• 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.

• 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.

• 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.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.118-125
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• 2018

An evaluation system for an electronic-nose concept using three types of metal oxide gas sensors that react similarly to the human olfactory cells was constructed for the quantitative and qualitative evaluation of aroma fragrances. Four types of aroma fragrances (lavender, orange, jasmine, and Roman chamomile), which are commonly used in aromatherapy, were evaluated. All the gas sensors reacted remarkably to the aroma fragrances and the good correlation of r=0.58-0.88 with the aromatic odor intensities by olfaction was confirmed. From the results of the analysis of an electronic-nose concept for classifying the characteristics of aroma oil fragrances, aroma oils could be classified using the fragrance characteristics and oil extraction methods with the cumulative variability contribution rate of 95.65% (F1: 69.65%, F2: 26.03%) by principal component analysis. In the pattern recognition based on the artificial neural network, the four aroma fragrances were 100% recognized through the training data of 56 cases (70%) out of 80 cases, and the pattern recognition rate was 57.1%-71.4% through the validation and testing data of 24 cases (30%). The pattern recognition success rate through all confusion matrices was 82.1%, indicating that the classification of aroma oil fragrances using the three types of gas sensors was successful.

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.29-34
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• 2015

This study aimed to evaluate the characterization of a metal oxide semiconductor and electrochemical gas sensor array for measuring wastewater odor. The sensitivity of all gas sensors observed in sampling method by stripping was 6.7 to 20.6 times higher than that by no stripping, except sensor D (electrochemical gas sensor). The average reduction ratio of sensor signal as a function of initial dilution rate of wastewater was in the order of food plant > food waste reutilization facility > plating plant. The sensitivity of gas sensors was dependent on both the type of wastewater and the dilution rate. The sensor signals observed by the gas sensor array were correlated with the dilution factor (OU) calculated by the air dilution sensory test with several wastewater ($r^2=0.920{\sim}0.997$), except the sensor signals of sensor D measured in the plating plant wastewater. It seems likely that the gas sensor array plays a role in the evaluation of odor in wastewater and is useful tool for on-site odor monitoring in the wastewater facilities.

• Journal of Sensor Science and Technology
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• v.17 no.6
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• pp.406-413
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• 2008

We have been implemented simple statistical pattern recognition methods for harmful gases classification using gas sensors array fabricated by MEMS (Micro Electro Mechanical System) technology. The performance of pattern recognition method as a gas classifier is highly dependent on the choice of pre-processing techniques for sensor and sensors array signals and optimal classification algorithms among the various classification techniques. We carried out pre-processing for each sensor's signal as well as sensors array signals to extract features for each gas. We adapted simple statistical pattern recognition algorithms, which were PCA (Principal Component Analysis) for visualization of patterns clustering and MLR (Multi-Linear Regression) for real-time system implementation, to classify harmful gases. Experimental results of adapted pattern recognition methods with pre-processing techniques have been shown good clustering performance and expected easy implementation for real-time sensing system.

• Journal of Sensor Science and Technology
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• v.22 no.4
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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.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.1
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• pp.1-6
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• 2004

Air qualify monitoring if a primary activity for industrial and social environment. Especially, the VOCs(Volatile Organic Compounds) are very harmful for human and environment. Throughout this research. we designed sensor array with various kinds of gas sensor, and the recognition algorithm with ANN(Artificial Neural Network : BP), respectively. We have designed system to recognize various kinds and quantities of VOCs, such as benzene, tolylene, and xylene.

• Journal of the Korean Institute of Gas
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• v.6 no.1 s.17
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• pp.10-16
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• 2002

This paper introduces developed prototype intelligent pig which detects corrosion on pipeline by using Magnetic Flux Leakage technology. The 8 inch developed MFL(Magnetic Flux Leakage) pig is composed of 5 yokes which magnetize pipeline wall and 45 Hall sensors which detect MFL signal. The designed MFL modules are analyzed by using magnetic circuit method in order to confirm whether pipeline wall is fully saturated. A variety of artificial defects are manufactured on 8 inch diameter steel pipeline in order to acquire MFL signals. So leakage flux of the axial, radial and circumferential component was acquired as defects. The results of this paper show that design technique for 8 inch MFL pig can be applied to large diameter MFL pig and 0.5mm defect depth can be detected.