• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.606-615
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• 1997

This paper proposes an dffcient method for test pattern generation.Current test pattern genration systems generate a test vester for fault $F_{i+l}$ independently of the computation previously done for faults F1,F2...,Fi The proposed algorithm generates a test vector for fault $F_{i+l}$ by inheriting the test vector for fault Fi. A new test vector is grnerated from inherited values by gradually changing the inhderited values .The inherited values may partially activate a fauog and propagate the fault signal,Normally,this reduses the number of decision steps and backtracks in the second search.Experimental results for well-Known benchmark circuts show that the proposed algorithm is very efficient with small backtrack kimit;in combination eith other algorithms,it is very efficient for arbitrary backtrack limits.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.3
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• pp.48-55
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• 2006

This paper describes the antenna based GPS anti-jamming technology called CRPA(Controlled Reception Pattern Antenna), which used $2{\times}2$ array elements. In this system, the main functions are the antenna complex weight control and the GPS digital I/Q VM(Vector Modulator). To update the VM's I/Q complex weights, the PC based DAC(Digital to Analog Converter) module was also used and the two analog output voltages were applied to the $2{\times}2$ array elements to synthesize the null pattern. In the study, we also simulated the $2{\times}2$ GPS array null patterns to compare the null depth with experimental results. The VM was also modified at the frequency of 1.575GHz for the GPS L1 and controlled by the PC based VM software.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.833-834
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• 2014

In recent years, a pattern recognition method has been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov model has recently been used as pattern recognition methods in various fields. In this study, a HMM method for the fault diagnosis of a mechanical system is introduced, and a rotating machine with mass unbalance is selected for fault diagnosis. Moreover, a diagnosis procedure to identity the size of a defect is proposed in this study.

• Applied Microscopy
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• v.32 no.3
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• pp.185-193
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• 2002

The works of texture electron diffraction patterns for crystal structure analysis are written in Russian or introduced briefly in books written in English, which makes it difficult to be understood. In addition to working out the equations, vector theory corrects some errors included in the established formulas for texture electron diffraction patterns.

• International Journal of Reliability and Applications
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• v.10 no.1
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• pp.1-15
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• 2009

Even though the impact of manufacturing quality to reliability is not considered much as well as that of design area, a major cause of an early failure of the product is known as manufacturing problem. This research applies two different types of neural network algorithms, the Back propagation (BP) algorithm and Learning Vector Quantization (LVQ) algorithm, to identify and classify the nonrandom variation pattern on the control chart based on knowledge-based diagnosis of dimensional variation. The performance and efficiency of both algorithms are evaluated to choose the better pattern recognition system for auto body assembly process. To analyze hundred percent of the data obtained by Optical Coordinate Measurement Machine (OCMM), this research considers an application in which individual observations rather than subsample means are used. A case study for analysis of OCMM data in underbody assembly process is presented to demonstrate the proposed knowledge-based pattern recognition system.

• Journal of information and communication convergence engineering
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• v.3 no.2
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• pp.105-109
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• 2005

This paper proposed a fault diagnosis technique of induction motors winding fault based on an artificial neural network (ANN). This method used Park's vector pattern as input data of ANN. The ANN are firstly learned using this pattern, and then classify between 'healthy' and 'winding fault' (with 2, 10, and 20 shorted turn) induction motor under 0, 50, and $100\%$ load condition. Also the possibility of classification of untrained turn-fault and load condition are tested. The proposed method has been experimentally tested on a 3-phase, 1 HP squirrel-cage induction motor. The obtained results provided a high level of accuracy especially in small turn fault, and showed that it is a reliable method for industrial application

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.701-710
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• 2012

Retinitis Pigmentosa(RP) is a common hereditary disease. While they have been normally living, those who have this symptom feel frustration and pain by the damage of visual acuity. At the national level, the loss of the economic activity due to the reduction of economically active population will be also greater. There is an urgent need for the base study that can provide the clinical prognosis information of RP disease. In this study, we suggest that it is possible to predict prognosis through the pattern classification of RP data. Statistical processing results through statistical software like SPSS(Statistical Package for the Social Service) were mainly applied for the conventional study in data analysis. However, machine learning and automatic pattern classification was applied to this study. SVM(Support Vector Machine) and other various pattern classifiers were used for it. The proposed method confirmed the possibility of prognostic prediction based on the result of automatically classified RP data by SVM classifier.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.655-662
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• 2012

Classification is an important research field in pattern recognition with high-dimensional predictors. The support vector machine(SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the non-convex penalty function, and the smoothly clipped absolute deviation(SCAD) suggested by Fan and Li (2001). We developed the algorithm for the multiclass SVM with the SCAD penalty function using the local quadratic approximation. For multiclass problems we compared the performance of the SVM with the $L_1$, $L_2$ penalty functions and the developed method.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.449-455
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.