• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.292-297
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• 2004

The conventional computerized numerical controller (CNC) of machine tools has been increasingly replaced by a PC-based open architecture CNC (OAC) which is independent of the CNC vendor. The OAC and machine tools with OAC led the convenient environment where it is possible to implement user-defined application programs efficiently within CNC. Tis paper proposes a method of operational fault cause diagnosis which is based on the status of programmable logic controller (PLC) in machine tools with OAC. The operational fault is defined as a disability state occurring during normal operation of machine tools. The faults are occupied by over 70% of all faults and are also unpredictable as most of them occur without any warning. Two diagnosis models, the switching function (SF) and the step switching function (SSF), are propose in order to diagnose the fault cause quickly and exactly. The cause of an occurring fault is logically diagnosed through a fault diagnosis system (FDS) using the diagnosis models. A suitable interface environment between CNC and develope application modules is constructed in order to implement the diagnostic functions in the CNC domain. The diagnosed results were displayed on a CNC monitor for machine operators and provided to a remote site through a web browser. The result of his research could be a model of the fault cause diagnosis and the remote monitoring for machine tools with OAC.

• Journal of Veterinary Clinics
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• v.29 no.1
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• pp.63-67
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• 2012

Mosquitoes are the vectors of a number of viral diseases in cattle, such as Akabane disease, bovine ephemeral fever, Ainovirus infection, Chuzan virus infection, and Ibaraki disease. These diseases are transmitted from an infected animal to a non-infected host via the blood feeding of the vector. In Korea, the National Veterinary Research and Quarantine Services, Ministry for Food, Agriculture, Forestry and Fisheries is responsible for planning, implementation, laboratory investigations and reporting the results of the national surveillance program for mosquito-borne bovine diseases (MBD). The surveillance program, which was started in 1993, focused to determine the seroprevalence of each disease in cattle herds in space and time. From the epidemiological point of view, more important component of the surveillance program is to monitor infection rates in vectors for specific pathogens because this information is essential for a more precise understanding the dynamics of these diseases in a given environment and for determining risk of transmission. The aim of this study was to describe and compare methods for estimation of vector infection rates using maximum likelihood (MLE) and minimum infection rate in pooled samples. Factors affecting MLE such as number of pools, pooling size and diagnostic test performance are also discussed, assuming some hypothetical sampling scenarios for MBD.

• Journal of the Korean Vacuum Society
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• v.14 no.3
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• pp.110-114
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• 2005

This study proposes an accurate method of monitoring precursor consumption in chemical vapor deposition (CVD) systems. Since precursor costs are significant, finding an efficient method to monitor precursor consumption is necessary One example is the use of non-contact and inexpensive ultrasonic sensors for determining the liquid level in a container. In this study, sensors based on ultrasonic techniques have been developed for monitoring the precursor consumption in a CVD system. Moreover, the prototype sensors developed in this study can be useful in the field of semiconductors.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.64-70
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results. it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• Journal of Surface Science and Engineering
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• v.38 no.2
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• pp.73-78
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• 2005

A real-time monitoring of ICP(inductively coupled plasma) assisted magnetron sputtering of MgO was carried out using a QMS(quadrupole mass spectrometer), an OES(optical emission spectrometer), and a digital oscilloscope with a high voltage probe and a current monitor. At the time of ICP ignition, the most distinct impurity was OH emission (308.9 nm) which was dissociated from water molecules. For reactive deposition oxygen was added to Ar and the OH emission intensity was reduced abruptly When the discharge voltage was regulated by a PID controller from 240V(metallic mode) to 120V(oxide mode), the emission intensity from Mg (285.2 nm) changed proportionally to the discharge voltage, but the intensity of Ar I(811.6 nm) was constant. At 100V of discharge voltage, Mg sputtering was almost stopped. Emissions from Ar I(420.1 nm) and Mg I were dropped down to 1/10, but Ar I(811.6 nm) didn't change. And the emission from atomic oxygen (O I, 777.3 nm) was increased to 10 times. These results are compatible with those from QMS study.

• Tuberculosis and Respiratory Diseases
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• v.80 no.4
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• pp.351-357
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• 2017

In 2011, a cluster of peripartum patients were admitted to the intensive care unit of a tertiary hospital in Seoul with signs and symptoms of severe respiratory distress of unknown etiology. Subsequent epidemiological and animal studies suggested that humidifier disinfectant (HD) might represent the source of this pathology. Epidemiological studies, animal studies, and dose-response analysis demonstrated a strong association between HD use and lung injuries. The diagnostic criteria for HD-associated lung injury (HDALI) was defined on the basis of the clinical, pathological, and radiological attributes of the patients. The clinical spectrum of HDALI appears to range from asymptomatic to full-blown acute respiratory failure, and some patients have required actual lung transplantation for survival. The overall mortality of the exposed population was not significant, although peripartum patients and children who were admitted to the intensive care unit did show high mortality rates. Persistent clinical findings such as diffuse ill-defined centrilobular nodules and restrictive lung dysfunction were observed in some of the survivors. The findings of this review emphasize the importance of assessment of the level of toxicity of chemical inhalants utilized in a home setting, as well as the need to identify and monitor afflicted individuals after inhalational injury.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.485-491
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• 2015

This paper shows the design improvement and test model of FAU (Frequency Analysis Unit) in PDD (Partial Discharge Diagnosis system) for 800kV GIS (Gas Insulated Switchgear). We found some problems during operation of previous FAU, such as the aging of fiber-optic converter that can cause communication error, the malfunction of signal analysis circuit etc. And then we solved those problems by design improvement and verified the performance through type test. To monitor partial discharge, the performance of UHF sensor is important but the performance of frequency analysis unit is also very important. So we solved communication error, the malfunction of signal analysis circuit and then increased the operation reliability of FAU by improving fiber-optic converter and signal analysis circuit. Accredited testing laboratory carried out the performance verification test according to performance test criteria and procedure of reliability test standards, IEC-60225, 61000 and 60068 etc. We confirmed the test results which correspond with the performance test criteria.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.389-395
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• 2012

In this paper, we analyzed the frequency spectrum of the electromagnetic waves radiated by an electric discharge as a basic study to develop an on-line diagnostic technique for power equipment installed inside closed-switchboards. In order to simulate local and series arc discharges caused by an electric field concentration and poor connections, three types of electrode systems were fabricated, consisting of needle and plane electrodes and an arc generator meeting the specifications of UL 1699. The experiment was carried out in an electromagnetic anechoic chamber, and the measurement system consisted of a PD free transformer, a loop antenna with a frequency bandwidth of 150 kHz-30 MHz, an ultra log periodic antenna with a frequency bandwidth of 30 MHz-2 GHz, and an EMI test receiver with a frequency bandwidth of 3 Hz-3 GHz. According to the experimental results, the frequency spectra of the electrical discharges were widely distributed across a range of 150 kHz-400 MHz, depending on the defects, while commonly found between 150 kHz and 10 MHz. Therefore, considering the ambient noise and antenna characteristics, the best frequency bandwidth for a measurement system to monitor abnormal conditions by detecting electromagnetic waves in closedswitchboards is 150 kHz-10 MHz.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.295-300
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• 1998

In this paper, a real time diagnostic algorithm fur estimating the impact location by loose parts is proposed. It is composed of two modules such as the alarm discrimination module (ADM) and the impact-location estimation module(IEM). ADM decides whether the detected signal that triggers the alarm is the impact signal by loose parts or the noise signal. When the decision from ADM is concluded as the impact signal, the beginning time of burst-type signal, which the impact signal has usually such a form in time domain, provides the necessary data fur IEM. IEM by use of the arrival time method estimates the impact location of loose parts. The overall results of the estimated impact location are displayed on a computer monitor by the graphical mode and numerical data composed of the impact point, and thereby a plant operator can recognize easily the status of the impact event. This algorithm can perform the diagnosis process automatically and hence the operator's burden and the possible operator's error due to lack of expert knowledge of impact signals can be reduced remarkably. In order to validate the application of this method, the test experiment with a mock-up (flat board and reactor) system is performed. The experimental results show the efficiency of this algorithm even under high level noise and potential application to Loose Part Monitoring System (LPMS) for improving diagnosis capability in nuclear power plants.