• Journal of Korea Water Resources Association
• /
• v.31 no.6
• /
• pp.821-832
• /
• 1998

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. Fuzzy neuron, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an adaptive learning method by which a membership function and fuzzy rules were adapted for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water supplied to the city of Kwangju. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supplied (b) the mean temperature, and (c)the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.35% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of KIISE:Software and Applications
• /
• v.32 no.7
• /
• pp.674-682
• /
• 2005

This paper proposes classification of multi-spectral satellite image based on fusion of fuzzy G-K (Gustafson-Kessel) algorithm and PCM algorithm. The suggested algorithm establishes the initial cluster centers by selecting training data from each category, and then executes the fuzzy G-K algorithm. PCM algorithm perform using classification result of the fuzzy G-K algorithm. The classification categories are allocated to the corresponding category when the results of classification by fuzzy G-K algorithm and PCM algorithm belong to the same category. If the classification result of two algorithms belongs to the different category, the pixels are allocated by Bayesian maximum likelihood algorithm. Bayesian maximum likelihood algorithm uses the data from the interior of the average intracluster distance. The information of the pixels within the average intracluster distance has a positive normal distribution. It improves classification result by giving a positive effect in Bayesian maximum likelihood algorithm. The proposed method is applied to IKONOS and Landsat TM remote sensing satellite image for the test. As a result, the overall accuracy showed a better outcome than individual Fuzzy G-K algorithm and PCM algorithm or the conventional maximum likelihood classification algorithm.

• Journal of Korea Port Economic Association
• /
• v.31 no.1
• /
• pp.85-110
• /
• 2015

The purpose of this paper is to show the clustering trend and the empirical comparison and to choose the clustering ports for 3 Korean ports(Busan, Incheon and Gwangyang Ports) by using the Fuzzy(Average Index Transformation) DEA and Cross-efficiency models for 38 Asian ports during 11 years(2001-2011) with 4 input variables(birth length, depth, total area, and number of crane) and 1 output variable(container TEU). The main empirical results of this paper are as follows. First, clustering results by using Fuzzy(AIT)DEA show that 3 Korean ports[Busan(56.29%), Incheon(57.96%), and Gwangyang(66.80%) each]can increase the efficiency. Second, according to Cross-efficiency model, Busan(Hongkong, Kobe, Manila, Singapore, and Kaosiung etc.), Incheon(Aquaba, Dammam, Karachi, Mohammad Byin Oasim and Davao), and Gwangyang(Damman, Yokohama, Nogoya, Keelong, Kaosiung, and Bangkok) should be clustered with those ports in parentheses. Third, when both Fuzzy(AIT)DEA and Cross-efficiency models are mixed, the empirical result shows that 3 Korean ports[Busan(71.38%), Incheon(103.89%), and Gwangyang(168.55%) each]can increase the efficiency. The efficiency ranking comparison among the three models by using Wilcoxon Signed-rank Test was matched with the average level of 66%-67%. The policy implication of this paper is that Korean port policy planner should introduce the Fuzzy(AIT)DEA, and Cross-efficiency models with the mixed two models when clustering is needed among the Asian ports for enhancing the efficiency of inputs and outputs. Also, the results of SWOT analysis among the clustering ports should be considered.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.12
• /
• pp.3718-3726
• /
• 1999

Color image is more effective than gray one in human visual perception. Therefore, color image processing becomes important area. Color images are often corrupted by noises due to the input sensor, channel transmission errors and so on. Some filtering techniques such as vector median, mean filter, and vector $\alpha-trimmed$ mean filter have been used for color noise removal. Among them, vector $\alpha-trimmed$ mean filter gave the best performance in the mixed color noise. But, there are edge shift and blurring effect because vector $\alpha-trimmed$ mean filter is uniformly processed across the image. So, we proposed a conditional fuzzy cluster filter to improve this problems. Simulation results showed that the proposed scheme improves the NCD measure and visual quality over the conventional vector $\alpha-trimmed$ mean filter in the mixed color noise.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.6
• /
• pp.61-70
• /
• 2011

In this paper, it is realized an image filter for a noise elimination using a recurrent neural networks with fuzzy. The proposed fuzzy neural networks structure is to converge weights and the number of iteration for a certain value by using basically recurrent neural networks structure and is simplified computation and complexity of mathematics by applying the hybrid fuzzy membership function operator. In this paper, the proposed method, the recurrent neural networks applying fuzzy which is collected a certain value, has been proved improving average 0.38dB than the conventional method, the generalied recurrent neural networks, by using PSNR. Also, a result image of the proposed method was similar to the original image than a result image of the conventional method by comparing to visual images.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.10
• /
• pp.59-70
• /
• 2010

Each pixels become got pixel value for color of only one from among colors because of bayer pattern that light receiving device of image sensor which is used in HHP and digital camera writes only one color. Information of the missing pixels could infer perfect color image from using information of neighbor pixels by using CFA(Color Filter Array). In this paper, we derive relation between the average of the data from the light receiving device of image sensor and each color channel data. And by using this relation, a new efficient edge color fuzzy method for color interpolation is proposed. Also, missing luminance signal channel interpolation was fuzzy interpolation along any edges direction for reducing color noise and interpolating efficiently it. And in this paper, the proposed method has been proved improving average 2.4dB than the conventional method by using PSNR. Also, resolution of the image of the proposed method was similar to the original image by visual images, we has been verified to be decreased a chromatic aberration than image of conventional algorithms with simulation result.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.5
• /
• pp.526-537
• /
• 1999

In the paper. we propose an evaluation model based the adaptive fuzzy systems, which can transform the physical features of a color pattern to the emotional features. The model is motivated by the Soen's psychological experiments, in which he found the physical features such as average hue, saturation, intensity and the dynamic components of the color patterns affects to the emotional features represented by a pair of adjective words having the opposite meanings. Our proposed model consists of two adaptive fuzzy rule-bases and the y-model, a l i r ~ r ys et operator, to fuze the evaluation values produced by them. The model shows con~parablep erformances to the neural network for the approximation of the nonlinear transforms, and it has the advantage to obtain the linbwistic interpretation from the trained results. We believe the evaluated results of a color pattern can be used to the emotion-based color image retrievals.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.3
• /
• pp.252-259
• /
• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection of a image. The FCS is based on the fuzzy logic system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. There are two different approaches, Michigan and Pittsburgh approaches, to acquire appropriate fuzzy rules by evolutionary computation. In this paper, we use the Michigan style in which a single fuzzy if-then rule is coded as an individual. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. The proposed method is evaluated by applying it to the edge detection of a gray-level image that is a pre-processing step of the computer vision. the differences of average gray-level of the each vertical/horizontal arrays of neighborhood pixels are represented into fuzzy sets, and then the center pixel is decided whether it is edge pixel or not using fuzzy if-then rules. We compare the resulting image with a conventional edge image obtained by the other edge detection method such as Sobel edge detection.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.12
• /
• pp.1514-1519
• /
• 2019

IVUS is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. IVUS is regularly used to locate the atherosclerosis lesions in the coronary arteries. Auto-segmentation of the vessel structure is important to detect the disorder of coronary artery. In this paper, we propose a simple strategy to extract Intima/Adventitia area effectively using fuzzy binarization from intravascular images. The proposed method apply fuzzy binarization to find the adventitia but apply average binarization to locate the intima since they have different homogeneity of pixel intensity comparing with the environment. In this paper, we demonstrate an effective auto-segmentation method for detecting the interior/exterior of the vessel walls by differentiating the fuzzy binarization result and average binarization result from IVUS image. Important statistics such as Intima-Media Thickness (IMT) or volume of a target area can be easily computed from result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.171-174
• /
• 2010

본 논문에서는 렌즈의 흠집을 추출할 수 있는 퍼지 기법을 이용한 렌즈 흠집 검출 방법을 제안한다. 제안된 방법은 렌즈 영상을 그레이 영상으로 변환한 후, 캐니 마스크를 이용하여 렌즈의 경계선을 추출한다. 추출된 렌즈의 경계선에 대해 평균 이진화와 모폴로지를 이용하여 렌즈 경계선을 보정한다. 렌즈 경계선이 보정된 영상에서 Seed Fill 알고리즘을 적용하여 렌즈의 내부 영역만을 추출한다. 추출된 렌즈의 내부 영역에 해당하는 원 영상에서 소벨 마스크를 적용하여 렌즈 내부 영역의 에지를 추출한다. 렌즈 내부 영역에서 추출된 에지 객체들의 정보를 이용하여 흠집과 비흠집을 분류하는 퍼지 기법을 적용하여 흠집 영역을 추출한다. 본 논문에서 제안된 렌즈의 흠집 검출 방법의 성능을 평가하기 위해 CHEMI, MID, HL, HM 시력 보정용 렌즈를 대상으로 실험한 결과, 제안된 방법이 흠집을 효과적으로 검출하는 것을 확인하였다.