1998 Fiscal Year Final Research Report Summary
DEVELOPMENT OF OLFACTORY SIMULATOR BY USlNG A NEURAL NETWORK
| Project/Area Number |
09650497
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
計測・制御工学
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| Research Institution | SUZUKA COLLEGE OF TECHNOLOGY |
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Principal Investigator |
KUWABARA Hirofumi SUZUKA COLLEGE OF TECHNOLOGY,ELECTRONIC & INFORMATION ENGINEERING, PROFFSSOR, 電子情報工学科, 教授 (30043326)
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| Project Period (FY) |
1997 – 1998
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| Keywords | ATMOSPHERIC PRESSURE MASS SPECTROMETRY / OLFACTORY SIMULATOR / NEURAL NETWORK / SENSOR |
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| Research Abstract |
I try to develop a human olfactory simulator using a neural network with an atmospheric pressure mass spectrometer as a sensor of the smell.
1. Improvement of existing atmospheric pressure mass spectrometer
I aimed at rising the sensitivity of the atomospheric pressure mass spectrometer with the improvement of lens group of the ion introduction part and the ion detection system by using the conversion di-node. Sensitivity in a high mass part has doubled by these improvements.
2. Developing the hardware and the software to interface between the mass spectrometer and the computer.
I try to make the data acquisition system which is measuring the spectrum continuously and accumulating them to improve accuracy of measurements. Because an unstable part existed in the power-supply unit etc. of the spectrometer, the measurement of the number of mass became unstable. As the completion of the system in the current year was not able to be expected by this trouble, the experiment is continued by off-line operation of the spectrometer.
3. Development of software for simulator of olfactory and operation result
The smell is classified into seven kinds of basic smells according to a traditional research. Some compounds which are classified each basic smell were prepared and the mass spectra were measured. Each mass spectrum pattern was put into the neural network in input layer 200, middle layer 50, and output layer 7, which was made to study until settling to the classification of the corresponding smell After that, a mass spectrum pattern of a new sample was put into the simulator, and whether it was possible to classify the sample into the corresponding smell.
As a result, it was confirmed to be able to classify the smell from the mass spectrum by using the neural network.
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