Electrothermal simulation of the self-heating effects in 4H-SiC MESFETs

A thermal model of 4H-SiC MESFET is developed based on the temperature dependences of material parameters and three-region I - V model. The static current characteristics of 4H-SiC MESFET have been obtained with the consideration of the self-heating effect on related parameters including electron mobility, saturation velocity and thermal conductivity. High voltage performances are analysed using equivalent thermal conductivity model. Using the physicalbased simulations, we studied the dependence of self-heating temperature on the thickness and doping of substrate. The obtained results can be used for optimization of the thermal design of the SiC-based high-power field effect transistors.

Using Multi-input-layer Wavelet Neural Network to Model Product Quality of Continuous Casting Furnace and Hot Rolling Mill

A new architecture of wavelet neural network with multi-input-layer is proposed and implemented for modeling a class of large-scale industrial processes. Because the processes are very complicated and the number of technological parameters, which determine the final product quality, is quite large, and these parameters do not make actions at the same time but work in different procedures, the conventional feed-forward neural networks cannot model this set of problems efficiently. The network presented in this paper has several input-layers according to the sequence of work procedure in large-scale industrial production processes. The performance of such networks is analyzed and the network is applied to model the steel plate quality of continuous casting furnace and hot rolling mill. Simulation results indicate that the developed methodology is competent and has well prospects to this set of problems.

Technique Based on Image Pyramid and Bayes Rule for Noise Reduction in Unsupervised Change Detection

In this paper,a technique based on image pyramid and Bayes rule for reducing noise effects in unsupervised change detection is proposed.By using Gaussian pyramid to process two multitemporal images respectively,two image pyramids are constructed.The difference pyramid images are obtained by point-by-point subtraction between the same level images of the two image pyramids.By resizing all difference pyramid images to the size of the original multitemporal image and then making product operator among them,a map being similar to the difference image is obtained.The difference image is generated by point-by-point subtraction between the two multitemporal images directly.At last,the Bayes rule is used to distinguish the changed pixels.Both synthetic and real data sets are used to evaluate the performance of the proposed technique.Experimental results show that the map from the proposed technique is more robust to noise than the difference image.

Improved Verifiability Scheme for Data Storage in Cloud Computing

In Cloud computing, data and service requests are responded by remote processes calls on huge data server clusters that are not totally trusted. The new computing pattern may cause many potential security threats. This paper explores how to ensure the integrity and correctness of data storage in cloud computing with user＇s key pair. In this paper, we aim mainly at constructing of a quick data chunk verifying scheme to maintain data in data center by implementing a balance strategy of cloud computing costs, removing the heavy computing load of clients, and applying an automatic data integrity maintenance method. In our scheme, third party auditor （TPA） is kept in the scheme, for the sake of the client, to periodically check the integrity of data blocks stored in data center. Our scheme supports quick public data integrity verification and chunk redundancy strategy. Compared with the existing scheme, it takes the advantage of ocean data support and high performance.