• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.153-158
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• 1995

All of the nuclear power plants in Korea we operating with analog instrumentation and control (I&C) equipment which are increasingly faced with frequent troubles, obsolescence and high maintenance expenses. Electrical and computer technology has improved rapidly in recent years and has been applied to other industries. So it is strongly recommended we adopt modern digital and computer technology to improve plant safety and availability. The advanced I&C system, namely, Integrated Intelligent Instrumentation and Control System (I$^3$CS) will be developed for beyond the next generation nuclear power plant. I$^3$CS consists of three major parts, the advanced compact workstation, distributed digital control and protection system including Automatic Start-up/shutdown Intelligent Control System (ASICS) and the computer-based alarm processing and operator support system, namely, Diagnosis, Response, and operator Aid Management System (DREAMS).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.212-216
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• 1996

Recently, with the development of NC and CNC machine tools and the high labor wage, the cutting process requires the high speed and automatic system which uses industrial robots and the flexible manufacturing system(FMS) that combines several machine tools. In this system, the whole system can be influenced by just one of the machin tools. So it needs to detect a problem and to solve it immediately In in-process state. The monitoring system through measuring the motor current with current sensor has been attracting the attention of lots of researchers view of its low cost and flexibility. By using the pattern discriminant with the detected three-phase-current signal, that is, $I_{RMS}$, a system which can monitor and analyze abnormal machining process condition of the workpiece during the machining will be able to be developed in this research.h.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.993-1002
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• 2016

Hepatitis is a major public health problem all around the world. This paper proposes an automatic disease diagnosis system for hepatitis based on Genetic Algorithm (GA) Wavelet Kernel (WK) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by ELM learning method. The hepatitis disease datasets are obtained from UCI machine learning database. In Wavelet Kernel Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. Therefore, values of these parameters and numbers of hidden neurons should be tuned carefully based on the solved problem. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using Genetic Algorithm (GA). The performance of proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specivity analysis and ROC curves. The results of the proposed GA-WK-ELM method are compared with the results of the previous hepatitis disease studies using same database as well as different database. When previous studies are investigated, it is clearly seen that the high classification accuracies have been obtained in case of reducing the feature vector to low dimension. However, proposed GA-WK-ELM method gives satisfactory results without reducing the feature vector. The calculated highest classification accuracy of proposed GA-WK-ELM method is found as 96.642 %.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.95-98
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• 2011

System to urinalysis using FT-IR instruments is presented based on fuzzy logic knowledge. Linguistic expressions of the possibility of infection and the importance were quantified and membership functions were determined based on general quantitative criteria. Diseases considered were Diabetes Mellitus, Proteinuria, Microalbuminuria. Glucose, Protein, Albumin, Creatinine in 30 samples were analyzed by the present system, which resulted in 74% accuracy. The simple mathematical formulation of present system would enable an easy implementation in commercial analysis instruments. Also, the identical fuzzy logic can be applied to similar diagnostic environments in general.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.119-126
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• 2022

A growing number of gas-insulated transformers in underground power substations in urban areas are approaching 20 years of operation, the time when failures begin to occur. It is thus essential to prevent failure through accurate condition diagnosis of the given facility. Various solid insulation materials exist inside of the transformers, and the generated decomposition gas may differ for each gas-insulated equipment. In this study, a simulation system was designed to analyze the deterioration characteristics of SF₆ decomposition gas and insulation materials under the conditions of partial discharge and thermal fault for diagnosis of gas-insulated transformers. Degradation characteristics of the insulation materials was determined using an automatic viscometer and FT-IR. The analysis results showed that the pattern of decomposition gas generation under partial discharge and thermal fault was different. In particular, acetaldehyde was detected under a thermal fault in all types of insulation, but not under partial discharge or an arc condition. In addition, in the case of insulation materials, deterioration of the insulation itself rapidly progressed as the experimental temperature increased. It was confirmed that it was possible to diagnose the internal discharge or thermal fault occurrence of the transformer through the ratio and type of decomposition gas generated in the gas-insulated transformer.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.53-62
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• 2023

Due to the recent increase in new and renewable energy, gas turbine generators start and stop every day to supply high-quality power, and accordingly, the life span of high-temperature parts is shortened and the failure of combustion chamber temperature sensors increases. Therefore, in this study, we proposed a fuzzy logic-based failure diagnosis algorithm that can accurately diagnose and systematically detect the failure of the sensor when the dual temperature sensor used for gas turbine control fails, and to confirm the usefulness of the proposed algorithm We tried to confirm the usefulness of the proposed algorithm by performing various simulations under the matlab/simulink environment.

• Journal of the Korea Society of Computer and Information
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• v.4 no.1
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• pp.47-53
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• 1999

In medical field, the computer has been used in the automatic processing of data derived in hospital. the automation of diagonal devices, and processing of medical digital images. In this paper, we classify red blood cell into 16 class including normal cell to the automation of blood analysis to diagnose disease. First, using UNL Fourier and invariant moment algorithm, we extract features of red blood cell from blood cell image and then construct multi-layer backpropagation neural network to recognize. We proof that the system can give support to blood analyzer through blood sample analysis of 10 patients.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1221-1224
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• 2004

This paper presents the programmable logic controller (PLC) networking development for communication with the mobile phone. The proposed technique is suitable for remote sensing control systems, which can display the operation status and monitoring fault diagnosis of a system. The system operation is based on the use of personal computer (PC) to logically analyze data from PLC, and existing internet protocol for sending information messages to mobile phones.

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.179-188
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• 1996

A clinically oriented 32-channel electroencephalogram (EEG) and evoked potential (EP) mapping system has been developed EEG and EP signals acquired from 32-channel electrodes attached on the heroid surface are amplified by a pre-amplifier which is separated from main amplifier and is located near the patient to reduce signal attenuation and noise contamination between electrodes and the amplifier. The amplified signals are further amplified by a main amplifier where various filtering and gain contr61 are achieved An automatic artifact rejection scheme is employed using neural network-based EEG and artifact classifier, by which examination time is substantially reduce4 The continuously measured EEG sigrlals are used for spectral mapping, and auditory and visual evoked potentials measured in synchronous to the auditory and visual stimuli are used for temporal evoked potential mapping. A user-friendly graphical interface based on the Microsoft Window 3.1 is developed for the operation of the system. Statistical databases for comparisons of group and individual are included to support a statistically-based diagnosis.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.89-92
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• 1992

The sensory nerve study is the important index to diagnosis peripheral neuromyotic disease. This paper discusses about the design of parameter - latency, amplitude, conduction velocity - measurement system in the sensory nerve. This system consists of three parts which are Main Control Unit(MCU), Stimulator, and external output unit. Also new measurement algorithms which is adaptive threshold method is presented in this paper. The designed system is controlled by MCU includes automatic detection algorithms and self-diagnostic functions.