On optimal physically nonlinear trusses

Optimization problems for physically nonlinear hyperelastic trusses statically loaded by a single system of loads are investigated. Several possible formulations of the problems for a prescribed truss layout are considered. It is demonstrated that the problems lead to the design of equal absolute stress values in some rods and of active technology constraints in other rods. Some general properties of the trusses are examined. Optimal physically nonlinear hyperelastic truss layout problems under static loading are also considered. It is shown that the well-known results of Maxwell, Michell and Prager concerning linear-elastic structures are generalized for the considered problems. It is demonstrated that the results of the paper are extended to optimization problems for structures in stationary creep with an arbitrary concave stress-strain velocity relation.     

基于ASP的安全性研究

ASP作为开发网站应用的快速工具，从一开始就受到众多漏洞、后门的困扰。对ASP技术进行了详细的分析，找出并罗列了20个左右具体的ASP安全漏洞。阐述了出现这些漏洞的技术原因，给出了较为可行的漏洞解决方案。针对广大ASP用户的需要，提出了系统的ASP安全建议以供参考，为大众提供一个较为安全可靠的ASP网络服务。     

Conformant plans and beyond: Principles and complexity

Conformant planning is used to refer to planning for unobservable problems whose solutions, like classical planning, are linear sequences of operators called linear plans. The term ‘conformant’ is automatically associated with both the unobservable planning model and with linear plans, mainly because the only possible solutions for unobservable problems are linear plans. In this paper we show that linear plans are not only meaningful for unobservable problems but also for partially-observable problems. In such case, the execution of a linear plan generates observations from the environment which must be collected by the agent during the execution of the plan and used at the end in order to determine whether the goal had been achieved or not; this is the typical case in problems of diagnosis in which all the actions are knowledge-gathering actions.Thus, there are substantial differences about linear plans for the case of unobservable or fully-observable problems, and for the case of partially-observable problems: while linear plans for the former model must conform with properties in state space, linear plans for partially-observable problems must conform with properties in belief space. This differences surface when the problems are allowed to express epistemic goals and conditions using modal logic, and place the plan-existence decision problem in different complexity classes.Linear plans is one extreme point in a discrete spectrum of solution forms for planning problems. The other extreme point is contingent plans in which there is a branch point for every possible observation at each time step, and thus the number of branch points is not bounded a priori. In the middle of the spectrum, there are plans with a bounded number of branch points. Thus, linear plans are plans with zero branch points and contingent plans are plans with unbounded number of branch points.In this work, we lay down foundations and principles for the general treatment of linear plans and plans of bounded branching, and provide exact complexity results for novel decision problems. We also show that linear plans for partially-observable problems are not only of theoretical interest since some challenging real-life problems can be dealt with them.     

Formation‐containment analysis and design for high‐order linear time‐invariant swarm systems

Formation‐containment analysis and design problems for high‐order linear time‐invariant swarm systems with directed interaction topologies are dealt with respectively. Firstly, protocols are presented for leaders and followers respectively to drive the states of leaders to realize the predefined time‐varying formation and propel the states of followers to converge to the convex hull formed by the states of leaders. Secondly, formation‐containment problems of swarm systems are transformed into asymptotic stability problems, and an explicit expression of the formation reference function is derived. Sufficient conditions for swarm systems to achieve formation containment are proposed. Furthermore, necessary and sufficient conditions for swarm systems to achieve containment and time‐varying formation are presented respectively as special cases. An approach to determine the gain matrices in the protocols is given. It is shown that containment problems, formation control problems, consensus problems and consensus tracking problems can all be treated as special cases of formation‐containment problems. Finally, numerical simulations are provided to demonstrate theoretical results. Copyright © 2014 John Wiley & Sons, Ltd.     

Dynamic programming equations for discounted constrained stochastic control

In this paper, the application of the dynamic programming approach to constrained stochastic control problems with expected value constraints is demonstrated. Specifically, two such problems are analyzed using this approach. The problems analyzed are the problem of minimizing a discounted cost infinite horizon expectation objective subject to an identically structured constraint, and the problem of minimizing a discounted cost infinite horizon minimax objective subject to a discounted expectation constraint. Using the dynamic programming approach, optimality equations, which are the chief contribution of this paper, are obtained for these problems. In particular, the dynamic programming operators for problems with expectation constraints differ significantly from those of standard dynamic programming and problems with worst-case constraints. For the discounted cost infinite horizon cases, existence and uniqueness of solutions to the dynamic programming equations are explicitly shown by using the Banach fixed point theorem to show that the corresponding dynamic programming operators are contractions. The theory developed is illustrated by numerically solving the constrained stochastic control dynamic programming equations derived for simple example problems. The example problems are based on a two-state Markov model that represents an error prone system that is to be maintained.     

决策问题结构的形式化研究

运用分形理论研究决策问题结构，对决策问题进行形式化描述，定义其结构函数和结构指数，并将决策问题从定性上分为良好结构、未结构化和纯不良结构。给出了关于决策问题结构的几个定理，讨论了决策问题结构化的测度，提出了决策问题简化的判据。     

有约束无模型控制

研究了有约束无模型控制问题,首先对一般的MISO无模型控制(NMC)给出了一个简单合理的收敛性条件.由于实际应用中完全无约束的控制系统并不存在,为此考虑输入输出有上下界约束的无模型控制问题(NMCC).通过无模型控制方法中的泛模型,将对输出的约束转化为对输入的约束,进一步分析了有约束无模型控制问题的可行条件,并在不可行时给出了对软约束的调整方法,进而得出求解这种约束问题的一般算法.最后给出了一个仿真算例说明了方法的有效性,并在一定条件下给出了约束问题的收敛性证明.     

Job distribution algorithms

Two job distribution problems are studied, extremum for several criteria are estimated, and an approximate algorithm for solving these problems is designed. Such kind of problems are encountered in distributing jobs to processors in multi-processor computing systems, commodities to warehouses, and in many other cases.     

Logspace Optimization Problems and Their Approximability Properties

Logspace optimization problems are the logspace analogues of the well-studied polynomial-time optimization problems. Similarly to them, logspace optimization problems can have vastly different approximation properties even though their underlying decision problems have the same computational complexity. Natural problems - including the shortest path problems for directed graphs, undirected graphs, tournaments, and forests - exhibit such a varying complexity. In order to study the approximability of logspace optimization problems in a systematic way, polynomial-time approximation classes and polynomial-time reductions between optimization problems are transferred to logarithmic space. It is proved that natural problems are complete for different logspace approximation classes. This is used to show that under the assumption L ≠ NL some logspace optimization problems cannot be approximated with a constant ratio; some can be approximated with a constant ratio, but do not permit a logspace approximation scheme; and some have a logspace approximation scheme, but optimal solutions cannot be computed in logarithmic space.     

Unconditionally stable algorithms for nonlinear heat conduction

It is shown that many commonly used one-step algorithms which are unconditionally stable for linear transient heat conduction problems become conditionally stable in the nonlinear regime. Alternative algorithms are proposed which for linear problems are identical to those commonly used, whereas for nonlinear problems the unconditional stability behavior of the linear case is retained.     

An improved electromagnetism-like mechanism algorithm for constrained optimization

Many problems in scientific research and engineering applications can be decomposed into the constrained optimization problems. Most of them are the nonlinear programming problems which are very hard to be solved by the traditional methods. In this paper, an electromagnetism-like mechanism (EM) algorithm, which is a meta-heuristic algorithm, has been improved for these problems. Firstly, some modifications are made for improving the performance of EM algorithm. The process of calculating the total force is simplified and an improved total force formula is adopted to accelerate the searching for optimal solution. In order to improve the accuracy of EM algorithm, a parameter called as move probability is introduced into the move formula where an elitist strategy is also adopted. And then, to handle the constraints, the feasibility and dominance rules are introduced and the corresponding charge formula is used for biasing feasible solutions over infeasible ones. Finally, 13 classical functions, three engineering design problems and 22 benchmark functions in CEC’06 are tested to illustrate the performance of proposed algorithm. Numerical results show that, compared with other versions of EM algorithm and other state-of-art algorithms, the improved EM algorithm has the advantage of higher accuracy and efficiency for constrained optimization problems.     

Tabu search for a class of single-machine scheduling problems

In this paper we develop a tabu search-based solution procedure designed specifically for a certain class of single-machine scheduling problems with a non-regular performance measure. The performance of the developed algorithm is tested for solving the variance minimization problem. Problems from the literature are used to test the performance of the algorithm. This algorithm can be used for solving other problems such as minimizing completion time deviation from a common due date.Scope and purposeScheduling problems with non-regular performance measures has gained a great importance in modern manufacturing systems. These problems are found to be hard to solve and analyze. The purpose of this paper is to present a tabu search approach for solving a certain class of single-machine scheduling problems with non-regular performance measure. Minimizing the variance of completion times and the total deviation from a common due date are two examples of such problems. The proposed approach is found to perform better than the simulated annealing approach for the variance minimization problem.     

0-1 Quadratic programming approach for optimum solutions of two scheduling problems

Two scheduling problems are considered: (1) scheduling n jobs non-preemptively on a single machine to minimize total weighted earliness and tardiness (WET); (2) scheduling n jobs non-preemptively on two parallel identical processors to minimize weighted mean flow time. In the second problem, a pre-ordering of the jobs is assumed that must be satisfied for any set of jobs scheduled on each specific machine. Both problems are known to be NP-complete. A 0-1 quadratic assignment formulation of the problems is presented. An equivalent 0-1 mixed integer linear programming approach for the problems are considered and a numerical example is given. The formulations presented enable one to use optimal and heuristic available algorithms of 0-1 quadratic assignment for the problems considered here.     

Heuristics for the Unrelated Parallel Machine Scheduling Problem with Setup Times

The problem addressed in this paper is the non-preemptive unrelated parallel machine scheduling problem with the objective of minimizing the makespan. Machine-dependent and job sequence-dependent setup times are considered, all jobs are available at time zero, and all times are deterministic. This is a NP-hard problem and in this paper, optimal solutions are found for small problems only. For larger problems, a new meta-heuristic, Meta-RaPS, is introduced and its performance is evaluated by comparing its solutions to the solutions of an existing heuristic for the same problem. The results show that Meta-RaPS found all optimal solutions for the small problems and outperformed the solutions obtained by the existing heuristic for larger problems.     

A Heuristic Genetic Algorithm for Solving Resource Allocation Problems

In the paper, a heuristic genetic algorithm for solving resource allocation problems is proposed. The resource allocation problems are to allocate resources to activities so that the fitness becomes as optimal as possible. The objective of this paper is to develop an efficient algorithm to solve resource allocation problems encountered in practice. Various genetic algorithms are studied and a heuristic genetic algorithm is proposed to ameliorate the rate of convergence for resource allocation problems. Simulation results show that the proposed algorithm gives the best performance.     

Securing electronic data interchange

One of the growing areas of computer technology is electronic data interchange (EDI). Electronic data interchange is the electronic transfer of data from one agent to another. One of the most important problems with computer programs today is that they are not reliable. The reason for this is quite simple. Computer programs are built to solve particular problems and these problems can be quite large. By large we mean having lots of constraints, cases and rules as to what constitutes the problem. Real‐world problems are so complex that it is hard to be sure that when a program has been built it will never fail for an input that has not been taken into account. Hence, reliability of computer programs for EDI has serious implications because if a program fails to work then this can lead to problems with security. If the field of computer science concentrates on better techniques for increasing computer reliability then we will be able to build more reliable and secure programs for EDI.     

The sector bound approach to quantized feedback control

This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.     

New computational paradigms in solving fault detection and isolation problems

A representative set of fault detection and isolation problems are formulated for linear time-invariant systems with additive faults. For all problems general existence conditions of their solutions are given. An overview of recently developed computational methods for the synthesis of fault detection filters is presented for all formulated problems. Two remarkable computational paradigms emerged in these developments, which are instrumental in developing generally applicable, numerically reliable and computationally efficient synthesis methods. The first paradigm is the use of integrated synthesis algorithms, where the resulting fault detection filters are determined by successive updating of partial syntheses addressing specific requirements. The second paradigm is the use of the nullspace method as a first synthesis step to reduce all synthesis problems to a simple standard form which allows to easily check solvability conditions and address least order synthesis problems.     

A preference aggregation rule approach for structural optimization

In this paper, preference aggregation rules are used to define overall design evaluation measures in optimal design problems. A methodology for the efficient solution of the corresponding design optimization problems is presented. Each design criterion as well as the constraints imposed on the design variables and problem parameters are characterized by preference functions. The nondifferentiable nature of the optimization problems which arise in this formulation is coped with using a first-order algorithm combined with approximation concepts. High-quality approximations for the system response functions are constructed using the concepts of intermediate response quantities and intermediate variables. These approximations are used to replace the original problem by a sequence of approximate problems. Example problems are presented to study the performance of the proposed optimization technique as well as the methodology based on approximation concepts.     

A solution of nonlinear TPBVP's occuring in optimal control

A new method is presented to obtain feedback solution for nonsingular optimal control problems, which lead to polynomial type nonlinear two-point boundary-value problems. Examples of such problems treated in the paper are quadratic state regulators for nonlinear systems with an analytic generator including the special case of a bilinear problem, linear quadratic regulators with magnitude constraints on the control variables, and also linear problems with nonquadratic performance indices.The solution method is based on solving a related two-point boundary value problem by using an equivalent functional equation and the theory of the so-called polynomial operators. Considerations are made in discrete form and the resulting algorithms are readily applicable to direct computing and control. Due to computational burden the method is restricted to low dimensional problems. Demonstration of the use of the method is given by designing nonlinear regulators for two computer controlled pilot processes.