• Journal of Biomedical Engineering Research
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• v.31 no.5
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• pp.395-400
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• 2010

Hypertension is the chronic disease that the 16% of total population are suffering, and it needs to be studied to find alternative treatment because of the tolerance and side effect of medications that may bother some patients. in this paper, we verified practicality of implantable electrical stimulator that can readily change stimulus magnitude and frequency. And this device is possible to stimulate baroreflex or parasympathetic nerve. Therefore we performed in vitro tests and animal experiment for device's operating conditions. This device consist of implantable electrical stimulator and extracorporeal control/monitoring system. Stimulator was designed to make 1Hz~100Hz pulses and it can change continuous or periodic pulse train type. And this device can control stimulator's function and monitor stimulator's status and patients' blood pressure at exterior of body using ZigBee module as wireless telecommunication. We verified that stimulator have error rate under 5% at 50mm depth of organs and, stimulator makes high-efficiency energy with closer position of two electrodes. Also we can confirm the performance of device that decreasing blood pressure and heart rate of a rat by electrical stimulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.2
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• pp.142-149
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• 2004

This paper presents a neural network controller of a grid-connected wind energy conversion system for extracting maximum power from wind and a power controller to transfer the maximum power extracted into a utility grid. It discusses the modeling and simulation of the wind energy conversion system with the controllers, which consists of an induction generator, a transformer, a link of a rectifier, and an inverter. The paper describes tile drive train model, induction generator model and grid-interface model for dynamics analysis. Maximum power extraction is achieved by controlling the pitch angle of the rotor blades by a neural network controller. Pitch control method is mechanically complicated, but the control performance is better than that of the stall regulation. The simulation results performed on MATLAB show the variation of the generator torque, the generator rotor speed, the pitch angle, and real/reactive power injected into the grid, etc. Based on the simulation results, the effectiveness of the proposed controllers is verified.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.1
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• pp.152-159
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• 2010

High power density and power conversion efficiency have been required in the power converters according to the rapid growth of industry. In this context, the next generation High Speed Train(HST) requires power converter which has high-efficiency, high-performance and high-density. In this paper, the new control technique for battery charger used for the next generation HST is proposed. The phase shift ZVZCS converter is classified according to a resonant circuit which is located in the primary or secondary side. In this paper, The PWM switching technique using all-pass filter is proposed to control ZVZCS converter which has resonant circuit in the secondary side. ATmega_128 micro controller based in all-pass filter in substitute for phase shift IC is presented to have digital control. To verify the proposed topology, the simulation and experiment are performed by using PSIM software and 1[kW] experimental set-up.

• 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.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.299-303
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• 2017

By piling up hidden layers in artificial neural networks, deep learning is delivering outstanding performances for high-level abstraction problems such as object/speech recognition and natural language processing. Alternatively, deep-learning users often struggle with the tremendous amounts of time and resources that are required to train deep neural networks. To alleviate this computational challenge, many approaches have been proposed in a diversity of areas. In this work, two of the existing Apache Spark-based acceleration frameworks for deep learning (SparkNet and DeepSpark) are compared and analyzed in terms of the training accuracy and the time demands. In the authors' experiments with the CIFAR-10 and CIFAR-100 benchmark datasets, SparkNet showed a more stable convergence behavior than DeepSpark; but in terms of the training accuracy, DeepSpark delivered a higher classification accuracy of approximately 15%. For some of the cases, DeepSpark also outperformed the sequential implementation running on a single machine in terms of both the accuracy and the running time.

• Journal of the Korean Society of Systems Engineering
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• v.7 no.2
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• pp.7-11
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• 2011

A high-speed railway system represents a typical example of large-scale multi-disciplinary system, consisting of subsystems such as rolling-stock, electrical hardware, electronics, control, information, communication, civil technology etc. The system design and acquisition data of the large-scale system must be the subject under strict configuration control and management. Systems engineering technology development project for Korea next generation High-speed Electric Multiple Unit (HEMU) system in progress is a national large system development project that is not only a large-size and complex but also multi-disciplinary in nature. Therefore, all stakeholders must understand and share the functional and performance requirements of HEMU throughout its life-cycle phases. Also in the test and evaluation phase, all systems requirements must be verified. In 2011, the prototype train manufacturing will be completed. It will do test run on the commercial line and all systems requirements are verified until 2012. For the system verification, the test and evaluation process have to be established before the test trial run. Using a systems engineering tool, the system design database(SDD) with requirements traceability and development process management in the course of the development have to be established. This paper represents the test and evaluation process development based on the SEMP(Systems Engineering Management Plan) developed in the design stage. The test and evaluation process is refined and updated in comparison to the design stage one. The test and evaluation process consists of procedure, test and evaluation method and schedule. So through this process, it is defined that each systems requirements is verified on which test and about what time.

• Journal of Korea Multimedia Society
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• v.4 no.4
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• pp.333-338
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• 2001

In this paper, fuzzy-ARTMAP equalizer is developed mainly for overcoming the obstacles, such as complexity and long training, in implementing the previously developed neural-basis equalizers. The proposed fuzzy-ARTMAP equalizer is fast and easy to train and includes capabilities not found in other neural network approaches a small number of parameters, no requirements for the choice of initial weights, no risk of getting trapped in local minima, and capability of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random from linear channel with Gaussian noise. The performance of the proposed equalizer is compared with other neural net basis equalizers, such as MLP and RBF equalizers. The fuzzy ARTMAP equalizer combines relatively simple structure and fast processing speed; it gives accurate results for nonlinear problems that cannot be solved with a linear equalizer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9A
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• pp.755-761
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• 2005

In this paper, we proposed a channel estimation method by impulse signal train in OFDM. In order to estimate the channel response, 4 impulse signals are generated and transmitted during one OFDM (Orthogonal Frequency Division Multiplexing) symbol. The intervals between the impulse signals are all equal in time domain. At the receiver, the impulse response signals are summed and averaged. And then, the averaged impulse response signal is zero padded and fast Fourier transformed to obtain the channel estimation. The BER performance of the proposed method is compared with those of conventional estimation method using the long training sequence in fast fading environments. The simulation results show that the proposed method improves by 3 dB in terms of Eb/No, compared with the conventional method.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1257-1268
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• 2014

Since the introduction of KTX (Korea Tranin eXpress) in Korea reilway market, number of passengers using KTX has been greatly increased in the market. Thus, demand forecasting for KTX passengers has been played a importantant role in the train operation and management. In this paper, we study several time series models and compare the models based on considering special days and others. We used the MAPE (Mean Absolute Percentage Errors) to compare the performance between the models and we showed that the Reg-AR-GARCH model outperformanced other models in short-term period such as one month. In the longer periods, the Reg-ARMA model showed best forecasting accuracy compared with other models.

• Hwankyungkyoyuk
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• v.14 no.2
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• pp.95-105
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• 2001

Out-door learning activity in environmental education has been emphasized as an effective method in environmental education since the aims of environmental education emphasize students'value, attitude, actions as well as knowledge. In order to implement successfully out-door learning activity in environmental education classrooms, teachers'perceptions to environmental problems and experiences at fields are essential. An environmental education network among the metropolitan city and provincial office of education, nongovernmental organization of environmental movement and education and university was established and a teachers'in-service training program of out-door learning centered environmental education was implemented. The program was developed in order to 1) connect environmental education with the regional environmental situations, 2) provide teachers with opportunities to participate in an out-door learning program, and 3) train teachers to be environmental education leaders of out-door learning. For evaluation of the program, responses of participants to questionnaire were analyzed. Most of teachers responded that their perception of environment was changed positively after the participation in the program. This study suggested that a future planning of a teachers'in-service training program of out-door learning centered environmental education should be developed in considerations of arranging enough hours for out-door learning at regional environmental sites, applying performance assessment, providing teachers with multiple opportunities with programs in different levels including enriched programs, and establishing an environmental education network among nongovernmental organization of environment movement and education, university, and local offices and department of education.