Ranking grey numbers based on dominance grey degrees

With respect to the decision making problems where a lot of fuzzy and grey information always exists in the real-life decision making information system methods as fuzzy mathematics, it is difficult for such uncertainty probability, and interval numbers to deal with. To this end, based on the thought and method of grey numbers, grey degrees and interval numbers, the concept of dominance grey degree is defined. And then a method of ranking interval grey numbers based on the dominance grey degree is proposed. After discussing the relevant properties, the paper finally uses an example to demonstrate the effectiveness and applicability of the model. The result shows that the proposed model can more accurately describe uncertainty decision making problems, and realize the total ordering process for multiple-attribute decision-making problems.     

Image decomposition using adaptive regularization and div （BMO）

In order to avoid staircasing effect and preserve small scale texture information for the classical total variation regularization, a new minimization energy functional model for image decomposition is proposed. Firstly, an adaptive regularization based on the local feature of images is introduced to substitute total variational regularization. The oscillatory component containing texture and/or noise is modeled in generalized function space div (BMO). And then, the existence and uniqueness of the minimizer for proposed model are proved. Finally, the gradient descent flow of the Euler-Lagrange equations for the new model is numerically implemented by using a finite difference method. Experiments show that the proposed model is very robust to noise, and the staircasing effect is avoided efficiently, while edges and textures are well remained.     

Novel passive localization algorithm based on weighted restricted total least square

A novel multi-observer passive localization algorithm based on the weighted restricted total least square (WRTLS) is proposed to solve the bearings-only localization problem in the presence of observer position errors. Firstly, the unknown matrix perturbation information is utilized to form the WRTLS problem. Then, the corresponding constrained optimization problem is transformed into an unconstrained one, which is a generalized Rayleigh quotient minimization problem. Thus, the solution can be got through the generalized eigenvalue decomposition and requires no initial state guess process. Simulation results indicate that the proposed algorithm can approach the Cramer-Rao lower bound (CRLB), and the localization solution is asymptotically unbiased.     

H_ index based fault estimation for micro quad-rotor

Taking the attitude control system of micro quad-rotor as a research object, a design scheme of fault estimator based on generalized Kalman-Yakubovic-Popov （GKYP） lemma is put forward to deal with the problem of estimating multiple actuators malfunctions with couplings. Using an H_index and an appropriate algorithm, the goal of weakening the coupling can be achieved by limiting the fault frequency to a certain range, then different kinds of actuator faults can be estimated correctly. The simulations demonstrate the reliability and validity of the proposed method.     

Single foggy image restoration based on spatial correlation analysis of dark channel prior

Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.     

Simulated annealing spectral clustering algorithm for image segmentation

The similarity measure is crucial to the performance of spectral clustering. The Gaussian kernel function based on the Euclidean distance is usual y adopted as the similarity measure. However, the Euclidean distance measure cannot ful y reveal the complex distribution data, and the result of spectral clustering is very sensitive to the scaling parameter. To solve these problems, a new manifold distance measure and a novel simulated anneal-ing spectral clustering （SASC） algorithm based on the manifold distance measure are proposed. The simulated annealing based on genetic algorithm （SAGA）, characterized by its rapid convergence to the global optimum, is used to cluster the sample points in the spectral mapping space. The proposed algorithm can not only reflect local and global consistency better, but also reduce the sensitivity of spectral clustering to the kernel parameter, which improves the algorithm’s clustering performance. To efficiently apply the algorithm to image segmentation, the Nystrom method is used to reduce the computation complexity. Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms, the proposed algorithm can achieve better clustering performances on several synthetic datasets, texture images and real images.     

Global minimization of adaptive local image fitting energy for image segmentation

The active contour model based on local image fitting （LIF） energy is an effective method to deal with intensity inhomo- geneities, but it always conflicts with the local minimum problem because LIF has a nonconvex energy function form. At the same time, the parameters of LIF are hard to be chosen for better per- formance. A global minimization of the adaptive LIF energy model is proposed. The regularized length term which constrains the zero level set is introduced to improve the accuracy of the bound- aries, and a global minimization of the active contour model is presented, in addition, based on the statistical information of the intensity histogram, the standard deviation σ with respect to the truncated Gaussian window is automatically computed according to images. Consequently, the proposed method improves the performance and adaptivity to deal with the intensity inhomo- geneities. Experimental results for synthetic and real images show desirable performance and efficiency of the proposed method.     

基于增强高阶非凸全变分模型的图像去噪算法

为了在缓解阶梯效应的同时更好地保留去噪后图像的细节信息,提出一种基于增强高阶非凸全变分(higher order non-convex total variation, HONTV)模型的图像去噪算法。该算法将每一次去噪后的图像和原始图像取平均作为增强HONTV模型下一次循环的输入并更新参数,然后采用增广拉格朗日乘子法和交替方向乘子法进行循环求解,经过多次迭代,最终得到的去噪图像包含较多的细节信息。在基于全变分的图像去噪方法中,对添加不同标准差大小的高斯白噪声的测试图像和视频进行实验。实验结果表明,所提算法在视觉性能和客观评价指标方面均优于对比算法。     

基于组稀疏表示和加权全变分的图像压缩感知重构

传统的基于组稀疏表示(group sparse representation, GSR)的压缩感知(compressd sensing, CS)重构算法利用信号的稀疏性和非局部相似性来重构图像信号,但没有充分考虑图像的局部平滑特性,影响了算法的重构性能。考虑信号的稀疏性、非局部相似性、平滑性3种先验信息,提出一种基于GSR和加权全变分(weighted total variation, WTV)的图像CS重构算法,并针对传统的WTV采用全局加权会引入错误的纹理以及边缘状伪影的问题,利用一种新的WTV策略,只对图像的高频分量设置权重来保证图像重构质量。此外，针对硬阈值迭代法忽略低频的主分量系数,采用硬阈值-模平方方法来更好地保护非主分量系数。实验表明,相同采样率下,所提算法的峰值信噪比比非局部正则化全变分和基于GSR的CS算法平均分别提高5.4 dB和0.62 dB,验证了所提算法有效保护图像的细节信息。     

基于组稀疏表示和加权全变分的图像压缩感知重构

传统的基于组稀疏表示(group sparse representation, GSR)的压缩感知(compressd sensing, CS)重构算法利用信号的稀疏性和非局部相似性来重构图像信号,但没有充分考虑图像的局部平滑特性,影响了算法的重构性能。考虑信号的稀疏性、非局部相似性、平滑性3种先验信息,提出一种基于GSR和加权全变分(weighted total variation, WTV)的图像CS重构算法,并针对传统的WTV采用全局加权会引入错误的纹理以及边缘状伪影的问题,利用一种新的WTV策略,只对图像的高频分量设置权重来保证图像重构质量。此外，针对硬阈值迭代法忽略低频的主分量系数,采用硬阈值-模平方方法来更好地保护非主分量系数。实验表明,相同采样率下,所提算法的峰值信噪比比非局部正则化全变分和基于GSR的CS算法平均分别提高5.4 dB和0.62 dB,验证了所提算法有效保护图像的细节信息。     

Image segmentation algorithm based on high-dimension fuzzy character and restrained clustering network

An image segmentation algorithm of the restrained fuzzy Kohonen clustering network （RFKCN） based on high- dimension fuzzy character is proposed. The algorithm includes two steps. The first step is the fuzzification of pixels in which two redundant images are built by fuzzy mean value and fuzzy median value. The second step is to construct a three-dimensional （3-D） feature vector of redundant images and their original images and cluster the feature vector through RFKCN, to realize image seg- mentation. The proposed algorithm fully takes into account not only gray distribution information of pixels, but also relevant information and fuzzy information among neighboring pixels in constructing 3- D character space. Based on the combination of competitiveness, redundancy and complementary of the information, the proposed algorithm improves the accuracy of clustering. Theoretical anal- yses and experimental results demonstrate that the proposed algorithm has a good segmentation performance.     

WHO WON THE 2008 OLYMPICS？ A MULTICRITERIA DECISION OF MEASURING INTANGIBLES

It often happens that at the end of Olympics games the medals won by more than one country are many and close in total number as in the 2008 games where China won 100 medals with many gold ones and the United States won 110 medals but with a lesser number of gold medals. The question is： Although it is often done arbitrarily, is there a way to quantify the values of gold, silver and bronze medals legitimately to resolve this concern？ This short exposition shows that there is by using the author＇s theory for the measurement of intangibles, the Analytic Hierarchy Process.     

Asymptotic and stable proper ties of general stochastic functional differential equations

The asymptotic and stable properties of general stochastic functional differential equations are investigated by the multiple Lyapunov function method, which admits non-negative up-per bounds for the stochastic derivatives of the Lyapunov functions, a theorem for asymptotic properties of the LaSal e-type described by limit sets of the solutions of the equations is obtained. Based on the asymptotic properties to the limit set, a theorem of asymptotic stability of the stochastic functional differential equations is also established, which enables us to construct the Lyapunov functions more easily in application. Particularly, the wel-known classical theorem on stochastic stability is a special case of our result, the operator LV is not required to be negative which is more general to fulfil and the stochastic perturbation plays an important role in it. These show clearly the improvement of the traditional method to find the Lyapunov functions. A numerical simulation example is given to il ustrate the usage of the method.     

Spectral analysis and stabilization of a coupled wave-ODE system

In this paper, an interconnected wave-ODE system with K-V damping in the wave equation and unknown parameters in the ODE is considered. It is found that the spectrum of the system operator is composed of two parts： Point spectrum and continuous spectrum. The continuous spectrum consists of an isolated point 1 1/d, and there are two branches of the asymptotic eigenvalues： The first branch is accumulating towards 1 -2, and the other branch tends to -∞. It is shown that there is a sequence of generalized eigenfunctions, which forms a Riesz basis for the Hilbert state space. As a consequence, the spectrum-determined growth condition and exponential stability of the system are concluded.     

Robust bounded control for uncertain flight dynamics using disturbance observer

The robust bounded flight control scheme is developed for the uncertain longitudinal flight dynamics of the fighter with control input saturation invoking the backstepping technique. To enhance the disturbance rejection ability of the robust flight control for fighters, the sliding mode disturbance observer is designed to estimate the compounded disturbance including the unknown external disturbance and the effect of the control input saturation. Based on the backstepping technique and the compounded disturbance estimated output, the robust bounded flight control scheme is proposed for the fighter with the unknown external disturbance and the control input saturation. The closed-loop system stability under the developed robust bounded flight control scheme is rigorously proved using the Lyapunov method and the uniformly asymptotical convergences of all closed-loop signals are guaranteed. Finally, simulation results are presented to show the effectiveness of the proposed robust bounded flight control scheme for the uncertain longitudinal flight dynamics of the fighter.     

Spiking neural P systems with anti-spikes and without annihilating priority as number acceptors

Spiking neural P systems with anti-spikes （ASN P systems） are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes wil immediately annihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, cal ed ASN P systems without anni-hilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation wil consume one time unit. As a result, such systems using two categories of spiking rules （identified by （a, a） and （a,a^-）） can achieve Turing completeness as number accepting devices.     

Multi-agent consensus with time-varying delays and switching topologies

The consensus problems of multi-agents with time-varying delays and switching topologies are studied. First, assume that an agent receives state information from its neighbors with fixed communication delays and processes its own state information with time-varying self-delay respectively. The state time-delay feedback is introduced into the existing consensus protocol to begenerate an improved protocol. Then a sufficient condition is derived which can make the system with time-varying self-delays achieve the consensus. On this basis, a specific form of consensus equilibrium influenced by the initial states of agents, time-delays and state feedback intensity is figured out. In addition, the multi-agent consensus is considered with time-varying topologies. Finally, simulations are presented to il ustrate the validity of theoretical results.     

Shallow water source localization using a mobile shor t horizontal array

This paper presents an approach to the challenging is- sue of passive source localization in shallow water using a mobile short horizontal linear array with length less than ten meters. The short array can be conveniently placed on autonomous underwa- ter vehicles and deployed for adaptive spatial sampling. However, the use of such small aperture passive sonar systems makes it difficult to acquire sufficient spatial gain for localizing long-range sources. To meet the requirement, a localization approach that employs matched-field based techniques that enable the short ho- rizontal linear array is used to passively localize acoustic sources in shallow water. Furthermore, the broadband processing and inter-position processing provide robustness against ocean en- vironmental mismatch and enhance the stability of the estimation process. The proposed approach＇s ability to localize acoustic sources in shallow water at different signal-to-noise ratios is examined through the synthetic test cases where the sources are located at the endfire and some other bearing of the mobile short horizontal linear array. The presented results demonstrate that the positional parameters of the estimated source build up over time as the array moves at a low speed along a straight line at a constant depth.     

FAMILIES OF p-ARY SEQUENCES WITH LOW CORRELATION AND LARGE LINEAR COMPLEXITY

This paper generalizes a method of generating shift sequences in the interleaved construc- tion proposed by Gong. With the new shift sequences, some new families of p-ary sequences with desired properties can be obtained. A lower bound on the number of new families of binary sequences is also established.