• 한국가스학회지
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• 제28권2호
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• pp.38-46
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• 2024

도시가스 배관망은 넓은 지역에 분포되며, 계층적으로 복잡하게 연결되어 있는 것을 특징으로 한다. 넓은 지역에 분포된 배관망의 압력 상태를 정밀하게 감시하기 위하여 PCA 기반의 센서 고장탐지 및 진단 기법을 적용하였다. 도시가스 배관망은 센서의 수가 많기 때문에 상호 연관성이 높은 센서들을 하나의 블록으로 묶어 블록 차원에서 전체 센서를 다루는 다중블록 PCA(MBPCA)가 추천된다. 그런데 MBPCA는 고장센서 식별 성능이 우수한 장점이 있지만, 센서의 개수가 증가할수록 고장탐지 성능이 나빠지는 경우가 많고, 지엽적인 변화에도 모델 전체를 갱신해야 하는 문제점이 있다. 이러한 이유로 모든 센서에 대해 MBPCA를 적용하는 것보다 선택적으로 MBPCA를 적용하는 것이 필요하다. 본 연구에서는 고장탐지 성능(fault detectability) 지수와 고장진단 성능(fault identificability) 지수를 제안하였으며, 이를 통해 블록별로 MBPCA와 PCA의 성능을 비교할 수 있도록 하였다. 이 지수를 기준으로 MBPCA와 PCA 블록을 구분하여 (주)해양에너지의 도시가스 배관망의 고장 탐지 및 진단 시스템을 개발하였으며, 센서의 개수가 많은 경우에 발생하는 문제점을 해결할 수 있었다.

• 한국군사과학기술학회지
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• 제21권6호
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• pp.744-751
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• 2018

In this paper, we propose a fast detection and identification algorithm of chemical gases with a passive FTIR spectrometer. We use a pre-detection algorithm that can reduce the spatial region effectively for gas detection and the candidates of the target. It is possible to remove background spectra effectively from measured spectra with the least-squares method. The CC(Correlation Coefficients) and the SNR(Signal-to-Noise Ratio) methods are used for the detection of target gases. The proposed pre-detection algorithm allows the total process of chemical gas detection to be performed with lower complexity compared with the conventional algorithms. This paper can help developing real-time chemical detection instruments and various applications of FTIR spectrometers.

• 한국가스학회지
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• 제20권5호
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• pp.40-49
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• 2016

지하 매설배관을 이용한 유체 수송 중 임의의 배관 위치에서 누설이 발생할 경우 육안 식별의 어려움으로 진동 센서 등을 이용하여 누설 위치를 탐지한다. 이러한 누설 위치 검출 시스템은 물과 같은 비 압축성 유체를 대상으로 센서 간의 신호 도달 시간차와 음파의 전파속도를 이용하여 검출하고 있다. 본 논문에서는 가스와 같은 압축성 유체의 누설 검출을 위한 시스템을 개발하고자 기존 검출 방법을 검토하고, 온도와 압력을 고려한 압축성 유체의 전파속도 식을 개선하고 압축 공기를 이용한 실험 장치를 구축하여 실험 수행을 통해 검증하였다. 검증 결과 매설배관 내 압축성 유체의 누설 위치 검출이 가능함을 확인하였으며, 가스 수송용 매설배관 내 누설 위치 검출 시스템 개발에 적용 가능함을 확인하였다.

• 한국가스학회:학술대회논문집
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• 한국가스학회 2007년도 춘계학술발표회 논문집
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• pp.92-97
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• 2007

• 센서학회지
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• 제17권3호
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• pp.183-187
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• 2008

A sub-ppm level MEMS gas sensor that can be used for the detection of formaldehyde (HCHO) is presented. It is realized by using a zinc oxide (ZnO) thin-film material with a Ni-seed layer as a sensing material and by bulk micromachining technology. To enhance sensitivity of the MEMS gas sensor with Ni-seed layer was embedded with ZnO sensing material and sensing electrodes. As experimental results, the changed sensor resistance ratio for HCHO gas was 9.65 % for 10 ppb, 18.06 % for 100 ppb, and 35.7 % for 1 ppm, respectively. In addition, the minimum detection level of the fabricated MEMS gas sensor was 10 ppb for the HCHO gas. And the measured output voltage was about 0.94 V for 10 ppb HCHO gas concentration. The noise level of the fabricated MEMS gas sensor was about 50 mV. The response and recovery times were 3 and 5 min, respectively. The consumption power of the Pt micro-heater under sensor testing was 184 mW and its operating temperature was $400^{\circ}C$.

• 한국전기전자재료학회논문지
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• 제24권4호
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• pp.290-296
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• 2011

Carbon nanotubes (CNTs) have excellent electrical, chemical stability, mechanical and thermal properties. In this paper, networks of Multi-walled carbon nanotube (MWCNT) materials were investigated as a resistive gas sensors for the $H_2$ gas detection. Sensor films were fabricated by the air spray method using the multi-walled CNTs dispersion solution on the glass substrates cured with plasma and nitrocellulose. Sensors were characterized by the resistance measurements in the self-fabricated oven in order to find the optimum detection properties for the hydrogen gas molecular. The sensitivity and the linearity of the MWVNT sensors using the glass substrate cured with plasma for the $H_2$ gas concentration of 0.06~0.6 ppm are 0.013~0.097%/sec and 0.131~0.959%FS, respectively. The MWCNT film was excellent in the response for the hydrogen gas moleculars and its reaction speed was very fast, which could be using as hydrogen gas sensor. The resistance of the fabricated sensors decreases when the sensors are exposed to $H_2$ gas.

• KSBB Journal
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• 제13권5호
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• pp.477-482
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• 1998

A biosensor with PZT piezoelectric ceramic crystal was developed for the detection of formaldehyde gas. Poled PZT piezoelectric ceramic disk was made from ZrO2, TiO2 and Nb2O5, together with the addition of PbO and polyvinyl alcohol, through various processes of mixing, calcination drying, crushing, forming, sintering, polishing, ion coating and poling. Oscillator circuit of sensor was made of operational amplifier(AD811AN). Formaldehyde dehydrogenase was immobilized onto a piezoelectic ceramic crystal, together with the cofactors, reduced glutathione and nicotinamide adenine dinucleotide. The effect of flow rate on the sensitivity was determined by varing the flow rate of carrier gas from 24.7mL/min to 111.7mL/min through detector cell. The results indicated that as the flow rate was increased, the recovery rate was increased. And a significant increase in the sensitivity was observed in enhanced flow rate of carrier gas. Frequency difference(ΔF) of immobilized PZT piezoelectic disk increased proportionally to the concentration gas and reproduced to repeated exposures of formaldehyde gas(28ppm, Δ68Hz).

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 춘계학술대회 논문집
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• pp.365-366
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• 2009

• Transactions on Electrical and Electronic Materials
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• 제14권5호
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• pp.254-257
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• 2013

In-situ optical emission spectroscopy (OES) is employed for PECVD chamber monitoring. OES is used as an addon sensor to monitoring and cleaning end point detection (EPD). On monitoring plasma chemistry using OES, the process gas and by-product gas are simultaneously monitored. Principal component analysis (PCA) enhances the capability of end point detection using OES data. Through chamber cleaning monitoring using OES, cleaning time is reduced by 53%, in general. Therefore, the gas usage of fluorine is also reduced, so satisfying Green Fab challenge in semiconductor manufacturing.

• Smart Structures and Systems
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• 제23권4호
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• pp.393-403
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• 2019

Applying more features gives us better accuracy in modeling; however, increasing the inputs causes the curse of dimensions. In this paper, a new structure has been proposed for fault detecting and identifying (FDI) of high-dimensional systems. This structure consist of two structure. The first part includes Auto-Encoders (AE) as Deep Neural Networks (DNNs) to produce feature engineering process and summarize the features. The second part consists of the Local Model Networks (LMNs) with LOcally LInear MOdel Tree (LOLIMOT) algorithm to model outputs (multiple models). The fault detection is based on these multiple models. Hence the residuals generated by comparing the system output and multiple models have been used to alarm the faults. To show the effectiveness of the proposed structure, it is tested on single-shaft industrial gas turbine prototype model. Finally, a brief comparison between the simulated results and several related works is presented and the well performance of the proposed structure has been illustrated.