Potential-based obstacle avoidance in formation control

Based on the double integrator mathematic model, a new kind of potential function is presented in this paper by referring to the concepts of the electric field; then a new formation control method is proposed, in which the potential functions are used between agent-agent and between agent-obstacle, while state feedback control is applied for the agent and its goal. This strategy makes the whole potential field simpler and helps avoid some local minima. The stability of this combination of potential functions and state feedback control is proven. Some simulations are presented to show the rationality of this control method.     

Sparse representation and blind source separation of ill-posed mixtures

Bofill et al. discussed blind source separation (BSS) of sparse signals in the case of two sensors. However, as Bofill et al. pointed out, this method has some limitation. The potential function they introduced is lack of theoretical basis. Also the method could not be extended to solve the problem in the case of more than three sensors. In this paper, instead of the potential function method, a K-PCA method (combining K-clustering with PCA) is proposed. The new method is easy to be used in the case of more than three sensors. It is easy to be implemented and can provide accurate estimation of mixing matrix. Some criterion is given to check the effect of the mixing matrix A . Some simulations illustrate the availability and accuracy of the method we proposed.     

Using confidence interval to summarize the evaluating results of DSM systems

Distributed shared Memory(DSM) systems have gained popular acceptance by combining the scalability and low cost of distributed system with the ease of use of single address space.Many new hardware DSM and software DSM systems have been proposed in recent years.In general,benchmarking is widely used to demonstrate the performance advantages of new systems.Howerver,the common method used to summarize the measured results is the arithmetic mean of ratios,which is incorrect in some cases.Furthermore,many published papers list a lot of data only,and do not summarize them effectively,which confuse users greatly.In fact,many users want to get a single number as conclusion,which is not provided in old summarizing techniques.Therefore,a new data-summarizing technique based on confidence interval is proposed in this paper.The new technique includes two data-summarizing methods:(1) paried confidence interval method;(2) unpaired confidence interval method.With this new technique,it is concluded that at some confidence one system is better than others.Four examples are shown to demonstrate the advantages of this new technique.Furthermore,with the help of confidence level,it is proposed to standardize the benchmaks used for evaluating DSM systems so that a convincing result can be got,In addition,the new summarizing technique fits not only for evaluating DSM systems,but also for evaluating other systems,such as memory system and communication systems.     

Parameterization based on maximum curvature minimization model

Parameterization is one of the key problems in the construction of a curve to interpolate a set of ordered points.We propose a new local parameterization method based on the curvature model in this paper.The new method determines the knots by minimizing the maximum curvature of quadratic curve.When the knots by the new method are used to construct interpolation curve,the constructed curve have good precision.We also give some comparisons of the new method with existing methods,and our method can perform better in interpolation error,and the interpolated curve is more fairing.     

Image decomposition on the basis of an inverse pyramid with 3-layer neural networks

The contemporary information technologies and Internet impose high requirements on the image compression efficiency. Great number of methods for information redundancy reduction had already been developed, which are based on the image processing in the spatial or spectrum domain. Other methods for image compression use some kinds of neural networks. In spite of their potentialities, the methods from the last group do not offer high compression efficiency. New adaptive method for image decomposition on the basis of an inverse pyramid with neural networks is presented in this paper. The processed image is divided in blocks and then each is compressed in the space of the hidden layers of 3-layer BPNNs, which build the so-called inverse difference pyramid. The results of the new method modeling are presented for sequence of static images in comparison with results for single images from the same group.     

One Fairing Method of Cubic B-spline Curves Based on Weighted Progressive Iterative Approximation

A new method to the problem of fairing planar cubic B-spline curves is introduced in this paper. The method is based on weighted progressive iterative approximation （WPIA for short） and consists of following steps： finding the bad point which needs to fair, deleting the bad point, re-inserting a new data point to keep the structm-e of the curve and applying WPIA method with the new set of the data points to obtain the faired curve. The new set of the data points is formed by the rest of the original data points and the new inserted point. The method can be used for shape design and data processing. Numerical examples are provided to demonstrate the effectiveness of the method.     

A New Method of Solving Kernels in Algebraic Decomposition for the Synthesis of Logic Cell Array

In this paper,an improvement has been made on the algorithm of solving the kernels of new functions which are generated after a common divisor appearing in the original functions is replaced by a new intermediate variable.And an efficient method based on kernel heritage is presented.This method has been successfully used in synthesis of LCA (Logic Cell Array).     

A fault-tolerant routing scheme in dynamic networks

In dynamic networks,links and nodes will be deleted or added regularly.It is very essential for the routing scheme to have the ability of fault-tolerance.The method to achieve such a goal is to generate ore than one path for a given set of source and destination.In this paper,the idea of inderval routing is used to construct a new scheme(Multi-Node Label Interval Routing Scheme,or MNILIR scheme)to realizee fault-tolerance.Interval routing is a space-efficinet routing method for netwroks ,but the method is static and determinative,and it cannot realize faulttoloerance.In MNLIR scheme some nodes will have more than one label,thus some parirs of destination and source will have more than one path;the pairs of nodes, which have inheritance relation,will have the shortest path.Using this character,MNLIR scheme has better overall routing performance than the former interval routing scheme,which can be proven by simulations.The common problem concerning the insertion and deletion of nodes and links is considered in this paper.So if the networks have some changes in topology,MNLIR scheme may find alternative path for certain paris of nodes.In this way,fault-tolerance can be realized with only a little space added to store the multi-node labels.     

An Analysis of Newly Built Forecasting Method to the Sanitary Materials Data

Correct sales forecasting is inevitable in industries. In industries, how to improve forecasting accuracy such as sales, shipping is an important issue. There are many researches made on this. In this paper, a hybrid method is introduced and plural methods are compared. Focusing that the equation of ESM （exponential smoothing method） is equivalent to （1,1） order ARMA model equation, a new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. In this paper, trend removing by the combination of linear and 2nd order non-linear function and 3rd order non-linear function is carried out to the manufacturer＇s data of sanitary materials. The new method shows that it is useful for the time series that has various trend characteristics and rather strong seasonal trend. The effectiveness of this method should be examined in various cases.     

MNP: A Class of NP Optimization Problems

A large class of NP optimization problems called MNP are studied.It is shown that Rmax(2)is in this class and some problems which are not likely in Rmax(2) are in this class.A new kind of reductions,SL-reductions,is defined to preserve approximability and nonapproximability,so it is a more general version of L-reductions and A-reductions.Then some complete problems of this class under SL-reductions are shown and it is proved that the max-clique problem is one of them.So all complete problems in this class are as difficult to approximate as the max-clique problem.     

Synchronization Criteria for an Array of Neutral-Type Neural Networks with Hybrid Coupling: A Novel Analysis Approach

This paper is concerned with an array of neutral-type neural networks with hybrid nonlinear coupling, which is composed of discrete-delay coupling and distributed-delay coupling. The discrete-delay considered in this system is assumed to vary over an interval (0 < h ₁ ≤ τ ₁(t) ≤ h ₂), where the lower and the upper bounds are known. Based on Lyapunov–Krasovskii (L–K) functional and Kronecker product technique, by introducing several new free-weighting matrices, two novel criterions are acquired to ensure the global synchronization of the proposed networks, which are proved to be much less conservative than some exiting results. Moreover, the derivative of discrete-delay can take any value. Finally, numerical examples are provided to show the effectiveness of the proposed results.     

The δ-process for acceleration of outer iterations in reactor problems

A new δ-process method is proposed and justified for the acceleration of outer iterations in reactor problems of the eigenvalue (K _eff) calculation in a multigroup approximation. It is proved that the δ-process is asymptotically equivalent to Newton’s method. To investigate the efficiency of this method, the initial state of critical assembly BZD/1 in the ZEBRA experiments is computed in approximation of the discrete ordinates method in X-Y-Z geometry with acceleration for the different value of parameter δ in the interval (0, 1). The best acceleration factor of 3 was obtained in the S ₈ P ₃-approximation for the value δ = 0.8.     

Exact and Approximation Algorithms for Clustering

In this paper we present an n^ O(k ^1-1/d ) -time algorithm for solving the k -center problem in \reals ^d , under L _∈ fty - and L ₂ -metrics. The algorithm extends to other metrics, and to the discrete k -center problem. We also describe a simple (1+ɛ) -approximation algorithm for the k -center problem, with running time O(nlog k) + (k/ɛ)^ O(k ^1-1/d ) . Finally, we present an n^ O(k ^1-1/d ) -time algorithm for solving the L -capacitated k -center problem, provided that L=Ω(n/k ^1-1/d ) or L=O(1) . Received July 25, 2000; revised April 6, 2001.     

Disturbance observer for non-minimum phase linear systems

Disturbance observer (DOB) approach has been widely employed in the industry thanks to its powerful ability to attenuate disturbances and compensate plant uncertainties. Motivated by the fact that the application of the DOB approach has been limited to minimum phase systems, we propose a new DOB configuration for non-minimum phase systems. Rigorous analysis for robust stability and performance recovery is presented in terms of a new filter Θ (s). By restricting the plant uncertainty to a multiplicative perturbation, we also present a systematic design methodology for the filter Θ(s) by virtue of the H_∞ synthesis technique. An illustrative example of autopilot design is presented to show the effectiveness of the proposed method for which it is not easy to design H_∞ controller to achieve some control goals. The simulation results show that the response of the perturbed system in the presence of disturbance can be recovered to the nominal system response in the absence of disturbance.     

New approaches to robust l2?l∞ and H∞ filtering for uncertain discrete-time systemsfiltering for uncertain discrete-time systems

The problems of robust l ₂−l _∞ and H _∞ filtering for discrete-time systems with parameter uncertainty residing in a polytope are investigated in this paper. The filtering strategies are based on new robust performance criteria derived from a new result of parameter-dependent Lyapunov stability condition, which exhibit less conservativeness than previous results in the quadratic framework. The designed filters guaranteeing a prescribed l ₂−l _∞ or H _∞ noise attenuation level can be obtained from the solution of convex optimization problems, which can be solved via efficient interior point methods. Numerical examples have shown that the filter design procedures proposed in this paper are much less conservative than earlier results.     

A new approach for transient response control via characteristic ratio assignment method

This paper proposes a new direct approach to control the transient response of a LTI system. The characteristic of a repeated-pole system is first studied. It can be shown that the step response of this kind of system has no overshoot. μ-scaled characteristic ratios, β _i = μα _i , i = 1, … n, and τ are introduced, where α _i is the principal characteristic ratio and τ is the generalized time constant. It is shown that two parameters, μ and τ, are used to successfully control the overshoot and the transient time of all minimum phase LTI systems. The result shows that the proposed method is comparable to the method proposed by Bhattacharyya [1], but more luminous and simple in approach.     

Minimization of a Detail-Preserving Regularization Functional for Impulse Noise Removal

Recently, a powerful two-phase method for restoring images corrupted with high level impulse noise has been developed. The main drawback of the method is the computational efficiency of the second phase which requires the minimization of a non-smooth objective functional. However, it was pointed out in (Chan et al. in Proc. ICIP 2005, pp. 125–128) that the non-smooth data-fitting term in the functional can be deleted since the restoration in the second phase is applied to noisy pixels only. In this paper, we study the analytic properties of the resulting new functional ℱ. We show that ℱ, which is defined in terms of edge-preserving potential functions φ _α , inherits many nice properties from φ _α , including the first and second order Lipschitz continuity, strong convexity, and positive definiteness of its Hessian. Moreover, we use these results to establish the convergence of optimization methods applied to ℱ. In particular, we prove the global convergence of some conjugate gradient-type methods and of a recently proposed low complexity quasi-Newton algorithm. Numerical experiments are given to illustrate the convergence and efficiency of the two methods.

Modeling and Control of Linear Two-time Scale Systems: Applied to Single-Link Flexible Manipulator

This paper deals with the problem of H _∞ control of linear two-time scale systems. The authors’ attention is focused on the robust regulation of the system based on a new modeling approach under the assumption of norm-boundedness of the fast dynamics. In the proposed approach, the fast dynamics are treated as a norm-bounded disturbance (dynamic uncertainty). In this view, the synthesis is performed only for the certain dynamics of the two-time scale system, whose order is less than that of the original system. It should be noted, however, that this scheme is significantly different from the conventional approaches of order reduction for linear two-time scale systems. Specifically, in the present work, explicitly or implicitly, all the dynamics of the system are taken into consideration. In other words, the portion that is treated as a perturbation is incorporated in the design by its maximum possible gain – in the L ₂ sense – over different values of the inputs. One of the advantagesof this approach is that – unlike in the conventional approaches of the order reduction – the reduced-order system still keeps some information of the ‘deleted’ subsystem. Also, we consider the robust stability analysis and stability bound improvement of perturbed parameter (ɛ) in the two-time scale systems by using linear fractional transformations and structured singular values (μ) approach. In this direction, by introducing the parametric uncertainty and dynamic uncertainty in the two-time scale systems, we represent the system as a standard μ-interconnection framework by using linear fractional transformations, and derive a set of new stability conditions for the system in the frequency domain. The exact solution of ɛ-bound is characterized. It is shown that, in spite of the coupling between the dynamic uncertainties and certain dynamics, the designed H _∞ controller stabilizes the overall closed-loop system, in the presence of norm-bounded disturbances. To show the effectiveness of the approach, the modeling of the single-link flexible manipulator and control of the Tip-position of the manipulator by utilizing the mentioned method are presented in the case study.     

A family of adaptive H �� controllers with full information for dissipative hamiltonian systems

This paper investigates a parameterization method of adaptive H _∞ controllers for dissipative Hamiltonian systems with disturbances and unknown parameters. The family of adaptive H _∞ controllers with full information is obtained by interconnecting an adaptive H _∞ controller with a generalized zero-energy-gradient (ZEG) detectable, free generalized Hamiltonian system. The present parameterization method avoids solving Hamilton-Jacobi-Issacs equations and thus the controllers obtained are easier in operation as compared to some existing ones. Simulations show the effectiveness and feasibility of the adaptive control strategy proposed in this paper.     

Efficient design/simulation algorithms for simultaneous switching noise in leadframe packages

 Two new modeling and simulation approaches for Simultaneous Switching Noise (SSN) are described and compared to “brute force” simulation by SPICE. Both simulation accuracy and simulation run-time are considered. The two new approaches are: 1) the “effective inductance” method, in which an approximate, very efficient method of extracting an SSN L _eff  is utilized; and 2) the “macromodel” method, in which the complex inductance network responsible for SSN is represented by only a few dominant poles in the frequency domain and the time domain response is obtained by an efficient convolution algorithm. Both approaches are shown to be accurate and fast, but only the effective inductance algorithm is robust in numerical convergence. Received: 19 March 1997 / Accepted: 25 March 1997