• Title/Summary/Keyword: Intelligent Electronic Nose System

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Intelligent Electronic Nose System for Detection of VOCs in Exhaled Breath

  • Byun, Hyung-Gi;Yu, Joon-Bu
    • Journal of Sensor Science and Technology
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    • v.28 no.1
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    • pp.7-12
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    • 2019
  • Significant progress has been made recently in detection of highly sensitive volatile organic compounds (VOCs) using chemical sensors. Combined with the progress in design of micro sensors array and electronic nose systems, these advances enable new applications for detection of extremely low concentrations of breath-related VOCs. State of the art detection technology in turn enables commercial sensor systems for health care applications, with high detection sensitivity and small size, weight and power consumption characteristics. We have been developing an intelligent electronic nose system for detection of VOCs for healthcare breath analysis applications. This paper reviews our contribution to monitoring of respiratory diseases and to diabetic monitoring using an intelligent electronic nose system for detection of low concentration VOCs using breath analysis techniques.

Development of Intelligent Mobile Robot with electronic nose

  • Byun, Hyung-Gi;Ham, Yu-Kyung;Kim, Jung-Do;Park, Ji-Hyeok;Shon, Won-Ryul
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.137.2-137
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    • 2001
  • We have been developed an intelligent mobile robot with an artificial olfactory function to recognize odours and to track odour source location. This mobile robot also has been installed an engine for speech recognition and synthesis, and is controlled by wireless communication. An artificial olfactory system based on array of 7 gas sensors has been installed in the mobile robot for odour recognition, and 11 gas sensors also are located in the bottom of robot to track odour sources. 3 optical sensors are also included in the intelligent mobile robot, which is driven by 2 D.C. motors, for clash avoidance in a way of direction toward an odour source. Throughout the experimental trails, it is confirmed that the intelligent mobile robot is capable of not only the odour recognition using artificial neural network algorithm, but also the tracking odour source using the step-by-step approach method ...

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Mobile Robot with Artificial Olfactory Function

  • Kim, Jeong-Do;Byun, Hyung-Gi;Hong, Chul-Ho
    • Transactions on Control, Automation and Systems Engineering
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    • v.3 no.4
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    • pp.223-228
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    • 2001
  • We have been developed an intelligent mobile robot with an artificial olfactory function to recognize odours and to track odour source location. This mobile robot also has ben installed an engine for speech recognition and synthesis and is controlled by wireless communication. An artificial olfactory system based on array of 7 gas sensors has been installed in the mobile robot for odour recognition, and 11 gas sensors also are located in the obttom of robot to track odour sources. 3 optical sensors are also in cluded in the intelligent mobile robot, which is driven by 2 D. C. motors, for clash avoidance in a way of direction toward an odour source. Throughout the experimental trails, it is confirmed that the intelligent mobile robot is capable of not only the odour recognition using artificial neural network algorithm, but also the tracking odour source using the step-by-step approach method. The preliminary results are promising that intelligent mobile robot, which has been developed, is applicable to service robot system for environmental monitoring, localization of odour source, odour tracking of hazardous areas etc.

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A Hierarchical Clustering Method Based on SVM for Real-time Gas Mixture Classification

  • Kim, Guk-Hee;Kim, Young-Wung;Lee, Sang-Jin;Jeon, Gi-Joon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.5
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    • pp.716-721
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    • 2010
  • In this work we address the use of support vector machine (SVM) in the multi-class gas classification system. The objective is to classify single gases and their mixture with a semiconductor-type electronic nose. The SVM has some typical multi-class classification models; One vs. One (OVO) and One vs. All (OVA). However, studies on those models show weaknesses on calculation time, decision time and the reject region. We propose a hierarchical clustering method (HCM) based on the SVM for real-time gas mixture classification. Experimental results show that the proposed method has better performance than the typical multi-class systems based on the SVM, and that the proposed method can classify single gases and their mixture easily and fast in the embedded system compared with BP-MLP and Fuzzy ARTMAP.

Classification of Aroma Using Neural Network (신경회로망을 이용한 아로마 분류)

  • Kim, Yong Soo;Kim, Han-Soo;Kim, Sun-Tae;Lim, Mi-Hye
    • 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.