• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.1199-1203
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• 2005

Combined sewer overflows(CSOs) are themselves a significant source of water pollution. Therefore, the control of urban drainage for CSOs reduction and receiving water quality protection is needed. Examples in combined sewer systems include downstream storage facilities that detain runoff during periods of high flow and allow the detained water to be conveyed by an interceptor sewer to a centralized treatment plant during periods of low flow. The design of such facilities as stormwater detention storage is highly dependant on the temporal variability of storage capacity available(which is influenced by the duration of interevent dry periods) as well as the infiltration capacity of soil and recovery of depression storage. As a result, a contiunous approach is required to adequately size such facilities. This study for the continuous long-term analysis of urban dranage system used analytical Probabilistic model based on derived probability distribution theory. As an alternative to the modeling of urban drainage system for planning or screening level analysis of runoff control alternatives, this model have evolved that offer much ease and flexibility in terms of computation while considering long-term meteorology. This study presented rainfall and runoff characteristics or the subject area using analytical Probabilistic model. Runoff characteristics manifasted the unique characteristics of the subject area with the infiltration capacity of soil and recovery of depression storage and was examined appropriately by sensitivity analysis. This study presented the average annual COSs and number of COSs when the interceptor capacity is in the range 3xDWF(dry weather flow). Also, calculated the average annual mass of pollutant lost in CSOs using Event Mean Concentration. Finally, this study presented a dicision of storage volume for CSOs reduction and water quality protection.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.12
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• pp.1476-1484
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• 1999

TV 방송 분야에서 다양한 정보와 상호 작용성을 제공하기 위해서 최근 기존 방송 내용인 A/V 스트림 외 부가정보 방송이 시도되고 있다. 데이타 방송에 대한 기존 연구는 대부분 고정된 내용의 데이타를 방송하는 환경을 가정하고 있어서 그 결과가 방송 내용의 변화가 많은 환경에 부적합하다. 본 논문에서는 데이타에 대한 접근이 반복되지 않을 가능성이 높고 사용자 접근 확률을 예상하기 어려운 상황에서 응답 시간을 개선하는 방안으로 수신 데이타를 무조건 캐쉬에 반입하고 교체가 필요한 경우 다음 방송 시각이 가장 가까운 페이지를 축출하는 사용자 단말 시스템에서의 캐슁 정책을 제안하였다. 제안된 캐쉬 관리 정책은 평균적인 캐쉬 접근 실패 비용을 줄임으로써 사용자 응답 시간을 개선하며, 서로 다른 스케줄링 기법을 사용하는 다양한 방송 제공자가 공존하는 환경에서 보편적으로 효과를 가져올 수 있다.Abstract Recently, many television broadcasters have tried to disseminate digital multimedia data in addition to the traditional content (audio-visual stream). The broadcast data need to be cached by a client system, to provide a reasonable response time for a user request. Previous studies assumed the dissemination of a fixed set of items, and the results are not suitable when broadcast items are frequently changed. In this paper, we propose a novel cache management scheme that chooses the replacement victim based on the remaining time to the next broadcast instance. The proposed scheme reduces response time, where it is hard to predict the probability distribution of user accesses. The caching policy we present here significantly reduces expected response time by minimizing expected cache miss penalty, and can be applied without difficulty to different scheduling algorithms.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.27-38
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• 2005

Recently, the rapidly development of computing environments and the spread of Internet make possible to obtain and use of information easily. Immediately, by opposition function the Hacker's unlawful intrusion and threats rise for network environments as time goes on. Specially, the internet consists of Unix and TCP/IP had many vulnerability. the security techniques of authentication and access controls cannot adequate to solve security problem, thus IDS developed with 2nd defence line. In this paper, intrusion detection method using Bayesian Networks estimated probability values of behavior contexts based on Bayes theory. The contexts of behaviors or events represents Bayesian Networks of graphic types. We profiled concisely normal behaviors using behavior context. And this method be able to detect new intrusions or modificated intrusions. We had simulation using DARPA 2000 Intrusion Data.

• Journal of the Korean Society of Safety
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• v.28 no.5
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• pp.41-48
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• 2013

This paper is intended to provide the background information and justification for Korean highway bridge design code(limit state design)(2012). Limit state design method calculates reliability index and probability of failure through the analysis of the reliability of the experimental database. It has become possible to perform the economical and consistent design by evaluating the safety of a structure quantitatively. In this paper, we used the design specifications of RC slab bridge of superstructure form of Road Design Manual in Part 5 bridge built in highway bridge. This study conducted structural analysis using the method of frame structure theory, design and analysis of bridge by limit state design method, the design code including various standards and Load model applied Korean highway bridge design code limit state design(KHBDC;2012). As a result, it analyzed the effect of safety through comparison. Showing effect of improvement the safety factor and comparing the value of the result, it is determined to be capable of economical design and safety. Furthermore, limit state design method was able to determine many redundant force of cross-section compared with existing design method. It is determined that it can reduce the overall amount because of the reduction of the cross-section and girder depth.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.191-199
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• 1996

In this paper, We assume that the closed computer system model composed of multiprocessor system is analyzed by BCMP queueing network theory with priority levels. In this system that contains terminals, processors and I/O devices, We show maximum throughput and the number of active terminals in the optimum multiprogramming levels. It is compared the performance with the other. In the result, it is obtained the optimum number of processors and active terminals. Therefore, the system model consisted of the optimum number of processor and multiprogramming level m is analyzed by the servers with a priority level. Each server is applied to the type of server which is characterized terminal, processor I/O device etc.. This model is analyzed by the server with a probability ditribution. Ideal state is proposed by the modeling for priority levels. Finally, we try to increase the performance in overload system.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.681-687
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• 2001

In order to raise a class discrimination power by combining multiple classifiers under the Bayesian decision theory, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables obtained from training data samples should be minimized. Wang and Wong proposed a tree dependence first-order approximation scheme of a high order probability distribution composed of the class and multiple feature pattern variables for minimizing the upper bound of the Bayes error rate. This paper presents an extended high order product approximation scheme dealing with higher order dependency more than the first-order tree dependence, based on the minimization of the upper bound of the Bayes error rate. Multiple recognizers for unconstrained handwritten numerals from CENPARMI were combined by the proposed approximation scheme using the Bayesian formalism, and the high recognition rates were obtained by them.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.10
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• pp.575-587
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• 2004

In this paper. a new Fast Expectation-Maximization algorithm(FEM) is proposed. Firstly the K-means algorithm is modified to reduce the number of iterations for finding the initial values that are used as the initial values in EM process. Conventionally the Initial values in K-means clustering are chosen randomly. which sometimes forces the process of clustering converge to some undesired center points. Uniform partitioning method is added to the conventional K-means to extract the proper initial points for each clusters. Secondly the effect of posterior probability is emphasized such that the application of Maximum Likelihood Posterior(MLP) yields fast convergence. The proposed FEM strengthens the characteristics of conventional EM by reinforcing the speed of convergence. The superiority of FEM is demonstrated in experimental results by presenting the improvement results of EM and accelerating the speed of convergence in parameter estimation procedures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3675-3681
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• 2009

One of the most important factors in the lifetime of WSN(Wireless Sensor Network) is the limited resources and static control problem of the sensor nodes. In order to achieve energy efficiency and network utilities, sensor nodes can be well organized into one cluster and selected head node and normal node by dynamic conditions. Various clustering algorithms have been proposed as an efficient way to organize method based on LEACH algorithm. In this paper, we propose an efficient clustering algorithm using information entropy theory based on LEACH algorithm, which is able to recognize environmental differences according to changes from data of sensor nodes. To measure and analyze the changes of clusters, we simply compute the entropy of sensor data and applied it to probability based clustering algorithm. In experiments, we simulate the proposed method and LEACH algorithm. We have shown that our data balanced and energy efficient scheme, has high energy efficiency and network lifetime in two conditions.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.209-222
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• 1997

In this paper, we study a bargaining order problem where one buyer sequentially bargains with two sellers whose reservation prices are unknown to the buyer but correlated. Our main question is who the buyer should bargain first with to maximize his expected payoff. This type of problem is widely applicable to business and political situations where one party negotiates with multiple parties sequentially. One of the most important element in a sequential bargaining is "linkage effect" which exists when the aggreement of the previous bargaining affects the outcome of the following bargaining. To examine "linkage effect", we assume that the sellers'objects are similar so that the sellers' reservation prices are correlated. In addition, to consider incomplete information aspect regarding reservation prices, it is assumed that the sellers' reservation prices are unknown to the buyer. That is, we deal with one sided incomplete information case. In our model, there are two stages in each of which the buyer meets one seller. Since we are concerned with the bargaining order, we consider two different bargaining orders. Using game theory, we find a perfect Bayesian equilibrium and compute the buyer's expected payoff for each bargaining order. Finally we identify the advantageous bargaining order for the buyer by comparing the expected payoffs obtained under two different bargaining orders. Our results are as follows: the advantageous bargaining order depends on the prior probability of the seller type. However, in general, the buyer should bargain first with the seller whose object is less valuable to the buyer. The basic reason for our result is that the buyer wants to experiment in the first stage to find out the sellers' reservation prices and in doing so, to minimize the experimental cost and maximize potential gain in case of negotiation failure in the first stage. in the first stage.

• Journal of Broadcast Engineering
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• v.15 no.6
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• pp.853-867
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• 2010

Recently, thumbnail techniques are required to index a high-performance video at digital convergence-based multimedia service like IPTV and DMB. Therefore a thumbnail extraction method in H.264/AVC bitstreams has been proposed. However, thumbnail quality deterioration problem at converting the general equation of spatial domain to frequency domain which is generated by not considering about H.264/AVC transform and quantization processing and rounding-off operation in intra prediction. In this paper, we propose a new thumbnail extraction method in H.264/AVC bitstreams. The proposed scheme is based on H.264/AVC core-transform for a thumbnail extraction in frequency domain, and probability theory, intra rounding-off error compensation. Through the implementation and performance evaluation, the subjective quality difference between the output of our scheme and the output of reference decoder is negligible and better than the conventional method, and moreover PSNR gain by up to 8.66 dB.