• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.72-79
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• 2009

This paper describes the performance evaluation of NHD(nonhomogeneity detector) for STAP(space-time adaptive processing) airborne radar according to various normalization methods in the nonhomogeneous clutter environment. In practice, the clutter can be characterized as random variation signals, because it sometimes includes signals with very large magnitude like impulsive signal due to the system environment. The received interference signals are composed of homogeneous and nonhomogeneous data. In this situation, NHB is needed to maintain the STAP performance. The normalization using the NHD result is an effective method for removing the nonhomogeneous data. The optimum normalization can be performed by a representative value considered with a characteristic of the given data, so we propose the K-means clustering algorithm. The characteristic of random variation data due to nonhomogeneous clutters can be considered by the number of clusters, and then the representative value for selecting the homogeneous data is determined in the clustering result. In order to reflect a characteristic of the nonstationary interference data, we also investigate the algorithm for a calculation of the proper number of clusters. Through our simulations, we verified that the K-means clustering algorithm has very superior normalization and target detection performances compared with the previous introduced normalization methods.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.195-204
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• 2023

In this study, prompt engineering that improves prompts was explored to improve the performance of teaching and learning materials recommendations using generative artificial intelligence such as GPT and Stable Diffusion. Picture materials were used as the types of teaching and learning materials. To explore the impact of the prompt composition, a Zero-Shot prompt, a prompt containing learning target grade information, a prompt containing learning goals, and a prompt containing both learning target grades and learning goals were designed to collect responses. The collected responses were embedded using Sentence Transformers, dimensionalized to t-SNE, and visualized, and then the relationship between prompts and responses was explored. In addition, each response was clustered using the k-means clustering algorithm, then the adjacent value of the widest cluster was selected as a representative value, imaged using Stable Diffusion, and evaluated by 30 elementary school teachers according to the criteria for evaluating teaching and learning materials. Thirty teachers judged that three of the four picture materials recommended were of educational value, and two of them could be used for actual classes. The prompt that recommended the most valuable picture material appeared as a prompt containing both the target grade and the learning goal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.707-716
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• 2017

The advances in image display devices necessitate display images suitable for the user's purpose. The display devices should be able to provide object-based image information when a depthmap is required. In this paper, we represent the algorithm using a histogram-based image segmentation method for depthmap generation. In the conventional K-means clustering algorithm, the number of centroids is parameterized, so existing K-means algorithms cannot adaptively determine the number of clusters. Further, the problem of K-means algorithm tends to sink into the local minima, which causes over-segmentation. On the other hand, the proposed algorithm is adaptively able to select centroids and can stand on the basis of the histogram-based algorithm considering the amount of computational complexity. It is designed to show object-based results by preventing the existing algorithm from falling into the local minimum point. Finally, we remove the over-segmentation components through connected-component labeling algorithm. The results of proposed algorithm show object-based results and better segmentation results of 0.017 and 0.051, compared to the benchmark method in terms of Probabilistic Rand Index(PRI) and Segmentation Covering(SC), respectively.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3019-3021
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• 2000

In this paper. a new fuzzy modeling algorithm is proposed : it can express a given unknown system with a small number of fuzzy rules and be easily implemented. This method uses an independent array instead of a lattice form for a premise membership function. For the purpose of getting the initial value of fuzzy rules. the method uses the fuzzy c-means clustering method. To optimally tune the initial fuzzy rule. the DNA coding method is also utilized at same time. Box and Jenkins's gas furnace data is used to illustrate the validity of the proposed algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.177-180
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• 2003

텔레비젼 카메라, 비디콘 카메라(vidicon camera), 디지털 검지기, 스캐너 등 물리적 장치로 획득한 영상은 주위의 밝기로 인하여 어두운 영상을 얻거나 영상장치의 물리적 속성과 영상 전송에 기인하여 영상은 열악한 대비를 가질 수 있다. 본 논문에서는 획득한 저대비 영상을 대비 향상시켜주는 기법을 제안한다. 제안된 기법은 K-means 알고리듬을 사용하여 교차점을 자동으로 선정하는 방법을 사용한다. 이 최적의 교차점을 선정하는 과정은 획득한 영상을 물체와 배경으로 분리하는 두 개의 클래스 문제로 보고 K-means 알고리듬을 적용하였다. 구한 교차점을 사용하여 영상을 양분하여 히스토그램 평활화 방법을 적용하였다. 본 논문에서는 퍼지성 지수(index of fuzziness)를 사용하여 향상의 정도를 측정하였다. 제안된 기법을 저대비 영상에 적용하였으며 그 결과를 히스토그램 평활화 기법의 결과와 비교하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.188-191
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• 2004

야간에 비디오카메라로 촬영시 열악한 주위 환경과 영상 전송에 기인하여 다양한 잡음에 의하여 왜곡되거나 흐린 저대비(low contrast)영상을 가질 수 있다. 본 논문에서는 획득한 저대비 영상을 대비 향상시켜주는 기법을 제안한다. 동영상 압축표준인 MPEG-2는 인간의 시각 특성상 색차(chrominance)신호보다 밝기(luminance)신호에 더 민감하기 때문에 밝기신호와 색차 신호를 분리하여 압축한다. 밝기신호만을 추출한 후 K-means 알고리듬을 사용하여 교차점을 자동으로 선정하는 방법을 사용하는데, 이 최적의 교차점을 선정하는 과정은 획득한 영상을 물체와 배경으로 분리하는 두 개의 클래스 문제로 보고 K-means 알고리듬을 적용하였고 구한 교차점을 사용하여 영상을 양분하여 히스토그램 평활화 방법을 적용하였다 븐 논문에서는 퍼지성 지수(index of fuzziness)를 사용하여 향상의 정도를 측정하였다. 제안된 기법을 저대비 영상에 적용하였으며 그 결과를 히스토그램 평활화 기법의 결과와 비교하였다.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• Resources Recycling
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• v.26 no.2
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• pp.46-55
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• 2017

In this study, the design methodology of Radial Basis Function Neural Networks is developed with the aid of Laser Induced Breakdown Spectroscopy and also applied to the practical plastics sorting system. To identify black plastics such as ABS, PP, and PS, RBFNNs classifier as a kind of intelligent algorithms is designed. The dimensionality of the obtained input variables are reduced by using PCA and divided into several groups by using K-means clustering which is a kind of clustering techniques. The entire data is split into training data and test data according to the ratio of 4:1. The 5-fold cross validation method is used to evaluate the performance as well as reliability of the proposed classifier. In case of input variables and clusters equal to 5 respectively, the classification performance of the proposed classifier is obtained as 96.78%. Also, the proposed classifier showed superiority in the viewpoint of classification performance where compared to other classifiers.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.91-96
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• 2015

In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.853-866
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• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.