Reconstruction of B-spline surface by interpolating boundary curves and approximating inner points

In this paper,we present an algorithm for reconstruction of B-spline surface such that it interpolates the four given boundary curves and simultaneously approximates some given inner points.The main idea of our method is:first,we construct an initial surface which interpolates the four given boundary curves;then,while keeping the boundary control points of the initial surface unchanged,we reposition the inner control points of the surface with energy optimization method.Examples show that our algorithm is practicable and effective.     

Polygonal mesh regularization for subdivision surfaces interpolating meshes of curves

Published online: 14 February 2003     

Shape control of a bivariate interpolating spline surface

A bivariate rational interpolation was constructed in earlier works using both function values and partial derivatives of the function being interpolated as the interpolation data, and it can be expressed by the symmetric basis. This paper gives the point-control method for modifying the interpolating surfaces by selecting suitable values of the parameters under the conditions that the interpolation data are not changed. An example is given to show the effectiveness of this method.     

Optimal reduced-set vectors for support vector machines with a quadratic kernel

To reduce computational cost, the discriminant function of a support vector machine (SVM) should be represented using as few vectors as possible. This problem has been tackled in different ways. In this article,we develop an explicit solution in the case of a general quadratic kernel k(x. x') = (C + D xT x')2. For a given number of vectors, this solution provides the best possible approximation and can even recover the discriminant function if the number of used vectors is large enough. The key idea is to express the inhomogeneous kernel as a homogeneous kernel ona space having one dimension more than the original one and to follow the approach of Burges (1996).     

A plot drainage network as a conceptual tool for the spatial representation of surface flow pathways in agricultural catchments

The drainage network must take the farming systems and the landscape structure into consideration to describe flow pathways in the agricultural catchment. A new approach is proposed to build the drainage network which is based on the identification of the inlets and outlets for surface water flow on each farmers’ field (or plot), estimating the relative areas contributing to the surface yield. The delineation of these areas and their links in terms of surface flow pathways provides us with a pattern of relationships between individual plots, i.e. going from each plot to the other plots over the entire catchment. In this approach, flow directions are firstly calculated in the usual way by taking account of slope direction. Plot outlets are defined from the digital elevation model (DEM) and then linked together using a tree structure. If present, linear networks such as hedges modify both the flow directions and the location of plot outlets, hence modify this tree structure. In a final step, the plots are themselves linked together using a graph structure illustrated by an arrow diagram. This drainage network based on plot outlets is applied to a 15-km² catchment area represented by 38,300 pixels and 2000 plots. This new drainage network takes into consideration 5300 plot outlets, which greatly reduces the number of objects in comparison with a drainage network made up of pixels or DEM cells. This method leads to a simple and functional representation of surface flow pathways in an agricultural catchment. It allows us to identify the key plots controlling stream water pollution where converging flow pathways are coming from numerous or large-sized plots. Finally, it produces a functional representation for decision support.     

Rate-interval curves — A tool for the analysis and monitoring of network traffic

In this paper we propose a method, related to the theory of Network Calculus, for the analysis of aggregate network traffic by means of empirical rate-interval curves obtained from experimental data. The algorithm on which it is based differs from the commonly employed wavelet-based approach, although it retains some of its multiresolution features. We briefly introduce the theoretical aspects, analyze measurement accuracy and present results, obtained both by simulation and by the analysis of real traffic traces, which provide an assessment of the strengths and weaknesses of the proposed method.

Rate-interval curve analysis provides very robust and acceptably accurate estimates of the Hurst parameter value and, even in the presence of flow irregularities, results can be proved to be correct as far as scaling properties are concerned.

Further analyzes concerning peaks, bursts and similar localized phenomena that may have a significant impact on the performances of a network are allowed by considering maximal rate envelopes, showing the potential of this approach for monitoring applications.     

Implementing a generalized tool for network monitoring

Determining how you were attacked is essential to developing a response or countermeasure. Usually, a system or network manager presented with a successful intrusion has very little information with which to work: a possibly corrupted system log, a firewall log, and perhaps some tcpdump output.When hackers come up with a new technique for cracking a network, it often takes the security community a while to determine the method being used. In aviation, an aircraft's ‘black box’ is used to analyze the details of a crash. We believe a similar capability is needed for networks. Being able to quickly learn how an attack works will shorten the effective useful lifetime of the attack. Additionally, the recovered attack records may be helpful in tracking or prosecuting the attacker. Since we have developed a general purpose statistics-gathering system, we believe it will be useful for more than just security. For example, a network manager may desire an historical record of the usage growth of certain applications, or details about the breakdown of types of traffic at different times of day. Such records will provide useful information for network managers in diagnosing performance problems or planning growth.This paper describes an architecture and toolkit for building network traffic analysis and statistical event records: The Network Flight Recorder. The NFR uses a promiscuous packet interface to pass visible traffic into an internally meta-programmed decision engine which routes information about packets and their contents into statistical or logging backends. In addition to packet analysis and collection, the NFR's internal architecture permits network managers to sample interesting portions of network traffic for logging or statistical analysis. The NFR programming language is simple, but powerful enough that you can perform reasonable analysis on traffic before choosing to record it. For example, you might analyze SMTP transactions but only choose to record those relating to a user who is sending spam or abusive E-mail. The analysis language includes a capability for generating alert messages which the rest of the system queues, multiplexes, and delivers. A simplified hyper-query interface allows extensive browsing of the NFR's stored datasets and statistics from any Java-enabled browser. The NFR is currently being deployed at a number of ISPs and commercial sites, and is available for download in source code form from www.nfr.net.     

Optimal routing in multiple I/O data network using neural network with perturbed energy function

An optimal routing problem in multiple I/O data network is one of the most important problems related on the performance of the network basically, and is formulated as a constrained nonlinear optimization problem. When solving the problem considered using neural networks, we may obtain local minima, rather than global minimum, because the problem has multimodals. In this paper, we introduce a perturbed energy function into the neural network based on a penalty method to solve the multimodal nonlinear optimization problem.     

A spectral approach to clustering numerical vectors as nodes in a network

We address the issue of clustering examples by integrating multiple data sources, particularly numerical vectors and nodes in a network. We propose a new, efficient spectral approach, which integrates the two costs for clustering numerical vectors and clustering nodes in a network into a matrix trace, reducing the issue to a trace optimization problem which can be solved by an eigenvalue decomposition. We empirically demonstrate the performance of the proposed approach through a variety of experiments, including both synthetic and real biological datasets.     

A modification of theorem for the least bound of the minimum degree of interpolating unit

Theorem 1 for the least bound on the degree of interpolating unit in [1] requires to check both positive definiteness and negative definiteness of the Nevanlinna-Pick matrix.     

Optimal process design of sheet metal forming for minimum springback via an integrated neural network evolutionary algorithm

The process of sheet metal forming is characterized by various process parameters. Accurate prediction of springback is essential for the design of tools used in sheet metal forming operations. In this paper, an evolutionary algorithm is presented that is capable of handling single/multiobjective, unconstrained and constrained formulations of optimal process design problems. To illustrate the use of the algorithm, a relatively simple springback minimization problem (hemispherical cup-drawing) is solved in this paper, and complete formulations of the algorithm are provided to deal with the constraints and multiple objectives. The algorithm is capable of generating multiple optimal solutions in a single run. The evolutionary algorithm is combined with the finite element method for springback computation, in order to arrive at the set of optimal process parameters. To reduce the computational time required by the evolutionary algorithm due to actual springback computations via the finite element method, a neural network model is developed and integrated within the evolutionary algorithm as an approximator. The results clearly show the viability of the use of the evolutionary algorithm and the use of approximators to derive optimal process parameters for metal forming operations.     

Optimal coverage trajectories for a UGV with tradeoffs for energy and time

Area coverage is a common task for an unmanned ground vehicle (UGV) that requires time and energy to complete. We have developed a novel cost function that can be used to optimally traverse a path that covers a region. The UGV model and cost function are developed theoretically and verified experimentally. Our cost function weights force inputs, area covered and motor efficiency to create an optimal trajectory. This trajectory is constrained to follow a coverage path described in the literature. The path is modified based on the cost function by replacing turn-in-place maneuvers by moving turns. Tradeoffs are presented for three cases: (1) drive motor efficiency is not considered, (2) the motors are most efficient at the maximum velocity, and (3) the motors are most efficient below the maximum velocity. Optimality tradeoffs include the time required to cover the region, and the energy required to complete the trajectory. Experimental results using an iRobot Packbot are presented.     

Optimal parametrization of curves for robot trajectory design

A numerical method is presented for the offline determination of the minimum-time parameterization of a fixed path in robot joint space, assuming start and end at rest conditions and subject to constraints on manipulator joint torques. A numerical method for solving the problem is discussed in which the derivative of the change of variables is approximated by a cubic spline. Numerical results for a three-axis manipulator are presented     

Optimal 3D arm strategies for maximizing twist rotation during somersault of a rigid-body model

Multibody System Dynamics - Looking for new arm strategies for better twisting performances during a backward somersault is of interest for the acrobatic sports community while being a complex...     

Pattern recognition as a quest for minimum entropy

We shall show that many known algorithms of clustering and pattern recognition can be characterized as efforts to minimize entropy, when suitably defined.     

The selection of variable gear for minimum energy

The system under consideration is that of a motor, coupled by a gear train to an inertial load, where the requirement is to rotate the load by a given angle and come to a halt. The question considered is whether the use of a variable gear, instead of one with constant ratio, will reduce the required energy, and if so, what are the optimal gear ratio function and the corresponding control function.     

Optimal resource assignment to maximize multistate network reliability for a computer network

Computer network is a major tool to transmit data in our modern society. How to evaluate and enhance network reliability is thus an important issue for organizations, especially to maximize network reliability. A computer network is a multistate network in which each edge has several possible capacities with a probability distribution and may fail. The multistate network reliability is the probability that the maximal flow is no less than a given demand. From the standpoint of quality management, a further problem is to reassign the existing resources for maximizing multistate network reliability without changing the network topology. Hence, this paper focuses on the resource assignment problem to propose an efficient approach based on the simple genetic algorithm. In which, a resource assignment is represented as a chromosome and the corresponding multistate network reliability is the fitness value of the chromosome. The experimental results show that the proposed algorithm can derive the optimal resource assignment in a reasonable time.     

Optimal speedup for backtrack search on a butterfly network

We present a generic algorithm for implementing backtrack search on anN processor butterfly network. For a backtrack search tree havingM nodes the heighth, our algorithm requires timeO(M/N +h) with high probability. This is optimal and is obtained without making assumptions about the shape of the tree being searched.This research was supported in part by NSF-DARPA Grant No. CCR-9005448 and Air Force Office of Scientific Research, Grant No. F49620-90-C-0029. A preliminary version of this paper appeared in theProceedings of the Third ACM Symposium on Parallel Algorithms and Architectures, July 1991, Hilton Head, South Carolina.     

Identification for control: Optimal input intended to identify a minimum variance controller

It is well known that the quality of the parameters identified during an identification experiment depends on the applied excitation signal. Prediction error identification using full order parametric models delivers an ellipsoidal region in which the true parameters lie with some prescribed probability level. This ellipsoidal region is determined by the covariance matrix of the parameters. Input design strategies aim at the minimization of some measure of this covariance matrix. We show that it is possible to optimize the input in an identification experiment with respect to a performance cost function of a closed-loop system involving explicitly the dependence of the designed controller on the identified model. In the present contribution we focus on finding the optimal input for the estimation of the parameters of a minimum variance controller, without the intermediate step of first minimizing some measure of the model parameter accuracy. We do this in conjunction with using covariance formulas which are not asymptotic in the model order, which is rather new in the domain of optimal input design. The identification procedure is performed in closed-loop. Besides optimizing the input power spectrum for the identification experiment, we also address the question of optimality of the controller. It is a wide belief that the minimum variance controller should be the optimal choice, since we perform an experiment for designing a minimum variance controller. However, we show that this may not always be the case, but rather depends on the model structure.