• 대한환경공학회지
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• 제35권4호
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• pp.233-239
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• 2013

본 연구에서는 실험실 규모의 스크러버 전단과 후단에 설치된 가스센서의 출력값으로부터 흡수제의 실시간 흡수효율을 평가하기 위한 연구를 진행하였다. 스크러버 전단과 후단에 설치된 가스센서는 센서표면에서의 가스 흡 탈착반응으로 발생되는 전기적인 변화를 출력신호로 나타내는 측정장치이며, 스크러버의 흡수제와 암모니아 가스와의 반응시간(3시간, 6시간 및 12시간)에 따른 암모니아 가스의 흡수량을 산출하였다. 또한 가스센서의 출력값으로부터 산출된 암모니아 흡수량을 기존의 암모니아 분석방법인 인도페놀법에 의한 흡수량 산정방법과 비교해 보았으며, 약 20%의 차이를 보이긴 하나 0.99 이상의 높은 상관성을 보이고 있음을 확인하였다. 또한, 반응시간에 따른 pH와 흡수량과의 높은 상관성을 확인할 수 있었으며, 흡수제에 대한 암모니아 흡수량을 실시간으로 파악함으로써 흡수제의 파과시간을 예측할 수 있었다. 향후 다양한 연구를 통하여 악취배출시설의 스크러버에 이와 같은 가스센서를 적용하여 흡수제의 흡수 효율을 실시간으로 평가하여 교체주기 및 효율 등을 실시간으로 평가할 수 있는 시스템으로 발전이 가능함을 확인하였다.

• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2006년도 하계학술발표대회 논문집
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• pp.454-459
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• 2006

There are two main streams for pattern classification technology One is the method using PCA (Principal Component Analysis) and the other is the method using Neural network. Both of them have merits and demerits. In general, using PCA is so simple while using neural network can improve algorithm continually. Algorithm using neural network needs so many calculations rendering very slow response. In this work, an attempt is made to develop algorithms adopting both PCA and neural network merits for simpler, but faster and smarter.

• 센서학회지
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• 제25권6호
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• pp.423-430
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• 2016

This study used a single metal oxide semiconductor (MOS) sensor to classify the major odorous gases hydrogen sulfide ($H_2S$), ammonia ($NH_3$) and toluene ($C_6H_5CH_3$). In order to classify these odorous substances, the voltage on the MOS sensor heater was gradually reduced in 0.5 V steps 5.0 V to examine the changes to the response by the cooling effect on the sensor as the voltage decreased. The hydrogen sulfide gas showed the highest sensitivity compared to odorless air under approximately 2.5 V and the ammonia and toluene gases showed the highest sensitivity under approximately 5.0 V. In other words, the hydrogen sulfide gas reacted better in the low temperature range of the MOS sensor, and the ammonia and toluene gases reacted better in the high-temperature range. In order to analyze the response characteristics of the MOS sensor by temperature in a pattern, a two-dimensional (2D) x-y pattern analysis was introduced to clearly classify the hydrogen sulfide, ammonia, and toluene gases. The hydrogen sulfide gas was identified by a straight line with a slope of 1.73, whereas the ammonia gas had a slope of 0.05 and the toluene gas had a slope of 0.52. Therefore, the 2D x-y pattern analysis is suggested as a new way to classify these odorous substances.

• 센서학회지
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• 제20권3호
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• pp.151-155
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• 2011

A variety of pattern recognition algorithms including neural networks may be applicable to the identification of odors. In this paper, an identification technique for an electronic odor sensing system applicable to wound state monitoring is presented. The performance of the radial basis function(RBF) network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those parameters are initialized by an ill-conditioned genetic fuzzy c-means algorithm, and the distribution of input patterns in the very first stage, the stochastic gradient(SG), is adapted. The adaptive RBF network with singular value decomposition(SVD), which provides additional adaptation capabilities to the RBF network, is used to process data from array-based gas sensors for early detection of wound infection in burn patients. The primary results indicate that infected patients can be distinguished from uninfected patients.

• 센서학회지
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• 제23권2호
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• pp.99-104
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• 2014

Sensor arrays based on chemical sensors produce multidimensional patterns of data that may be used discriminate between different chemicals. For the human observer, visualization of multidimensional data is difficult, since the eye and brain process visual information in two or three dimensions. To devise a simple means of data inspection from the response of sensor arrays, PCA (Principal Component Analysis) or Sammon's nonlinear mapping technique can be applied. The PCA, which is a well-known statistical method and widely used in data analysis, has disadvantages including data distortion and the axes for plotting the dimensionally reduced data have no physical meaning in terms of how different one cluster is from another. In this paper, we have investigated two techniques and proposed a combination technique of PCA and nonlinear Sammom mapping for visualization of multidimensional patterns to two dimensions using data sets from odor sensing system. We conclude the combination technique has shown more advantages comparing with the PCA and Sammon nonlinear technique individually.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권2호
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• pp.67-72
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• 2022

최근 인공지능을 활용한 다양한 지능형 응용서비스 개발이 활발히 진행 중이다. 특히, 제조 산업 현장에서는 인공지능 기반 실시간 예측서비스 연구가 활발히 진행 중이며 이중 화재 및 악취를 감지·예측할 수 있는 인공지능 서비스에 대한 요구가 매우 높다. 하지만 기존 감지·예측시스템은 화재 및 악취 발생 예측이 아닌 발생 후 감지 서비스가 대부분이다. 이는 인공지능 기반 예측서비스 기술이 적용되어 있지 않기 때문이다. 또한, 화재 예측 및 악취 감지·예측서비스는 초저지연 특징을 가진 서비스이다. 따라서 초저지연 예측서비스를 제공하기 위해 엣지 컴퓨팅 기술이 인공지능 모델과 결합되어 클라우드에 비해 빠른 추론 결과를 현장에 빠르게 적용할 수 있도록 개발 중이다. 따라서 본 논문에서는 제조 산업 현장에서 가장 많이 요구되는 화재 예측 및 악취 감지·예측에 사용할 수 있는 LSTM 알고리즘 기반 학습모델을 제안한다. 또한, 제안하는 학습모델은 엣지 다바이스에 구현이 가능하도록 설계하였으며 사물인터넷 단말로부터 실시간 센서데이터를 수신하고 이 데이터를 추론 모델에 적용하여 화재 및 악취 상태를 실시간으로 예측할 수 있도록 제안한다. 제안된 모델은 3가지 성능 지표를 통해 학습모델의 예측 정확도를 평가하였으며 평가 결과는 평균 90% 이상 성능을 보였다.

• Journal of information and communication convergence engineering
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• 제4권1호
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• pp.18-22
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• 2006

A small-sized gas identification system has been fabricated and characterized using an integrated gas sensor array and artificial neural-network. The sensor array consists of four thick-film oxide semiconductor gas sensors whose sensing layers are $In_{2}O_{3}-Sb_{2}O_{5}-Pd-doped\;SnO_2$ + Pd-coated layer, $La_{2}O_{5}-PdCl_{2}-doped\;SnO_2,\;WO_{3}-doped\;SnO_{2}$ + Pt-coated layer and $ThO_{2}-V_{2}O_{5}-PdCl_{2}\;doped\;SnO_{2}$. The small-sized gas identification instrument is composed of a GMS 81504 containing an internal ROM (4k bytes), a RAM (128 bytes) and four-channel AD converter as MPU, LEDs for displaying alarm conditions for three gases (liquefied petroleum gas: LPG, liquefied natural gas: LNG and carbon monoxide: CO) and interface circuits for them. The instrument has been used to identify alarm conditions for three gases among the real circumstances and the identification has been successfully demonstrated.

• International Journal of Advanced Culture Technology
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• 제5권1호
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• pp.64-69
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• 2017

The rapid growth of algae occurs can induce the algae bloom when nutrients are supplied from anthropogenic sources such as fertilizer, animal waste or sewage in runoff the water currents or upwelling naturally. The algae blooms creates the human health problem in the environment as well as in the water resource managers including hypoxic dead zones and harmful toxins and pose challenges to water treatment systems. The algal blooms in the source water in water treatment systems affects the drinking water taste & odor while clogging or damaging filtration systems and putting a strain on the systems designed to remove algal toxins from the source water. This paper propose the emerging In-Situ self-diagnosable smart algae sensing device with wireless connectivity for smart remote monitoring and control. In this research, we developed the In-Site Algae diagnosable sensing device with wireless sensor network (WSN) connectivity with Optical Biological Sensor and environmental sensor to monitor the water treatment systems. The proposed system emulated in real-time on the water treatment plant and functional evaluation parameters are presented as part of the conceptual proof to the proposed research.

• 센서학회지
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• 제22권4호
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• pp.286-291
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• 2013

This paper presents to analyze patterns from single and complex malodors using gas sensor array based on metal oxide semiconductors. The aim of research is to identify and discriminate single malodors such as $NH_3$, $CH_3SH$ and $H_2S$ and their mixtures according to concentration levels. Measurement system for analyzing patterns from malodors is constructed by an array of metal oxide semiconductor sensors which are available commercially together with associate electronics. The patterns from sensory system are analyzed by Principal Component Analysis (PCA) which is simple statistical pattern recognition technique. Throughout the experimental trails, we confirmed the experimental procedure for measurement system such as sensors calibration time and gas flow rate, and discriminated malodors using pattern analysis technique.

• 한국재료학회지
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• 제34권5호
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• pp.235-241
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• 2024

Gas identification techniques using pattern recognition methods were developed from four micro-electronic gas sensors for noxious gas mixture analysis. The target gases for the air quality monitoring inside vehicles were two exhaust gases, carbon monoxide (CO) and nitrogen oxides (NO_x), and two odor gases, ammonia (NH₃) and formaldehyde (HCHO). Four MEMS gas sensors with sensing materials of Pd-SnO₂ for CO, In₂O₃ for NO_X, Ru-WO₃ for NH₃, and hybridized SnO₂-ZnO material for HCHO were fabricated. In six binary mixed gas systems with oxidizing and reducing gases, the gas sensing behaviors and the sensor responses of these methods were examined for the discrimination of gas species. The gas sensitivity data was extracted and their patterns were determined using principal component analysis (PCA) techniques. The PCA plot results showed good separation among the mixed gas systems, suggesting that the gas mixture tests for noxious gases and their mixtures could be well classified and discriminated changes.