• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.64-69
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• 1998

This paper presents a lip print recognition by the pattern kernels for a personal identification. A lip print recognition is developed less than the other physical attributes of a fingerprint, a voice pattern, a retinal blood/vessel pattern, or a facial recognition. A new method is proposed to recognize a lip print bi the pattern kernels. The pattern kernels are a function consisted of some local lip print pattern masks. This function converts the information on a lip print into the digital data. The recognition in the multi-resolution system is more reliable than recognition in the single-resolution system. The results show that the proposed algorithm by the multi-resolution architecture can be efficiently realized.

• Journal of Korea Multimedia Society
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• v.13 no.11
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• pp.1586-1593
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• 2010

Vocabulary recognition error correction method has probabilistic pattern matting and dynamic pattern matting. In it's a sentences to based on key-word by semantic analysis. Therefore it has problem with key-word not semantic analysis for morphological changes shape. Recognition rate improve of vocabulary unrecognized reduced this paper is propose. In syllable restoration algorithm find out semantic of a phoneme recognized by a phoneme semantic analysis process. Using to sentences restoration that morphological analysis and morphological analysis. Find out error correction rate using phoneme likelihood and confidence for system parse. When vocabulary recognition perform error correction for error proved vocabulary. system performance comparison as a result of recognition improve represent 2.0% by method using error pattern learning and error pattern matting, vocabulary mean pattern base on method.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.103-111
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• 2002

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.417-422
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• 2005

This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.121-125
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• 2015

In this paper, we propose a method that extracts features from character patterns using the fractal dimension of chaos theory. The input character pattern image is converted into time-series data. Then, using the modified Henon system suggested in this paper, it determines the last features of the character pattern image after calculating the box-counting dimension, natural measure, information bit, and information (fractal) dimension. Finally, character pattern recognition is performed by statistically finding each information bit that shows the minimum difference compared with a normalized character pattern database.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2249-2251
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• 1998

This paper presents a lip print recognition by the pattern kernels for a personal identification. A lip print recognition is developed less than the other physical attribute that is a fingerprint, a voice pattern, a retinal blood-vessel pattern, or a facial recognition. A new method by the pattern kernels is pro for a lip print recognition. The pattern kerne function consisted of some local lip print p masks. This function identifies the lip print known person or an unknown person. The results show that the proposed algorithm the pattern kernels can the efficiently realized.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.112-119
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• 2000

A die-casting junction method is usually used to manufacture the tub of an IH(induction heating) jar. If there is a very small air bubble in the junction area, the thermal conductivity is deteriorated and local overheat occurs. Such problem brings serious inferiority of the IH jar. In this paper, we propose a new method to detect such defect with simply measured thermal data. Thermal distribution of preheated tubs is obtained by scanning with infrared thermal sensors and analyzed with the statistic pattern recognition method. By defining the characteristic feature as the temperature difference between sensors and using ellipsoid function as decision boundary, a supervised learning method of genetic algorithm is proposed to obtain the required parpameters. After applying the proposed method to experiment, we have proved that the rate of recognition is high even for a small number of data set.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.675-678
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• 1988

This paper describes the method of syntactic-semantic pattern recognition and description for two dimensional object which is adjusted or changed in size and its orientation. To avoid the complexity and ambiguity which is arised in the case of syntactic or decision-theoretic method is used individually, an attributed grammar is introduced which applies computative attributes to pattern primitives, and then uses decision-theoretic method for attributes and syntactic method for pattern structure. A primitive extraction embedding parsing and grobal rule for classification is also applied for more effective pattern recognition and description.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.455-464
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• 1994

The intelligent trajectory control method that controls moving direction and average velocity for a prosthetic arm is proposed by pattern recognition and force estimations using EMG signals. Also, we propose the real time trajectory planning method which generates continuous accelleration paths using 3 stage linear filters to minimize the impact to human body induced by arm motions and to reduce the muscle fatigue. We use combination of MLP and fuzzy filter for pattern recognition to estimate the direction of a muscle and Hogan's method for the force estimation. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. The simulation results of proposed prosthetic arm control system using the EMG signals show that the arm is effectively followed the desired trajectory depended on estimated force and direction of muscle movements.