• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.356-360
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• 2002

Polluting the air with such pollutants as CO, H₂S and SO₂, industrial development huts increased the danger of gas toxication. Futhermore, as the: living standard goes higher, the consumption of explosive hydrocarbonic gases such as butane(C₄H/sub 10/) or propane(C₃H/sub 8/) has been soaring, which results in the danger of a gas explosion. As measures to cope with such dangers, the development of highly sensitive gas sensors, gas detectors adopting gas-sensing technologies, and gas recognition systems are urgently required. The objective of the present research is to develop a gas recognition system that is capable of identifying specific types of selected gases by formulating a semiconductor-typed gas sensor array, which not only improves the selectivity of semiconductor-typed gas sensors but also minimizes the erect of drifts on a single sensor signal, and applying the input pattern data of gases detected by the array to a neural network.

• Journal of Sensor Science and Technology
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• v.8 no.6
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• pp.461-468
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• 1999

In this paper, we have implemented a gas recognition system for classification and identification of explosive gases such as methane, propane, and butane using a sensor array and an artificial neural network. Such explosive gases which can be usually detected in the oil factory and LPG pipeline are very dangerous for a human being. We analyzed the characteristics of a multi-dimensional sensor signals obtained from the nine sensors using the principal component analysis(PCA) technique. Also, we implemented a gas pattern recognizer using a multi-layer neural network with error back propagation learning algorithm, which can classify and identify the sorts of gases and concentrations for each gas. The simulation and experimental results show that the proposed gas recognition system is effective to identify the explosive gases. And also, we used DSP board(TMS320C31) to implement the proposed gas recognition system using the neural network for real time processing.

• Journal of Sensor Science and Technology
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• v.11 no.5
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• pp.286-293
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• 2002

We fabricated gas recognition system using conducting polymer sensor array for recognizing and analyzing VOCs(Volatile Organic Compounds) gases. The polypyrrole and polyaniline thin film sensors which were made by chemical polymerization were employed to detect VOCs. The multi-dimensional sensor signals obtained from the sensor array were analyzed using PCA(principal component analysis) technique and RBF(radial basis function) Network. Throughout the experimental trails, we confirmed that RBF Network is effective than PCA technique in identifying VOCs.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.431-435
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• 2013

Aroma has been used for healing for a long time. The healing effects depend on aroma used. We made gas sensor array system to classify aromas systematically. We used outputs of sensors as the input to IAFC neural network. Results show that the neural network successfully classified jasmine, orange, roman chamomile, and lavender into 4 classes, and classified without any error.

• Electrical & Electronic Materials
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• v.8 no.4
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• pp.509-517
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• 1995

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.361-364
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• 2005

우리의 삶과 환경에 많은 영향을 주는 냄새들을 해석하고 이들을 감지하는 후각기관에 대한 이해는 많은 연구자들의 오랜 소망 이였다. 최근 전자 공학 및 컴퓨터 기술의 눈부신 발전에 힘입어 인간의 감각을 모방하는 전자코 시스템의 개발이 활발하게 추진되고 있다. 특히 인공지능 연구와 맞물린 센서기술의 비약적인 발전은 냄새를 인간의 후각기관과 비슷하게 감지하고 분석할 수 있는 인공 후각인식시스템의 개발을 가능하게 하고 있다. 하지만 현재 전자코 시스템은 습도와 온도에 민감하고 시료를 분석하기 위해 긴 응답시간이 필요하므로 연구소와 같이 제한적인 환경에서 사용되고 잇다. 이에 본 연구는 10초 내외의 짧은 시간에 인식이 가능한 전자코 시스템을 제작하고자 하였다.

• Journal of Sensor Science and Technology
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• v.6 no.6
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• pp.476-482
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• 1997

A portable electronic nose system has been fabricated and characterized using an oxide semiconductor gas sensor array and pattern recognition techniques such as principal component analysis and back-propagation artificial neural network. The sensor array consists of six thick-film gas sensors whose sensing layers are Pd-doped $WO_{3}$, Pt-doped $SnO_{2}$, $TiO_{2}-Sb_{2}O_{5}-Pd$-doped $SnO_{2}$, $TiO_{2}-Sb_{2}O_{5}-Pd$-doped $SnO_{2}$ + Pd coated layer, $Al_{2}O_{3}$-doped ZnO and $PdCl_{2}$-doped $SnO_{2}$. The portable electronic nose system consists of an 16bit Intel 80c196kc as CPU, an EPROM for storing system main program, an EEPROM for containing optimized connection weights of artificial neural network, an LCD for displaying gas concentrations. As an application the system has been used to identify 26 carbon monoxide/hydrocarbon (CO/HC) car exhausting gases in the concentration range of CO 0%/HC 0 ppm to CO 7.6%/HC 400 ppm and the identification has been successfully demonstrated.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.9-14
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• 2010

In this paper, we propose method of correlation coefficients between sensors by statistical analysis that selects optimal sensors in odor recognition system of selective multi-sensors. The proposed sensor decision method obtains odor data from Metal Oxide Semiconductor(MOS) sensor array and then, we decide optimal sensors based on correlation of obtained odors. First of all, we select total number of 16 sensors eliminated sensor of low response and low reaction rate response among similar sensors. We make up DB using 16 sensors from input odor and we select sensor of low correlation after calculated correlation coefficient of each sensor. Selected sensors eliminate similar sensors' response therefore proposed method are able to decide optimal sensors. We applied to floral scent recognition for performance evaluation of proposed sensors decision method. As a result, application of proposed method with floral scent recognition using correlation coefficient obtained recognition rate of 95.67% case of using 16 sensors while applied floral scent recognition system of proposed sensor decision method confirmed recognition rate of 94.67% using six sensors and 96% using only 8 sensors.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.11a
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• pp.602-605
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• 2000

Recently, a study of intellectual smell recognition system is applied for the various fields such as control of food processing and survey of decay. A basic gas recognition system was implemented gases using four metal oxides semiconductor sensors as inputs. A CPLD chip of twenty thousand gates level was used for this purpose. The CPLD chip was designed and the availability of the one chip smell recognition system was tested.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.95-99
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• 2023

Forest healing is an activity that enhances immunity and human health using various elements of nature, such as fragrance and scenery. Particularly, phytoncide composed of terpene, a natural volatile substance emitted by forest plants, activates the immune function and is an important raw material in health-related products, such as antibacterial and insect repellents. Moreover, phytoncide is used as a measure to evaluate the impact of the forest atmosphere on the human body. This study aims to implement a highly sensitive gas sensor system that can measure phytoncide in real-time, which is an essential element for realizing a forest healing environment. A gas generation apparatus was implemented by using an adsorption tube in consideration of filed applicability in a laboratory atmosphere to enable the measurement of α-pinene and limonene, which are among the main components of phytoncide. Throughout the experimental trials, the sensitivity of gas sensor arrays to α-pinene and limonene was confirmed. In addition, the classification results demonstrated the AVOCs and NVOCs can be well discriminated using PCA. The primary results confirmed the possibility of developing a high-sensitivity gas sensor system for phytoncide sensing in real time.