• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.25-32
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• 2007

In this paper, a new method for first order plus dead time(FOPDT) model identification is proposed, which can identity multiple points on a process step response in terms of classification of time response. The process input and output to the test are decomposed into the transient part and the steady-state part. The steady-state part express one FOPDT model and the transient part express variously FOPDT model using least square estimation method. The optimum parameter tuning algorithm for PID controller of the Smith Predictor is proposed through ITAE as performance index. The Simulation results show the validity and improvement of performance for various processes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.173-180
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• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.207-209
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• 2007

In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

• Genomics & Informatics
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• v.5 no.1
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• pp.1-5
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• 2007

Embryonic stem cells can be differentiated into various types of cells, requiring a tight regulation of transcription. Biomarkers related to each lineage of cells are used to guide the differentiation into neural or any other fates. In previous experiments, we reported the guided differentiation (GD)-specific genes by comparing profiles of random differentiation (RD). Interestingly 68% of differentially expressed genes in GD overlap with that of RD, which makes it difficult for us to separate the lineages by examining several markers. In this paper, we design a prediction model to identify the differentiation into neural fates from any other lineage. From the profiles of 11,376 genes, 203 differentially expressed genes between neural and random differentiation were selected by random variance T-test with 95% confidence and 5% false discovery rate. Based on support vector machine algorithm, we could select 79 marker genes from the 203 informative genes to construct the optimal prediction model. Here we propose a prediction model for the prediction of neural fates from random differentiation which is constructed with a perfect accuracy.

• Journal of the Korea Computer Industry Society
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• v.7 no.3
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• pp.155-162
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• 2006

Because of their lower cost, higher speed, and longer travel, a belt drive system are quite desirable over screw driven system. However, a belt drive system are inherently difficult to control due to belt flexibility, friction, vibration, backlash and other non-linearities. This thesis presents servo control algorithm and the designing method of controller appliable to a belt drive system. In this paper, a LQ servo controller for a belt drive system is proposed to accomplish an optimal design of improved control system. In this scheme a mathematical model for the control system is obtained in state space form. Finally, the effectiveness of the proposed servo controller was verified through the computer simulation results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1637-1642
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• 2015

Next-generation applications using technology IT fused to biological signals from the emotional state to extract a lot of research has been, and the sensitivity of the human sensory functions influences the physiological condition known to be the fact that. In this paper, Propose an Emotional-lighting control algorithm using bio-signals. LED lighting for Emotion light is environmentally friendly and has a high efficiency and long life. In particular, LED lights are different colors represent the possible single light sphere advantages. And, Human sensitivity for determining a more accurate biological signals using EEG was collected using EEG equipment sensitivity was determined to analyze the EEG.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1169-1177
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• 2009

This paper presents an energy efficient MAC protocol for delay-sensitive data transmission over wireless sensor network. In general, energy consumption and delay depend on Channel Monitoring Interval and data sensing period at each sensor node. Based on this fact, we propose a new preamble structure to effectively advertise Channel Monitoring Interval and avoid the overhearing problem. In order to pursue an effective tradeoff between energy consumption and delay, we also develop a Channel Monitoring Interval determining algorithm that searches for a sub-optimal solution with a low computational complexity. Finally, experimental results are provided to compare the proposed MAC protocol with existing sensor MAC protocols.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.442-448
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• 2011

There are many research results about a self-localization technique of mobile robot. In this paper we present a self-localization technique based on the features of ceiling vision using a fisheye lens. The features obtained by SIFT(Scale Invariant Feature Transform) can be used to be matched between the previous image and the current image and then its optimal function is derived. The fisheye lens causes some distortion on its images naturally. So it must be calibrated by some algorithm. We here propose some methods for calibration of distorted images and design of a geometric fitness model. The proposed method is applied to laboratory and aile environment. We show its feasibility at some indoor environment.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.1
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• pp.84-91
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• 2013

Ultrasonic flip-chip bonding needs a precise bonding tool which delivers ultrasonic energy into chip bumps effectively to use the selected resonance mode and frequency of the horn structure. The bonding tool is excited at the resonance frequency and the input and output ports should locate at the anti-nodal points of the resonance mode. In this study, we propose new design method with topology optimization for ultrasonic bonding tools. The SIMP(solid isotropic material with penalization) method is used to formulate topology optimization and OC(optimal criteria) algorithm is adopted for the update scheme. MAC(modal assurance criterion) tracking is used for the target frequency and mode. We fabricate two prototypes of ultrasonic tools which are based on 3D optimization models after reviewing 2D and 3D topology optimization results. The prototypes are satisfied with the ultrasonic frequency and vibration amplitude as the ultrasonic bonding tools.