• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.420-427
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• 2017

This paper proposes a new method using Wide Line Detector based statistical shape Feature (WLDF) to identify whether or not a tongue is cracked; a cracked tongue is one of the most frequently used visible features for diagnosis in traditional Chinese Medicine (TCM). We first detected a wide line in the tongue image, and then extracted WLDF, such as the maximum length of each detected region, and the ratio between maximum length and the area of the detected region. We trained a binary support vector machine (SVM) based on the WLDF to build a classifier for cracked tongues. We conducted an experiment based on our proposed scheme, using 196 samples of cracked tongues and 245 samples of non-cracked tongues. The results of the experiment indicate that the recognition accuracy of the proposed method is greater than 95%. In addition, we provide an analysis of the results of this experiment with different parameters, demonstrating the feasibility and effectiveness of the proposed scheme.

• Proceedings of the KSME Conference
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• 2001.06e
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• pp.702-707
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• 2001

The characteristics of flow in dividing regions are precise, therefore their classification is very important not only in industry but also in hydrodynamics. By now, many studies of flow in dividing regions have been performed, but flow characteristics that use visualization in dividing regions have not been studied. The present study of the PIV and the CFD exhibit average velocity distributions, kinetic energy distributions and total pressure distributions etc of the total flow field due to the development of the accurate visualization optical laser and of optical equipment. Also, PIV is accurate with the flows characteristics of the dividing region as continuous analysis is done using input equipment. The study analyzes average velocity vector field, average kinetic energy, x-axis stress distributions, average and total pressure distributions of dividing regions with flow for visualization of the PIV and the CFD measurement in a dividing rectangular duct.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.37-42
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• 1990

In order to obtain optimal design criteria and operating parameters, a Single-sided Linear Induction Motor (SLIM) is analysed by using a 2-D finite element method with magnetic and current vector potential. In the analysing procedures, the governing equation is derived from Maxwell's equation combined with the magnetic vector potential. As a forcing term, 3-phase voltage source is employed using the Kirchhoff's voltage law in order to look into effects of the unbalanced 3-phase currents and air gap flux density. Also, 2ndary eddy current distribution, longitudinal end and transverse edge effects are in turns visualized by flux lines in 3 different analysing planes as functions of frequency and input power.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.5
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• pp.167-172
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• 1984

This paper describes the finite element analysis of magnetostatic field problems with permanent magnets. Two kinds of algorithms, one using the magnetic vector potential and the other using the magnetic scalar potential, are introduced. The magnetization of the pemanent magnet is used as the source instead of the magnetic equivalent current in both of the formulations using the magnetic vector potential and the magnetic scalar potential. A simple functional, which has only the region integral instead of the region integral and boundary integral, is derived in the formulation using the magnetic scalar potential. These make the formulation of the system equations simpler and more convenient than the conventional methods. The numerical results by the two proposed algorithms for a C-type permanent magnet model are compared with the analytic solutions respectively. The numerical results are in good agreement with the analytic solutions.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.619-622
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• 2001

In this paper, an efficient approach to classification of transient and harmonic disturbances in power systems is proposed. First, the Stop-and-Go CA CFAR Detector is utilized to detect a disturbance from the power signals which are mixed with other disturbances and noise. Then, (i) Wigner Distribution, SVD(Singular Value Decomposition) and Fisher´s Criterion (ii) DWT and Fisher´s Criterion, are applied to extract an efficient feature vector. For the classification procedure, a combined neural network classifier is proposed to classify each corresponding disturbance class. Finally, the 10 class data simulated by Matlab power system blockset are used to demonstrate the performance of the proposed classification system.

• Proceedings of the Optical Society of Korea Conference
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• 1990.02a
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• pp.64-68
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• 1990

Boundary element method using linear basis function is applied to obtain fields scattered from a 3-D dielectric sphere. Electric field integral equation is used on the surfaces of the dielectric material where its surface is discretized into trilateral cells. For plane wave incidence, scattered fields by a dielectric sphere is calculated and compared with its analytic solution. The total electric fields are calculated on the great circle of the sphere boundary as well as the outside of the sphere in the plane of the wave vector and the polarization vector of the incident electric field.

• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.345-352
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• 1995

The LPC cepstrum coefficients, which are an acoustic features of speech signal, have been widely used as the feature parameter for various speech recognition systems and showed good performance. The vocal tract area function is a kind of articulatory feature, which is related with the physiological mechanism of speech production. This paper proposes the vocal tract area function as an alternative feature parameter for speech recognition. The linear predictive analysis using Burg algorithm and the vector quantization are performed. Then, recognition experiments for 5 Korean vowels and 10 digits are executed using the conventional LPC cepstrum coefficients and the vocal tract area function. The recognitions using the area function showed the slightly better results than those using the conventional LPC cepstrum coefficients.

• Journal of electromagnetic engineering and science
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• v.7 no.1
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• pp.7-11
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• 2007

PCB Power-Bus (comprising power/ground planes) impedance and fields are evaluated by an efficient series expansion method that is suggested in this paper. It is used to investigate the structure's radiated emission(RE) levels and find acceptable ways of loading the power/ground planes such as decoupling capcitor(DeCap)s, balanced feeding and slits, in order to reduce the interferences. Also, the calculations and measurements of a proposed geometry are verified by vector fitting as a analysis model to check the behavior of the slit.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.2
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• pp.144-150
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• 2000

Surface roughness is one of the most important parameters to estimate quality of products. As this reason so many studies were car-ried out through various attempts that were contact or non-contact using computer vision. Even through these efforts there were few good results in this research., however texture analysis making a important role to solve these problems in various fields including universe aviation living thing and fibers. In this study feature value of co-occurrence matrix was calculated by statistic method and roughness value of worked surface was classified, of it. Experiment was carried out using input vector of neural network with characteristic value of texture calculated from worked surface image. It's found that recognition rate of 74% was obtained when adapting texture features. In order to enhance recogni-tion rate combination type in characteristics value of texture was changed into input vector. As a result high recognition rate of 92.6% was obtained through these processes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.