高度复杂拟凸体的实时触觉绘制

触觉绘制目前已成为虚拟现实的研究热点之一,其核心是触觉代理与目标物体的实时碰撞检测与距离计算,对高度复杂模型的触觉绘制仍然是虚拟现实领域的难题,原因是对模型复杂度敏感的算法均不能满足触觉绘制苛刻的时间要求.提出触觉包围盒概念,并针对拟凸体提出一种基于触觉包围盒的实时触觉绘制算法.触觉包围盒记录了离散的采样光线与目标物体的相交信息,进行触觉计算时,只需要执行1次线段与包围盒求交运算和5次双线性插值运算即可高速求出碰撞信息,且所得到的碰撞信息可直接用于反馈力计算.实验结果表明,该算法不仅快速有效,而且具有与触觉绘制模型复杂度不相关的优点.     

Optimally solving Markov decision processes with total expected discounted reward function: Linear programming revisited

We compare the computational performance of linear programming (LP) and the policy iteration algorithm (PIA) for solving discrete-time infinite-horizon Markov decision process (MDP) models with total expected discounted reward. We use randomly generated test problems as well as a real-life health-care problem to empirically show that, unlike previously reported, barrier methods for LP provide a viable tool for optimally solving such MDPs. The dimensions of comparison include transition probability matrix structure, state and action size, and the LP solution method.     

Underflow revisited

Underflow is a floating-point phenomenon. Although the use of gradual underflow as defended in [2] and [3] is now widespread, most numerical analysts may not be aware of the fact that several implementations of the same principle are in existence, leading to different behavior of code on different platforms, mainly with respect to exception signaling. We intend to thoroughly discuss the slight differences among these implementations. Examples will be taken from current hardware and from our own multiprecision software class library. Throughout the discussion the focus is on the analysis of the phenomenon and not on any implementation issues. Many programmers are also unaware of the fact that the IEEE 754 and 854 standards do not guarantee that a program will deliver identical results on all conforming systems. Of all the differences that can occur cross-platform, the underflow exception is just one. Received: January 2002 / Accepted: June 2002 RID="*" ID="*"Research Director, FWO-Vlaanderen RID="**" ID="**"Supported by an NOI-grant from the Universiteit Antwerpen (UIA) RID="***" ID="***"Postdoctoral Fellow, FWO-Vlaanderen RID="****" ID="****"Research Fellow, IWT (Institute for Science and Technology)     

Match-bounds revisited

The use of automata techniques to prove the termination of string rewrite systems and left-linear term rewrite systems is advocated by Geser et al. in a recent sequence of papers. We extend their work to non-left-linear rewrite systems. The key to this extension is the introduction of so-called raise rules and the use of tree automata that are not quite deterministic. Furthermore, to increase the applicability of the method we show how it can be incorporated into the dependency pair framework. To achieve this we introduce two new enrichments which take the special properties of dependency pair problems into account.     

Quicksort revisited

H.D. Mills and R.C. Linger (1986) propose adding the datatype set to existing programming languages. During some investigations using sets, it became apparent that Quicksort can be written without using stacks (or recursion). Using sets can lead to efficient multiprocessor usage, because if the elements of a set can be processed in any order, they can frequently be processed simultaneously. An example of the possibilities is an intelligent disk control unit based on External Quicksort, using four processors and four read/write heads., The control unit can sort a large disk file in about 1/3 of the time taken by the one-processor version     

Textures revisited

We describe texture generation methods for complex objects. Recently developed 3D scanning devices and high-resolution cameras can capture the complex geometry of an object and yield high-resolution images. However, generating a textured model from this input data is still a difficult problem. This task is divided into three subproblems: parameterization, texture combination, and texture restoration. A low-distortion parameterization method is presented, which minimizes geometry stretch energy. Photographs of the object taken from multiple viewpoints under modestly uncontrolled illumination conditions are merged into a seamless texture using our new texture combination method. We also demonstrate a texture restoration method that can fill in missing pixel information when the input photographs do not provide sufficient information to cover the entire surface due to self-occlusion or registration errors. Our methods are fully automatic, except for the registration process between a 3D model and input photographs. We demonstrate the application of our method to human face models for evaluation. The techniques presented in this paper make a consistent and complete pipeline to generate the texture of a complex object.     

Revised multi-segment goal programming: Percentage goal programming

The multi-segment goal programming (MSGP) model is an extension model of GP wherein the core thinking is inherited from the multi-choice goal programming (MCGP) model. In this paper, we recommend certain points of the MSGP model and offer a Revised MSGP Model as an aid to burdened decision makers who cannot expect an either-or selection of coefficients in practice. The proposed model takes into account a scenario in which the selection of all possible coefficients pertaining to each decision variable in the MSGP model can be an in-between selection instead of an exclusive-or selection. We hope this study can fill in a possible gap that might exist when applying the MSGP model, and can offer an extension model for practitioners when they use this model to solve related decision problems.     

Integrating answer set programming and constraint logic programming

We introduce a knowledge representation language ${\cal AC(C)}$ extending the syntax and semantics of ASP and CR-Prolog, give some examples of its use, and present an algorithm, $\mathcal{AC}\!solver$ , for computing answer sets of ${\cal AC(C)}$ programs. The algorithm does not require full grounding of a program and combines “classical” ASP solving methods with constraint logic programming techniques and CR-Prolog based abduction. The ${\cal AC(C)}$ based approach often allows to solve problems which are impossible to solve by more traditional ASP solving techniques. We believe that further investigation of the language and development of more efficient and reliable solvers for its programs can help to substantially expand the domain of applicability of the answer set programming paradigm.     

Revised multi-segment goal programming: Percentage goal programming

The multi-segment goal programming (MSGP) model is an extension model of GP wherein the core thinking is inherited from the multi-choice goal programming (MCGP) model. In this paper, we recommend certain points of the MSGP model and offer a Revised MSGP Model as an aid to burdened decision makers who cannot expect an either-or selection of coefficients in practice. The proposed model takes into account a scenario in which the selection of all possible coefficients pertaining to each decision variable in the MSGP model can be an in-between selection instead of an exclusive-or selection. We hope this study can fill in a possible gap that might exist when applying the MSGP model, and can offer an extension model for practitioners when they use this model to solve related decision problems.     

Revision programming

In this paper we introduce revision programming — a logic-based framework for describing constraints on databases and providing a computational mechanism to enforce them. Revision programming captures those constraints that can be stated in terms of the membership (presence or absence) of items (records) in a database. Each such constraint is represented by a revision rule ← ₁,…,_k, where and all ga_i are of the form in(a) and out(b). Collections of revision rules form revision programs. Similarly as logic programs, revision programs admit both declarative and imperative (procedural) interpretations. In our paper, we introduce a semantics that reflects both interpretations. Given a revision program, this semantics assigns to any database B a collection (possibly empty) of P-justified revisions of B. The paper contains a thorough study of revision programming. We exhibit several fundamental properties of revision programming. We study the relationship of revision programming to logic programming. We investigate complexity of reasoning with revision programs as well as algorithms to compute P-justified revisions. Most importantly from the practical database perspective, we identify two classes of revision programs, safe and stratified, with a desirable property that they determine for each initial database a unique revision.     

Quasiparallel programming

This paper describes SIMONE, an extension of PASCAL,¹ which provides the quasiparallel programming facility of SIMULA 67, but without classes or references. The language is intended to be suitable for the design, testing and simulation of operating system algorithms. It is illustrated by simple examples, suitable as project material in a course on operating systems. A simple, restricted, but efficient implementation is described. It is suggested that the language might be suitable for more general simulation purposes, and an example of a general job shop simulation is given.     

Immune programming

This paper describes ‘Immune Programming’, a paradigm in the field of evolutionary computing taking its inspiration from principles of the vertebrate immune system. These principles are used to derive stack-based computer programs to solve a wide range of problems.An antigen is used to represent the programming problem to be addressed and may be provided in closed form or as an input/output mapping. An antibody set (a repertoire), wherein each member represents a candidate solution, is generated at random from a gene library representing computer instructions. Affinity, the fit of an antibody (a solution candidate) to the antigen (the problem), is analogous to shape-complementarity evident in biological systems. This measure is used to determine both the fate of individual antibodies, and whether or not the algorithm has successfully completed.When a repertoire has not yielded affinity relating algorithm completion, individual antibodies are replaced, cloned, or hypermutated. Replacement occurs according to a replacement probability and yields an entirely new randomly-generated solution candidate when invoked. This randomness (and that of the initial repertoire) provides diversity sufficient to address a wide range of problems. The chance of antibody cloning, wherein a verbatim copy is placed in the new repertoire, occurs proportionally to its affinity and according to a cloning probability. The chances of an effective (high-affinity) antibody being cloned is high, analogous to replication of effective pathogen-fighting antibodies in biological systems. Hypermutation, wherein probability-based replacement of the gene components within an antibody occurs, is also performed on high-affinity entities. However, the extent of mutation is inversely proportional to the antigenic affinity. The effectiveness of this process lies in the supposition that a candidate showing promise is likely similar to the ideal solution.This paper describes the paradigm in detail along with the underlying immune theories and their computational models. A set of sample problems are defined and solved using the algorithm, demonstrating its effectiveness and excellent convergent qualities. Further, the speed of convergence with respect to repertoire size limitations and probability parameters is explored and compared to stack-based genetic programming algorithms.     

Synthetic programming

Given a formal specification for a desired program, our goal is to transform it step-by-step into executable code. We proceed in a top-down fashion—as suggested by ‘structured programming’ methodology. Each step consists of applying a synthesis rule to rewrite a segment of the developing program in increased detail. If every step is transparent enough to ensure correctness, each partial program in the series is sure to be equivalent to its predecessor. In particular, the final program is guaranteed to satisfy the initial specifications.In this paper, we concentrate on automatable strategies for the formation of iterative loops, giving rules based on ‘invariant assertions’ and on ‘subgoal assertions’.     

Contractor programming

This paper describes a solver programming method, called contractor programming, that copes with two issues related to constraint processing over the reals. First, continuous constraints involve an inevitable step of solver design. Existing softwares provide an insufficient answer by restricting users to choose among a list of fixed strategies. Our first contribution is to give more freedom in solver design by introducing programming concepts where only configuration parameters were previously available. Programming consists in applying operators (intersection, composition, etc.) on algorithms called contractors that are somehow similar to propagators.Second, many problems with real variables cannot be cast as the search for vectors simultaneously satisfying the set of constraints, but a large variety of different outputs may be demanded from a set of constraints (e.g., a paving with boxes inside and outside of the solution set). These outputs can actually be viewed as the result of different contractors working concurrently on the same search space, with a bisection procedure intervening in case of deadlock. Such algorithms (which are not strictly speaking solvers) will be made easy to build thanks to a new branch & prune system, called paver.Thus, this paper gives a way to deal harmoniously with a larger set of problems while giving a fine control on the solving mechanisms. The contractor formalism and the paver system are the two contributions. The approach is motivated and justified through different cases of study. An implementation of this framework named Quimper is also presented.     

基于空间的编程

全面地介绍了一种基于空间的中间层编程方法。这是一种以分布式虚拟共享内存为技术基础设施的新型编程方法。其重要性在于它将分布计算的语义环境和问题领域的语义环境在极大程度上相分离 ,从而可以为今天软件体系结构中存在的种种问题提供具有持续可靠性的解决方案。     

Adaptive programming

An adaptive program is one that changes its behavior base on the current state of its environment. This notion of adaptivity is formalized, and a logic for reasoning about adaptive programs is presented. The logic includes several composition operators that can be used to define an adaptive program in terms of given constituent programs; programs resulting from these compositions retain the adaptive properties of their constituent programs. The authors begin by discussing adaptive sequential programs, then extend the discussion to adaptive distributed programs. The relationship between adaptivity and self-stabilization is discussed. A case study for constructing an adaptive distributed program where a token is circulated in a ring of processes is presented