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Titlebook: Computer and Computing Technologies in Agriculture IV; 4th IFIP TC 12 Inter Daoliang Li,Yande Liu,Yingyi Chen Conference proceedings 2011 I

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楼主: ALOOF
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A Non-linear Model of Nondestructive Estimation of Anthocyanin Content in Grapevine Leaves with Viss for estimating Anth content in grapevine leaves. Although Anth in validation data set was widely variable, the new methods were capable of accurate predicting Anth content in grapevine leaves with a root mean square error below 1.65 mg/m., which is lower than that of MARI or ARI [20]. It documents
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Application of Improved BP Neural Network in Controlling the Constant-Force Grinding Feed,cy domain, and overcomes the limitation of the method which picks up feature parameters only in time domain or in frequency domain. At the same time this method provides a new clue of integrating other feature parameters in the grinding. Practice proves its good effect.
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Comparative Study of Distance Discriminant Analysis and Bp Neural Network for Identification of Rapas validated by the samples of prediction sets formed by the remaining 75 samples. The result error of BPNN model was set to be ±0.15, and the result indicated that no samples exceeded threshold value, therefore the distinguishing rate was 100%. The result of DDA model displayed that the recognition
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Design Method and Implementation of Ternary Logic Optical Calculator,ical calculator with the same basic units group by virtue of the criterion. Followed the design criterion, a hundreds-bit reconfigurable ternary logic optical calculator has been designed till June, 2007. In this paper we introduce the Decrease-Radix Design rule and the design criterion of multi-val
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Checkpoint and Restore for SystemC ModelsDS. The predicted grain protein content (GPC) model by leaf layers spectra using 0° view angle was the best with root mean squares (RMSE) of 0.7500 for Jingdong8 and 0.6461 for Zhongyou9507. The coefficients of determination, R. between measured and estimated grain protein contents were 0.7467 and 0
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Isabella Silva,Pedro Cardoso,Bruno Giesteiras for estimating Anth content in grapevine leaves. Although Anth in validation data set was widely variable, the new methods were capable of accurate predicting Anth content in grapevine leaves with a root mean square error below 1.65 mg/m., which is lower than that of MARI or ARI [20]. It documents
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