• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.33-43
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• 2010

In this paper, we propose a floral scent recognition using odor sensors and a odor display using odor distribution system. Proposed odor recognition has method of correlation coefficient between sensors that select optimal sensors in floral scent recognition system of selective multi-sensors. Proposed floral scent recognition system consists of four module such as floral scent acquisition module, optimal sensor decision module, entropy-based floral scent detection module, and floral scent recognition module. Odor distribution system consists of generation module of distribution information, control module of distribution, output module of distribution. 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 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 96% using only 8 sensors. Also, we applied to odor display of proposed method and obtained 3.18 thorough MOS experimentation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.77-85
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• 2009

In this paper, we propose data format for smell, taste, touch with speech and vision which can be transmitted and implement a floral scent detection and recognition system. We provide representation method of data of smell, taste, and touch. Also, proposed floral scent recognition system consists of three module such as floral scent acquisition module using Metal Oxide Semiconductor (MOS) sensor array, entropy-based floral scent detection module, and floral scent recognition module using correlation coefficients. The proposed system calculates correlation coefficients of the individual sensor between feature vector(16 sensors) from floral scent input point until the stable region and 12 types of reference models. Then, this system selects the floral scent with the maximum similarity to the calculated average of individual correlation coefficients. To evaluate the floral scent recognition system using correlation coefficients, we implemented an individual floral scent recognition system using K-NN with PCA and LDA that are generally used in conventional electronic noses. In the experimental results, the proposed system performs approximately 95.7% average recognition rate.

• 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.