• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.439-441
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• 2007

The seoul metropolitan subway has installed 8 lines and about 500 stations to transport 5 million passengers everyday. The underground air pollution level in the subway stations is very severe status, which is very harmful to the commutators and its personals. Although subway roles as such a massive and huge transportation system, the subway doesn't adapt yet any real-time air monitoring system. They have only some hand-held type detector equipments for monitoring air pollution. Therefore subway passengers are exposed to the harmful air pollution environment. The most harmful environmental parameters among the air pollution are known as the dust and sound noise dB level in the subway station. Because the dust is consisted of very small particles, we can't see them easily in dark condition on the platform, but it is very harmful. The monitoring system for air pollution is developed using embedded system attached with 6 different environmental sensors. This system monitors air pollution of dust sound noise, gas, temperature, humidity, inflammable gas, toxic gas in the subway ?station. The sensor unit of the ARM-CPU board and sensor transmits real time environmental data to the main server using Zigbee wireless communication module and TCP/IP network. The main control server receives and displays the real-time environmental data, and it send alarms to the personals when high level value.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.1-8
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• 2004

In this paper, we propose 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, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2628-2634
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• 2012

The GHG(green house gas) monitoring has been an essential method to prevent climate change. Service scenarios for GHG monitoring over NGN(next generation network), a global infrastructure, was proposed in the previous study, which stays at the abstract level that may lead to difficulties for the actual implementation of the monitoring service in NGN. This paper proposes GHG monitoring service scenarios, based on functions to be incorporated in NGN, that provides the basis for actual implementation in NGN.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.1
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• pp.22-30
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• 2002

ln this paper, it was reported the developed results of the monitoring unit for 72.5kY GIS which it is one part of the monitoring system for the substation that Korea Railway Company is promoting. In order to monitor the operational status of GIS, four parameters were chosen: the number of times of the circuit breaker switching, tightness of the gas-sealed units (circuit breaker / disconnecting switch / earthing switch), the number of times of the lightning arrestor operating and the leakage current of the lighting arrester. We constructed the monitoring system that can be judged the operating condition of the GIS from the signal of the suitable sensor for the purpose. Therefore, it is possible to on line monitoring for the condition of the GlS without efforts of the Periodic inspection.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1869-1873
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• 2010

For reliable operation of oil-filled electrical equipment, monitoring and maintenance of insulating oil is essential. Dissolved gas analysis(DGA) is widely used for monitoring faults in high voltage electrical equipment in service. Therefore, oil analysis should be monitored regularly during its service life. KEPCO has investigated thousands of dissolved gas analysis data since 1985, and conducted studies on the relationship of gas in oil analysis and internal inspection results of transformer. As the results, KEPCO revised criteria for transformer diagnosis and has applied it since 2008. Almost of 100 cases of internal inspection results since 2001 have been investigated. This paper presents the correlation of the fault-identifying gases with faults found in actual transformers and how should we approach to internal inspection of transformer by dissolved gas analysis.

• Corrosion Science and Technology
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• v.5 no.3
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• pp.112-116
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• 2006

This study has been conducted to monitor the corrosion rate of cathodically protected structure and corrosion inhibited system using multi-line thin-film electrical resistance (TFER) sensor in various environments. The field test data of TFER sensor for the corrosion monitoring of cathodically protected underground pipeline in soil environments and of corrosion inhibited gas heaters were also presented. The sensor was found to be a powerful method to commit the sensitive pick-up of small corrosion rate which can be observed in the cathodically protected and corrosion inhibited systems.

• Journal of the Korean Institute of Gas
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• v.25 no.6
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• pp.92-97
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• 2021

In accordance with the revision of the Enforcement Regulations of the High-Pressure Gas Safety Management Act in February 2021, from August 27, 2021, the operation status of safety devices such as gas leak detection and alarm devices, emergency shut-off devices and flame detectors installed at hydrogen vehicle charging stations can be monitored in real time. It is transmitted and operated by the computer system managed by Korea Gas Safety Corporations. We intend to share the results of statistical analysis of abnormal signals that have occurred along with the results of the monitoring system construction so that they can be used for the safety management of hydrogen refueling stations, and to seek future safety management directions.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.5
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• pp.617-628
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• 2018

Several techniques have been developed in the last years for energy conversion and aeronautic propulsion plants monitoring and diagnostics, to ensure non-stop availability and safety, mainly based on machine learning and pattern recognition methods, which need large databases of measures. This paper aims to describe a simulation based monitoring and diagnostic method to overcome the lack of data. An application on a gas turbine powered frigate is shown. A MATLAB-SIMULINK(R) model of the frigate propulsion system has been used to generate a database of different faulty conditions of the plant. A monitoring and diagnostic system, based on Mahalanobis distance and artificial neural networks have been developed. Experimental data measured during the sea trials have been used for model calibration and validation. Test runs of the procedure have been carried out in a number of simulated degradation cases: in all the considered cases, malfunctions have been successfully detected by the developed model.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.6
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• pp.1-6
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• 2008

This work proposes a fuzzy trend monitoring method for the fault detection of a gas turbine engine through analyzing measured performance data trend. The proposed trend monitoring technique can diagnose the engine status by monitoring major engine measured parameters such as fuel flow rate, exhaust gas temperature, rotor rotational speed and vibration, and then analyzing their time deppendent changes. In order to perform this, firstly the measured engine performance data variation is formulated using Linear Regression, and then faults are isolated and identified using fuzzy logic.

• 한국가스학회:학술대회논문집
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• 2005.10a
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• pp.216-221
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• 2005