Advanced Materials Research Vols. 1049-1050

Abstract: In this letter, we investigate asymmetric simple exclusion processes (ASEPs) with zoned inhomogeneity and off-ramp by the means of theoretical analysis and simulations. According to the theoretical analysis, we can find that the phase diagrams existing in this one-lane system varies with different hopping rate p and off-ramp rate q and the condition for p<0.5 and p>0.5 is distinctly different . It should be noticed that LD/LD, LD/HD and MC/HD can exist in this system no matter how hopping rate p and off-ramp rate q change.

Abstract: ASM is a statistical model applied to match contours of non-rigid object. The actual contour may much different from the initial contour and the result is likely to converge to an error contour. Kalman filter is adopted to track the current frame for the prediction and acts as the initial state of the ASM, and then applies the ASM to correct the contour of the object. Experimental results show that the method proposed in this paper allows the model to converge to the target contour quickly and accurately. It has good stability and robustness.

Abstract: According to actual requirements and situation, a novel automatic image registration algorithm was presented based on bag of words and features point. Firstly, pyramid delaminating was used to preprocess the pre-registration images. Then, image normalized could be obtained. Secondly, the dense SIFT features points could be extracted. The paper could get the feature eigenvectors descriptions of image by k-means clustering algorithm. Thirdly, the feature eigenvectors descriptions of image were train and the reference and pre-registration images were classification through Support Vector Machine (SVM). Finally, image registration could implement by features matching points. The results of experiment with the with office room and building test images shown the proposed algorithm was effective and could obtain the better registration image.

Abstract: The methods for quantity of the influence coefficient are put forward in this paper. The effect of the influence coefficient in voting actions is also explained. However, the simple means of the voting results such as evaluation, calculation, operation and control are introduced in the article.

Abstract: In this paper, based on the basic theory of GM(1,1), we present a new method based on cosine function transform the discrete data sequence disposed through the standardization processing to improve the smoothness, first we theoretically proved that this transformation can improve smoothness of the original data sequence after a certain standardization, and more effective than some former method, an example is used to demonstrate the effectiveness of this method in the last of this paper.

Abstract: The Cox model is commonly used to model survival data as a function of covariates. In this paper we compare the three methods to estimate the variance of the parameters in Cox model and presents the simulation result.

Abstract: Based on the DoDAF V2.0, the paper proposes a three-phased development process for the creation of the executable model of SoS capability architecture. The process not only takes a hybrid framework which supported the performance evaluation of SoS into consideration, but also gives emphasis to the dynamic logical relationship among activities, referred as syntax rules, which is not well highlighted in the current existing executable model creation method. The three phases are: analysis, synthesis/transform and refinement. The goal of the analysis phase is to create the SoS capability architectural description, including CV, OV, DIV, and SV/SvcV. The synthesis/transform phase shows the guideline and principle that govern transformation between the architecture model and PN. The refinement phase illustrates the transformation between OPN and CPN, and refines the interface between event driven dynamics and time driven dynamics to carry out the hybrid simulation.

Abstract: One-against-rest (OAR) is a well known multiclassification structure, which is an extension from binary classifiers. It has shown its great potential in pattern recognition and hyperspectral data processing. However, existence of unclassified region limits its application. In this paper, a new multiclassifier based on OAR combined with one-against-one (OAO) structure is proposed. In the multiclassifier, OAO is used to classify the unclassified region to improve performance of OAR. At the same time, the formation of unclassified region is discussed, and the pattern of selecting classifiers for secondary classification on unclassified region is proposed. To compare secondary classifiers and prove the conclusion, other six classifiers are selected , which are decision tree (DT), minimum distance (MD) based on Euclidean distance, MD based on Euclidean distance with kernel function, MD based on Mahalanobis distance, spectral mapping classifier (SMC) and maximum likelihood classifier (MLC). The SVM is used for OAR, OAO and DT in experiment and a hyperspectral remote sensing image is used as testing samples.

Abstract: In order to improve the correct recognition rate of EEG(Electroencephalogram,EEG) signals to meet the needs of Brain-Computer Interface system,this paper put forward a new method of signal recognition which combines wavelet packet decomposition and LVQ neural network.First,using the method of wavelet packet to analyze the signal,and then extract the specific frequency band’s energy of wavelet packet as characteristics.Then using the LVQ neural network model to study the distinguishing between the two EEG datas of Motor Imagery.The simulation experiment uses Matlab software to design LVQ neural network model to judge the two kinds of Motor Imagery task.In the process of judgment,respecti-vely to classify the data by using BP neural network and LVQ neural network.Experimental results show that the LVQ neural network can have a higher correct accuracy to recognize the motor imaginary task than BP neural.

Abstract: In order to analysis seismic data, we propose a cluster analysis method based on region scalable fitting model which aims to adapt the image segmentation algorithm to cluster analysis. The method can be divided into 4 parts: dimension reduction, data segmentation, feature extraction, and feature cluster. Finally, we do cluster validity evaluation. The validity evaluation results show that the computational complexity of the new algorithm is relatively lower than the ordinary ones and the inhomogeneous data of high dimension can be analyzed effectively by our model. In this paper, the RSF model was applied in the high-dimensional space. Since the original sample quantity is dramatically reduced by the new algorithm, our model can be incorporated into the algorithms which have strict requirements for the size of sample.