• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.87-98
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• 2011

Detecting a moving object from videos and tracking it are basic and necessary preprocessing steps in many video systems like object recognition, context aware, and intelligent visual surveillance. In this paper, we propose a method that is able to detect a moving object quickly and accurately in a condition that background and light change in a real time. Furthermore, our system detects strongly an object in a condition that the target object is covered with other objects. For effective detection, effective Eigen-space and FCM are combined and employed, and a CONDENSATION algorithm is used to trace a detected object strongly. First, training data collected from a background image are linear-transformed using Principal Component Analysis (PCA). Second, an Eigen-background is organized from selected principal components having excellent discrimination ability on an object and a background. Next, an object is detected with FCM that uses a convolution result of the Eigen-vector of previous steps and the input image. Finally, an object is tracked by using coordinates of an detected object as an input value of condensation algorithm. Images including various moving objects in a same time are collected and used as training data to realize our system that is able to be adapted to change of light and background in a fixed camera. The result of test shows that the proposed method detects an object strongly in a condition having a change of light and a background, and partial movement of an object.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.1
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• pp.58-64
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• 2001

정밀을 요하는 자동차 부품의 측정 시스템은 온도에 따라 보상이 필수적이다. 부품의 측정값의 신뢰도를 유지하기 위해서 단순히 제품의 합격 영역을 상온에서 51.786~51.819mm로 했을 때, 온도가 상온에서 따러져 있는 경우 그 부품의 측정영역을 신뢰하기가 어려워진다. 본 논문에서는 이 문제를 해결하기 위해서 2개의 카메라를 사용하여 한쪽은 표준 제품을 두고, 다른 쪽은 실제 제품을 둠으로서 온도에 따라 달라지는 표준 제품의 측정값의 Offset를 실제 제품에 반영함으로써 측정값을 보상하려고 하였다. 자동차의 부품은 여러 가지가 있으나, 이 중에서 현재 공장에서 측정에 어려움을 겪고 있는 에어콘 스윗치인 마그네트 코일 하우징을 대상으로 하였다. 특히 측정 대상이 크고, 카메라의 화소수가 40만 이하일 경우, 측정의 중요한 포인트는 화소수와 배경과 대상의 구별이다. 이를 정확히 알아내는데, FCM (Fuzzy C-means) 알고리듬이 좋은 결과를 주지만 속성 공간에서 유사성만을 고려하고, 공간영역에서 유사성은 고려되지 않기 때문에 FCM은 \"equal evidence\"와 \"ignorance\"를 구분하지 못한다. 이를 개선하기 위해서 FCM를 수정하여 먼저 FCM로 처리하고 이를 바탕으로 PCM (Possibilistic C-means)를 사용하였다. 결과를 모니터에 보여주고, RSC-232 포트를 통하여 신호를 마이크로 프로세서에 전달하여 제품의 양호(good), 불량(bad)을 판별하는 신호를 발생하게 하였다.을 판별하는 신호를 발생하게 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.497-503
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• 2005

본 논문에서는 퍼지 RBF 네트워크를 이용한 운송 컨테이너 식별자 인식 시스템을 제안한다. 일반적으로 운송 컨테이너의 식별자들은 크기나 위치가 정형화되어 있지 않고 외부 잡음으로 인하여 식별자의 형태가 변형될 수 있기 때문에 일정한 규칙으로 찾기는 힘들다. 본 논문에서는 이러한 특성을 고려하여 컨테이너 영상에 대해 Canny 마스크를 이용하여 에지를 검출하고, 검출된 에지 정보에서 영상획득 시 외부 광원에 의해 수직으로 길게 발생하는 잡음들을 퍼지 추론 방법을 적용하여 제거한 후에 수직 블록과 수평 블록을 검출하여 컨테이너의 식별자 영역을 추출하고 이진화한다. 이진화된 식별자 영역에 대해 검정색의 빈도수를 이용하여 흰바탕과 민바탕을 구분하고 4방향 윤광선 추적 알고리즘을 적용하여 개별 식별자를 추출한다. 개별 식별자 인식을 위해 퍼지 C-Means 알고리즘을 이용한 퍼지 RBF 네트워크를 제안하여 개별 식별자에 적용한다. 제안된 퍼지 RBF 네트워크는 퍼지 C-Means 알고리즘을 중간층으로 적용하고 중간층과 출력층 간의 학습에는 일반화된 델타 학습 방법과Delta-bar-Delta 알고리즘을 적용하여 학습 성능을 개선한다. 실제 컨테이너 영상을 대상으로 실험한 결과, 기존의 식별자 추출 방법보다 제안된 식별자 추출방법이 개선되었다. 그리고 기존의 ART2 기반 RBF 네트워크보다 제안된 퍼지 RBF 네트워크가 컨테이너 식별자의 학습 및 인식에 있어서 우수함을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1291-1300
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• 2008

In this paper, we propose a intelligent system to improve the accuracy of pattern recognition. The proposed intelligent system consist in SOFM, LVQ and FCM algorithm. We are confirmed the effectiveness of the proposed intelligent system through the several experiments that classify Fisher's Iris data and face image data that offered by ORL of Cambridge Univ. and EMG data. As the results of experiments, the proposed intelligent system has better accuracy of pattern recognition than general LVQ.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.477-486
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• 1998

In this paper, we implemented the urine analysis system capable of measuring a qualitative and semi-quantitative and assay using strip. The analysis algorithm of urine analysis was adopted a fuzzy logic-based classifiers that was robust to external error factors such as temperature and electric power noises. The spectroscopic properties of 9 pads In a strip were studied to developing the urine analysis system was designed for robustnesss and stability. The urine analysis system was consisted of hardware and software. The hardware of the urine analysis system was based on one-chip microprocessor, and Its peripherals which composed of optic modulo, tray control, preamplifier, communication with PC, thermal printer and operating status indicator. The software of the urine analysis system was composed of system program and classification program. The system program did duty fort system control, data acquisition and data analysis. The classification program was composed of fuzzy inference engine and membership function generator. The membership function generator made triangular membership functions by statical method for quality control. Resulted data was transferred through serial cable to PC. The transferred data was arranged and saved be data acquisition program coded by C+ + language. The precision of urine analysis system and the stability of fuzzy classifier were evaluated by testing the standard urine samples. Experimental results showed a good stability states and a exact classification.

• Journal of Korea Multimedia Society
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• v.10 no.4
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• pp.441-449
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• 2007

In clustering for image segmentation, large amount of computation and typical segmentation errors have been important problems. In the paper, we suggest a new method for minimizing these problems. Markers in marker-controlled watershed transform represent segmented areas because they are starting-points of extending areas. Thus, clustering restricted by marker pixels can reduce computational complexity. In our proposed method, the markers are selected by Gabor texture energy, and cluster information of them are generated by FCM (fuzzy c-mean) clustering. Generated areas from watershed transform are merged by using cluster information of markers. In the test of Brodatz' texture images, we improved typical partition-errors obviously and obtained less computational complexity compared with previous FCM clustering algorithms. Overall, it also took regular computational time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.357-365
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• 2022

Refrigerant mischarging is one of the most frequently occurring failure modes in air conditioners, and both undercharging and overcharging degrade cooling performance. Therefore, it is important to accurately determine the amount of charged refrigerant. In this study, a support vector machine (SVM) model was developed to multi-classify the refrigerant mischarge through steady-state identification via fuzzy clustering techniques. For steady-state identification, a fuzzy clustering algorithm was applied to the air conditioner operation data using the difference between moving averages. The identification results using the proposed method were compared with those using existing steady-state determination techniques studied through the inversed Fisher's discriminant ratio (IFDR). Subsequently, the main features were selected using minimum redundancy maximum relevance (mRMR) considering the correlation among candidate features, and an SVM multi-classification model was devised using the derived features. The proposed method achieves satisfactory accuracy and robustness from test data collected in the new domain.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.635-638
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• 2005

The purpose of video segmentation is to segment video sequence into shots where each shot represents a sequence of frames having the same contents, and then select key frames from each shot for indexing. There are two types of shot changes, abrupt and gradual. The major problem of shot change detection lies on the difficulty of specifying the correct threshold, which determines the performance of shot change detection. As to the clustering approach, the right number of clusters is hard to be found. Different clustering may lead to completely different results. In this thesis, we propose a video segmentation method using a color-X$^2$ intensity histogram-based fuzzy c-means clustering algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.59-64
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• 2007

In these days, malicious codes have become reality and evolved significantly to become one of the greatest threats to the modern society where important information is stored, processed, and accessed through the internet and the computers. Computer virus is a common type of malicious codes. The standard techniques in anti-virus industry is still based on signatures matching. The detection mechanism searches for a signature pattern that identifies a particular virus or stain of viruses. Though more accurate in detecting known viruses, the technique falls short for detecting new or unknown viruses for which no identifying patterns present. To cope with this problem, anti-virus software has to incorporate the learning mechanism and heuristic. In this paper, we propose a fuzzy diagnosis system(FDS) using fuzzy c-means algorithm(FCM) for the cluster analysis and a decision status measure for giving a diagnosis. We compare proposed system FDS to three well known classifiers-KNN, RF, SVM. Experimental results show that the proposed approach can detect unknown viruses effectively.