• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1727-1731
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• 2004

We propose an ART2 neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, fault classifier by ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters is used for fault isolation. The performances of the proposed fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.284-287
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• 2017

This paper introduces liquid crystal (LC) alignment and its electro-optical properties in the LC cells with silver nanowire (AgNW) networks. The AgNW network was used as an electrode of LC cell as a substitute for an indium-tin-oxide (ITO) film. LC alignment characteristics in the LC cell using AgNW networks, which have two different sheet resistances of $60{\Omega}/m^2$ and $80{\Omega}/m^2$, were observed. The LC alignment characteristics including pretilt angle, LC alignment state, and thermal stability are similar irrespective of sheet resistance of AgNW network. However, twisted-nematic (TN)-LC cell normally operated when using AgNW network with sheet resistance of $80{\Omega}/m^2$. Electrooptical properties of TN-LC cell exhibited competitive performance compared to those of TN-LC cell based on conventional ITO electrode, which allow new approaches to replace conventional ITO electrode in display technology.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.265-273
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• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.

• 한국가시화정보학회:학술대회논문집
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• 2004.11a
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• pp.52-55
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• 2004

Thermo-chromic Liquid Crystal(TLC) particles were used as temperature sensor for thermal fluid flow. $1K\times1K$ CCD color camera and Xenon Lamp(500W) were used for the visualization of a Hele-Shaw cell. The characteristic between the reflected colors from the TLC and their corresponding temperature shows strong non-linearity. A neural network known as having strong mapping capability for non-linearity is adopted to quantify the temperature field using the image of the flow. Improvements of color-to-temperature mapping was attained by using the local color luminance (Y) and hue (H) information as the inputs for the constructed neural network.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.948-951
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• 2008

Ubiquitous sensor network (USN) based on ZigBee communication protocol has been used in various application fields, such as home-network, intelligent building and machine, logistics, environmental monitoring, military field, security field and etc. The ZigBee is targeted at radio-frequency application that require a low data rate, long battery life and secure network. Especially, the USN system can be applied efficiently to building-indoor where the complex geometry is adopted. In this study, all 90 points of railway cabin indoor were monitored for temperature and humidity using USN technology. All sensors were pre/post-calibrated and the temperature/humidity change were analyzed in a railway cabin in real-time. The results would be useful to develop the cabin heating, ventilating and air conditing (HVAC) system to meet all passengers' thermal comfort regardless of their seat position.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.5
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• pp.22-29
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• 2012

By using the cyclic properties of symmetric balance incomplete block design(BIBD) codes and arrayed-waveguide grating(AWG) routers, a compact optical CDMA network coder-decoder(codec) can be constructed. It can be observed that the various code families obtained by BIBD improve the BER performance compared to M-sequence code.

• ETRI Journal
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• v.29 no.3
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• pp.371-380
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• 2007

This paper proposes a new automatic compensation network (ACN) for a system-on-chip (SoC) transceiver. We built a 5 GHz low noise amplifier (LNA) with an on-chip ACN using 0.18 ${\mu}m$ SiGe technology. This network is extremely useful for today's radio frequency (RF) integrated circuit devices in a complete RF transceiver environment. The network comprises an RF design-for-testability (DFT) circuit, capacitor mirror banks, and a digital signal processor. The RF DFT circuit consists of a test amplifier and RF peak detectors. The RF DFT circuit helps the network to provide DC output voltages, which makes the compensation network automatic. The proposed technique utilizes output DC voltage measurements and these measured values are translated into the LNA specifications such as input impedance, gain, and noise figure using the developed mathematical equations. The ACN automatically adjusts the performance of the 5 GHz LNA with the processor in the SoC transceiver when the LNA goes out of the normal range of operation. The ACN compensates abnormal operation due to unusual thermal variation or unusual process variation. The ACN is simple, inexpensive and suitable for a complete RF transceiver environment.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.702-708
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• 2019

This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5% -100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.3-5
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• 1999

This paper deals with the effect on the stress and deformation of the rotor bar of a cage induction motor by the thermal stress, centrifugal force and electromagnetic force. We use both the thermal network method(TNM) and the finite element method(FEM) to analyze the temperature and stress of the rotor.

• Proceedings of the KIEE Conference
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• 1997.07a
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• pp.34-37
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• 1997

The nonuniform thermal sources of a squirrel cage induction motor are obtained from the losses calculated by the nonlinear complex finite element analysis considering skew effects. Then, temperature rises of the motor are calculated using thermal equivalent network analysis and compared with some experimental values.