Robust H∞ control of uncertain linear impulsive stochastic systems

This paper develops robust stability theorems and robust H_∞ control theory for uncertain impulsive stochastic systems. The parametric uncertainties are assumed to be time varying and norm bounded. Impulsive stochastic systems can be divided into three cases, namely, the systems with stable/stabilizable continuous‐time stochastic dynamics and unstable/unstabilizable discrete‐time dynamics, the systems with unstable/unstabilizable continuous dynamics and stable/stabilizable discrete‐time dynamics, and the systems in which both the continuous‐time stochastic dynamics and the discrete‐time dynamics are stable/stabilizable. Sufficient conditions for robust exponential stability and robust stabilization for uncertain impulsive stochastic systems are derived in terms of an average dwell‐time condition. Then, a linear matrix inequality‐based approach to the design of a robust H_∞ controller for each system is presented. Finally, the numerical examples are provided to demonstrate the effectiveness of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.     

Granular Neural Networks With Evolutionary Interval Learning

To deal with different membership functions of the same linguistic term, a new interval reasoning method using new granular sets is proposed based on Yin Yang methodology. To make interval-valued granular reasoning efficiently and optimize interval membership functions based on training data effectively, a granular neural network (GNN) with a new high-speed evolutionary interval learning is designed. Simulation results in nonlinear function approximation and bioinformatics have shown that the GNN with the evolutionary interval learning is able to extract interval-valued granular rules effectively and efficiently from training data by using the new evolutionary interval learning algorithm.     

Video Annotation Based on Kernel Linear Neighborhood Propagation

The insufficiency of labeled training data for representing the distribution of the entire dataset is a major obstacle in automatic semantic annotation of large-scale video database. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, are promising to solve this problem. In this paper, a novel graph-based semi-supervised learning method named kernel linear neighborhood propagation (KLNP) is proposed and applied to video annotation. This approach combines the consistency assumption, which is the basic assumption in semi-supervised learning, and the local linear embedding (LLE) method in a nonlinear kernel-mapped space. KLNP improves a recently proposed method linear neighborhood propagation (LNP) by tackling the limitation of its local linear assumption on the distribution of semantics. Experiments conducted on the TRECVID data set demonstrate that this approach outperforms other popular graph-based semi-supervised learning methods for video semantic annotation.     

K(Knowledge)-net: building up and its dynamics

The essence of intelligence is to use certain abilities to obtain knowledge, to use that knowledge, and to operate with that knowledge. New knowledge learned by a human is often related to old existing knowledge, and sometimes we could have more conceptual knowledge based on old knowledge. So, the knowledge in the brain exists in a related structural form, and this structure is dynamic, and therefore is evolvable. Based on the understanding of the real process of learning by a human being, we discuss how to make a model to describe the dynamic structure of knowledge. This model is also a principle of artificial brain design. Most of the knowledge a child learns is from natural language and perception information, and we define this as semantic knowledge. The model to describe the process and structure of knowledge growing in a network form is called a K-net. It is a dynamic network with two main dynamics: one is new knowledge added, and the other is aggregating knowledge existing in the network with some probability. Under these very natural conditions, we found that the network is originally a simple random net, and then some characteristics of a complex network gradually appear when more new knowledge is added and aggregated. A more interesting phenomenon is the appearance of a random hierarchical structure. Does this mean emergence?     

Convergent Smoothing and Segmentation of Noisy Range Data in Multiscale Space

With few exceptions, most of the existing noise reduction and data segmentation algorithms are only suited to image data. Therefore, an adaptive smoothing algorithm, with model-based masks, within a scale space framework is proposed for range data in this paper. This algorithm smoothes range data that conform to predefined, geometric models, while leaving other data points unaffected. The convergence of the algorithm in yielding dominant features is shown based on its compliance with the anisotropic diffusion concept. The weights of the smoothing masks are adaptively calculated according to the Mahalanobis distances between range data and model-based predictions. These behave as the diffusion coefficient in the anisotropic diffusion equation, thus satisfying the requirements of the causality criterion that no new features are introduced from fine to coarse scales. The computational complexity of this algorithm is examined and compared to that of the well-known RANSAC feature extraction algorithm. Unlike RANSAC, it has the advantage that the computational complexity is less affected by increasing the order of the model, and is independent of the number of model outliers. The proposed algorithm can be used to smooth range data in multiscale space by increasing the number of smoothing iterations. Robust, robot-occlusion-invariant features are then easily extracted from the smoothed data by least squares fitting algorithms.     

Overexpression and elevated plasma level of tumor-associated antigen 90K/Mac-2 binding protein in colorectal carcinoma

The cancer cell secretome may contain potentially useful biomarkers. Previously, we have analyzed the colorectal carcinoma (CRC) cell secretome. In this study, tumor‐associated antigen 90K (TAA90K)/Mac‐2 binding protein (Mac‐2BP), one of the CRC cell secreted proteins, was chosen for evaluation as a potential CRC biomarker because its mRNA level was also found to be significantly elevated in CRC tissues and in a more metastatic CRC cell line from the analysis of two public domain array‐based datasets. Immunohistochemical analysis of 241 CRC specimens showed that TAA90K/Mac‐2BP was positively detected in 52.7% of the tumors, but weakly or not detected in over 95% of the adjacent nontumor epithelial cells. The plasma TAA90K/Mac‐2BP levels were significantly higher in CRC patients (N = 280) versus healthy controls (N = 147) (7.77 ± 3.49 vs. 5.72 ± 2.67 μg/mL, p<0.001). Moreover, combination of TAA90K/Mac‐2BP and carcinoembryonic antigen (CEA) could outperform CEA alone in discriminating CRC patients from healthy persons in this case‐control study. Our results collectively indicate that analysis of cancer cell secretome is a feasible strategy for identifying cancer biomarker candidates, and the TAA90K/Mac‐2BP may be a potential CRC biomarker.     

Visual lobe shape and search performance for targets of different difficulty

In quantitative models of visual search it has usually been assumed that visual lobe area shape was sufficiently regular to be approximated by a circle or ellipse. However, the irregularities in visual lobe shapes that have been found in studies involving extensive lobe mapping have suggested that lobe shape may have important implications for visual search performance and for the accuracy of mathematical models used for performance prediction. However, no systematic research on the relationship between the shape aspect of visual lobes and search performance seems to have been carried out and no comparisons of visual lobe shape characteristics under the effect of target difficulty have been reported. The current study was conducted to achieve two major objectives in two experiments. Experiment 1 used two different targets (letter 'O' and letter 'Y') to map the visual lobes of subjects in order to provide a systematic and quantitative comparison of lobe shape characteristics and experiment 2 was to investigate the correlation of visual lobe shape characteristics with visual search time under the effect of target difficulty. The visual lobes of 28 subjects were mapped on 24 imaginary and regularly spaced meridians originating from the centre of the visual field to resemble the full field mapping situation. Five categories of shape indices, viz. roundness, boundary smoothness, symmetry, elongation and shape regularity were investigated. The results of this study demonstrated that the visual lobe shapes of subjects elongate horizontally with medium level of roundness, high levels of boundary smoothness, symmetry and regularity for an easy target (O) against a homogeneous background of 'X's. When a difficult target (Y) was used, the visual lobes of the subjects were still elongated horizontally but to a smaller extent and with a low level of roundness, medium level of boundary smoothness and regularity and a similar high level of symmetry to the easy target. Moreover, significant correlations between shape indices and visual search time were found, suggesting mathematical models for predicting search time should not merely rely on area but also should consider visual lobe shape indices. Finally, a universal mathematical model containing several visual lobe shape indices was developed, which was applicable in the prediction of visual search time for a range of similar search tasks.     

A Compact Fourth-Order Finite Difference Scheme for Unsteady Viscous Incompressible Flows

In this paper, we extend a previous work on a compact scheme for the steady Navier–Stokes equations [Li, Tang, and Fornberg (1995), Int. J. Numer. Methods Fluids, 20, 1137–1151] to the unsteady case. By exploiting the coupling relation between the streamfunction and vorticity equations, the Navier–Stokes equations are discretized in space within a 3×3 stencil such that a fourth order accuracy is achieved. The time derivatives are discretized in such a way as to maintain the compactness of the stencil. We explore several known time-stepping approaches including second-order BDF method, fourth-order BDF method and the Crank–Nicolson method. Numerical solutions are obtained for the driven cavity problem and are compared with solutions available in the literature. For large values of the Reynolds number, it is found that high-order time discretizations outperform the low-order ones.     

Java移动代码的授权访问研究

Java移动代码是一种可以通过网络从一台计算机传珐另一台计算机上运行的Ｊava程序,在现代网络计算及电子商务中具有广泛应用,Ｊava的这一显著特性也蕴藏着授权管理上的不足,未授权者可以很容易地非法使用这些程序,针对Ｊava移动代码的这些不足之处,分别对Ｊava Applet及Ｊava Applet及Ｊava Servlet提出了基于数字签名算法的授权与访问控制方案,安全,有效地解决了这种新兴的授权与访问控制问题。