Micro—PROLOG和DBASE间的数据传递

Ｍｉｃｒｏ—ＰＲＯＬＯＧ和ＤＢＡＳＥ间的数据传递吉林省计算机技术研究所（长春１３００１２）李景贵Ｍｉｃｒｏ－ＰＲＯＬＯＧ和ＤＢＡＳＥ都是功能很强的实用软件，它们各有自己的优点和不足。为了编写更好的实用程序，往往要把它们结合起来一起使用。在正文介绍之前...     

块角型约束线性规划问题的内点分解算法

块角型约束线性规划问题的内点分解算法吴力（中国科学院计算数学与科学工程计算研究所）ＡＤＥＣＯＭＰＯＳＩＴＩＯＮＡＬＧＯＲＩＴＨＭＦＯＲＬＩＮＥＡＲＰＲＯＧＲＡＭＭＩＮＧＰＲＯＢＬＥＭＳＷＩＴＨＢＬＯＣＫＡＮＧＵＬＡＲＣＯＮＳＴＲＡＩＮＴＳ￥ＷｕＬｉ（...     

用个人计算机解较大型混合整数线性规划问题

用个人计算机解较大型混合整数线性规划问题胡清淮（武汉化工学院）魏一鸣（北京科技大学）ＳＯＬＶＩＮＧＲＥＬＡＴＩＶＥＬＹＬＡＲＧＥＳＩＺＥＭＩＸＥＤＩＮＴＥＧＥＲＬＩＮＥＡＲＰＲＯＧＲＡＭＭＩＮＧＰＲＯＢＬＥＭＳＵＳＩＮＧＰＥＲＳＯＮＡＬＣＯＭＰＵＴＥ...     

PROLOG—DBMS系统实现中的子句间优化技术

实用ＰＲＯＬＯＧ—ＤＢＭＳ系统实现中的首要问题是要保证系统的正确性．然而,从数据库中提取冗余数据不但严重地影响系统的效率,而且影响系统的正确性．本文所提出的子句间优化技术可以解决这一问题．因为采用这一技术可以保证仅从数据库中提取有用事实并且没有任何元组多于一次被提取．     

混合型符号几何规划的递归二次规划算法

混合型符号几何规划的递归二次规划算法张希，张可村（西安交通大学科学计算与应用软件系）ＡＳＵＣＣＥＳＳＩＶＥＱＵＡＤＲＡＴＩＣＰＲＯＧＲＡＭＭＩＮＧＡＬＧＯＲＩＴＨＭＦＯＲＭＩＸＥＤＳＩＧＮＯＭＩＡＬＧＥＯＭＥＴＲＩＣＰＲＯＧＲＡＭＭＩＮＧ￥Ｚｈａｎｇ...     

解非线性最小二乘的并行连续极小化算法及其数值试验

解非线性最小二乘的并行连续极小化算法及其数值试验李庆扬，朱鹏（清华大学）ＡＰＡＲＡＬＬＥＬＣＯＮＴＩＮＵＯＵＳＭＩＮＩＭＩＺＡＴＩＯＮＡＬＧＯＲＩＴＨＭＦＯＲＳＯＬＶＩＮＧＮＯＮＬＩＮＥＡＲＬＥＡＳＴ－ＳＱＵＡＲＥＳＰＲＯＢＬＥＭＳＡＮＤＮＵＭＥＲＩ...     

COLLISION DETECTION AMONG MOVING OBJECTSIN MACHINING PROCESS SIMULATION

ＣＯＬＬＩＳＩＯＮＤＥＴＥＣＴＩＯＮＡＭＯＮＧＭＯＶＩＮＧＯＢＪＥＣＴＳＩＮＭＡＣＨＩＮＩＮＧＰＲＯＣＥＳＳＳＩＭＵＬＡＴＩＯＮＹａｎｇＨｅｍｉｎｇ；ＬｕＡｎｓｈｅｎｇ；ＺｈｏｕＪｉＣＯＬＬＩＳＩＯＮＤＥＴＥＣＴＩＯＮＡＭＯＮＧＭＯＶＩＮＧＯＢＪＥＣ...     

统计数据凑整问题的网络流算法

统计数据凑整问题的网络流算法金大勇，杨承恩（长沙铁道学院）ＮＥＴＷＯＲＫＡＬＧＯＲＩＴＨＭＳＦＯＲＴＨＥＤＡＴＡＲＯＵＮＤＩＮＧＰＲＯＢＬＥＭ￥ＪｉｎＤａ－ｙｏｎｇ；ＹａｎｇＣｈｅｎｇ－ｅｎ（ＣｈａｎｇｓｈａＲａｉｌｗａｙＩｎｓｔｉｔｕｔｅ）Ａｂｓｔ...     

KBASE—P知识库系统的设计与实现

当前的实用知识库系统研究是将知识库查询语言嵌入到一个过程语言中。ＫＢＡＳＥ－Ｐ是一个通用的知识库程序设计语言。ＫＢＡＳＥ－Ｐ以ＫＢＡＳＥ作为查询语言，以ＦＤ－ＰＲＯＬＯＧ（我们开发的一个ＰＲＯＬＯＧ扩充）为过程性的宿主语言执行Ｉ／Ｏ和ＤＢ更新操作（用扩充的内部谓词）。由于良好的设计和实现，查询语言与宿主语言之间的阻抗不匹配问题相对较小。因而，ＫＢＡＳＥ－Ｐ是一个比较实用的知识库程序设计语言。ＫＢＡ     

解大型稀疏线性代数方程组的预条件Krylov子空间方法

解大型稀疏线性代数方程组的预条件Ｋｒｙｌｏｖ子空间方法邓健新（中国科学院计算中心）ＰＲＥＣＯＮＤＩＴＩＯＮＩＮＧＫＲＹＬＯＶＳＵＢＳＰＡＣＥＭＥＴＨＯＤＳＦＯＲＬＡＲＧＥＳＰＡＲＳＥＬＩＮＥＡＲＡＬＧＥＢＲＡＩＣＳＹＳＴＥＭＳ￥ＤｅｎｇＪｉａｎ－ｘｉ...     

An automatic feature generation approach to multiple instance learning and its applications to image databases

Automatic content-based image categorization is a challenging research topic and has many practical applications. Images are usually represented as bags of feature vectors, and the categorization problem is studied in the Multiple-Instance Learning (MIL) framework. In this paper, we propose a novel learning technique which transforms the MIL problem into a standard supervised learning problem by defining a feature vector for each image bag. Specifically, the feature vectors of the image bags are grouped into clusters and each cluster is given a label. Using these labels, each instance of an image bag can be replaced by a corresponding label to obtain a bag of cluster labels. Data mining can then be employed to uncover common label patterns for each image category. These label patterns are converted into bags of feature vectors; and they are used to transform each image bag in the data set into a feature vector such that each vector element is the distance of the image bag to a distinct pattern bag. With this new image representation, standard supervised learning algorithms can be applied to classify the images into the pre-defined categories. Our experimental results demonstrate the superiority of the proposed technique in categorization accuracy as compared to state-of-the-art methods.     

Pre-Emptive Scheduling Problems with Controllable Processing Times

We consider a range of single machine and identical parallel machine pre-emptive scheduling models with controllable processing times. For each model we study a single criterion problem to minimize the compression cost of the processing times subject to the constraint that all due dates should be met. We demonstrate that each single criterion problem can be formulated in terms of minimizing a linear function over a polymatroid, and this justifies the greedy approach to its solution. A unified technique allows us to develop fast algorithms for solving both single criterion problems and bicriteria counterparts.     

State agnostic planning graphs: deterministic,non-deterministic,and probabilistic planning

Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source.We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic (classical) planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in conformant and conditional planning (i.e., search in belief state space), where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between belief states collapses the set of sets of planning graphs to a single set. We describe another extension in conformant probabilistic planning that reuses planning graph samples of probabilistic action outcomes across search nodes to otherwise curb the inherent prediction cost associated with handling probabilistic actions. Finally, we show how to extract a state agnostic relaxed plan that implicitly solves the relaxed planning problem in each of the planning graphs represented by the state agnostic planning graph and reduces each heuristic evaluation to counting the relevant actions in the state agnostic relaxed plan. Our experimental evaluation (using many existing International Planning Competition problems from classical and non-deterministic conformant tracks) quantifies each of these performance boosts, and demonstrates that heuristic belief state space progression planning using our technique is competitive with the state of the art.     

A branch-and-bound approach to the optimization of redundant software under failure correlation

A technique for computing optimal redundancy levels for the components of a fault-tolerant system with dependently failing redundant module versions is presented. Optimization is done under a given budget constraint and with the overall reliability as the objective. Within each component, price and estimated reliability of each redundant version is assumed to be constant. For solving the optimization problem, a branch-and-bound technique, using the solutions of continuous problem relaxations for the determination of bounds, is developed. Computational experiments with randomly generated test instances show that the suggested algorithm still performs well in situations where a computation based on dynamic programming is not feasible anymore.Scope and purposeUsing redundant program versions and thereby increasing the system reliability is a well-established way to build fault-tolerant software systems, as they are required in safety-critical computing (flight control, power plants, medical technique, defense systems). Given a certain budget for software development or purchase of software components, it makes sense to ask how this budget can be spent in order to maximize the achieved reliability. A problem occurring in this context is that different versions of a program, even if they have been developed independently from each other, do not fail independently. Our approach takes this correlation between failures into account and allows an efficient computation of optimal redundancy levels for problem instance sizes of practical interest.     

Optimization of the new Saab 9-3 exposed to impact load using a space mapping technique

The aim of this work is to illustrate how a space mapping technique using surrogate models together with response surfaces can be used for structural optimization of crashworthiness problems. To determine the response surfaces, several functional evaluations must be performed and each evaluation can be computationally demanding. The space mapping technique uses surrogate models, i.e. less costly models, to determine these surfaces and their associated gradients. The full model is used to correct the gradients from the surrogate model for the next iteration. Thus, the space mapping technique makes it possible to reduce the total computing time needed to find the optimal solution. First, two analytical functions and one analytical structural optimization problem are presented to exemplify the idea of space mapping and to compare the efficiency of space mapping to traditional response surface optimization. Secondly, a sub-model of a complete vehicle finite element (FE) model is used to study different objective functions in vehicle crashworthiness optimization. Finally, the space mapping technique is applied to a structural optimization problem of a large industrial FE vehicle model, consisting of 350.000 shell elements and a computing time of 100 h. In this problem the intrusion in the passenger compartment area was reduced by 32% without compromising other crashworthiness parameters.     

COMPLEXITY REDUCTION FOR OPTIMIZATION OF DETERMINISTIC TIMED PETRI-NET SCHEDULING BY TRUNCATION

Methods of applying Petri nets to model and analyze scheduling problems, with constraints such as precedence relationships and multiple resource allocation, have been available in the literature. Searching for an optimum schedule can be implemented by combining the branch-and-bound technique with the execution of the timed Petri net. The resulting complexity problem in a large Petri net is handled by a truncation technique such that the original large Petri net is divided into several smaller subnets. The complexity involved in the analysis of each subnet individually is greatly reduced. However, as illustrated in this paper, the schedules for the subnets obtained by treating them separately may not lead to an optimal overall schedule for the original Petri net. To circumvent this problem, algorithms are developed that can be used to search for a proper schedule for each subnet such that the combination of these schedules yields an overall optimum schedule for the original timed Petri net. These algorithms are based on the idea of Petri net execution and branch-and-bound with modification. Finally, the practical application of the timed Petri net truncation technique to scheduling problems in manufacturing systems is illustrated by an example of multirobot task scheduling.     

Optimal control problems with multiple characteristic time points in the objective and constraints

In this paper, we develop a computational method for a class of optimal control problems where the objective and constraint functionals depend on two or more discrete time points. These time points can be either fixed or variable. Using the control parametrization technique and a time scaling transformation, this type of optimal control problem is approximated by a sequence of approximate optimal parameter selection problems. Each of these approximate problems can be viewed as a finite dimensional optimization problem. New gradient formulae for the cost and constraint functions are derived. With these gradient formulae, standard gradient-based optimization methods can be applied to solve each approximate optimal parameter selection problem. For illustration, two numerical examples are solved.     

基于支持向量机分类的回归方法

支持向量机(support vector machine,简称SVM)是一种基于结构风险最小化原理的分类技术,也是一种新的具有很好泛化性能的回归方法.提出了一种将回归问题转化为分类问题的新思想.这种方法具有一定的理论依据,与SVM回归算法相比,其优化问题几何意义清楚明确.     

受限本体相似

在从不同的语义Web上得到用本体表达的文档资源以后,这些文档资源通常被转换成基于同一个本体的本体描述,这样既便于对文档的分析,又便于在此基础上进行信息抽取.这些文档本体之间仅仅在实例和关系层上彼此相互不同,在类、属性、规则、谓词方面都基本相同.对这种文档的检索,一个最普通的操作就是计算本体之间的相似性.很多计算本体相似性的方法基本上都是以分别属于不同本体的实体之间配对比较来实现,而且往往要考虑所有相关的元素.这不仅增加了计算复杂度,还会遇到循环计算的问题.在对语义网本体语言的推理能力进行研究以后,提出了一种基于知识推理的二阶本体相似技术,解决了循环计算的问题.     

A heuristic algorithm based on Lagrangian relaxation for the closest string problem

The closest string problem that arises in both computational biology and coding theory is to find a string minimizing the maximum Hamming distance from a given set of strings. This study proposes an efficient heuristic algorithm for this NP-hard problem. The key idea is to apply the Lagrangian relaxation technique to the problem formulated as a mixed-integer programming problem. This enables us to decompose the problem into trivial subproblems corresponding to each position of the strings. Furthermore, a feasible solution can be easily obtained from a solution of the relaxation. Based on this, a heuristic algorithm is constructed by combining a Lagrangian multiplier adjustment procedure and a tabu search. Computational experiments will show that the proposed algorithm can find good approximate solutions very fast.