线性时变系统的区间稳定性与鲁棒稳定性

本文应用向量比较定理研究线性时变系统的区间稳定性和具非线性时变摄动的线性时变系统的鲁棒稳定性，所得的新结果包含文献的一些主要结果作为特例，本文的研究方法说明向量比较方法是分析区间稳定性和鲁棒稳定性的一种自然而有力的工具。     

Issues in nonlinear stochastic grey box identification

Grey box identification refers to the practice of identifying dynamical systems in model structures exploiting partial prior information. This contribution reviews a method for stochastic grey box identification and surveys experiences and lessons of applying it to a number of industrial processes. Issues to be addressed include advantages and costs of introducing stochastics into the model, the question of what contribution must be expected from the model designer as opposed to what can be formalized in computer algorithms, and an outlook on future plans to resolve present shortcomings.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.     

ADS expert

ADS Expert is an integrated system that (a) provides suggestions about suitable optimization algorithms for a particular structural optimization problem, (b) it offers explanations and guide lines, in the form of hypertext, that refer to the optimization strategy, optimizer and one dimensional search method, (c) it produces the required gradients for the objective function and the constraints using symbolic differentiation, (d) establishes a direct linking with ADS routines and (e) it monitors the evolution of the optimization process. The system can be used as an educational tool for structural optimization and as an efficient integrated system for small to medium size optimization problems for which the objective function and the constraints are given in the form of explicit functions of the design variables. The system is a Windows application and runs quite satisfactorily on PCs.     

An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system.The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence.A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

Estimation of the Substrate Concentration Profiles in Petroleum Pipelines Containing Biofilms

In industrial systems, the formation of biofilms can cause many problems, such as an increase in the flow resistance of pipelines, energy losses in fluid transport and heat exchangers, product contamination, materials deterioration, and biocorrosion. As a result, biofilms contribute substantially to economic losses in the industry. Corrosion is particularly an issue in the petroleum industry and its implications range from down-hole completion through petroleum processing units. Much of this corrosion is attributed to microbial activities. This paper proposes a mathematical model for predicting substrate concentration for such microbial growth. Substrate concentrations in the system and near the biofilm surface are one of the parameters that has a great effect in determining the extent of the problems associated with biofilms. In this study, a convective-diffusion model under various flow conditions (stagnant, laminar, and turbulent) has been solved using the finite difference technique, employing the alternating direction implicit (ADI) method. The model assumes that a liquid containing substrate and bacteria is flowing in a pipeline with known concentrations at the inlet and then predicts the variation of the transient (as a function of time) substrate concentration along the pipeline and as a function of the pipe radius. The model is then used to predict and estimate the substrate concentration profiles on the biofilm surface under different environmental conditions. A parametric study was also conducted to study the effect of the different parameters influencing the substrate concentration profiles in the system and on the biofilm surface.     

Asymptotic stabilization of some equilibria of an underactuated underwater vehicle

The stabilization problem for selected relative equilibria of an underactuated rigid body, modelling a simple underwater vehicle, moving in an ideal fluid is addressed. State feedback control laws achieving local asymptotic stability of a forward motion and of a diving/rising with forward/reverse motion are proposed. The control design exploits the Hamiltonian nature of the system to be controlled and it is based on the so-called interconnection and damping assignment (IDA) procedure. Simulation results complete the work.     

Interaction bounds in multivariable control systems

Time-domain limitations due to right half-plane zeros and poles in linear multivariable control systems are studied. Lower bounds on the interaction are derived. They show not only how the location of zeros and poles are critical in multivariable systems, but also how the zero and pole directions influence the performance. The results are illustrated on the quadruple-tank process, which is a new multivariable laboratory process.     

Intelligent handling of data by integration of commonsense reasoning

Any intelligent problem solving system should be able, given the known data on a case, to decide whether some item of information is true, false or unknown. In this paper the way in which various forms of commonsense reasoning can be integrated to provide such decisions is described. To this end three structural types of knowledge defined over data, and four strategies for exploiting these structures, are identified. ‘Decide-Status’ integrates the reasoning strategies into a task frame. This frame structure not only integrates the reasoning but also affords the appropriate facilities for providing strategic justifications for its conclusions, if required.