Construction of a support tool for the design of the activity structures based computer system architectures

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis is a reapproachment of diverse design concepts, brought to bear upon the computer system engineering problem of identification and control of highly constrained multiprocessing (HCM) computer machines. It contributes to the area of meta/general systems methodology, and brings a new insight into the design formalisms, and results afforded by bringing together various design concepts that can be used for the construction of highly constrained computer system architectures. A unique point of view is taken by assuming the process of identification and control of HCM computer systems to be the process generated by the Activity Structures Methodology (ASM). The research in ASM has emerged from the Neuroscience research, aiming at providing the techniques for combining the diverse knowledge sources that capture the 'deep knowledge' of this application field in an effective formal and computer representable form. To apply the ASM design guidelines in the realm of the distributed computer system design, we provide new design definitions for the identification and control of such machines in terms of realisations. These realisation definitions characterise the various classes of the identification and control problem. The classes covered consist of: 1. the identification of the designer activities, 2. the identification and control of the machine's distributed structures of behaviour, 3. the identification and control of the conversational environment activities (i.e. the randomised/ adaptive activities and interactions of both the user and the machine environments), 4. the identification and control of the substrata needed for the realisation of the machine, and 5. the identification of the admissible design data, both user-oriented and machineoriented, that can force the conversational environment to act in a self-regulating manner. All extent results are considered in this context, allowing the development of both necessary conditions for machine identification in terms of their distributed behaviours as well as the substrata structures of the unknown machine and sufficient conditions in terms of experiments on the unknown machine to achieve the self-regulation behaviour. We provide a detailed description of the design and implementation of the support software tool which can be used for aiding the process of constructing effective, HCM computer systems, based on various classes of identification and control. The design data of a highly constrained system, the NUKE, are used to verify the tool logic as well as the various identification and control procedures. Possible extensions as well as future work implied by the results are considered.Government of Ira

Robust, risk-sensitive, and data-driven control of Markov Decision Processes

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007.Includes bibliographical references (p. 201-211).Markov Decision Processes (MDPs) model problems of sequential decision-making under uncertainty. They have been studied and applied extensively. Nonetheless, there are two major barriers that still hinder the applicability of MDPs to many more practical decision making problems: * The decision maker is often lacking a reliable MDP model. Since the results obtained by dynamic programming are sensitive to the assumed MDP model, their relevance is challenged by model uncertainty. * The structural and computational results of dynamic programming (which deals with expected performance) have been extended with only limited success to accommodate risk-sensitive decision makers. In this thesis, we investigate two ways of dealing with uncertain MDPs and we develop a new connection between robust control of uncertain MDPs and risk-sensitive control of dynamical systems. The first approach assumes a model of model uncertainty and formulates the control of uncertain MDPs as a problem of decision-making under (model) uncertainty. We establish that most formulations are at least NP-hard and thus suffer from the "'curse of uncertainty." The worst-case control of MDPs with rectangular uncertainty sets is equivalent to a zero-sum game between the controller and nature.(cont.) The structural and computational results for such games make this formulation appealing. By adding a penalty for unlikely parameters, we extend the formulation of worst-case control of uncertain MDPs and mitigate its conservativeness. We show a duality between the penalized worst-case control of uncertain MDPs with rectangular uncertainty and the minimization of a Markovian dynamically consistent convex risk measure of the sample cost. This notion of risk has desirable properties for multi-period decision making, including a new Markovian property that we introduce and motivate. This Markovian property is critical in establishing the equivalence between minimizing some risk measure of the sample cost and solving a certain zero-sum Markov game between the decision maker and nature, and to tackling infinite-horizon problems. An alternative approach to dealing with uncertain MDPs, which avoids the curse of uncertainty, is to exploit directly observational data. Specifically, we estimate the expected performance of any given policy (and its gradient with respect to certain policy parameters) from a training set comprising observed trajectories sampled under a known policy.(cont.) We propose new value (and value gradient) estimators that are unbiased and have low training set to training set variance. We expect our approach to outperform competing approaches when there are few system observations compared to the underlying MDP size, as indicated by numerical experiments.by Yann Le Tallec.Ph.D

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

SIMULATING SEISMIC WAVE PROPAGATION IN TWO-DIMENSIONAL MEDIA USING DISCONTINUOUS SPECTRAL ELEMENT METHODS

We introduce a discontinuous spectral element method for simulating seismic wave in 2- dimensional elastic media. The methods combine the flexibility of a discontinuous finite element method with the accuracy of a spectral method. The elastodynamic equations are discretized using high-degree of Lagrange interpolants and integration over an element is accomplished based upon the Gauss-Lobatto-Legendre integration rule. This combination of discretization and integration results in a diagonal mass matrix and the use of discontinuous finite element method makes the calculation can be done locally in each element. Thus, the algorithm is simplified drastically. We validated the results of one-dimensional problem by comparing them with finite-difference time-domain method and exact solution. The comparisons show excellent agreement

ECOS 2012

The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology