• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.8
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• pp.33-40
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• 2009

This paper presents the high performance control of interior permanent magnet synchronous motor(IPMSM) using space vector(SV) PWM method based on hybrid artificial intelligent(HAI) controller. The HAI controller combines the advantages between adaptive fuzzy control and neural network The SV PWM method is applied to a speed control system of motor in the industry field until now and is feasible to improve harmonic rate of output current, switching frequency and response characteristics. This HAI controller is used instead of conventional PI controller in order to solve problems happening when calculating a reference voltage. The HAI controller improves speed performance by hybrid combination of reference model-based adaptive mechanism method, fuzzy control and neural network. This paper analyzes response characteristics of parameter variation, steady-state and transient-state using proposed HAI controller and this controller compares with conventional fuzzy neural network(FNN) and PI controller. Also, this paper proves validity of HAI controller.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.321-340
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• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• Journal of Microbiology and Biotechnology
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• v.12 no.3
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• pp.463-469
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• 2002

Rotaviruses are the world-wide leading causative agents of severe dehydrating gastroenteritis in young children and animals. The outer capsid glycoprotein VP7 and inner capsid glycoprotein VP6 of rotaviruses are highly antigenic and immunogenic. An SFV-based expression system has recently emerged as a useful tool for heterologous protein production in mammalian cells, exhibiting a much more efficient performance compared to other gene expression systems. Accordingly, the current study adopted an SFV-based expression system to express the VP7 of a group A human rotavirus from a Korean isolate, and the VP6 of a group B bovine rotavirus from a Korean isolate, in mammalian cells. The genes of the VP6 and VP7 were inserted into the SFV expression vector pSFV-1. The RNA was transcribed in vitro from pSFV-VP6 and pSFV-VP7 using SP6 polymerase. Each RNA was then electroporated into BHK-21 cells along with pSFV-helper RNA containing the structural protein gene without the packaging signal. The expression of VP6 and VP7 in the cytoplasm was then detected by immunocytochemistry. The recombinant virus was harvested by ultracentrifugation and examined under electron microscopy. After infecting BHK-21 cells with the defective viruses, the expressed proteins were separated by SDS-PAGE and analyzed by a Western blot. The results indicate that an SFV-based expression system fur the VP6 and VP7 of rotaviruses is an efficient tool for developing a diagnostic kit and/or preventive vaccine.

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• Analytical Science and Technology
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• v.24 no.2
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• pp.85-93
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• 2011

To protect the steel structure in a high story buildings from fire, the sprayed fire-resistive materials are applied during the construction. Current standard methods to check the quality of sprayed fire-resistive materials are real fire test in lab, which take a long time (several weeks) and expensive. In this study, a simple analytical method to check the quality of sprayed fire-resistive materials is developed using Near Infrared Spectroscopy (NIR). Total 9 kinds of sprayed fire-resisted materials and 3 kinds of normal sprayed material sets were used for the analysis. Each set of materials was 50 to 100 samples. Samples are grinded and make a fine powder. The spectral data acquisition was carried out using FT-NIR spectrometer with a integrating sphere. NIR methods successfully identify the sprayed fire resistive materials by a principle component analysis (PCA) after a vector normalization (SNV) pretreatment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.1
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• pp.58-66
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• 2009

In this paper, vector control to applied disturbance offset feedforward loop control for changing disturbances with various mechanical parameter is suggested. The proposed system estimate load torque based on induction motor torque using minimum diemension state observer. Because speed controller using state observer is used on condition of feedforward loop fur a torque, the robust speed control system realized. In this study, the proposed paper does to heighten reliability of system by presuming and use the speed by voltage and current that is detected without speed sensor. To prove the propriety of this paper, the various simulation carried out adequacy using a Matlab Simulink, and at the same time real system is made, using a ADMC300 digital signal processor, so it is proved. As the experimental result of embodying the system, the robust system is realized.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.378-385
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• 2010

The explosive increase in the use of email has made to need email classification efficiently and accurately. Current work on the email classification method have mainly been focused on a binary classification that filters out spam-mails. This methods are based on Support Vector Machines, Bayesian classifiers, rule-based classifiers. Such supervised methods, in the sense that the user is required to manually describe the rules and keyword list that is used to recognize the relevant email. Other unsupervised method using clustering techniques for the multi-category classification is created a category labels from a set of incoming messages. In this paper, we propose a new automatic email multi-category classification method using NMF for automatic category label construction method and dynamic category hierarchy method for the reorganization of email messages in the category labels. The proposed method in this paper, a large number of emails are managed efficiently by classifying multi-category email automatically, email messages in their category are reorganized for enhancing accuracy whenever users want to classify all their email messages.

• Spatial Information Research
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• v.22 no.2
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• pp.63-71
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• 2014

Ocean leisure sports have recently emerged as one of so-called blue ocean industries. They are sensitive to diverse environmental conditions such as current, temperature, and salinity, which can increase needs of forecasting data as well as in-situ observations for the ocean. In this context, a Web-based geovisualization system for coastal information produced by model forecasts was implemented for use in supporting various ocean activities. First, FVCOM(Finite Volume Coastal Ocean Model) was selected as a forecasting model, and its data was preprocessed by a spatial interpolation and sampling library. The interpolated raster data for water surface elevation, temperature, and salinity were stored in image files, and the vector data for currents including speed and direction were imported into a distributed DBMS(Database Management System). Web services in REST(Representational State Transfer) API(Application Programming Interface) were composed using Spring Framework and integrated with desktop and mobile applications developed on the basis of hybrid structure, which can realize a cross-platform environment for geovisualization.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.102-108
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• 2010

The photovoltaic system changes solar energy into DC by solar cell and this DC is inverted into AC which is used in general houses by inverter. Recently, the use of power of the photovoltaic system is increased. Therefore, the study of 3 phase solar system to transmit large power is very important. This paper proposes a method that finds simply faults and diagnoses the switch open faults of 3-phase pulse width modulation (PWM) inverter of grid-connected photovoltaic system. The proposed method in $\alpha\beta$ plane uses the patterns of trajectory image as the characteristic parameters and differenciates a normal state and open states of switches. Then, the result is made into tree. The tree is composed of 21 fault patterns and the parameters to classify faults are a shape, a trajectory area, a distributed angle, and a typical vector angle. The result shows that the proposed method diagnosed fault diagnoses, classified correctly them, and made a pattern tree by fault patterns.

• Journal of Korean Association for Spatial Structures
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• v.8 no.4
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• pp.73-80
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• 2008

The objective of current study is to develop an optimization technique for the seismic actively controlled building structures using active tendon devices by an efficient solution of LQR control gain. In order to solve the active control system, the Ricatti closed-loop algorithm has been applied, and the state vector has been formulated by the transfer matrix and solved by a numerical technique of the trapezoidal rule. The time-delay problem has been also considered by phase compensation. To optimize the performance index, the ratio of the weighted matrix is the design variable, allowable story drift limits of IBC 2000 and tendon forces have been applied as restraint conditions, and the optimum control program has been developed with the algorithm of the SUMT technique. In examples of the optimization problem of eight stories shear buildings, it is evaluated that the optimum controlled building is more suitable in the control of earthquake response than the uncontrolled system and can reduce the performance index to compare with the controlled system with a constant ratio of the weighted matrix.