• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.1-12
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• 1996

We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.296-303
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• 2007

This paper proposes a new width control system composed of an ANWC(Automatic Neural network based Width Control) and a fuzzy-PID controller in hot strip finishing mills which aims at obtaining the desirable width. The ANWC is designed using a neural network based width prediction model to minimize a width variation between the measured width and its target value. Input variables for the neural network model are chosen by using the hypothesis testing. The fuzzy-PlD control system is also designed to obtain the fast looper response and the high width control precision in the finishing mill. It is shown through the field test of the Pohang no. 1 hot strip mill of POSCO that the performance of the width margin is considerably improved by the proposed control schemes.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.515-525
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• 2007

In this paper, we develop a co-design methodology of stochastic optimal controllers and network parameters that optimizes the overall quality of control (QoC) in networked control systems (NCSs). A new dynamic model for NCSs is provided. The relationship between the system stability and performance and the sampling frequency is investigated, and the analysis of co-design of control and network parameters is presented to determine the working range of the sampling frequency in an NCS. This optimal sampling frequency range is derived based on the system dynamics and the network characteristics such as data rate, time-delay upper bound, data-packet size, and device processing time. With the optimal sampling frequency, stochastic optimal controllers are designed to improve the overall QoC in an NCS. This co-design methodology is a useful rule of thumb to choose the network and control parameters for NCS implementation. The feasibility and effectiveness of this co-design methodology is verified experimentally by our NCS test bed, a ball magnetic-levitation (maglev) system.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.4
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• pp.371-376
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• 2004

Distributed Network Protocol Version 3.0 (DNP3.0) is the communication protocol developed for the interoperability between a RTU and a central control station of SCADA in the power utility industry. In this paper DNP3.0 is implemented by using HDL with FPGA and C program on Hitachi H8/532 processor. DNP3.0 is implemented from physical layer to network layer in hardware level to reduce the computing load on a CPU. Finally, the ASIC for DNP3.0 has been manufactured from Hynix Semiconductor. The commercial feasibility of the hardware through the communication test with ASE2000 and DNP Master Simulator is performed. The developed protocol becomes one of IP, and can be used to implement SoC for the terminal device in SCADA systems. Also, the result can be applicable to various industrial controllers because it is implemented in HDL.

• Proceedings of the IEEK Conference
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• 2000.11b
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• pp.184-187
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• 2000

A network is an important portion of communications in these days. Because of many inconveniences of a wired-network, wireless solutions have been studied for many years. One of the results of those efforts is IEEE 802.11, wireless LAN. This paper briefly summarizes wireless LAN and specially focuses on the design of a network processor for the wireless LAN system. The processor has 16-bit instruction set suitably selected for network processing and low-power consumption. It is implemented and verified with a wireless LAN system model. The wireless LAN system is modeled in RTL excluding the RF module. The processor can be used in many wireless systems as a controller and utilized as a test module for the research of low-power schemes.

• ETRI Journal
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• v.14 no.4
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• pp.10-18
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• 1992

Intelligent Network offers network operators significant opportunities to provide new services such as Freephone service and Credit Calling service. To realize the IN capabilities it is required to introduce several new components into the existing telecommunication network. Among them Service Control Point is regarded as a keystone node of Intelligent Network. This paper discusses the architecture design and implementation of NICS(Network Information Control System), which is a combined system of SCP and SMS. Service processing aspects are discussed in detail among the functions of NICS, and service management and test environment of service logic are presented also. The field trial of NICS which has been developed to provide Freephone Service and Credit Calling Service is scheduled at the end of 1993.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.3
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• pp.125-131
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. In this paper, two fault detection methods were considered. One is a generic neural network, and the other is an moving average neural network. In order to compare the performance of fault detection results from these methods, two different types of faults in an air-conditioning system were applied. These are the condenser 30% fouling and the evaporator fan 25% slowdown. Test results showed that the moving average neural network was more effective for the detection of partial faults in the air-conditioning system.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1221-1226
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• 2015

This paper proposes a multiple switch mode power supply (SMPS) system based on the wireless network which controls variable load. The system enables power supply of up to 600W using 200W SMPS as a unit module and provides a controlling function of output power based on variable load and a monitoring function based on wireless network. The controlling function for output power measures the variation of output power and facilitates efficient power supply by controlling output power based on the measured variation value. The monitoring function guarantees a stable power supply by observing the multiple SMPS system in real time via wireless network. The performance of the proposed system was examined by various experiments. In addition, it was verified through standardized test of Korea Testing Certification. The results were given and discussed.

• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.468-475
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• 2008

This paper presents a method for estimating the transient stability status of the power system using radial basis function(RBF) neural network with a fast hybrid training approach. A normalized transient energy margin(${\Delta}V_n$) has been obtained by the potential energy boundary surface(PEBS) method along with a time-domain simulation technique, and is used as an output of the RBF neural network. The RBF neural network is then trained to map the operating conditions of the power system to the ${\Delta}V_n$, which provides a measure of the transient stability of the power system. The proposed approach has been successfully applied to the 10-machine 39-bus New England test system, and the results are given.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.25-34
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• 1999

In this paper, an algorithm is presented to recognize lane and obstacles based on highway road image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, edge detection, and identification of lanes. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction and the presence of absence of an obstacle. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing lane and obstacles. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning of assistance system