Application of localization for the fabrication of large curved plates in shipbuilding

Various problems associated with localization during curved plate fabrication are discussed. Localization is a necessary step for automation of curved plate fabrication that aligns a designed shape with a fabricated one as closely as possible for comparison of their shapes. On top of this localization, various conditions are introduced to reflect requirements occurring during fabrication such as minimum cutting length, maintenance of cutting length, localization for non-penetration and data types for localization. Each condition is formulated as a constraint which is provided as input to the optimization problem for localization. Algorithms for localization with each constraint based on iteration are proposed. Examples are used to demonstrate the performance of the algorithms.     

柔性结构形态重构的感知网络优化配置研究

以航天结构健康监测为研究背景,针对柔性板状结构形态重构的感知网络进行优化配置研究.在基于应变模态的结构形态重构算法中,以应变位移转换矩阵的条件数作为传感器优化配置的优化准则,以结构形态重构误差作为优化效果评价指标,采用一种改进的模拟退火算法对传感器优化配置进行了研究.针对传感器优化配置中的传感器数量、位置、方向以及多个目标的综合进行了传感器配置优化设计,仿真结果显示通过传感器配置的优化设计,可以有效地提高柔性结构的形态重构效果.最后,通过实验验证了传感器优化配置的有效性,为结构形态重构的感知网络优化配置提供了一定的理论依据.     

Multi-objective structural robust optimization under stress criteria based on mixed plate super-elements and genetic algorithms

This paper presents a methodology for the multi-objective (MO) robust optimization of plate structures under stress criteria, based on Mixed Super-Elements (MSEs). The optimization is performed with a classical Genetic Algorithm (GA) method based on Pareto-optimal solutions. It considers antagonist objectives among them stress criteria and thickness parameters distributed along the plate. This work aims at providing fast and efficient objective calculations. Our method is based on the implementation of MSEs for each zone of the plate featured by its own thickness. They are constructed with a Mixed Finite Element Model (MFEM) based on a displacement-stress mechanical formulation, and is enhanced with a sub-structuring modal reduction method in order to reduce the size of each constant thickness MSE. Those methods combined enable a fast and stress-wise efficient structure analysis, which improves the performance of the repetitive GA. A few cases minimizing the mass and the maximum Von Mises stress within a plate structure under dynamic loads put forward the relevance of our method with promising results. For the sake of robustness, both discrete frequencies and frequency bands are studied. The MO optimization is able to satisfy multiple damage criteria with different thickness distributions. It brings simplicity, saves computational time and the Pareto-front presentation with stress objective provides a good overview of the possibilities for the designers.     

Periodic Motion Planning and Control for Double Rotary Pendulum via Virtual Holonomic Constraints

Periodic motion planning for an under-actuated system is rather difficult due to differential dynamic constraints imposed by passive dynamics, and it becomes more difficult for a system with higher underactuation degree, that is with a higher difference between the number of degrees of freedom and the number of independent control inputs. However, from another point of view, these constraints also mean some relation between state variables and could be used in the motion planning.We consider a double rotary pendulum, which has an underactuation degree 2. A novel periodic motion planning is presented based on an optimization search. A necessary condition for existence of the whole periodic trajectory is given because of the higher underactuation degree of the system. Moreover this condition is given to make virtual holonomic constraint (VHC) based control design feasible. Therefore, an initial guess for the optimization of planning a feasible periodic motion is based on this necessary condition. Then, VHCs are used for the system transformation and transverse linearization is used to design a static state feedback controller with periodic matrix function gain. The controller gain is found through another optimization procedure. The effectiveness of initial guess and performance of the closed-loop system are illustrated through numerical simulations.      

Six-axis force sensor evaluation and a new type of optimal frame truss design for robotic applications

The different means currently available for six-axis wrist force sensor evaluation are discussed, and a unified criteria is proposed that is based on the condition number, the overall static and dynamic stiffness of the sensor, and the strain gauge sensitivity. In this light a new frame/truss type of sensor body design is introduced. The uniqueness of the design lies in the elastic members that exhibit truss (axial deformation), as opposed to the commonly used beam (bending) behavior. Several improvements over previous designs result, including: increased force sensitivity with a consistently low condition number, increased rigidity, and improved design flexibility. In addition, a design methodology is presented that uses optimization theory in combination with finite element analysis, to yield the best possible frame/truss force sensor design for a given set of specified principal forces.     

A method for optimal limit design of structures with alternative loads

Three problems of structural optimization are formulated and solved either by a direct or an iterative method. The first problem, the most general considered here, concerns “cost-optimization” of perfectly plastic structures for alternative loads. The second and third ones are more specific problems with linear objective functions, quadratic yield conditions and either a single or several load conditions. The linear-quadratic case can be solved directly when only one load condition is considered. This problem is shown to be related to elastic analysis. For alternative loads an iterative method based on this analysis is proposed. It shows strong convergence and forms the basis of an iterative solution of the general problem.     

A boundary integral method applied to plates of arbitrary plan form

An integral equation method for the solution of thin elastic plates of arbitrary plan form has been presented. The method involves embedding the real plate in a fictitious plate for which the Green's function is known. An unknown load vector is then introduced on the boundary of the real plate (line load and line normal moment). The deflection field due to both known transverse and unknown boundary loads can then be found everywhere by superposition. Satisfaction of the boundary conditions on the real plate results in a vector integral equation in the unknown boundary vector.In concept, any consistent set of boundary conditions will yield a solution. Practically, boundary conditions requiring higher derivatives of the deflection are both very cumbersome and yield singularities in the integral equations which cause numerical difficulties. For these reasons only clamped boundary conditions are treated numerically in the present paper.For interior bending moments and deflections (greater than distances of the order of one boundary subdivision from the boundary) the method is both highly accurate and inexpensive. Errors right on the boundary, e.g. the clamping moment in the clamped boundary condition case, can be appreciable, however. While this can be improved by a more sophisticated treatment of the unknown boundary vector in the numerical solution (increased expense) it is shown in the paper that a simple boundary extrapolation procedure gives excellent accuracy there.     

Evolutionary Algorithms and Matroid Optimization Problems

We analyze the performance of evolutionary algorithms on various matroid optimization problems that encompass a vast number of efficiently solvable as well as NP-hard combinatorial optimization problems (including many well-known examples such as minimum spanning tree and maximum bipartite matching). We obtain very promising bounds on the expected running time and quality of the computed solution. Our results establish a better theoretical understanding of why randomized search heuristics yield empirically good results for many real-world optimization problems.     

Multi-dimensional optimization of functionally graded material composition using polynomial expansion of the volume fraction

The work of this paper proposes a method for multi-dimensional optimization of functionally graded materials (FGMs) composition. The method is based on using polynomial expansion of the volume fraction of the constituent materials. In this approach, the design variables are the coefficients of the polynomial expansion which to be determined through the optimization process. This method provides much more flexibility in the design compared to the methods based on the power-law, or the exponential-law which will in turn lead to more optimal designs. Also it requires much less number of design variables compared to the grid based approaches which is also utilized for two-dimensional optimization of FGMs structures. As an application of the proposed method, the optimization of a simply supported Aluminum plate reinforced with Silicon Carbide nano-particles is considered. Cost plays a very important role for this type of structures, since the cost of the reinforcements such as Silicon Carbide nano-particles, or carbon nano-tubes is too high. So the aim of the optimization process is to minimize the amount of the reinforcement required to satisfy certain performance criteria. Both static, and dynamic cases are considered in this work; a plate under a transverse pressure distribution is considered as the static case, and the panel flutter problem as the dynamic case.     

Topology optimization of three-dimensional structures using optimum microstructures

In this paper, optimum three-dimensional microstructures derived in explicit analytical form by Gibianski and Cherkaev (1987) are used for topology optimization of linearly elastic three-dimensional continuum structures subjected to a single case of static loading. For prescribed loading and boundary conditions, and subject to a specified amount of structural material within a given three-dimensional design domain, the optimum structural topology is determined from the condition of maximum integral stiffness, which is equivalent to minimum elastic complicance or minimum total elastic energy at equilibrium.The use of optimum microstructures in the present work renders the local topology optimization problem convex, and the fact that local optima are avoided implies that we can develop and present a simple sensitivity based numerical method of mathematical programming for solution of the complete optimization problem.Several examples of optimum topology designs of three-dimensional structures are presented at the end of the paper. These examples include some illustrative full three-dimensional layout and topology optimization problems for plate-like structures. The solutions to these problems are compared to results obtained earlier in the literature by application of usual two-dimensional plate theories, and clearly illustrate the advantage of the full three-dimensional approach.     

A performance index for topology and shape optimization of plate bending problems with displacement constraints

This paper presents a performance index for topology and shape optimization of plate bending problems with displacement constraints. The performance index is developed based on the scaling design approach. This performance index is used in the Performance-Based Optimization (PBO) method for plates in bending to keep track of the performance history when inefficient material is gradually removed from the design and to identify optimal topologies and shapes from the optimization process. Several examples are provided to illustrate the validity and effectiveness of the proposed performance index for topology and shape optimization of bending plates with single and multiple displacement constraints under various loading conditions. The topology optimization and shape optimization are undertaken for the same plate in bending, and the results are evaluated by using the performance index. The proposed performance index is also employed to compare the efficiency of topologies and shapes produced by different optimization methods. It is demonstrated that the performance index developed is an effective indicator of material efficiency for bending plates. From the manufacturing and efficient point of view, the shape optimization technique is recommended for the optimization of plates in bending. Received November 27, 1998?Revised version received June 6, 1999     

再制造成本随机分布的生产优化问题研究

研究单一厂商制造/再制造集成系统的两期生产优化问题.首先,在回收率一定的条件下,建立回收产品的再制造成本与再制造率之间的函数关系;然后,建立以追求利润最大化为目标的模型,验证了该模型为凸规划,给出了K-T条件表达式,并分析了伽马分布条件下解的特征及其临界条件;最后,通过算例对该模型的性质和规律作进一步分析.     

Large scale structural optimization: Computational methods and optimization algorithms

Summary The objective of this paper is to investigate the efficiency of various optimization methods based on mathematical programming and evolutionary algorithms for solving structural optimization problems under static and seismic loading conditions. Particular emphasis is given on modified versions of the basic evolutionary algorithms aiming at improving the performance of the optimization procedure. Modified versions of both genetic algorithms and evolution strategies combined with mathematical programming methods to form hybrid methodologies are also tested and compared and proved particularly promising. Furthermore, the structural analysis phase is replaced by a neural network prediction for the computation of the necessary data required by the evolutionary algorithms. Advanced domain decomposition techniques particularly tailored for parallel solution of large-scale sensitivity analysis problems are also implemented. The efficiency of a rigorous approach for treating seismic loading is investigated and compared with a simplified dynamic analysis adopted by seismic codes in the framework of finding the optimum design of structures with minimum weight. In this context a number of accelerograms are produced from the elastic design response spectrum of the region. These accelerograms constitute the multiple loading conditions under which the structures are optimally designed. The numerical tests presented demonstrate the computational advantages of the discussed methods, which become more pronounced in large-scale optimization problems.     

微粉生产过程中磨机进出口温度多目标优化

微粉是钢铁废渣经过研磨后所形成的一种粉末,是一种高效且环保的建筑材料添加剂.在微粉生产过程中磨机进出口温度之间存在正相关的关系,但在正常工况下进口温度的升高将有助于提高产量,而出口温度的降低却有利于保证生产的安全性,因此温度设定值的求解将是一个多目标优化问题,较难获得最优值.从实际生产工况出发,采用非支配排序遗传算法II、多目标粒子群优化算法和多目标灰狼优化算法多种多目标优化算法求解此问题,并进行对比分析获得最优可行解集.优化后得到的解集能更好的为温度的设定提供参考,从而提升产量与生产的安全.     

RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm

Under mild conditions, it can be induced from the Karush-Kuhn-Tucker condition that the Pareto set, in the decision space, of a continuous multiobjective optimization problem is a piecewise continuous (m - 1)-D manifold, where m is the number of objectives. Based on this regularity property, we propose a regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) for continuous multiobjective optimization problems with variable linkages. At each generation, the proposed algorithm models a promising area in the decision space by a probability distribution whose centroid is a (m - 1)-D piecewise continuous manifold. The local principal component analysis algorithm is used for building such a model. New trial solutions are sampled from the model thus built. A nondominated sorting-based selection is used for choosing solutions for the next generation. Systematic experiments have shown that, overall, RM-MEDA outperforms three other state-of-the-art algorithms, namely, GDE3, PCX-NSGA-II, and MIDEA, on a set of test instances with variable linkages. We have demonstrated that, compared with GDE3, RM-MEDA is not sensitive to algorithmic parameters, and has good scalability to the number of decision variables in the case of nonlinear variable linkages. A few shortcomings of RM-MEDA have also been identified and discussed in this paper.     

基于多目标优化多任务学习的端到端车牌识别方法

本文针对多个车牌识别任务之间存在竞争和冲突,导致难以同时提高多个车牌的识别率的问题,提出基于多目标优化多任务学习的端到端车牌识别方法.首先,通过分析某些车牌识别任务容易占主导地位,而其他任务无法得到充分优化的问题,建立基于多任务学习的车牌识别模型.接着,针对字符分割造成车牌识别准确率较低、鲁棒性较差的问题,提出基于多任务学习的端到端车牌识别方法.最后,针对多个车牌识别任务间难以权衡的问题,提出一种基于多目标优化的多任务学习方法,以提高多个车牌识别的准确率.将本文所提方法在标准车牌数据集上进行测试,实验结果验证了该方法的有效性和优越性,其他代表性方法相比可以提高车牌识别的准确率、快速性和鲁棒性.     

A compact association of particle filtering and kernel based object tracking

Particle filtering (PF) and kernel based object tracking (KBOT) algorithms have shown their promises in a wide range of visual tracking contexts. This paper mainly addresses the association of PF and KBOT. Unlike other related association approaches which usually directly use KBOT to refine the position states of propagated particles for more accurate mode seeking, we elucidate the problem of what kind of particles is suitable for employing KBOT to refine their position states from a theoretical point of view. In accordance with the theoretical analysis, a two-stage solution is also proposed to resample propagated particles that are suitable for invoking KBOT from a computational perspective. The incremental Bhattacharyya dissimilarity (IBD) based stage is designed to consistently distinguish the particles located in the object region from the others placed in the background, while the matrix condition number based stage is formulated to further eliminate the particles positioned at the ill-posed conditions for running KBOT. Once the appropriate particles are obtained, constrained gradient based mean shift optimization enables us to efficiently refine the particles' position states. Besides, a state transition model embodying object-scale oriented information and prior motion cues is presented to adapt to fast movement scenarios. These ingredients lead to a new tracking algorithm. Experiments demonstrate that the proposed association approach is more robust to handle complex tracking conditions in comparison with related methods. Also, a limited number of particles are used in our association algorithm to maintain multiple hypotheses.     

An active approach to space-reduced NCO tracking and output feedback optimizing control for batch processes with parametric uncertainty

Necessary conditions of optimality (NCO) tracking is a promising approach to run-to-run optimization of batch processes, by converting the optimization problem into a feedback control problem. Since batch processes often contain numerous decision variables that hamper input adaptation in a feedback control manner, the directional effect of uncertainty has been utilized to reduce adaptation directions. This paper proposes an active approach that can further simplify the design of NCO tracking controllers for run-ro-run optimization of batch processes. The idea is to actively restrict the plant inputs in an optimal subspace, prior to the separation of constraint- and sensitivity-seeking directions of plant inputs. For this purpose, an extended system is constructed and then the system is operated by the so-called surrogate variables. Depending on the dimensions of active constraints and uncertain parameters, two cases are distinguished and their NCO tracking controllers are designed respectively. In addition, when the number of parameters is greater than the constraints, the neighboring-extremal based output feedback is incorporated into the active approach, such that the time-consuming gradient evaluations are avoided hence convergence is accelerated. In both cases, the number of adapted directions equals to the number of uncertain parameters. A numerical example and a batch distillation column are investigated to show the new methodology.     

Multi-objective optimization of a stacked neural network using an evolutionary hyper-heuristic

The present paper deals with the development and optimization of a stacked neural network (SNN) through an evolutionary hyper-heuristic, called NSGA-II-QNSNN. The proposed hyper-heuristic is based on the NSGA-II (Non-dominated Sorting Genetic Algorithm - II) multi-objective optimization evolutionary algorithm which incorporates the Quasi-Newton (QN) optimization algorithm. QN is used for training each neural network from the stack. The final global optimal solution provided by NSGA-II-QNSNN algorithm is a Pareto optimal front. It represents all the equally good compromises that can be made between the structural complexity of the stacked neural network and its modelling performance. The set of decision variables, which led to obtaining the set of points in the Pareto optimal front, represents the optimum values for the parameters of the stacked neural network: the number of networks in the stack, the weights for every output of the composing networks, and the number of hidden neurons in each individual neural network. Each stacked neural network determined through the optimization process was trained and tested by applying it to a real world problem: the modelling of the polyacrylamide-based multicomponent hydrogels synthesis. The neural modelling established the influence of the reaction conditions on the reaction yield and the swelling degree. The results provided by NSGA-II-QNSNN were superior, not only in terms of performance, but also in terms of structural complexity, to those obtained in our previous works, where individual or aggregated neural networks were used, but the stacks were developed manually, based on successive trials.     

A robust mean absolute deviation model for portfolio optimization

In this paper we develop a robust model for portfolio optimization. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. We construct a simple robust mean absolute deviation (RMAD) model which leads to a linear program and reduces computational complexity of existing robust portfolio optimization methods. This paper tests the robust strategies on real market data and discusses performance of the robust optimization model empirically based on financial elasticity, standard deviation, and market condition such as growth, steady state, and decline in trend. Our study shows that the proposed robust optimization generally outperforms a nominal mean absolute deviation model. We also suggest precautions against use of robust optimization under certain circumstances.