• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.607-615
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• 2020

The purpose of this study was to analyze the research status and trends related to the industrial mathematics based on text mining techniques with a sample of 4910 papers collected in the SIAM Journal on Applied Mathematics from 1970 to 2019. The R program was used to collect titles, abstracts, and key words from the papers and to analyze topic modeling techniques based on LDA algorithm. As a result of the coherence score on the collected papers, 20 topics were determined optimally using the Gibbs sampling methods. The main results were as follows. First, studies on industrial mathematics were conducted in a variety of mathematics fields, including computational mathematics, geometry, mathematical modeling, topology, discrete mathematics, probability and statistics, with a focus on analysis and algebra. Second, 5 hot topics (mathematical biology, nonlinear partial differential equation, discrete mathematics, statistics, topology) and 1 cold topic (probability theory) were found based on time series regression analysis. Third, among the fields that were not reflected in the 2015 revised mathematics curriculum, numeral system, matrix, vector in space, and complex numbers were extracted as the contents to be covered in the high school mathematical curriculum. Finally, this study suggested strategies to activate industrial mathematics in Korea, described the study limitations, and proposed directions for future research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.5
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• pp.437-444
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• 2020

This study was focused on the stabilization of surge motions of a moored vessel under irregular head seas. A two-point moored vessel shows strong non-linearity even in regular sea, owing to its inherent non-linear restoring force. A long-crested irregular wave is subjected to the vessel system, resulting in more complex nonlinear behavior of the displacement and velocities than in the case of regular waves. Sliding mode control (SMC) is implemented in the moored vessel to control both surge displacement and surge velocity. The SMC can provide a closed-loop system with performance and robustness against parameter uncertainties and disturbances; however, chattering is the main drawback for implementing SMC. The goal of minimizing the chattering and state convergence with accuracy is achieved using a quasi-sliding mode that approximates the discontinuous function via a continuous sigmoid function. Numerical simulations were conducted to validate the effectiveness of the proposed control algorithm.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.385-394
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• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.5
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• pp.11-24
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• 2011

A real-time hybrid test of a 3 story-3 bay reinforced concrete frame which is divided into numerical and physical substructure models under uniaxial earthquake excitation was run using a fixed iteration implicit HHT time integration method. The first story inner non-ductile column was selected as the physical substructure model, and uniaxial earthquake excitation was applied to the numerical model until the specimen failed due to severe damage. A finite-element analysis program, Mercury, was newly developed and optimized for a real-time hybrid test. The drift ratio based on the top horizontal displacement of the physical substructure model was compared with the result of a numerical simulation by OpenSees and the result of a shaking table test. The experiment in this paper is one of the most complex real-time hybrid tests, and the description of the hardware, algorithm and models is presented in detail. If there is an improvement in the numerical model, the evaluation of the tangent stiffness matrix of the physical substructure model in the finite element analysis program and better software to reduce the computational time of the element state determination for the force-based beam-column element, then the comparison with the results of the real-time hybrid test and the shaking table test deserves to make a recommendation. In addition, for the goal of a "Numerical simulation of the complex structures under dynamic loading", the real time hybrid test has enough merit as an alternative to dynamic experiments of large and complex structures.

• Journal of IKEEE
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• v.8 no.1 s.14
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• pp.96-107
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• 2004

In this paper, a new integral variable structure regulation controller(IVSRC) is designed by using a special integral sliding surface and a disturbance observer for the improved regulation control of highly nonlinear robot manipulators with prescribed output performance. The sliding surface having the integral state with a special initial condition is employed in this paper to exactly predetermine the ideal sliding trajectory from a given initial condition to origin without any reaching phase. And a continuous sliding mode input using the disturbance observer is also introduced in oder to effectively follow the predetermined sliding trajectory within the prescribed accuracy without large computation burden. The performance of the prescribed tracking accuracy to the predetermined sliding trajectory is clearly investigated in detail through the two theorems together with the closed loop stability. The design of the proposed IVSRC is separated into the performance design and robustness design in each independent link. The usefulness of the algorithm has been demonstrated through simulation studies on the regulation control of a two link manipulator under parameter uncertainties and payload variations, in view of no reaching phase, no overshoot, predetermined response with prescribed accuracy, easy change of output performance, separation of design phase, and so on.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.171-185
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• 2006

A sophisticated reliability analysis method is proposed to evaluate the reliability of real nonlinear complicated dynamic structural systems excited by short duration dynamic loadings like earthquake motions by intelligently integrating the response surface method, the finite element method, the first-order reliability method, and the iterative linear interpolation scheme. The method explicitly considers all major sources of nonlinearity and uncertainty in the load and resistance-related random variables. The unique feature of the technique is that the seismic loading is applied in the time domain, providing an alternative to the classical random vibration approach. The four-parameter Richard model is used to represent the flexibility of connections of real steel frames. Uncertainties in the Richard parameters are also incorporated in the algorithm. The laterally flexible steel frame is then reinforced with reinforced concrete shear walls. The stiffness degradation of shear walls after cracking is also considered. The applicability of the method to estimate the reliability of real structures is demonstrated by considering three examples; a laterally flexible steel frame with fully restrained connections, the same steel frame with partially restrained connections with different rigidities, and a steel frame reinforced with concrete shear walls.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.413-423
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• 2003

The CMA algorithmis most widely investigated blind algorithm and the most widely used one in practice. But, since nonlinear CM cost function have not closed form solution about the optimum weight. There have been difficultiesto analyze the CMA equalizer's theoretical performance. Recently, Zeng presents the notable theoretical resultabout the MSE of CM-minimizing estimators for the FIR linear channel in the presence of AWGN. Through this method, It wouldbe possible to campare the theoretical performance between CMA and Wiener equalizer in terms of MSE. In this paper, based on Zeng's method, we first calculate the theoretical MSE bound of CMA equalizer in partial response channel which is widely used in HDD, digital VCR such as high-density digital recording.playback systems. We confirmedthis result withthe computer simulation. Except this, we also performedthe theoretical and simulation analysis about the modified CMA equalizer, which was proposed to improve the performance of CMA equalizer in partial response channel. Finally, we compare and evaluate the performance analysis results between CMA and Modified CMA equalizer.