• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.11
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• pp.1543-1550
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• 2015

Ferroresonance is a non-linear vibrational phenomenon that is generated by the electrical interaction of the inductance component with the capacitor component of a certain capacitance as the device of the inductance component such as a transformer is saturated due to the degradation, the waveform distortion of current and voltage, and the oscillation of overcurrent and overvoltage in a system. Recently, ferroresonance was generated from the waveform distortion of current and voltage, or the overvoltage or undervoltage phenomenon caused by the nature of an electrical power system and design technology of the transformer in the three phase transformer system. Hence, in general, ferroresonance analyzed by converting to the LC equivalent circuit. However, in general, the aforementioned analytical method only applies to the resonance phenomenon that is generated by the interaction of the capacitance of bussbar and grounding, and switching as the capacitor component with PT and the transformer as the inductance component in a system. Subsequently, the condition where ferroresonance was generated since overvoltage was supplied as line voltage to the phase voltage and thus the iron core is saturated due to the interconnection between grounded and ungrounded systems could not be analyzed when single phase PT was connected in a ${\Delta}$/Y connection system. In this study, voltage swell in the configuration of grounded circuit of a step-up transformer with the ${\Delta}-{\Delta}$ connection linked to PT for control power and the ferroresonance generated by overvoltage when the line voltage of the ${\Delta}-{\Delta}$ connection was connected to the phase voltage of the grounded Y-Y connection were analyzed using PSCAD / EMTDC through the failure case of the transformer caused by ferroresonance in the system with the ${\Delta}-{\Delta}$/Y-Y connection, and subsequently, the preventive measure of ferroresonance was proposed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.817-827
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• 2015

In this modern society, people are having a close relationship with smartphone. This makes easier for hackers to gain the user's information by installing the malware in the user's smartphone without the user's authority. This kind of action are threats to the user's privacy. The malware characteristics are different to the general applications. It requires the user's authority. In this paper, we proposed a new classification method of user requirements method by each application using the Principle Component Analysis(PCA) and Probabilistic K-Nearest Neighbor(PKNN) methods. The combination of those method outputs the improved result to classify between malware and general applications. By using the K-fold Cross Validation, the measurement precision of PKNN is improved compare to the previous K-Nearest Neighbor(KNN). The classification which difficult to solve by KNN also can be solve by PKNN with optimizing the discovering the parameter k and ${\beta}$. Also the sample that has being use in this experiment is based on the Contagio.

• Journal of the Korean Applied Science and Technology
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• v.30 no.2
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• pp.225-232
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• 2013

Omija component was known to possess natural odor, taste, color, and various general characteristics. Omija extraction was extracted using ethanol as a solvent. Omija extract showed a red-purple color of some viscous liquid state. Some conclusions from natural Omija extract were obtained as follow. The result of antimicrobial experiment to add Omija extract, the number of microbial population showed negative reaction from 3 days after it cultivated. This phenomenon could confirm that Omija component affected to antimicrobial effect. The result of dyeing experiment to add Omija extract, fiber dyeing effect showed with some ivory color after dyed to cotton and silk. Also, this phenomenon could confirm that Omija component affected to natural dyeing effect from observated dye state with biological microscope(BM). The result of instrumental analysis, inorganic components of K(109.60ppm), Na(3.500ppm), Ca(1.205ppm), Mg(0.900ppm), Li(0.350ppm), Si(0.380ppm), Cu(0.250ppm), Fe(0.125ppm), Zn(0.090ppm), etcs from Omija were ascertained with ICP/OES, and organic components of benzene(10.808), borny lacetate(11.289), phenol(14.183), ${\beta}$-terpinene(15.840), ${\alpha}$-terpinolene(17.616) etcs from Omija were ascertained with GC/MSD.

• Bulletin of the Korean Mathematical Society
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• v.55 no.4
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• pp.1303-1315
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• 2018

For principal component analysis (PCA) to efficiently analyze large scale matrices, it is crucial to find a few singular vectors in cheaper computational cost and under lower memory requirement. To compute those in a fast and robust way, we propose a new stochastic method. Especially, we adopt the stochastic variance reduced gradient (SVRG) method [11] to avoid asymptotically slow convergence in stochastic gradient descent methods. For that purpose, we reformulate the PCA problem as a unconstrained optimization problem using a quadratic penalty. In general, increasing the penalty parameter to infinity is needed for the equivalence of the two problems. However, in this case, exact penalization is guaranteed by applying the analysis in [24]. We establish the convergence rate of the proposed method to a stationary point and numerical experiments illustrate the validity and efficiency of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.263-266
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• 2007

This paper discusses the Bayesian statistical inference. This paper discusses the Bayesian inference, MCMC (Markov Chain Monte Carlo) integration, MCMC method, Metropolis-Hastings algorithm, Gibbs sampling, Maximum likelihood estimation, Expectation Maximization algorithm, missing data processing, and BMA (Bayesian Model Averaging). The Bayesian statistical inference is used to process a large amount of data in the areas of biology, medicine, bioengineering, science and engineering, and general data analysis and processing, and provides the important method to draw the optimal inference result. Lastly, this paper discusses the method of principal component analysis. The PCA method is also used for data analysis and inference.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.721-731
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• 2017

In this study, principal component analysis methods using Lasso penalty are introduced. There are two popular methods that apply Lasso penalty to principal component analysis. The first method is to find an optimal vector of linear combination as the regression coefficient vector of regressing for each principal component on the original data matrix with Lasso penalty (elastic net penalty in general). The second method is to find an optimal vector of linear combination by minimizing the residual matrix obtained from approximating the original matrix by the singular value decomposition with Lasso penalty. In this study, we have reviewed two methods of principal components using Lasso penalty in detail, and shown that these methods have an advantage especially in applying to data sets that have more variables than cases. Also, these methods are compared in an application to a real data set using R program. More specifically, these methods are applied to the crime data in Ahamad (1967), which has more variables than cases.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.2
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• pp.85-93
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• 2021

In this paper, the aerodynamic characteristics of general tail controlled missile were predicted and corrected the result using semi-empirical analysis tool. The cause of the error was confirmed by comparing the aerodynamic characteristics prediction result of the semi-empirical analysis tool with the wind tunnel test result, and the main error factor of the semi-empirical analysis tool was the interference component between the main wing and the tail wing. The semi-empirical analysis results were corrected using the wind tunnel test results and the computational analysis results, and it was confirmed that the corrected data agrees well with the wind tunnel test results. Through this study, it was confirmed that the wing-tail interference component correction is needed when predicting the aerodynamic characteristics of a general tail controlled missile using a semi-empirical analysis tool.

• Journal of Sericultural and Entomological Science
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• v.32 no.1
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• pp.17-30
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• 1990

A 6${\times}$4 top-cross set was made from the ten silkworm parental lines selected by the first principal component scores. They were also analysed for the relationship between the combining ability and the first principal component score. The highest general combining ability effects were detected in the parental lines of Japanese, N39 and chinese, C46, for the most quantitative characters in the study. The first principal component score of factors related to silk productivity in the parents was significantly and positively correlated to the general combining ability of the twelve characters such as cocoon yield, cocoon weight, cocoon shell weight, cocoon shell percentage, duration of the 5th instar larvae, total larval period, length of a bave, weight of a have, non-breaking length of a bave, non-breaking weight of a have, raw silk percentage, and neatness. Similarity distance (D$^2$) was related to the specific combining ability of the characters such as cocoon yield, non-breaking length of a bave, non-breaking weight of a have, non-breaking ratio of a bave, raw silk percentage, neatness. From the results, it is possible to predict the general combining ability effects by the principal component scores for the 12 characters of the parents related to silk productivity.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.1
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• pp.24-30
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• 1991

This paper deals with the analysis of the transient characteristics of a superconducting a.c. generator(SCG) using Finite Element Method. Since the magnetic field induced by the field current and the armature currents are not sinusoidally distributed in a generator, the conventional equivalent circuit method, in general, uses the fundamental component only and is done in frequency domain. But the finite element analysis makes it possible to analyze the transient magnetic field distribution and the electrical characteristics of the double shields of SCG in time domain.

• Smart Structures and Systems
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• v.6 no.4
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• pp.349-362
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• 2010

Ultrasonic Guided Waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes a SHM method based on UGWs, discrete wavelet transform (DWT), and principal component analysis (PCA) able to detect and quantify the onset and propagation of fatigue cracks in structural waveguides. The method combines the advantages of guided wave signals processed through the DWT with the outcomes of selecting defect-sensitive features to perform a multivariate diagnosis of damage. This diagnosis is based on the PCA. The framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a PXI platform that controls the generation and measurement of the ultrasonic signals by means of piezoelectric transducers made of Lead Zirconate Titanate. Although the approach is demonstrated in a beam test, it is argued that the proposed method is general and applicable to any structure that can sustain the propagation of UGWs.