes. Also, the F-measure was increased by 5-10% compared to the previous background modeling methods in the video of crowded situations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.435-438
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• 2007

움직이는 객체를 검출하기 위해서 정확한 배경을 사용하기 위해 널리 사용되는 방법으로는 가우시안 혼합 모델이다. 가우시안 혼합 모텔은 확률적 학습 방법을 사용하는데, 이 방법은 움직이는 배경일 경우와 이동하던 물체가 정지하는 경우 배경을 정확히 모델링하지 못한다. 본 논문에서는 확률적 모델링을 통해 혼잡한 배경을 모델링하고 객체의 계층적 처리를 통해 보다 정확한 배경으로 갱신할 수 있는 학습 방법을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.387-391
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• 2008

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06d
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• pp.343-347
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• 2010

TFMCC(TCP-Friendly Multicast Congestion Control)방식은 equation 기반의 멀티캐스트 혼잡 제어 메커니즘으로 TFRC(TCP-Friendly Rate Control) 프로토콜을 유니캐스트에서 멀티캐스트 도메인으로 확장한 방식이다. TFMCC 방식은 현재 무선 환경에 적용 시 유선 환경에서의 혼잡에 의한 패킷 손실뿐만 아니라, 무선 환경에서 무선 링크 에러를 네트워크의 혼잡으로 인식하며, single-rate 멀티캐스트 혼잡제어의 특성인 가장 낮은 수신단의 성능으로 전체 네트워크 전송률이 급격히 저하된다. 이에 본 논문에서는 무선 환경에서의 TFMCC의 성능 향상을 위해 네트워크의 무선 환경의 손실률과 유선 환경 손실률을 모델링하여 구분한 ARC(Analytical Rate Control)의 TCP 전송률 equation 을 TFMCC에 맞게 적용하였으며, 멀티캐스트 도메인에서 전송률 제어 시 무선 손실률을 별도로 고려하는 방식(M-ARC)을 제안하였다. 또한 성능 평가를 위해서 시뮬레이션 한 결과 무선 환경을 고려한 M-ARC(Multicast-Analytical Rate Control)가 기존의 TFMCC에 비해 더 높은 전송률을 유지함을 볼 수 있었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.283-289
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• 2005

Background Subtraction은 움직이는 물체 검출에 가장 많이 사용되는 방법 중 하나이다. 배경이 복잡하고 변화가 심한 경우, 배경을 실시간으로 얼마나 정확하게 학습하는가가 물체 검출의 정확도를 결정한다. Gaussian Mixture Model은 이러한 배경의 모델링에 가장 많이 쓰이는 방법이다. Gaussian Mixture Model은 확률적 학습 방법을 사용하는데, 이러한 방법은 물체가 자주 지나다니거나 물체가 멈춰있는 경우, 배경을 정확하게 모델링하지 못한다. 본 논문에서는 밝기 값에 대한 확률적 모델링과 밝기 값의 변화에 따른 처리를 결합하여 혼잡한 환경에서 배경을 정확하게 모델링할 수 있는 학습 방법을 제안한다.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.31-41
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• 2010

So far, many simulation researches associated with naval ships have been concentrated on the tactical systems and naval ship development. However, studies on the improvement of living conditions for naval ship crews were limited. This study targets a crew mess room which is a typical welfare facility in naval ships and existing modeling and simulation methodologies for naval tactical and strategic systems cannot be applied to the crew room simulations. This paper suggests a simulation modeling framework based on 3-dimensional discrete event simulation methodology for crew mess room congestion assessment and operation scenarios. The simulation modeling framework is verified through practical case simulations. The purpose of simulation modeling framework consists of process and system architecture for operation feasibility tests of welfare facilities in naval ships and is to guide an assessment of the operability performance of a crew mess room.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.977-987
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• 1999

The current traffic signal control systems are operated depending on the pre-planned control scheme or the selected control scheme according to a period of time. The problem with these types of traffic control systems is that they can not cope with variant traffic flows appropriately. Such a problem can be difficult to solve by using binary logic. Therefore, in this 0paper, we propose a traffic signal control system which can deal wit various traffic flows quickly and effectively. The proposed controller is operated under uncertainty and in a fuzzy environment. It show the congestion of road traffic by using fuzzy logic, and it determines the length of green signal by means of a fuzzy inference engine. It modeled using petri-net to verify its validation.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.1-6
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• 2020

This paper aims to integrate the previous models about mean object transfer latency in one framework and analyze the result through the computational experience. The analytical object transfer latency model assumes the multiple packet losses and the Internet of Things(IoT) environment including multi-hop wireless network, where fast re-transmission is not possible due to small window. The model also considers the initial congestion window size and the multiple packet loss in one congestion window. Performance evaluation shows that the lower and upper bounds of the mean object transfer latency are almost the same when both transfer object size and packet loss rate are small. However, as packet loss rate increases, the size of the initial congestion window and the round-trip time affect the upper and lower bounds of the mean object transfer latency.

• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.1
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• pp.10-18
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• 2007

In this paper, we extend the multiple time scale control framework to window-based congestion control, in particular, TCP. This is performed by interfacing TCP with a large time scale control module which adjusts the aggressiveness of bandwidth consumption behavior exhibited by TCP as a function of "large time scale" network state. i.e., conformation that exceeds the horizon of the feedback loop as determined by RTT. Performance evaluation of multiple time scale TCP is facilitated by a simulation bench-mark environment which is based on physical modeling of self-similar traffic. If source traffic is not extended exceeding, when RTT is 450ms, in self similar burst environment, performance gain of TCP-SSC is up to 45% for ${\alpha}$=1.05. However, its is acquired only 20% performance gain for ${\alpha}$=1.95 relatively. Therefore we showed that by TCP-MTS at large time scale into a rate-based feedback congestion control, we are able to improve two times performance significantly.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.87-97
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• 2007

This paper proposes drivers' rational belief formation under a bounded traffic environment. This is to escape the criticism that excessive rationality (e.g., a driver's calculating ability and memory capacity) is required of drivers. Under bounded traffic environments. drivers do not have structural knowledge of traffic conditions and others' decisions. Simulations are carried out using a program coded in C. Consequently, the author found the learning process of drivers and the value of information can be differentiated by route conditions and the characteristics of driver groups. Also, it was found that rational drivers form different beliefs about traffic conditions even though they have the same traffic environment in a bounded traffic environment.