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基于D—S證據(jù)理論的雞蛋新鮮度多傳感器融合識別
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國家高技術(shù)研究發(fā)展計劃(863計劃)資助項目(2007AA10Z213);江蘇省科技攻關(guān)項目(BE2007320);南京農(nóng)業(yè)大學(xué)青年創(chuàng)新基金資助項目(Y200827)


Non-destructive Egg Freshness Recognition Using Multi-sensor Fusion Based on D—S Evidence Theory
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    為提高無損檢測在判斷雞蛋新鮮度方面的穩(wěn)定性和模型適應(yīng)性, 通過D—S證據(jù)理論和BP神經(jīng)網(wǎng)絡(luò)將電子鼻和機器視覺兩種傳感器在特征層進行融合,構(gòu)建了雞蛋新鮮度的融合模型。探討了一種可以彌補D—S證據(jù)在信息融合過程中不足的改進方法。驗證試驗結(jié)果表明:通過融合優(yōu)化,不確定性的基本概率賦值下降到0.01以內(nèi),解決了單一檢測方法檢測模型存在識別空白區(qū)間或穩(wěn)定性差的問題。經(jīng)過D〖—S融合的多傳感器融合BP模型在判別效果和穩(wěn)定性方面都有較大提高,判別雞蛋新鮮度準確率平均值達到92.6%。

    Abstract:

    For the purpose of enhancing the detecting stability and the model adaptability of egg freshness by non-destructive detection method, a sensor fusion was taken by the machine vision and electronic nose in the sensor level of characteristics while D—S evidence theory was chosen as the sensor information fusion method and BP artificial neural network as the specific modeling method. An improved method that could remedy for the deficiency of D—S evidence theory was discussed. Verification results showed that the basic probability assignment of uncertainty decreased to less than 0.01 by sensor fusion optimization. The problem of low detecting range in single sensor method has been well solved. Meanwhile, the egg freshness discriminating accuracy and stability has been improved compared with no sensor fusion situation. The average discriminating accuracy reached to 92.6%.

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劉鵬,屠康,潘磊慶,張偉.基于D—S證據(jù)理論的雞蛋新鮮度多傳感器融合識別[J].農(nóng)業(yè)機械學(xué)報,2011,42(8):122-127. Liu Peng, Tu Kang, Pan Leiqing, Zhang Wei. Non-destructive Egg Freshness Recognition Using Multi-sensor Fusion Based on D—S Evidence Theory[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(8):122-127.

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