• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.769-775
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• 2011

Custom power devices such as dynamic voltage restorer (DVR) and DSTATCOM are used to improve the power quality in distribution systems. These devices require real power to compensate the deep voltage sag during sufficient time. An interline DVR (IDVR) consists of several DVRs in different feeders. In this paper, a neural network is proposed to control the IDVR performance to achieve optimal mitigation of voltage sags, swell, and unbalance, as well as improvement of dynamic performance. Three multilayer perceptron neural networks are used to identify and regulate the dynamics of the voltage on sensitive load. A backpropagation algorithm trains this type of network. The proposed controller provides optimal mitigation of voltage dynamic. Simulation is carried out by MATLAB/Simulink, demonstrating that the proposed controller has fast response with lower total harmonic distortion.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2299-2301
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• 1998

In this paper, a modified RBF (Radial Basis Function) neural network structure is suggested for the prediction of time series with non-linear, non-stationary characteristics. Conventional RBF neural network predicting time series by using past outputs is for sensing the trajectory of the time series and for reacting when there exists strong relation between input and hidden neuron's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden neurons are modified to react to the increments of input variable and multiplied by increments(or decrements) of out puts for prediction. When the suggested structure is applied to prediction of Lorenz equation, and Rossler equation, improved performances are obtainable.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.238-241
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• 2002

With the popularity of utilization of the Internet among people, the amount of data in the network rapidly increased. So that, the fall of response time from WWW server, which is caused by the network traffic and the burden on m server, has become more of an issue. This problem is encouraged the rearch by redundancy of requesting the same pages by many people, even though they browse the same the ones. To reduce these redundancy, WWW cache server is used commonly in order to store m page data and reuse them. However, the technical uses of WWW cache that different from CPU and Disk cache, is known for its difficulty of improving the cache hit rate. Consecuently, it is difficult to choose effective WWW data to be stored from all data flowing through the WWW cache server. On the other hand, there are room for improvement in commonly used cache replacement algorithms by WWW cache server. In our study, we try to realize a WWW cache server that stresses on the improvement of the stresses of response time. To this end, we propose the new cache replacement algorithm by focusing on the utilizable information of network distance from the WWW cache server to WWW server that possessing the page data of the user requesting.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.305-313
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• 2011

Due to the expansion of high-speed Internet access, the need for secure and reliable networks has become more critical. The sophistication of network attacks, as well as their severity, has also increased recently. As such, more and more organizations are becoming vulnerable to attack. The aim of this research is to classify network attacks using neural networks (NN), which leads to a higher detection rate and a lower false alarm rate in a shorter time. This paper focuses on two classification types: a single class (normal, or attack), and a multi class (normal, DoS, PRB, R2L, U2R), where the category of attack is also detected by the NN. Extensive analysis is conducted in order to assess the translation of symbolic data, partitioning of the training data and the complexity of the architecture. This paper investigates two engines; the first engine is the back-propagation neural network intrusion detection system (BPNNIDS) and the second engine is the radial basis function neural network intrusion detection system (BPNNIDS). The two engines proposed in this paper are tested against traditional and other machine learning algorithms using a common dataset: the DARPA 98 KDD99 benchmark dataset from International Knowledge Discovery and Data Mining Tools. BPNNIDS shows a superior response compared to the other techniques reported in literature especially in terms of response time, detection rate and false positive rate.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.213-219
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• 2018

In this paper, we introduce a target position reasoning system based on Bayesian network that selects destinations of robots on a map to explore compound disaster environments. Compound disaster accidents have hazardous conditions because of a low visibility and a high temperature. Before firefighters enter the environment, the robots notify information in advance, such as victim's positions, number of victims, and status of debris of building. The problem of the previous system is that the system requires a target position to operate the robots and the firefighter need to learn how to use the robot. However, selecting the target position is not easy because of the information gap between eyewitness accounts and map coordinates. In addition, learning the technique how to use the robots needs a lot of time and money. The proposed system infers the target area using Bayesian network and selects proper x, y coordinates on the map based on image processing methods of the map. To verify the proposed system, we designed three example scenarios based on eyewetinees testimonies and compared time consumption between human and the system. In addition, we evaluate the system usability by 40 subjects.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1984.10a
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• pp.7-10
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• 1984

In this paper, the advantages of buffer insertion access method in comparison with other access methods to local area networks are analyzed. Sending and Receiving protocols in a data link layer are designed by a software method, We have derived both qeueing delays and the response time for the performance model that is proposed in this paper, and using the computer simulation, analyzed the performance for the proposed model in terms of the throughput rate- response time characteristrics. Based on the proposed model, the hardware design is implemented.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.195-195
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• 1999

This paper is Proposed a new method to deal with the optimized auto-tuning for the PID controller which is used to the process-control in various fields. First of all, in this method, 1st order delay system with dead time which is modelled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nickels method. So we can find the parameters of PID controller so as to minimize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. Finally, after studying the parameters of PID controller by Backpropagation of Neural-Network, when we give new K, L, T values to Neural-Network, the optimized parameter of PID controller is found by Neural-Network Program.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2242-2244
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• 2003

This paper considers a networked control system (NCS) that consists of an inverted cart pendulum, a digital controller, and a controller area network (CAN) in which the actuator and sensors of the pendulum are connected to form a closed-loop system. The worst-case message response time (WCMRT) in the CAN is analyzed and the analysis results are applied to the target control system. For the case where the control system cannot satisfy the WCMRT condition and therefore time delays are inevitable, the Luck and Ray method is used to compensate the network-induced time delays. Simulations are carried out to show the feasibility of the proposed scheme.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.1
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• pp.24-30
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• 2006

The Controller Area Network (CAN) is being widely used for real-time control application and small-scale distributed computer controller systems. When the stuff bits are generated by bit-stuffing mechanism in the CAN network, it causes jitter including variations in response time and delay In order to eliminate this jitter, stuff bits must be controlled to minimize the response time and to reduce the variation of data transmission time. This paper proposes the method to reduce the stuff bits by restriction of available identifier and bit mask using exclusive OR operation. This da manipulation method are pretty useful to the real-time control strategy with respect to performance. However, the CAN may exhibit unfair behavior under heavy traffic conditions. When there are both high and low priority messages ready for transmission, the proposed precedence priority filtering method allows one low priority message to be exchanged between any two adjacent higher priority messages. In this way, the length of each transmission delays is upper bounded. These procedures are implemented as local controllers for the ISHURO(Inha Semvung Humanoid Robot).

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1979-1990
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• 2021

Evacuation Time Estimate (ETE) can provide decision-makers with a likelihood to implement evacuation of a population with radiation exposure risk by a nuclear power plant. Thus, the ETE is essential for developing an emergency response preparedness. However, studies on ETE have not been conducted adequately in Korea to date. In this study, different cohorts were selected based on assumptions. Existing local data were collected to construct a multi-model network by TSIS-CORSIM code. Furthermore, several links were aggregated to make simple calculations, and post-processing was conducted for dealing with the stochastic property of TSIS-CORSIM. The average speed of each cohort was calculated by the link aggregation and post-processing, and the evacuation time was estimated. As a result, the average cohort-based evacuation time was estimated as 2.4-6.8 h, and the average clearance time from ten simulations in 26 km was calculated as 27.3 h. Through this study, uncertainty factors to ETE results, such as classifying cohorts, degree of model complexity, traffic volume outside of the network, were identified. Various studies related to these factors will be needed to improve ETE's methodology and obtain the reliability of ETE results.