• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.791-796
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• 2001

Welding using lasers can be mass-produced in high speed. In the laser welding, performing real-time evaluation of the welding quality is very important in enhancing the efficiency of welding. In this study, the plasma and molten metal which are generated during laser welding were measured using the UV sensor and IR sensor. The results of laser welding were classified into five categories such as optimal heat input, little low heat input, low heat input, focus off, and nozzle change. Also, a system was formulated which uses the measured signals with a fuzzy pattern recognition method which is used to perform real-time evaluation of the welding quality and the defects which can occur in laser welding. Weld quality prediction program was developed using previous weld results and statistical program which could show the trend of weld quality and signal was developed.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1998년도 추계학술대회 논문집
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• pp.164-169
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• 1998

This paper deals with condition monitoring for tool wear during tuning operation. To develop economic sensing and identification methods for turning processes, sound pressure measurement and digital signal processing technique are proposed. To identify noise sources of tool wear and reject background noise, noise rejection methodology is proposed. features to represent condition of tool wear are obtained through analysis using adaptive filter and FFT in time and frequency domain. By using fuzzy pattern recognition, we extract features, which are sensitive to condition of tool wear, from several features and make a decision on tool wear. The validity of the proposed system is condirmed through the large number of cutting tests in two cutting conditions.

• 한국레이저가공학회지
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• 제3권2호
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• pp.53-61
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• 2000

On the laser weld production line, a slight alteration of the welding condition produces many defects. The defects are monitored in real time, in order to prevent continuous occurrence of defects, reduce the loss of material, and guarantee good quality. The measurement system is produced by using three photo-diodes for detection of the plasma and spatter signal in CO$_2$ laser welding. For high speed CO$_2$ laser welding, laser tailored welded blanks for example, on-line weld quality monitoring system was developed by using fuzzy multi-feature pattern recognition. Weld qualities were classified optimal heat input, a little low heat input, low heat input, and focus misalignment, and final weld quality were classified good and bad.

• International Journal of Korean Welding Society
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• 제1권1호
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• pp.7-12
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• 2001

Real time monitoring has become critical as the use of laser welding increases. Plasma and spatter are measured and used as the signal for estimating weld quality. The estimating algorithm was made using the fuzzy pattern recognition with the area of data that is beyond the tolerance boundary. Also, an algorithm that detects the spatter and the localized defect was created in order to kd the partially produced pit and the sudden loss of weld penetration. These algorithms were used in quality monitoring of the $CO_2$ laser tailored blank weld. Statistical program that can display the laser weld quality result and the signal transition was made for the first stage of the remote control system.

• International Journal of Korean Welding Society
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• 제3권1호
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• pp.45-50
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• 2003

Welding using lasers can be mass-produced in high speed. In the laser welding, performing real-time monitoring system of the welding quality is very important in enhancing the efficiency of welding. In this study, the plasma and molten metal which are generated during laser welding were measured using the UV sensor and IR sensors. The results of laser welding were classified into five categories such as optimal heat input, little low heat input, low heat input, partial joining due to gap mismatch, and nozzle deviation. Also, a system was formulated which uses the measured signals with a fuzzy pattern recognition method which is used to perform real-time evaluation of the welding quality and the defects which can occur in laser welding.

• 전자공학회논문지B
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• 제31B권10호
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• pp.82-91
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• 1994

In this thesis, we perform the experiment of speaker recognition by identifying vowels in the pronounciation of each speaker. In detail, we extract the vowels from the pronounciation of each speaker first. From it, we check the frequency energgy of 29 channels. After changing these into fuzzy values, we employ the fuzzy inference to recognize the speaker by text-dependent and text-independent methods. For this experiment, an algorithm of extracting vowels is developed, and newly introduced parameter is the frequency energy of the 29 channels computed from the extracted vowels. It shows the features of each speakers better than existing parameters. The advanced point of this paramter is to use the reference pattern only without the help of any codebook. As a rewult, test-dependent method showed about 95.5% rate of recognition, and text-independent method showed about 94.2% rate of recognition.

• 전자공학회논문지C
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• 제34C권2호
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• pp.61-70
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• 1997

Human-hand gestures have been used a means of communication among people for a long time, being interpreted as streams of tokens for a language. The signed language is a method of communication for hearing impaired person. Articulated gestures and postures of hands and fingers are commonly used for the signed language. This paper presents a system which recognizes the korean sign language (KSL) and translates the recognition results into a normal korean text and sound. A pair of data-gloves are used a sthe sensing device for detecting motions of hands and fingers. In this paper, we propose a dynamic gesture recognition mehtod by employing a fuzzy feature analysis method for efficient classification of hand motions, and applying a fuzzy min-max neural network to on-line pattern recognition.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1287-1292
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• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권3호
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• pp.215-220
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• 2002

This paper proposes a method to apply the Backpropagation(BP) algorithm of MVL(Multi-Valued Logic) Neural Network to pattern recognition. It extracts the property of an object density about an original pattern necessary for pattern processing and makes the property of the object density mapped to MVL. In addition, because it team the pattern by using multiple valued logic, it can reduce time f3r pattern and space fer memory to a minimum. There is, however, a demerit that existed MVL cannot adapt the change of circumstance. Through changing input into MVL function, not direct input of an existed Multiple pattern, and making it each variable loam by neural network after calculating each variable into liter function. Error has been reduced and convergence speed has become fast.