Dislocation Model and Morphology Simulation of bcc<->fcc Martensitic Transformation

By using molecular dynamics computer simulation at atomic level, the effects of single dislocation and dipole dislocations on nucleation and growth of martensitic transformation have been studied. It was found that only the location of tension or compression stress fields of the dislocations are favorable for martensite nucleation in NiAl alloy and the dislocations can move to accommodate partly the transformation strain during the nucleation and growth of martensite. Combined with the molecular dynamics simulation, a two dimensional simulation for martensite morphology based on a dislocation model has been performed. Many factors related to martensitic transformation were considered, such as supercooling, interface energy, shear strain, normal strain and hydrostatic pressure. Different morphologies of martensites, similar to lath, lenticular, thin plate, couple-plate and lenticular couple-plate martensites observed in Fe-C and Fe-Ni-C alloys, were obtained.

Modeling curve dynamics and spatial geometry characteristics of rice leaves

The objective of this work was to develop a dynamic model for describing leaf curves and a the rice leaf （including sub-models for unexpanded leaf blades, expanded leaf blades, and dimensional （3D） dynamic visualization of rice leaves by combining relevant models detailed spatial geometry model of leaf sheaths）, and to realize three- Based on the experimental data of different cultivars and nitrogen （N） rates, the time-course spatial data of leaf curves on the main stem were collected during the rice development stage, then a dynamic model of the rice leaf curve was developed using quantitative modeling technology. Further, a detailed 3D geometric model of rice leaves was built based on the spatial geometry technique and the non-uniform rational B-spline （NURBS） method. Validating the rice leaf curve model with independent field experiment data showed that the average distances between observed and predicted curves were less than 0.89 and 1.20 cm at the tilling and jointing stages, respectively. The proposed leaf curve model and leaf spatial geometry model together with the relevant previous models were used to simulate the spatial morphology and the color dynamics of a single leaf and of leaves on the rice plant after different growing days by 3D visualization technology. The validation of the leaf curve model and the results of leaf 3D visualization indicated that our leaf curve model and leaf spatial geometry model could efficiently predict the dynamics of rice leaf spatial morphology during leaf development stages. These results provide a technical support for related research on virtual rice.

Modeling Study of Fruit Morphological Formation in Melon

Modeling of fruit morphological formation in melon is important for realizing virtual and digital plant growth.The objective of this study was to characterize the changes in patterns of fruit growth characters during plant development.In cultivar experiments,a high-resolution wireless vision sensor network has been developed to realize non-contact automatic uninterrupted measurement of the fruit shape micro-change （fruit size,color,and net）.Results showed that the fruit swelling process （vertical and horizontal diameters） exhibited a slow-rapid-slow pattern,which could be well described with a logistic curve against growing degree days （GDD）;fruit color changes based on the RGB values could be represented by quadratic relationship to cumulative GDD;the fruit net changes over growth progress could be partitioned into three phases according to the time interval.The first phase was from 1 to 30 days after pollination （DAP）,in which the vertical stripe appeared at fruit middle part and the horizontal stripe at fruit petiole and hilum part as well;the second phase was from 30 to 40 DAP,the horizontal stripe occurred at fruit middle part and the net was formed;the third phase was the process started from 40 DAP,the netted breadth and thickness were gradually increased.The model was validated with the independent data from the experiment,and the mean RMSE （root mean square error） of fruit were 0.36 and 0.28 cm for vertical and horizontal diameters,11.9 for fruit color,and 0.45 cm for stripe length and diameter at varied GDD,respectively.This work is beneficial to a reliable foundation for study the relationship between morphological formation and physiological change of the melon fruit internally and then realize the intelligent precision management to improve the yield and quality of greenhouse melon production.

A cellular automata model for simulating fed-batch penicillin fermentation process

A cellular automata model to simulate penicillin fed-batch fermentation process(CAPFM)was established in this study,based on a morphologically structured dynamic penicillin production model,that is in turn based on the growth mechanism of penicillin producing microorganisms and the characteristics of penicillin fed-batch fermentation.CAPFM uses the three-dimensional cellular automata as a growth space,and a Moore-type neighborhood as the cellular neighborhood.The transition rules of CAPFM are designed based on mechanical and structural kinetic models of penicillin batch-fed fermentation processes.Every cell of CAPFM represents a single or specific number of penicillin producing microorganisms,and has various state.The simulation experimental results show that CAPFM replicates the evolutionary behavior of penicillin batch-fed fermentation processes described by the structured penicillin production kinetic model accordingly.

Non-Destructive Crack Detection of Preserved Eggs Using a Machine Vision and Multivariate Analysis

Pidan or century egg, also known as preserved egg, is one of the most traditional and popular egg products in China. The crack detection of preserved eggshell is very important to guarantee its quality. In this study, we develop an image algorithm for preserved eggshell＇s crack detection by using natural light and polarized image. Four features including crack length, crack state coefficient, maximum projection and angular point are extracted from the natural light image by morphology calculus algorithms. The support vector machines（SVM） model with radial basis kernel function is established using the four features with an accuracy of about 92%. The detection accuracy is improved to 94% by using a new characteristic parameter of crack length on polarization image. The Multi-information fusion analysis indicates the potential for cracks detection by a real-time synthesis imaging system.