Stabilizing a Flexible Beam on a Cart: A Distributed Port-Hamiltonian Approach

Motion planning and stabilization of the inverted pendulum on a cart is a much-studied problem in the control community. We focus our attention on asymptotically stabilizing a vertically upright flexible beam fixed on a moving cart. The flexibility of the beam is restricted only to the direction along the traverse of the cart. The control objective is to attenuate the effect of disturbances on the vertically upright profile of the beam. The control action available is the motion of the cart. By regulating this motion, we seek to regulate the shape of the beam. The problem presents a combination of a system described by a partial differential equation (PDE) and a cart modeled as an ordinary differential equation (ODE) as well as a controller which we restrict to an ODE. We set our problem in the port-controlled Hamiltonian framework. The interconnection of the flexible beam to the cart is viewed as a power-conserving interconnection of an infinite-dimensional system to a finite-dimensional system. The energy-Casimir method is employed to obtain the controller. In this method, we look for some constants of motion that are invariant of the choice of controller Hamiltonian. These Casimirs relate the controller states to the states of the system. We finally prove the stability of the equilibrium configuration of the closed-loop system.     

一个渐近的分布概率式

本文给出了一个渐近分布概率式的推广形式，并运用概率论知识给出它的一个证明     

Optimal Asset Allocation: A Worst Scenario Expectation Approach

Mean-variance criterion has long been the main stream approach in the optimal portfolio theory. The investors try to balance the risk and the return on their portfolio. In this paper, the deviation of the asset return from the investor’s expectation in the worst scenario is used as the measure of risk for portfolio selection. One important advantage of this approach is that the investors can base on their own knowledge, information, and preference on various risks, in addition to the asset’s volatility, to adjust their exposure to various risks. It also pinpoints one main concern of the investors when they invest, the amount they lose in the worst situation.     

A Point Process Approach to Filtered Processes

We consider some models of filtered point processes such as those developped in Yue and Hashino (2001), and rephrase them in terms of point processes. We derive from this formulation some estimates for the probability of overflow in a rainfall process. This method allows us by considering a non deterministic model of filtering to compute some characteristics of the compound models of Cowpertwait (1994), Phelan (1991), and Rodriguez-Iturbe et al. (1987, 1988). A spatial version of this point process is also studied, using an analogy with the boolean model of stochastic geometry we compute bounds for the probability of dryness in a compound rainfall process.     

Optimal Scaling of a Gradient Method for Distributed Resource Allocation

We consider a class of weighted gradient methods for distributed resource allocation over a network. Each node of the network is associated with a local variable and a convex cost function; the sum of the variables (resources) across the network is fixed. Starting with a feasible allocation, each node updates its local variable in proportion to the differences between the marginal costs of itself and its neighbors. We focus on how to choose the proportional weights on the edges (scaling factors for the gradient method) to make this distributed algorithm converge and on how to make the convergence as fast as possible.We give sufficient conditions on the edge weights for the algorithm to converge monotonically to the optimal solution; these conditions have the form of a linear matrix inequality. We give some simple, explicit methods to choose the weights that satisfy these conditions. We derive a guaranteed convergence rate for the algorithm and find the weights that minimize this rate by solving a semidefinite program. Finally, we extend the main results to problems with general equality constraints and problems with block separable objective function.The authors are grateful to Professor Paul Tseng and the anonymous referee for their valuable comments that helped us to improve the presentation of this paper.Communicated by P. Tseng     

Optimum Allocation in Two-Stage Stratified Randomized Response Model

In the present paper a two-stage stratified Warner’s randomized response model is used to determine the optimum allocation in the presence of non-response. The problem is formulated as a Nonlinear Programming Problem. A complete method of solution of the formulated problem is proposed. Two numerical examples are worked out to illustrate the computational details of the proposed method.     

Insensitivity of Generalized Semi-Markov Processes Evolving in a Random Environment

The theory of insensitivity within generalized semi-Markov processes is extended to cover the case where such a process evolves in a random environment; that is, when the decay rates and transition probabilities are functions of the state of an extraneous environmental process.     

An Optimum Solution for One-Dimensional Slitting Problems: A Dynamic Programming Approach

One-dimensional slitting problems are often encountered in textile, paper or sheet-metal industries where a roll of this product needs to be divided into various narrower-width rolls. The rolls are slit into pieces such that the total value of the sale is maximized depending on the width and the number of defects in the sheet. As the width of a piece increases, the value of the piece increases also, but on the other hand, the number of defects in the piece increases as well, causing reduction in its value. Based on these constraints, a dynamic programming formulation is framed so as to maximize the total value of the sale. Computational complexity with a numerical example is discussed to demonstrate how the procedure works.     

R&D Project Selection in a Multidimensional Environment: A Practical Approach

This paper investigates the R&D project selection problem within government departments. The Department of National Defence is taken as a case in point. The multidimensional character of the problem is detailed, and existing methods for priority allocation are briefly surveyed. Two basic models for selecting projects are described. One of these, the ordinal intersection method, was found to be the most appropriate for the actual problem settings investigated. An illustrative example is given, and concluding remarks are made.     

Continuity and Boundedness of Infinitely Divisible Processes: A Poisson Point Process Approach

Sufficient conditions for boundedness and continuity are obtained for stochastically continuous infinitely divisible processes, without Gaussian component, {Y(t),t T}, where T is a compact metric space or pseudo-metric space. Such processes have a version given by Y(t)=X(t)+b(t),tT where b is a deterministic drift function and

Multi-Path Approach for Reliability-Redundancy Allocation Using a Scaling Method

The reliability-redundancy allocation problem is an optimization problem that achieves better system reliability by determining levels of component redundancies and reliabilities simultaneously. The problem is classified with the hardest problems in the reliability optimization field because the decision variables are mixed-integer and the system reliability function is nonlinear, non-separable, and non-convex. Thus, iterative heuristics are highly recommended for solving the problem due to their reasonable solution quality and relatively short computation time. At present, most iterative heuristics use sensitivity factors to select an appropriate variable which significantly improves the system reliability. The sensitivity factor represents the impact amount of each variable to the system reliability at a designated iteration. However, these heuristics are inefficient in terms of solution quality and computation time because the sensitivity factor calculations are performed only at integer variables. It results in degradation of the exploration and growth in the number of subsequent continuous nonlinear programming (NLP) subproblems. To overcome the drawbacks of existing iterative heuristics, we propose a new scaling method based on the multi-path iterative heuristics introduced by Ha (2004). The scaling method is able to compute sensitivity factors for all decision variables and results in a decreased number of NLP subproblems. In addition, the approximation heuristic for NLP subproblems helps to avoid redundant computation of NLP subproblems caused by outlined solution candidates. Numerical experimental results show that the proposed heuristic is superior to the best existing heuristic in terms of solution quality and computation time.     

Optimal Multi-Period Advertising Budget Allocation within a Competitive Environment

A mathematical model approach is developed for the purpose of aiding advertising and marketing executives in advertising budget allocation decision-making in the face of a competitive environment. Two alternative model formulations are examined to study the dynamic market response to advertising expenditures. These embody numerous realistic characteristics of the advertising phenomenon including carry-over of past expenditures, diminishing returns and saturation effects, response decay in the absence of advertising and product diffusion effects. Through mathematical programming, the model determines the optimal advertising expenditures over a predetermined planning horizon under alternative constraint options (including competitive advertising assumptions). Illustrations of model applications are also presented.     

A Linear Programming Approach to Designing an Optimum Tax Package

This paper describes the application of linear programming to the problem of designing an optimum tax package to make the 1967 British devaluation work. The objective was to show how to release sufficient domestic resources so as to meet the extra exports demanded as a result of devaluation. It is important to realize that what matters is not the total amount of resources taken from the consumers, but the matching of these resources with the exportable goods demanded overseas. If this matching is not obtained, the tax policy may only cut down domestic production and employment without solving the balance-of-payments problem. The linear programming technique proved useful in tackling this problem of matching of demand and supply in particular directions. The particular tax package derived is heavy on those goods which have good export potential and/or high import content, and it avoids imposing taxes which will reduce domestic consumption without helping the balance of payments.     

安全投资优化分配方法证明及应用

分析并证明了用 Cobb-Douglsa函数建立安全投资优化分配模型及确立安全优化分配比例系数的正确性 ,并将该方法用于工厂实际工作 ,取得了满意的结果 .文中提出的投资优化分配方法具有较高的实用价值和广泛的应用前景