In this paper a technical analysis of an ocean thermal energy conversion (OTEC) system is performed. Specifically, we present a general mathematical framework for the synthesis of OTEC power generating systems. The ov...
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In this paper a technical analysis of an ocean thermal energy conversion (OTEC) system is performed. Specifically, we present a general mathematical framework for the synthesis of OTEC power generating systems. The overall synthesis task is to minimize heat exchange area requirements, while generating some fraction of the maximum net power recoverable from hot and cold ocean water. The resulting problem formulation yields a nonlinear, nonconvex mathematical program;however, we show that globally optimal solutions for this program are easily obtained explicitly through a direct optimization approach with minimal computational effort over a wide range of thermodynamic conditions. The proposed analysis is demonstrated on a case study involving the generation of hydrogen by an OTEC system with a pure ammonia working fluid. (c) 2007 Elsevier Ltd. All rights reserved.
The problem of time-optimal de-tumbling control (TODTC) of a rigid spacecraft moving between two attitudes is studied in this article. Unlike conventional approaches, which involve solving a set of differential equati...
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The problem of time-optimal de-tumbling control (TODTC) of a rigid spacecraft moving between two attitudes is studied in this article. Unlike conventional approaches, which involve solving a set of differential equations, a novel numerical method is introduced. In the proposed method, by fixing the count of control steps and treating the sampling period as a variable, the TODTC problem is formulated as a nonlinear programming ( NLP) problem by utilizing an iterative procedure. Generating initial feasible solutions systematically is also discussed, since these are usually needed in solving a NLP problem. In this manner, the optimization process of the NLP problem can be started from many different points when searching for the optimal solution. Simulation results are included, to show the feasibility of the proposed method.
This article presents a new analytical method for estimating the location of a target using directional data. A three-dimensional (3-D) location estimation method is developed based on a nonlinear programming problem ...
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This article presents a new analytical method for estimating the location of a target using directional data. A three-dimensional (3-D) location estimation method is developed based on a nonlinear programming problem formulated for the line method, which is a well-known algorithm for 2-D location estimation. This method can be applied to situations in which one needs to find a target in space using observers on the ground or to find a target on the ground using observers at least one of which is not on the ground. In addition, based on the analysis of the maximum likelihood estimate of the target location, another 3-D location estimation method is developed for cases in which accuracies of directional data from different observers are different. The performance of the suggested methods is evaluated through simulation experiments;the results show that the methods give very accurate estimates in a reasonably short computation time.
In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonline...
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In this article we solve a nonlinear cutting stock problem which represents a cutting stock problem that considers the minimization of, both, the number of objects used and setup. We use a linearization of the nonlinear objective function to make possible the generation of good columns with the Gilmore and Gomory procedure. Each time a new column is added to the problem, we solve the original nonlinear problem by an Augmented Lagrangian method. This process is repeated until no more profitable columns is generated by Gilmore and Gomory technique. Finally, we apply a simple heuristic to obtain an integral solution for the original nonlinear integer problem.
Wolfe and Mond-Weir type second-order symmetric duals are formulated and appropriate duality theorems are established under eta-bonvexity/eta-pseudobonvexity assumptions. This formulation removes several omissions in ...
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Wolfe and Mond-Weir type second-order symmetric duals are formulated and appropriate duality theorems are established under eta-bonvexity/eta-pseudobonvexity assumptions. This formulation removes several omissions in an earlier second-order primal dual pair introduced by Devi [Symmetric duality for nonlinear programming problems involving eta-bonvex functions, European J. Oper. Res. 104 (1998) 615-621]. (c) 2007 Elsevier B.V. All rights reserved.
We consider a class of convex programming problems whose objective function is given as a linear function plus a convex function whose arguments are linear functions of the decision variables and whose feasible region...
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We consider a class of convex programming problems whose objective function is given as a linear function plus a convex function whose arguments are linear functions of the decision variables and whose feasible region is a polytope. We show that there exists an optimal solution to this class of problems on a face of the constraint polytope of dimension not more than the number of arguments of the convex function. Based on this result, we develop a method to solve this problem that is inspired by the simplex method for linear programming. It is shown that this method terminates in a finite number of iterations in the special case that the convex function has only a single argument. We then use this insight to develop a second algorithm that solves the problem in a finite number of iterations for an arbitrary number of arguments in the convex function. A computational study illustrates the efficiency of the algorithm and suggests that the average-case performance of these algorithms is a polynomial of low order in the number of decision variables.
Team Cornell's Skynet is an autonomous Chevrolet Tahoe built to compete in the 2007 DARPA Urban Challenge. Skynet consists of many unique subsystems, including actuation and power distribution designed in-house, a...
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Team Cornell's Skynet is an autonomous Chevrolet Tahoe built to compete in the 2007 DARPA Urban Challenge. Skynet consists of many unique subsystems, including actuation and power distribution designed in-house, a tightly coupled attitude and position estimator, a novel obstacle detection and tracking system, a system for augmenting position estimates with vision-based detection algorithms, a path planner based on physical vehicle constraints and a nonlinear optimization routine, and a state-based reasoning agent for obeying traffic laws. This paper describes these subsystems in detail before discussing the system's overall performance in the National Qualifying Event and the Urban Challenge. Logged data recorded at the National Qualifying Event and the Urban Challenge are presented and used to analyze the system's performance. (C) 2008 Wiley Periodicals, Inc.
The development of a two-timescale discretization scheme for collocation is presented. This scheme allows a coarser discretization to be used for slowly varying state variables and a second finer discretization to be ...
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The development of a two-timescale discretization scheme for collocation is presented. This scheme allows a coarser discretization to be used for slowly varying state variables and a second finer discretization to be used for state variables having higher-frequency dynamics. That is, the discretization scheme can be tailored to the dynamics of the particular state variables. Consequently, the size of the overall nonlinear programming problem can be reduced significantly. Two two-timescale discretization architecture schemes are described. Comparison of results between the two-timescale method and conventional single-timescale collocation shows very good agreement. When the two-timescale discretization is used in combination with the sparse nonlinear optimizer SNOPT, a significant reduction (by more than 60%) in the number of nonlinear programming parameters required for the transcription of the problem and iterations necessary for convergence can he achieved without sacrificing solution accuracy.
This paper investigates a discrete-time neural network model for solving nonlinear convex programming problems with hybrid constraints. The neural network finds the solution of both primal and dual problems and conver...
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This paper investigates a discrete-time neural network model for solving nonlinear convex programming problems with hybrid constraints. The neural network finds the solution of both primal and dual problems and converges to the corresponding exact solution globally. We prove here that the proposed neural network is globally exponentially stable. Furthermore, we extend the proposed neural network for solving a class of monotone variational inequality problems with hybrid constraints. Compared with other existing neural networks for solving such problems, the proposed neural network has a low complexity for implementation without a penalty parameter and converge an exact solution to convex problem with hybrid constraints. Some numerical simulations for justifying the theoretical analysis are also given. The numerical simulations are shown that in the new model note only the cost of the hardware implementation is not relatively expensive, but also accuracy of the solution is greatly good. (c) 2007 Elsevier Inc. All rights reserved.
Binary integrators are an important part of the receiver in many operating radar systems. The optimisation of a binary integrator is not a simple task, because it requires the solution of a (k x n)-dimensional nonline...
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Binary integrators are an important part of the receiver in many operating radar systems. The optimisation of a binary integrator is not a simple task, because it requires the solution of a (k x n)-dimensional nonlinear optimisation problem, where n is the number of integrated bits (or the number of sensors in a distributed radar or sensor network) and k is the number of the design parameters of the single-pulse detector. An algorithm that converts the multi-dimensional optimisation problem into a one-dimensional problem, so reducing considerably the computational complexity, is developed. This reduction in computational complexity makes the real-time optimisation possible and practical, so it is very helpful for mobile sites in which the optimisation should be performed continually. The proposed algorithm can be applied when either the 'AND' or the 'OR' integration rule is adopted. The results are illustrated by means of two study cases. In the first case, the binary integrator of a constant false alarm rate radar detector is optimised;in the second one a decentralised detection system composed by n similar sensors is considered and the decision rules are jointly optimised according to the Neyman-Pearson criterion.
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