A constrained nonlinear programming method is proposed for designing orthogonal waveforms applied to multiple input multiple output (MIMO) radar systems. The designed orthogonal waveforms are phase-coded signals with ...
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A constrained nonlinear programming method is proposed for designing orthogonal waveforms applied to multiple input multiple output (MIMO) radar systems. The designed orthogonal waveforms are phase-coded signals with a uniform amplitude and arbitrary phases. The optimal orthogonal waveforms have lower auto-correlation peak sidelobe levels (APSL) and lower peak cross-correlation levels (PCCL), compared with those of other methods. Additionally, the relationships among APSL, PCCL and the size of the orthogonal waveforms are studied via numerical experiments.
Disassembly sequence planning at the early conceptual stage of design leads to enormous benefits including simplification of products, lower assembly and disassembly costs, and design modifications which result in inc...
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Disassembly sequence planning at the early conceptual stage of design leads to enormous benefits including simplification of products, lower assembly and disassembly costs, and design modifications which result in increased potential profitability of end-of-life salvaging operations. However, in the early design stage, determining the best disassembly sequence is challenging. First, the required information is not readily available and very time-consuming to gather. In addition, the best solution is sometimes counterintuitive, even to those with experience and expertise in disassembly procedures. Integrating analytical models with immersive computing technology (ICT) can help designers overcome these issues. A two-stage procedure for doing so is introduced in this paper. In the first stage, a stochastic programming model together with the information obtained through immersive simulation is applied to determine the optimal disassembly sequence, while considering uncertain outcomes, such as time, cost, and the probability of causing damage. In the second stage, ICT is applied as a tool to explore alternative disassembly sequence solutions in an intuitive way. The benefit of using this procedure is to determine the best disassembly sequence, not only by solving the analytic model but also by capturing human expertise. The designer can apply the obtained results from these two stages to analyze and modify the product design. An example of a Burr puzzle is used to illustrate the application of the method.
In this paper, using the upper bound shakedown theorem by means of the displacement finite element method and nonlinear programming, a numerical method is proposed to obtain the shakedown limit of strip footing under ...
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In this paper, using the upper bound shakedown theorem by means of the displacement finite element method and nonlinear programming, a numerical method is proposed to obtain the shakedown limit of strip footing under repeated loading. Shakedown analysis is a powerful method that provides the possibility to determine the shakedown limit of a structure. The shakedown limit of a structure under repeated loading is a load limit below which the structure is in the safe zone and its behaviour is elastic. For cohesive-frictional soil, the Mohr-Coulomb yield criterion is considered without any linearization. The role of the unit weight of the soil in the shakedown limit of a footing under repeated load is studied. Also, the effect of repeated load on the reduction of the bearing capacity of footing is investigated in several examples.
Estimating forces in muscles and joints during locomotion requires formulations consistent with available methods of solving the indeterminate problem. Direct comparisons of results betwen differing optimization metho...
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Estimating forces in muscles and joints during locomotion requires formulations consistent with available methods of solving the indeterminate problem. Direct comparisons of results betwen differing optimization methods proposed in the literature have been difficult owing to widely varying model formulations, algorithms, input data, and other factors. We present an application of a new optimization program which includes linear and nonlinear techniques allowing a variety of cost functions and greater flexibility in problem formulation. Unified solution methods such as the one demonstrated here, offer direct evaluations of such factors as optimization criteria and constraints. This unified method demonstrates that nonlinear formulations (of the sort reported) allow more synergistic activity and in contrast to linear formulations, allow antagonistic activity. Concurrence of EMG activity and predicted forces is better with nonlinear predictions than linear predictions. The prediction of synergistic and antagonistic activity expectedly leads to higher joint force predictions. Relaxation of the requirement that muscles resolve the entire intersegmental moment maintains muscle synergism in the nonlinear formulation while relieving muscle antagonism and reducing the predicted joint contact force. Such unified methods allow more possibilities for exploring new optimization formulations, and in comparing the solutions to previously reported formulations.
The designs of salient pole generators may differ considerably from one hydroelectric plant to another and it is complex and need to experiment to design of hydro-generator Automatic optimization procedure is highly d...
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The designs of salient pole generators may differ considerably from one hydroelectric plant to another and it is complex and need to experiment to design of hydro-generator Automatic optimization procedure is highly desirable, because the designer may have little experience with a similar machine. The presented study;design space have been constituted nonlinear mixed variables which have the largest influence on the goal function at transient and dynamic analysis conditions. The optimization process is based on successive linearization of the goal function and the nonlinear mixed variables followed by a sequential procedure. The process is highly effective because the goal function is consist of nonlinear mixed variable, for this reason the optimum value is always on the boundary of the feasibility region. The procedure has been tested on a number of earlier designs. The goal function results could have been improved by using SMINLP and results are idealized better than QS, PS and SNLP between about % 3-%15 percent.
The continuous production of biodiesel is achieved through a sequence of stages such as reaction, absorption, decantation, and product distillation. These steps require certain performance criteria that must be optimi...
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The continuous production of biodiesel is achieved through a sequence of stages such as reaction, absorption, decantation, and product distillation. These steps require certain performance criteria that must be optimized. Several works have addressed the optimization of the design of biodiesel plants, and these have usually examined modifications to the dimensions and types of equipment or energy integration. However, there is only limited literature available on determining optimal operating conditions for existing processes. In this paper, the steady-state optimization of a soybean continuous biodiesel plant is proposed. To this end, a mathematical model to describe the chemical kinetics of soybean oil trans-esterification was developed and incorporated into a chemical process simulator. The optimization procedure is based on multidimensional Sequential Quadratic programming (SQP), in which the primary objectives were to minimize the plant's energy consumption subject to a minimum of 99 wt% biodiesel purity. The results reveal that the optimization of the current process allows a 4.45% reduction in energy consumption compared to the base case. Besides, the study also evidenced that the optimization approach can be applied to recalculate the optimal point when possible disturbances can deviate the system from a steady state. (C) 2019 Elsevier Ltd. All rights reserved.
We propose a class of inexact secant methods in association with the line search filter technique for solving nonlinear equality constrained optimization. Compared with other filter methods that combine the line searc...
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We propose a class of inexact secant methods in association with the line search filter technique for solving nonlinear equality constrained optimization. Compared with other filter methods that combine the line search method applied in most large-scale optimization problems, the inexact line search filter algorithm is more flexible and realizable. In this paper, we focus on the analysis of the local superlinear convergence rate of the algorithms, while their global convergence properties can be obtained by making an analogy with our previous work. These methods have been implemented in a Matlab code, and detailed numerical results indicate that the proposed algorithms are efficient for 43 problems from the CUTEr test set.
Augmented Lagrangian methods for derivative-free continuous optimization with constraints are introduced in this paper. The algorithms inherit the convergence results obtained by Andreani, Birgin, Martinez and Schuver...
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Augmented Lagrangian methods for derivative-free continuous optimization with constraints are introduced in this paper. The algorithms inherit the convergence results obtained by Andreani, Birgin, Martinez and Schuverdt for the case in which analytic derivatives exist and are available. In particular, feasible limit points satisfy KKT conditions under the Constant Positive Linear Dependence (CPLD) constraint qualification. The form of our main algorithm allows us to employ well established derivative-free subalgorithms for solving lower-level constrained subproblems. Numerical experiments are presented.
Caratheodory's lemma states that if we have a linear combination of vectors in R-n, we can rewrite this combination using a linearly independent subset. This lemma has been successfully applied in nonlinear optimi...
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Caratheodory's lemma states that if we have a linear combination of vectors in R-n, we can rewrite this combination using a linearly independent subset. This lemma has been successfully applied in nonlinear optimization in many contexts. In this work we present a new version of this celebrated result, in which we obtained new bounds for the size of the coefficients in the linear combination and we provide examples where these bounds are useful. We show how these new bounds can be used to prove that the internal penalty method converges to KKT points, and we prove that the hypothesis to obtain this result cannot be weakened. The new bounds also provides us some new results of convergence for the quasi feasible interior point l(2)-penalty method of Chen and Goldfarb [7].
The problem of inverting trained feedforward neural networks is to find the inputs which yield a given output, In general, this problem is an ill-posed problem because the mapping from the output space to the input sp...
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The problem of inverting trained feedforward neural networks is to find the inputs which yield a given output, In general, this problem is an ill-posed problem because the mapping from the output space to the input space is a one-to-many mapping. In this paper, we present a method for dealing with the inverse problem by using mathematical programming techniques. The principal idea behind the method is to formulate the inverse problem as a nonlinear programming (NLP) problem, a separable programming (SP) problem, or a linear programming (LP) problem according to the architectures of networks to be inverted or the types of network inversions to be computed. An important advantage of the method over the existing iterative inversion algorithm is that various designated network inversions of multilayer perceptrons (MLP's) and radial basis function (RBF) neural networks can be obtained by solving the corresponding SP problems, which can be solved by a modified simplex method, a well-developed and efficient method for solving LP problems. We present several examples to demonstrate the proposed method and the applications of network inversions to examining and improving the generalization performance of trained networks. The results show the effectiveness of the proposed method.
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