• Proceedings of the Korea Water Resources Association Conference
• /
• 2009.05a
• /
• pp.1136-1140
• /
• 2009

지상강우 관측망을 이용한 강우량 측정의 대안으로서 사용되는 기상 레이더를 활용한 강우량 추정의 경우, Z-R 방정식을 이용하여 반사도를 강우량으로 환산하는 방법을 일반적으로 사용한다. 이때 발생하는 각종 오차는 레이더 장비가 가지는 기계적인 오차뿐만 아니라 Z-R 방정식이 가지는 오차 등이 있으며, 이를 보정하기 위해서 레이더를 활용하여 추정된 강우량에 지상강우량계와 레이더강우량과의 비율인 G/R비를 보정하는 방법을 일반적으로 사용한다. 본 연구에서는 이와 같이 레이더 강우량을 보정하기 위해서 사용되는 G/R비를 산정하는데 미치는 지형적인 효과를 고려하기 위해서 광덕산 레이더 유효범위 100km 내(군사분계선 이북 미포함)의 지역에 대하여 군집분석을 실시하여 크게 산악지역과 평야지역으로 구분하고, 각각 구분된 지역에 대하여 G/R 비를 산정하여 초기추정 레이더 강우량에 곱하는 mean-field bias 보정을 실시하였다. 광덕산 레이더 기상관측소의 유효범위 100km 내의 2007년, 2008년 홍수기(6/21${\sim}$9/20)기간 동안 94개 Automatic Weather Station(AWS)지점에 대하여 크게 산악지역과 평야지역으로 지역화 시키는 방법은 비계층적 군집분석 기법 중 fuzzy-c mean 방법을 적용하였다. 또한 광덕산 레이더 반사도 기본 자료는 차폐영역으로 생기는 반사도 데이터 누락을 보완하기 위하여 0도와 1.5도 sweep 합성 10분단위 uf 자료를 사용하였으며, AWS와 보정이 이루어지는 레이더 격자의 크기는 최대 4km${\times}$4km로 선정하였다. 본 연구에 있어서 검증방법은 지역을 구분하기 전과 후를 AWS 실측 관측값과 절대상대오차, 평균제곱근 오차로써 비교하였다.

• The KIPS Transactions:PartB
• /
• v.11B no.2
• /
• pp.159-168
• /
• 2004

This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel's membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method Is applied to a Landsat TM satellite image for the verifying test.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.4 s.310
• /
• pp.1-8
• /
• 2006

This paper proposes an improved FCM(Fuzzy C-means) algorithm using intercluster and entropy-based weight in gray image. The fuzzy clustering methods have been extensively used in the image segmentation since it extracts feature information of the region. Most of fuzzy clustering methods have used the FCM algorithm. But, FCM algorithm is still sensitive to noise, as it does not include spatial information. In addition, it can't correctly classify pixels according to the feature-based distributions of clusters. To solve these problems, we applied a weight and intercluster to the traditional FCM algorithm. A weight is obtained from the entropy information based on the cluster's number of neighboring pixels. And a membership for one pixel is given based on the information considering the feature-based intercluster. Experiments has confirmed that the proposed method was more tolerant to noise and superior to existing methods.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2011.01a
• /
• pp.37-38
• /
• 2011

본 논문은 상대적으로 대비도 차이가 크게 나타나는 역광 이미지에 대해서 Retinex 알고리즘을 적용하여 보정 했을 경우 발생하는 밝은 영역에서의 컬러 성분의 손실을 개선하기 위한 새로운 기법을 제안한다. 역광 이미지의 경우 밝은 영역과 어두운 영역에 대한 밝기 차이가 매우 크게 발생하기 때문에 Retinex 알고리즘을 이용하여 영상의 대비도를 향상시킬 경우 밝은 영역에서의 컬러 성분이 손실되는 현상이 발생한다. 이러한 손실을 보완하기 위해서 원본 영상의 밝은 영역에 해당하는 컬러 성분을 Retinex 알고리즘으로 보정된 영상에 추가해준다. Fuzzy c-means 군집화 알고리즘을 이용하여 원본 영상에서의 밝은 영역과 어두운 영역에 대하여 모든 화소의 소속 정도를 나타내는 퍼지 소속 함수를 구한다. 밝은 영역에 대해서의 컬러 성분은 원본 영상 값에 밝은 영역 퍼지 소속 함수를 적용하고, 어두운 영역에 대해서의 컬러 성분은 Retinex 복원 영상 값에 어두운 영역 퍼지 소속 함수를 이용한다. 제안하는 알고리즘의 성능 평가를 위해 역광 현상이 강하게 나타나는 자연영상들을 대상으로 적용하여 기존의 Retinex 알고리즘(MSRCR) 보다 우수한 성능을 가지고 있음을 보였다.

• Proceedings of the Korean Information Science Society Conference
• /
• 1999.10b
• /
• pp.15-17
• /
• 1999

사례 기반 추론(Case-Based Reasoning)은 새로운 문제를 해결하기 위해 유사한 기존 문제를 추출하여 그 해결과정을 사용한다. 그러므로, 기존의 문제와의 유사성을 얼마만큼 잘 판별하는가가 매우 중요한 관건이다. 연구된 유사성 판단 방법으로는 퍼지 소속 함수(Fuzzy membership function)를 이용하여 사례마다 각 클래스에 대한 소속 함수 값을 주는 방법이 있다. 이 방법은 퍼지 소속 함수를 어떻게 주는가에 따라 성능이 달라진다. 본 논문에서는 적당한 퍼지 소속 함수를 주기 위하여 Fuzzy C-Means를 사용하는 방법을 제안하였다. 이 방법은 각 클래스에 대한 소속 함수 값을 결정하는데 있어서 좀 더 전체적인 데이터 분포 정보를 이용할 수 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.135-138
• /
• 1997

In this paper, we proposed effective modeling method to nonlinear complex system. Fuzzy Neural Network(FNN) was used as basic model. FNN was fused of Fuzzy Inference which has linguistic property and Neural Network which has learning ability and high tolerence level. This paper, we used FNN which was proposed by Yamakawa. The FNN used Simple Inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. This structure has better property than other structure at learning speed and convergence ability. But it has difficulty at definition of membership function. We used Hard c-Mean method to overcome this difficulty. To evaluate proposed method. We applied the proposed method to waste water treatment process. We obtained better performance than conventional model.

• Journal of the Korean Geotechnical Society
• /
• v.24 no.10
• /
• pp.57-70
• /
• 2008

The advantage of piezocone penetration test is a guarantee of continuous data, which is a source of reliable interpretation of target soil layer. Many researches have been carried out f3r several decades and several classification charts have been developed to classify in-situ soil from the cone penetration test result. Since most present classification charts or methods were developed based on the data which were compiled over the world except Korea, they should be verified to be feasible for Korean soil. Furthermore, sometimes their charts provide different soil classification results according to the different input parameters. However, unfortunately, revision of those charts is quite difficult or almost impossible. In this research a new soil classification model is proposed by using fuzzy C-mean clustering and neuro-fuzzy theory based on the 5371 CPT results and soil logging results compiled from 17 local sites around Korea. Proposed neuro-fuzzy soil classification model was verified by comparing the classification results f3r new data, which were not used during learning process of neuro-fuzzy model, with real soil log. Efficiency of proposed neuro-fuzzy model was compared with other soft computing classification models and Robertson method for new data.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.24 no.4
• /
• pp.543-554
• /
• 2000

The engineering equations, which have been used in many engineering companies, were employed for the dynamic modelling part in order to develop the naturally circulated boiler simulator. The fuzzy algorithm, which is similar to the algorithm of making decision by the human being, was developed for the boiler simulator controller and its simulated variables were compared with those of classical PID simulations to verify the stability and the effectiveness of fuzzy controller. The simulator is for the naturally circulated boiler and the main components are the furnace, the drum, the super heater, and the economizer. The combustion and thermal radiation dominant equations were used within the furnace and the mass conservation and the energy rate balance equations were employed for the drum part. The heat transfer rates were calculated using the logarithmic mean temperature differences both for the super heater and for the economizer. The simulations are very useful to understand the boiler operations and the engineering design of the main components. The main program was developed under the PC window condition by linking the fuzzy controller to the main boiler program using the Visual C++ language. The various operational conditions such as the abrupt changes of load, the changes of water supply pipes and the diameter of drum were simulated.

• Journal of Navigation and Port Research
• /
• v.42 no.2
• /
• pp.117-126
• /
• 2018

Container port is one of the most vital link of the transportation chain that plays an important role in trading with other countries. Having a proper understanding of port operations could change the role of the port from a transportation node to an efficient point in a transportation chain. Development of transportation chains, logistics and progress of these networks have enhanced the sustainable condition and level of transportation. Therefore, evaluating the competitiveness of ports is obligatory for port users to make a decision in investment or exploitation. This paper introduces the use of Fuzzy C-means and TOPSIS for competitiveness comparison among a sample of container terminals in Korea and China and determine how to improve Korean port competitiveness and particularly in Busan port.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.22 no.3
• /
• pp.303-309
• /
• 2004

In this paper, an adaptive Bayesian approach to image segmentation was developed for boundary detection. Both image intensities and texture information were used for obtaining better quality of the image segmentation by using the C programming language. Fuzzy c-mean clustering was applied fer the conditional probability density function, and Gibbs random field model was used for the prior probability density function. To simply test the algorithm, a synthetic image (256$\times$256) with a set of low gray values (50, 100, 150 and 200) was created and normalized between 0 and 1 n double precision. Results have been presented that demonstrate the effectiveness of the algorithm in segmenting the synthetic image, resulting in more than 99% accuracy when noise characteristics are correctly modeled. The algorithm was applied to the Antarctic mosaic that was generated using 1963 Declassified Intelligence Satellite Photographs. The accuracy of the resulting vector map was estimated about 300-m.