| Project/Area Number |
17360185
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
System engineering
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
OMATU Sigeru Osaka Prefecture University, Graduate School of Engineering, Professor (30035662)
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| Co-Investigator(Kenkyū-buntansha) |
FUJINAKA Toru Osaka Prefecture University, Graduate School of Engineering, Associate Professor (90190058)
YOSHIOJKA Michifumi Osaka Prefecture University, Graduate School of Engineering, Associate Professor (70285302)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥13,160,000 (Direct Cost: ¥12,500,000、Indirect Cost: ¥660,000)
Fiscal Year 2007: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2006: ¥5,000,000 (Direct Cost: ¥5,000,000)
Fiscal Year 2005: ¥5,300,000 (Direct Cost: ¥5,300,000)
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| Keywords | independent component analysis / Signal detection / Acoustic test / classification of products / spectral analysis / genetic algorithm / intellieent sienal nrocessin / Acoustic inspection device / 匂い計測装置 / オクターブバンド / 自己組織化特徴地図 / 競合型ニューラルネットワーク / 匂い特徴量 / 匂い識別 / 音響データ処理 |
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| Research Abstract |
In this research, we have set the final goal to achieve the intelligent acoustic classification system with compact size by using independent component analysis (ICA) and competitive neural networks. Using the ICA, we have developed the ICA noise separation system to obtain excellent separation of signals and noises and applied it to real acoustic data of compact shaving machine, which were observed under very noisy condition.
Then the separated acoustic signals were transformed into frequency domain by using Fourier transform. From the spectrum we have decided the feature vector to classify the compact shavers into good or not based on the competitive neural networks. The adopted neural network is the Kohonen's network of self-organizing feature map (SOM). Using the SOM, we select the most effective feature vector of the spectrum of acoustic data. Then we apply another competitive neural network of learning vector quantization (LVQ). Finally, we have tested the other products such as massage machine, caps of illumination lamps, bearing shaft, etc.
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