刘鹏,屠康,潘磊庆,张伟.基于D—S证据理论的鸡蛋新鲜度多传感器融合识别[J].农业机械学报,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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基于D—S证据理论的鸡蛋新鲜度多传感器融合识别   [下载全文]
Non-destructive Egg Freshness Recognition Using Multi-sensor Fusion Based on D—S Evidence Theory   [Download Pdf][in English]
  
DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  鸡蛋  新鲜度  D—S证据理论  多传感器融合  BP神经网络
基金项目:国家高技术研究发展计划(863计划)资助项目(2007AA10Z213);江苏省科技攻关项目(BE2007320);南京农业大学青年创新基金资助项目(Y200827)
作者单位
刘鹏 南京农业大学 
屠康 南京农业大学 
潘磊庆 南京农业大学 
张伟 南京农业大学 
中文摘要:为提高无损检测在判断鸡蛋新鲜度方面的稳定性和模型适应性, 通过D—S证据理论和BP神经网络将电子鼻和机器视觉两种传感器在特征层进行融合,构建了鸡蛋新鲜度的融合模型。探讨了一种可以弥补D—S证据在信息融合过程中不足的改进方法。验证试验结果表明:通过融合优化,不确定性的基本概率赋值下降到0.01以内,解决了单一检测方法检测模型存在识别空白区间或稳定性差的问题。经过D〖—S融合的多传感器融合BP模型在判别效果和稳定性方面都有较大提高,判别鸡蛋新鲜度准确率平均值达到92.6%。
Liu Peng  Tu Kang  Pan Leiqing  Zhang Wei
Nanjing Agricultural University;Nanjing Agricultural University;Nanjing Agricultural University;Nanjing Agricultural University
Key Words:Egg  Freshness  D—S evidence theory  Multi-sensor fusion  BP neural network
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%.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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