Convergence rates of Markov chain approximation methods for controlled diffusions with stopping

<正> This work is concerned with rates of convergence of numerical methods using Markov chainapproximation for controlled diffusions with stopping (the first exit time from a bounded region).In lieuof considering the associated finite difference schemes for Hamilton-Jacobi-Bellman (HJB) equations,a purely probabilistic approach is used.There is an added difficulty due to the boundary condition,which requires the continuity of the first exit time with respect to the discrete parameter.To prove theconvergence of the algorithm by Markov chain approximation method,a tangency problem might arise.A common approach uses certain conditions to avoid the tangency problem.Here,by modifying thevalue function,it is demonstrated that the tangency problem will not arise in the sense of convergencein probability and in L~1.In addition,controlled diffusions with a discount factor is also treated.     

An equivalency condition of nonsingularity in nonlinear semidefinite programming

<正> In this paper,an equivalency condition of nonsingularity in nonlinear semidefinite programming,which can be viewed as a generalization of the equivalency condition of nonsingularity for linearsemidefinite programming,is established under certain conditions of convexity.     

Integral-type tests for goodness-of-fit

<正> A family of integral-type goodness-of-fit tests is investigated.This family includes someexisting tests,such as the Cramér-von Mises test and Anderson-Darling test,etc.The asymptoticdistributions of the tests in the family under the null and local alternative hypotheses are established.The almost sure convergence under a fixed underlying distribution is obtained.Furthermore,simulationsare conducted to compare the powers of the tests in the family.Simulation results show that fordifferent alternatives,the more powerful tests are different,and the parameter λ has great influence onthe tests in small sample cases.     

DISTRIBUTED FINITE-TIME χ-CONSENSUS ALGORITHMS FOR MULTI-AGENT SYSTEMS WITH VARIABLE COUPLING TOPOLOGY

<正> This paper considers finite-time χ-consensus problem for a multi-agent system with firstorderindividual dynamics and switching interaction topologies.Several distributed finite-time consensusrules are constructed for multi-agent dynamics in a unified way with the help of Lyapunovfunction and graph theory as well as homogeneity.Time-invariant non-smooth forms of finite-timeneighbor-based controllers are proposed and a numerical example is shown for illustration.     

Systematic design of single carrier overlap frequency domain equalization

<正> This paper proposes a systematic design method of overlap frequency domain equalization(FDE) for single carrier (SC) transmission without a guard interval (GI).Based on the analysis of signal-to-interference-plus-noise ratio (SINR) of the equalizer output for each symbol,the authors adaptivelydetermine the block of the overlap FDE,where the block is defined as a set of symbols at the equalizeroutput with sufficiently low error rate,for a certain fixed sliding window size,which corresponds toa fast Fourier transform (FFT) window size.The proposed method takes advantage of the fact thatthe utility part of the equalized signal is localized around the center of the FFT window.In addition,the authors also propose to adjust the block size in order to control the computational complexity ofthe equalization per processed sample associating with the average bit error rate (BER) of the system.Simulation results show that the proposed scheme can achieve comparable BER performance to theconventional SC-FDE scheme with sufficient GI insertion for both the coded and uncoded cases withvarious modulation levels,while requiring lower computational complexity compared to the SC overlapFDE transmission with the fixed block.     

Adaptive tracking control for actuator failure compensation based on MT-filters

<正> This paper considers the problem of actuator failure compensation for a class of nonlinearsystems in the form of output-feedback.MT-filters are exploited to estimate the unmeasured states.The backsteppping technique is used to construct the controller recursively.The proposed compensationscheme can grantee the boundedness of all the closed-loop signals and asymptotic output tracking.Thetheoretical results are illustrated by a simulation example.     

A line search filter secant method for nonlinear equality constrained optimization

<正> This paper formulates and analyzes a line search method for general nonlinear equalityconstrained optimization based on filter methods for step acceptance and secant methods for searchdirection.The feature of the new algorithm is that the secant algorithm is used to produce a searchdirection,a backtracking line search procedure is used to generate step size,some filtered rules areused to determine step acceptance,second order correction technique is used to reduce infeasibility andovercome the Maratos effect.Global convergence properties of this method are analyzed:under mildassumptions it is showed that every limit point of the sequence of iterates generated by the algorithmis feasible,and that there exists at least one limit point that is a stationary point for the problem.Moreover,it is also established that the Maratos effect can be overcome in our new approach by addingsecond order correction steps so that fast local superlinear convergence to a second order sufficient localsolution is achieved.Finally,the results of numerical experiments are reported to show the effectivenessof the line search filter secant method.     

EVOKE: A Value-Driven Concept Selection Method for Early System Design

The development of new technologically advanced products requires the contribution from a range of skills and disciplines, which are often difficult to find within a single company or organization. Requirements establishment practices in Systems Engineering (SE), while ensuring coordination of activities and tasks across the supply network, fall short when it comes to facilitate knowledge sharing and negotiation during early system design. Empirical observations show that when system-level requirements are not available or not mature enough, engineers dealing with the development of long lead-time sub-systems tend to target local optima, rather than opening up the design space. This phenomenon causes design teams to generate solutions that do not embody the best possible configuration for the overall system. The aim of this paper is to show how methodologies for value-driven design may address this issue, facilitating early stage design iterations and the resolution of early stage design trade-offs. The paper describes how such methodologies may help gathering and dispatching relevant knowledge about the ‘design intent’ of a system to the cross-functional engineering teams, so to facilitate a more concurrent process for requirement elicitation in SE. The paper also describes EVOKE (Early Value Oriented design exploration with KnowledgE maturity), a concept selection method that allows benchmarking design options at sub-system level on the base of value-related information communicated by the system integrators. The use of EVOKE is exemplified in an industrial case study related to the design of an aero-engine component. EVOKE’s ability to raise awareness on the value contribution of early stage design concepts in the SE process has been further verified with industrial practitioners in ad-hoc design episodes.     

An Economic Model-Based Matching Approach Between Buyers and Sellers Through a Broker in an Open E-Marketplace

A broker in an open e-marketplace enables buyers and sellers to do business with each other. Although a broker plays an important role in e-marketplaces, theory and guidelines for matching between buyers and sellers in multi-attribute exchanges are limited. Therefore, a challenge for a broker’s responsibility is how to maximize a buyer’s total satisfaction degree as its goals under the consideration of trade-off between a buyer’s buying quantity and price paid to a seller, and other attributes. To solve this challenge, this paper proposes an economic model-based matching approach between a buyer’s requirements and a seller’s offers. The major contributions of this paper are that (i) a broker can model a seller’s price policy as per a buyer’s buying quantity through communication between a broker and a seller; (ii) due to each buyer’s different quantity demand, a broker models a buyer’s satisfaction degree as per a buyer’s buying quantity based on communication between a broker and a buyer; and (iii) to carry out a broker’s matching processes, an objective function and a set of constraints are generated to help a broker to maximize a buyer’s total satisfaction degree. Experimental results demonstrate the good performance of the proposed approach.     

Two Innovative Coalition Formation Models for Dynamic Task Allocation in Disaster Rescues

This paper addresses the problem of multi-objective coalition formation for task allocation. In disaster rescue, due to the dynamics of environments, heterogeneity and complexity of tasks as well as limited available agents, it is hard for the single-objective and single (task)-to-single (agent) task allocation approaches to handle task allocation in such circumstances. To this end, two multi-objective coalition formation for task allocation models are proposed for disaster rescues in this paper. First, through coalition formation, the proposed models enable agents to cooperatively perform complex tasks that cannot be completed by single agent. In addition, through adjusting the weights of multiple task allocation objectives, the proposed models can employ the linear programming to generate more adaptive task allocation plans, which can satisfy different task allocation requirements in disaster rescue. Finally, through employing the multi-stage task allocation mechanism of the dynamic programming, the proposed models can handle the dynamics of tasks and agents in disaster environments. Experimental results indicate that the proposed models have good performance on coalition formation for task allocation in disaster environments, which can generate suitable task allocation plans according to various objectives of task allocation.