Study on Artificial Neural Network Model for Crop Evapotranspiration

Based on potted plant experiment, BP-artifieial neural network was used to simulate crop evapotranspiration and 3 kinds of artificial neural network models were constructed as ET1 （meteorological factors）, ET2（ meteorological factors and sowing days） and ET3 （meteorological factors, sowing days and water content）. And the predicted result was compared with actual value ET that was obtained by weighing method. The results showed that the ET3 model had higher calculation precision and an optimum BP-artificial neural network model for calculating crop evapotranspiration.

A Large Conformational Change of a Bridged b-Cyclodextrin Dimer in Aqueous Solution

A novel bridged b-CD dimer in which two b-cyclodextrins were linked by a naphthalene at positions 2 and 7 has been synthesized. 1H and 13CNMR measurements showed that a large change in the conformation of the dimer occurred in aqueous solution. The dimer interacted with methyl and ethyl orange to form stable inclusion complexes via 搃nduced fit?mechanism.

Particle image velocimetry and numerical simulations of the hydrodynamic characteristics of an artificial reef

This article reports a particle image velocimetry study and the comparative results of a numerical simulation into the hydrodynamic characteristics around an artificial reef.We reveal the process of flow separation and vortex evolution,and compare the force terms generated by our artificial reef model.The numerical simulation agrees well with experimental results,showing the applicability of computational fluid dynamics to the hydrodynamics of an artificial reef.Furthermore,we numerically simulate the hydrodynamics of the reef model for seven velocities.The results show that the drag coefficient is approximately 1.21 in a self-modeling region for Reynolds numbers between 2.123×104and 9×104.Therefore,the upwelling height and current width of the flow field do not change significantly when the inflow velocity increases.Our study indicates that computational fluid dynamics can be applied to study the hydrodynamics of an artificial reef and offer clues to its construction.

Orthogonal moment based texture segmentation

Texture segmentation is a necessary step to identify the surface or an object in an image. We present a Legendre moment based segmentation algorithm. The Legendre moments in small local windows of the image are computed first and a nonlinear transducer is used to map the moments to texture features and these features are used to construct feature vectors used as input data. Then an RBF neural network is used to perform segmentation. A k-mean algorithm is used to train the hidden layers of the RBF neural network. The training of the output layer is the supervised algorithm based on LMS. The algorithm has been successfully used to segment a number of gray level texture images. Compared with the geometric moment-based texture segmentation, we can reduce the error rates using orthogonal moments.

Estimation of Sunflower Seed Yield Using Partial Least Squares Regression and Artificial Neural Network Models

Statistical models can efficiently establish the relationships between crop growth and environmental conditions while explicitly quantifying uncertainties. This study aimed to test the efficiency of statistical models established using partial least squares regression(PLSR) and artificial neural network(ANN) in predicting seed yields of sunflower(Helianthus annuus). Two-year field trial data on sunflower growth under different salinity levels and nitrogen(N) application rates in the Yichang Experimental Station in Hetao Irrigation District, Inner Mongolia, China, were used to calibrate and validate the statistical models. The variable importance in projection score was calculated in order to select the sensitive crop indices for seed yield prediction. We found that when the most sensitive indices were used as inputs for seed yield estimation, the PLSR could attain a comparable accuracy(root mean square error(RMSE) = 0.93 t ha-1, coefficient of determination(R^2) = 0.69) to that when using all measured indices(RMSE = 0.81 t ha-1,R^2= 0.77). The ANN model outperformed the PLSR for yield prediction with different combinations of inputs of both microplots and field data. The results indicated that sunflower seed yield could be reasonably estimated by using a small number of crop characteristic indices under complex environmental conditions and management options(e.g., saline soils and N application). Since leaf area index and plant height were found to be the most sensitive crop indices for sunflower seed yield prediction, remotely sensed data and the ANN model may be joined for regional crop yield simulation.

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging(MRI), image features from T2-weighted images(T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone(PZ) and central gland(CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features(10/12) had significant difference(P<0.01) between PCa and non-PCa in the PZ, while only five features(sum average, minimum value, standard deviation, 10 th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2 w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.