• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.821-831
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• 2014

Stochastic rainfall generators or stochastic simulation have been widely employed to generate synthetic rainfall sequences which can be used in hydrologic models as inputs. The calibration of Poisson cluster stochastic rainfall generator (e.g. Modified Bartlett-Lewis Rectangular Pulse, MBLRP) is seriously affected by local minima that is usually estimated from the local optimization algorithm. In this regard, global optimization techniques such as particle swarm optimization and shuffled complex evolution algorithm have been proposed to better estimate the parameters. Although the global search algorithm is designed to avoid the local minima, reliable parameter estimation of MBLRP model is not always feasible especially in a limited parameter space. In addition, uncertainty associated with parameters in the MBLRP rainfall generator has not been properly addressed yet. In this sense, this study aims to develop and test a Bayesian model based parameter estimation method for the MBLRP rainfall generator that allow us to derive the posterior distribution of the model parameters. It was found that the HBM based MBLRP model showed better performance in terms of reproducing rainfall statistic and underlying distribution of hourly rainfall series.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.3
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• pp.292-303
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• 2001

Object color can be determined by the characteristic of scene illuminant and surface. In this paper, perceived illumination effect is extended and with the highlight analysis, hybrid approach is proposed to estimate the illuminant chromaticity. The perceived illumination approach provides a stable candidate range for the estimation of illuminant chromaticity, however, the accuracy is slightly degraded depending on the image contents. The highlight approach does not depend on the image contents and provides an accurate solution of the scene illuminant chromaticity, however, it is difficult to determine the final solution among many cross-points. These two approaches are in effect mutually compensating. The solution from perceived illumination can be used as a starting point or as base information for the highlight approach to get the final solution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.407-412
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• 2007

본 논문에서는 비선형 블라인드 채널등화기의 구현을 위하여 가우시안 가중치(gaussian weights)를 이용한 개선된 퍼지 클러스터(Modified Fuzzy C-Means with Gaussian Weights: MFCM_GW) 알고리즘을 제안한다. 제안된 알고리즘은 기존 FCM 알고리즘의 유클리디언 거리(Euclidean distance) 값 대신 Bayesian Likelihood 목적함수(fitness function)와 가우시안 가중치가 적용된 멤버쉽 매트릭스(partition matrix)를 이용하여, 비선형 채널의 출력으로 수신된 데이터들로부터 최적의 채널 출력 상태 값(optimal channel output states)들을 직접 추정한다. 이렇게 추정된 채널 출력 상태 값들로 비선형 채널의 이상적 채널 상태(desired channel states) 벡터들을 구성하고, 이를 Radial Basis Function(RBF) 등화기의 중심(center)으로 활용함으로써 송신된 데이터 심볼을 찾아낸다. 실험에서는 무작위 이진 신호에 가우시안 잡음이 추가된 데이터를 사용하여 기존의 Simplex Genetic Algorithm(GA), 하이브리드 형태의 GASA(GA merged with simulated annealing (SA)), 그리고 과거에 발표되었던 MFCM 등과 그 성능을 비교 분석하였으며, 가우시안 가중치가 적용된 MFCM_GW를 이용한 채널등화기가 상대적으로 정확도와 속도 면에서 우수함을 보였다.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2879-2881
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• 2005

본 논문에서는 뉴로-퍼지 모델의 전제부 소속함수의 새로운 학습방법을 통한 모델링 기법을 제안한다. 모델의 크기와 학습시간을 줄이는 기법으로 클러스터링 기법을 이용한 모델의 초기 파라미터 결정 방법이 있다. 이는 클러스터링 후 이들 파라미터를 다시 모델에 적용하여 모델을 학습하는 순차적 방법으로써 모델의 학습이 끝난 후의 전제부 파라미터가 클러스터링 파라미터와 연관성을 가지지 못하는 경우가 발생하였다. 또한 오차미분 기반 학습에서는 전제부 초기치가 국부적 최적해에서 벋어나지 못하는 문제점을 가지고 있다. 본 논문에서는 자율적으로 클러스터의 수를 추정하며 이들 파라미터를 최적화하며 이를 이용하여 뉴로-퍼지 모델의 학습을 실시하는 학습기법을 제안하였다. 제안된 방법에서는 기존의 오차미분 기반 학습을 클러스터링 기반 학습으로 확장하였으며 이를 이용한 모델의 성능을 기존의 연구결과와 비교하여 우수성을 보인다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.303-304
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• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.1003-1006
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• 2005

무선 센서 네트워크는 자신의 주변 환경의 변화나 상태에 대해 감지가 가능한 센서를 가지는 센서 노드로 이루어진 네트워크이다. 하지만 센서 네트워크는 무선통신을 하고 이러한 통신을 통해 데이터가 유출되거나 변형되면 심각한 문제가 발생하게 된다. 따라서 본 논문에서는 USN의 정보보호 기술에 대해 수행된 연구들과 장단점을 살펴보고 클러스터 단위로 다항식을 사전에 분배하는 방식과 자가 위치 추정을 이용한 키 분배 메커니즘 방식을 이용 센서 노드간의 공유키가 존재할 가능성을 증가시켜 통신 채널을 쉽게 형성할 수 있도록 고안하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.479-485
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• 2011

A number of case studies that find distribution of longevity population and influencing factors through the spatial data fusion using GIS techniques are growing. The majority cases of these studies are adopt census administrative boundary data for the spatial analysis. However, these methods cannot fully explain the phenomenon of longevity because there are a variety of spatial characteristics within the census administrative boundaries. Therefore, studies of spatial unit are required that realistically reflect the phenomenon of human longevity. The dasymetric mapping method enables to product of spatial unit more realistic than census administrative boundary map and statistic estimates of small area utilizing diversity spatial information. In this study, elderly population of small area has been estimated within statistically significant level that applied the vector based dasymetric mapping method. Also, the cluster analysis confirmed that the variation of local spatial relationship within census administrative boundary. The result of this study implied that the need for local-level studies of the human longevity and the validity of the dashmetric mapping techniques.

• Journal of Korea Port Economic Association
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• v.30 no.4
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• pp.91-110
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• 2014

The Korean Government is planning to build commercial districts and amenities for the major port such as Busan, Gwangyang, and Incheon in order to activate the hinterland in each port. The foreign ports in Germany, Japan and China is competing with Korean ports are developing the commercial districts and amenities of seaport hinterland in order to support urban functions. The purpose of this study is to predict the demand for the facility of commercial districts and amenities planned in the Gwangyang seaport hinterland, then to propose its utilization plans. By the demand forecasting, the districts and amenities need to be full of office, accommodation and commercial facilities, etc. In addition, the districts need to be developed gradually for the target of 2035, considering the demand growth. Leasing out the property to secular tenants, it needs to charge rent for profits of port authority. Therefore, it is required to revise the National Ports Act for the private agency to take part in building the facilities of the commercial districts and amenities.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.105-112
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• 2012

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone