Message routing is a fundamental function of a network, and fault-tolerance is an important tool to ensure the quality of service of a network. Assume that network contain only one faulty element. In order to ensure the message can be arrived. We present a fault-tolerant message routing algorithm which being the secondary path, as the optimal path can't be connected in the double-loop networks.

Abstract ¡@¡@Double-loop networks have become one of the most popular architectures in the design of Local Area Networks and distributed memory multiprocessor systems. This is due to its characters of minimal diameter, easy routing, expandability and regularity. The switching mechanism at each node can easily be implemented using building blocks of the same specification. Therefore, double-loop networks have a high degree of reliability and hence very low vulnerability. Let N denote the number of nodes in a double-loop network and d(N) be the best possible diameter with N vertices. Given an N, Bermond et al. [5], Boesch and Wang [7], and Yebra et al. [23] have shown that . This is a well-known lower bound for d(N) and is usually denoted as lb(N). In this paper, what we discuss is finding an optimal topology such that d(N)= lb(N) for any given value of N. We provide a simple formula to find optimal topologies of double-loop networks with N nodes.

In this paper we consider the exact $D$-optimal design problem for linear trigonometric regression models with or without intercept on a partial circle. In a recent papper Dette, Melas and Pepelyshev (2001) found explicit solutions of approximate $D$-optimal designs for trigonometric regression models with intercept on a partial circle. The exact optimal designs are determined by means of moment sets of trigonometric functions. It is shown that the structure of the optimal designs depends on both the length of the design interval and the number of the design points.

In this work, we investigate c-optimal design for polynomial regression model without intercept. Huang and Chen (1996) showed that the c-optimal design for the dth degree polynomial with intercept is still the optimal design for the no-intercept model for estimating certain individual coe cients over [−1, 1]. We found the c-optimal designs explicitly for estimating other individual coe cients over [−1, 1], which have not been obtained earlier. For the no-intercept model, it is shown that the support points are scale invariant over [−b, b]. Finally some special cases are discussed for estimating the coe cients of the 2nd degree polynomial without intercept by Elfving theorem over nonsymmetric interval [a, b].

With the deregulation of power industry and the market competition, low cost, reliable power supply, and secured system operations are major concerns of the advanced deregulation markets. It is a natural extension to revise the objectives of the traditional optimal power flow (OPF) to help dispatch the power. Maximizing social welfare to create more values of the market is becoming an interesting topic. In the deregulation environment, a user-friendly mechanism is desirable to form an auction market information framework (AMIF) for power auction and market operation. This thesis proposed a prototype system to combine internet based technology, database system, and the auction market to construct an information framework of power auction market. The Internet technology used Dynamic HTML (DHTML) in WWW website to develop a convenient bidding environment for users. The database based on MS Access used open database connection (ODBC) technology to connect database and internet. The auction market integrates auction functions and re-designed OPF to support the auction mechanism and congestion management. This research could also provide a solid foundation for Taiwan¡¦s power system deregulation in the future. The proposed mechanism and its expansion could guarantee a smooth migration process and successful market/system operation.

The present study aims to determine the optimum blank shape design for the deep drawing of arbitrary shaped cups with a uniform trimming allowance at the flange i.e. cups without ears. This earing defect is caused by planar anisotropy in the sheet and the friction between the blank and punch/die. In this research, a new method for optimum blank shape design using finite element analysis has been proposed. Explicit non-linear finite element (FE) code LSDYNA is used to simulate the deep drawing process. FE models are constructed incorporating the exact physical conditions of the process such as tooling design like die profile radius, punch corner radius, etc., material used, coefficient of friction, punch speed and blank holder force. The material used for the analysis is mild steel. A quantitative error metric called shape error is defined to measure the amount of earing and to compare the deformed shape and target shape set for each stage of the analysis. This error metric is then used to decide whether the blank needs to be modified or not. The cycle is repeated until the converged results are achieved. This iterative design process leads to optimal blank shape. In order to verify the proposed method, examples of square cup and cylindrical cup have been investigated. In every case converged results are achieved after a few iterations. So through the investigation the proposed systematic method of optimal blank design is found to be very effective in the deep drawing process and can be further applied to other stamping applications.

The Bolza-form of the finite-time constrained optimal control problem leads to the Hamilton-Jacobi-Bellman (HJB) equation with terminal boundary conditions and tobe- determined parameters. In general, it is a formidable task to obtain analytical and/or numerical solutions to the HJB equation. This dissertation presents two novel polynomial expansion methodologies for solving optimal feedback control problems for a class of polynomial nonlinear dynamical systems with terminal constraints. The first approach uses the concept of higher-order series expansion methods. Specifically, the Series Solution Method (SSM) utilizes a polynomial series expansion of the cost-to-go function with time-dependent coefficient gains that operate on the state variables and constraint Lagrange multipliers. A significant accomplishment of the dissertation is that the new approach allows for a systematic procedure to generate optimal feedback control laws that exactly satisfy various types of nonlinear terminal constraints. The second approach, based on modified Galerkin techniques for the solution of terminally constrained optimal control problems, is also developed in this dissertation. Depending on the time-interval, nonlinearity of the system, and the terminal constraints, the accuracy and the domain of convergence of the algorithm can be related to the order of truncation of the functional form of the optimal cost function. In order to limit the order of the expansion and still retain improved midcourse performance, a waypoint scheme is developed. The waypoint scheme has the dual advantages of reducing computational efforts and gain-storage requirements. This is especially true for autonomous systems. To illustrate the theoretical developments, several aerospace application-oriented examples are presented, including a minimum-fuel orbit transfer problem. Finally, the series solution method is applied to the solution of a class of partial differential equations that arise in robust control and differential games. Generally, these problems lead to the Hamilton-Jacobi-Isaacs (HJI) equation. A method is presented that allows this partial differential equation to be solved using the structured series solution approach. A detailed investigation, with several numerical examples, is presented on the Nash and Pareto-optimal nonlinear feedback solutions with a general terminal payoff. Other significant applications are also discussed for one-dimensional problems with control inequality constraints and parametric optimization.

In this thesis, we consider a sampling design problem for a distributed parameter estimation system. The system contains a number of remotely located local sensors that can preprocess the observed signal and convey the processed data to a data fusion center to make the final estimate. Two issues are considered for this system. One is a sampling scheme design for a parameter estimation problem in a single context. The other is how to assign the appropriate number of sampling points to each of the sensors when a constraint on the total sample size is assumed. Here we propose to design this two issues by maximizing the criterion of Fisher's information or minimizing the Fisher's information loss . A sampling design procedure will be established and some numerical simulations will be also carried out for illustration purpose.

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In this thesis, we consider optimal control problems arising in high-speed integrated communication networks with Quality of Service (QOS) constraints. Integrated networks are expected to carry a large variety of traffic sources with widely varying traffic characteristics and performance requirements. Broadly, the traffic sources fall into two categories: (a) real-time sources with specified performance criteria, like small end to end delay and loss probability (sources of this type are referred to as Type 1 sources below), and (b) sources that do not have stringent performance criteria and do not demand performance guarantees from the network - the so-called Best Effort Type sources (these are referred to as Type 2 sources below). From the network's point of view, Type 2 sources are much more "controllable" than Type 1 sources, in the sense that the Type 2 sources can be dynamically slowed down, stopped or speeded up depending on traffic congestion in the network, while for Type 1 sources, the only control action available in case of congestion is packet dropping. Carrying sources of both types in the same network concurrently while meeting the performance objectives of Type 1 sources is a challenge and raises the question of equitable sharing of resources. The objective is to carry as much Type 2 traffic as possible without sacrificing the performance requirements of Type 1 traffic. We consider simple models that capture this situation. Consider a network node through which two connections pass, one each of Types 1 and 2. One would like to maximize the throughput of the Type 2 connection while ensuring that the Type 1 connection's performance objectives are met. This can be set up as a constrained optimization problem that, however, is very hard to solve. We introduce a parameter b that represents the "cost" of buffer occupancy by Type 2 traffic. Since buffer space is limited and shared, a queued Type 2 packet means that a buffer position is not available for storing a Type 1 packet; to discourage the Type 2 connection from hogging the buffer, the cost parameter b is introduced, while a reward for each Type 2 packet coming into the buffer encourages the Type 2 connection to transmit at a high rate. Using standard on-off models for the Type 1 sources, we show how values can be assigned to the parameter b; the value depends on the characteristics of the Type 1 connection passing through the node, i.e., whether it is a Variable Bit Rate (VBR) video connection or a Continuous Bit Rate (CBR) connection etc. Our approach gives concrete networking significance to the parameter b, which has long been considered as an abstract parameter in reward-penalty formulations of flow control problems (for example, [Stidham '85]). Having seen how to assign values to b, we focus on the Type 2 connection next. Since Type 2 connections do not have strict performance requirements, it is possible to defer transmitting a Type 2 packet, if the conditions downstream so warrant. This leads to the question: what is the "best" transmission policy for Type 2 packets? Decisions to transmit or not must be based on congestion conditions downstream; however, the network state that is available at any instant gives information that is old, since feedback latency is an inherent feature of high speed networks. Thus the problem is to identify the best transmission policy under delayed feedback information. We study this problem in the framework of Markov Decision Theory. With appropriate assumptions on the arrivals, service times and scheduling discipline at a network node, we formulate our problem as a Partially Observable Controlled Markov Chain (PO-CMC). We then give an equivalent formulation of the problem in terms of a Completely Observable Controlled Markov Chain (CO-CMC) that is easier to deal with., Using Dynamic Programming and Value Iteration, we identify structural properties of an optimal transmission policy when the delay in obtaining feedback information is one time slot. For both discounted and average cost criteria, we show that the optimal policy has a two-threshold structure, with the threshold on the observed queue length depending, on whether a Type 2 packet was transmitted in the last slot or not. For an observation delay k > 2, the Value Iteration technique does not yield results. We use the structure of the problem to provide computable upper and lower bounds to the optimal value function. A study of these bounds yields information about the structure of the optimal policy for this problem. We show that for appropriate values of the parameters of the problem, depending on the number of transmissions in the last k steps, there is an "upper cut off" number which is a value such that if the observed queue length is greater than or equal to this number, the optimal action is to not transmit. Since the number of transmissions in the last k steps is between 0 and A: both inclusive, we have a stack of (k+1) upper cut off values. We conjecture that these (k + l) values axe thresholds and the optimal policy for this problem has a (k + l)-threshold structure. So far it has been assumed that the parameters of the problem are known at the transmission control point. In reality, this is usually not known and changes over time. Thus, one needs an adaptive transmission policy that keeps track of and adjusts to changing network conditions. We show that the information structure in our problem admits a simple adaptive policy that performs reasonably well in a quasi-static traffic environment. Up to this point, the models we have studied correspond to a single hop in a virtual connection. We consider the multiple hop problem next. A basic matter of interest here is whether one should have end to end or hop by hop controls. We develop a sample path approach to answer this question. It turns out that depending on the relative values of the b parameter in the transmitting node and its downstream neighbour, sometimes end to end controls are preferable while at other times hop by hop controls are preferable. Finally, we consider a routing problem in a high speed network where feedback information is delayed, as usual. As before, we formulate the problem in the framework of Markov Decision Theory and apply Value Iteration to deduce structural properties of an optimal control policy. We show that for both discounted and average cost criteria, the optimal policy for an observation delay of one slot is Join the Shortest Expected Queue (JSEQ) - a natural and intuitively satisfactory extension of the well-known Join the Shortest Queue (JSQ) policy that is optimal when there is no feedback delay (see, for example, [Weber 78]). However, for an observation delay of more than one slot, we show that the JSEQ policy is not optimal. Determining the structure of the optimal policy for a delay k>2 appears to be very difficult using the Value Iteration approach; we explore some likely policies by simulation.