A matched-profile method for simple and robust vapor recognition in electronic nose (E-nose) system

Yang, Yoon Seok; Ha, Sol; Kim, Yong Shin

doi:10.1016/j.snb.2004.08.008

Cited by 23 publications

(30 citation statements)

References 8 publications

(12 reference statements)

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“…These phenomena would be a serious problem in the case of gas chromatography, but in the case of the electronic nose it can be used as important information. The time variable techniques have already been suggested by several researchers [23][24][25][26]. Fig.…”

Section: Recovery Of the Voc In The Two-step Preconcentrationmentioning

confidence: 99%

Two-step preconcentration for analysis of exhaled gas of human breath with electronic nose

Cho

Kim

Heo

et al. 2006

Sensors and Actuators B: Chemical

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Section: Recovery Of the Voc In The Two-step Preconcentrationmentioning

confidence: 99%

Two-step preconcentration for analysis of exhaled gas of human breath with electronic nose

Cho

Kim

Heo

et al. 2006

Sensors and Actuators B: Chemical

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“…While human olfaction sense tends to be easily fatigued, an e-nose has advantages in consistently detecting vapors, including those harmful to the human body. In an early electronic nose, calorimetric sensors were used to perform measurements on vapors, and the measurements were usually expressed in arrays of colors [7]. Such an e-nose system, which was used only in a laboratory environment, utilized complicated analytic procedures, including precise equipment such as gas chromatography (GC) systems or mass spectrometers (MS) combined with sophisticated machine intelligence [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The e-nose sensor used measures vapors with a speed of 10 Hz, which corresponds to a sampling rate of 2,000 points per 200 seconds [7]. Since a sensor array has 16 channels, each measured data sample contains 32,000 primitive variables, which is likely to result in computational burden.…”

Section: Introductionmentioning

confidence: 99%

Classification of Odorants in the Vapor Phase Using Composite Features for a Portable E-Nose System

Choi

Jeong

Kim

2012

Sensors

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We present an effective portable e-nose system that performs well even in noisy environments. Considering the characteristics of the e-nose data, we use an image covariance matrix-based method for extracting discriminant features for vapor classification. To construct composite vectors, primitive variables of the data measured by a sensor array are rearranged. Then, composite features are extracted by utilizing the information about the statistical dependency among multiple primitive variables, and a classifier for vapor classification is designed with these composite features. Experimental results with different volatile organic compounds data show that the proposed system has better classification performance than other methods in a noisy environment.

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“…Pattern recognition has received much attention due to its theoretical challenges as well as applications in face recognition [1][2][3], finger print recognition [7] and gas recognition [8,9]. As a result, numerous methods have been developed for pattern recognition in the last few decades.…”

Section: Introductionmentioning

confidence: 99%

Pattern recognition using feature feedback: Application to face recognition

Jeong

Ahn

Choi

et al. 2010

Int. J. Control Autom. Syst.

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In this paper, we propose a new pattern recognition method using feature feedback and present its application to face recognition. Conventional pattern recognition methods extract the features employed for classification using PCA, LDA and so on. On the other hand, in the proposed method, the extracted features are analyzed in the original space using feature feedback. Using reverse mapping from the extracted features to the original space, we can identify the important part of the original data that affects the classification. In this way, we can modify the data to obtain a higher classification rate, make it more compact or abbreviate the required sensors. To verify the applicability of the proposed method, we apply it to face recognition using the Yale Face Database. Each face image is divided into two parts, the important part and unimportant part, using feature feedback, and the classification performed using the feature mask obtained from feature feedback. Also, we combine face recognition with image compression. The experimental results show that the proposed method works well.

show abstract

A matched-profile method for simple and robust vapor recognition in electronic nose (E-nose) system

Cited by 23 publications

References 8 publications

Two-step preconcentration for analysis of exhaled gas of human breath with electronic nose

Two-step preconcentration for analysis of exhaled gas of human breath with electronic nose

Classification of Odorants in the Vapor Phase Using Composite Features for a Portable E-Nose System

Pattern recognition using feature feedback: Application to face recognition

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