Natural Excitation Technique and Eigensystem Realization Algorithm for Phase I of the IASC-ASCE Benchmark Problem: Simulated Data

A benchmark study in structural health monitoring based on simulated structural response data was developed by the joint IASC–ASCE Task Group on Structural Health Monitoring. This benchmark study was created to facilitate a comparison of various methods employed for the health monitoring of structures. The focus of the problem is simulated acceleration response data from an analytical model of an existing physical structure. Noise in the sensors is simulated in the benchmark problem by adding a stationary, broadband signal to the responses. A structural health monitoring method for determining the location and severity of damage is developed and implemented herein. The method uses the natural excitation technique in conjunction with the eigensystem realization algorithm for identification of modal parameters, and a least squares optimization to estimate the stiffness parameters. Applying this method to both undamaged and damaged response data, a comparison of results gives indication of the location and extent of damage. This method is then applied using the structural response data generated with two different models, different excitations, and various damage patterns. The proposed method is shown to be effective for damage identification. Additionally the method is found to be relatively insensitive to the simulated sensor noise.     

Identification of Structural Systems Using an Evolutionary Strategy

The problem of system identification is an inverse problem of difficult solution. Currently, difficulties lie in the development of algorithms that use measured data from the system to characterize it without significant a priori knowledge of the system. In this paper, a parameter estimation technique based on an evolution strategy (an optimization algorithm inspired by natural evolution) is presented to overcome some of the difficulties encountered in the field. Using this method, a set of direct problems is solved instead of directly tackling the inverse problem. If the uniqueness of the identification solution is guaranteed for the assumed model and the available data, this heuristic method is able to find a solution without incurring restrictions of other classical optimization methods, like the need for reliable initial estimates and convergence to local optima. Some results obtained with this algorithm are presented for the identification of 3 degrees of freedom (DOF) and a 10?DOF structural system under conditions including limited input/output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness of the system.     

棒材轧制椭圆孔型的多种群遗传优化

 针对目前棒材连轧孔型全域优化问题,提出了多种群遗传算法,可以解决标准遗传算法存在的全域收敛差问题,并结合某钢厂连轧棒材椭圆孔的几何参数进行了多种群遗传算法优化分析。采用刚塑性有限元法模拟优化前后的两道次连轧过程,并分析了连轧过程轧制力的变化情况。结合Matlab曲线拟合和符号积分方法,计算优化前后的轧制能耗并进行比较。同时,借助实际铅件轧制过程的轧制力测试结果,较好地验证了模拟结果的可靠性。结果表明：优化后的孔型可以有效地降低轧制能耗和轧辊的轧制压力。     

基于模拟退火多种群遗传算法的C-Mn钢生产优化

模拟退火和多种群进化是改进遗传算法的两种主要手段，文章将这两种方法相结合，建立了基于模拟退火的多种群遗传算法，并将该算法应用于C—Mn钢生产时内控成分和轧制工艺的优化，以满足用户对钢产品的力学性能和化学成分的要求。     

Optimizer convergence and local minima errors and their clinical importance

Two of the errors common in the inverse treatment planning optimization have been investigated. The first error is the optimizer convergence error, which appears because of non-perfect convergence to the global or local solution, usually caused by a non-zero stopping criterion. The second error is the local minima error, which occurs when the objective function is not convex and/or the feasible solution space is not convex. The magnitude of the errors, their relative importance in comparison to other errors as well as their clinical significance in terms of tumour control probability (TCP) and normal tissue complication probability (NTCP) were investigated. Two inherently different optimizers, a stochastic simulated annealing and deterministic gradient method were compared on a clinical example. It was found that for typical optimization the optimizer convergence errors are rather small, especially compared to other convergence errors, e.g., convergence errors due to inaccuracy of the current dose calculation algorithms. This indicates that stopping criteria could often be relaxed leading into optimization speed-ups. The local minima errors were also found to be relatively small and typically in the range of the dose calculation convergence errors. Even for the cases where significantly higher objective function scores were obtained the local minima errors were not significantly higher. Clinical evaluation of the optimizer convergence error showed good correlation between the convergence of the clinical TCP or NTCP measures and convergence of the physical dose distribution. On the other hand, the local minima errors resulted in significantly different TCP or NTCP values (up to a factor of 2) indicating clinical importance of the local minima produced by physical optimization.     

分布式一致性最优化的梯度算法与收敛分析

研究了多智能体网络中受集合约束的一致性最优化问题,提出了基于原始–对偶梯度的定步长分布式算法。算法中包括步长在内的参数会影响收敛性,需要先进行收敛分析,再根据收敛条件设置合适的参数。本文首先针对一般的定步长迭代格式,提出一种基于李雅普诺夫函数的收敛分析范式,它类似于一般微分方程关于李雅普诺夫稳定的分析方法。然后,针对所考虑的分布式梯度算法,构造了合适的李雅普诺夫函数,并根据收敛条件得到了算法参数设定范围,避免了繁冗复杂的分析论证。本文提出的理论与方法也为其他类型的分布式算法提供了一个框架性、系统性的论证方法。      

The approximate inverse and conjugate gradient: non-symmetrical algorithms for fast attenuation correction in SPECT

Hybrid methods have been known for a long time as very efficient algorithms for attenuation correction in single-photon emission computed tomography, but only recently have efforts been made to formulate them with more rigorous mathematics. This has allowed us to explain their efficiency in terms of approximate inversion, and to establish a convergence condition. The present study focuses on the convergence problem and emphasizes the question of symmetry. Hybrid method operators are not symmetrical; therefore the convergence condition is not easily verified. New schemes based on a modified conjugate gradient method are presented. Convergence is proved and performances are shown to be at least as good as the standard hybrid schemes on perfect and noisy simulated data.     

Parallel computing of physical maps--a comparative study in SIMD and MIMD parallelism

Ordering clones from a genomic library into physical maps of whole chromosomes presents a central computational problem in genetics. Chromosome reconstruction via clone ordering is usually isomorphic to the NP-complete Optimal Linear Arrangement problem. Parallel SIMD and MIMD algorithms for simulated annealing based on Markov chain distribution are proposed and applied to the problem of chromosome reconstruction via clone ordering. Perturbation methods and problem-specific annealing heuristics are proposed and described. The SIMD algorithms are implemented on a 2048 processor MasPar MP-2 system which is an SIMD 2-D toroidal mesh architecture whereas the MIMD algorithms are implemented on an 8 processor Intel iPSC/860 which is an MIMD hypercube architecture. A comparative analysis of the various SIMD and MIMD algorithms is presented in which the convergence, speedup, and scalability characteristics of the various algorithms are analyzed and discussed. On a fine-grained, massively parallel SIMD architecture with a low synchronization overhead such as the MasPar MP-2, a parallel simulated annealing algorithm based on multiple periodically interacting searches performs the best. For a coarse-grained MIMD architecture with high synchronization overhead such as the Intel iPSC/860, a parallel simulated annealing algorithm based on multiple independent searches yields the best results. In either case, distribution of clonal data across multiple processors is shown to exacerbate the tendency of the parallel simulated annealing algorithm to get trapped in a local optimum.     

Toward an Improvement in the Identification of Bridge Deck Flutter Derivatives

This paper presents a short review of the state-of-the-art methods to identify bridge deck flutter derivatives and proposes a new algorithm to simultaneously extract the aeroelastic coefficients from free-vibration section-model tests, which is based on the improvement of the unifying least-squares (ULS) method and is therefore called modified unifying least-squares method. The advantages with respect to ULS are the faster and better convergence and the improvement in accuracy due to the introduction of weighting factors in the unifying error function. The method has been validated through numerically simulated noisy signals and experimental heaving and pitching time histories for two different bridge deck cross sections: a single-box and a multiple-box girder section model. The analysis of the artificial signals shows that a few system parameters are very difficult to be identified due to the fact that the problem is strongly ill-conditioned. Nevertheless, all the diagonal and off-diagonal components of the stiffness and damping matrices which significantly contribute to the output of the system are correctly estimated. The improvement with respect to other methods is extensively discussed. For the wind-tunnel test cases the accuracy of the identification procedure is evaluated through the comparison between measured signals and those simulated through the estimated mechanical and aerodynamic system parameters with very satisfactory results. With respect to many previous attempts of validation, this approach clearly shows the degree of accuracy that can be expected from the identification algorithm. Finally, for the considered test cases the linear model which stands behind the method seems to be an acceptable approximation of the physics of the phenomenon.     

Application of Improved PSO Algorithm in Hydraulic Pressing System Identification

 In view of characteristics of particle swarm optimization (PSO) algorithm of fast convergence but easily falling into local optimum value, a novel improved particle swarm optimization algorithm is put forward, and it is applicable to identify parameters of hydraulic pressure system model in strip rolling process. In order to maintain population diversity and enhance global optimization capability, the algorithm is firstly improved by means of decreasing its inertia weight linearly from the maximum to the minimum and then combined with chaotic characteristics of ergodicity, randomness and sensitivity to initial value. When the improved algorithm is used to identify parameters of hydraulic pressure system, the comparison of simulation curves and measured curves indicates that the identification results are reliable and close to actual situation. A new method was provided for hydraulic AGC system model identification.     

基于多目标遗传算法的工作辊温度场计算与分析

等效换热系数是热连轧机工作辊温度场仿真模型的核心输入参数,多采用遗传算法优化得到,某1800 mm 热连轧机存在品种、规格交替轧制,等效换热系数的准确计算比较困难.选取多组典型工艺条件下的工作辊下机后表面温度作为优化目标,采用多目标遗传算法进行优化,并通过改变遗传算子有效避免了算法早熟及局部收敛等问题,获取了具有较强适应性的等效换热系数.仿真和实测数据的对比结果证明了优化模型的可靠性.利用仿真模型分析了主要工艺参数对工作辊热凸度的影响,并提出同宽交替时,工作辊热凸度随轧制进程呈指数变化,而在品种、规格交替编排轧制工艺下相邻带钢轧制时工作辊热凸度存在6-21.8μm 的波动,且随轧制进程趋于稳定.      

一种基于差分进化的正弦余弦算法

正弦余弦算法是一种新型仿自然优化算法,利用正余弦数学模型来求解优化问题。为提高正弦余弦算法的优化精度和收敛速度,提出了一种基于差分进化的正弦余弦算法。该算法通过非线性方式调整参数提高算法的搜索能力、利用差分进化策略平衡算法的全局探索能力及局部开发能力并加快收敛速度、通过侦察蜂策略增加种群多样性以及利用全局最优个体变异策略增强算法的局部开发能力等优化策略来改进算法,最后通过仿真实验和结果分析证明了算法的优异性能。      

A Multi-Agent Immune Network Algorithm and Its Application to Murphree Efficiency Determination for the Distillation Column

Artificial Immune Network (aiNet) algorithms have become popular for global optimization in many modern industrial applications. However, high-dimensional systems using such models suffer from a potential premature convergence problem. In the existing aiNet algorithms, the premature convergence problem can be avoided by implementing various clonal selection methods, such as immune suppression and mutation approaches, both for single population and multi-population cases. This paper presents a new Multi-Agent Artificial Immune Network (Ma-aiNet) algorithm, which combines immune mechanics and multiagent technology, to overcome the premature convergence problem in high-dimensional systems and to efficiently use the agent ability of sensing and acting on the environment. Ma-aiNet integrates global and local search algorithms. The performance of the proposed method is evaluated using 10 benchmark problems, and the results are compared with other well-known intelligent algorithms. The study demonstrates that Ma-aiNet outperforms other algorithms tested. Ma-aiNet is also used to determine the Murphree efficiency of a distillation column with satisfactory results.     

Modelling a Skin-Pass Rolling Process by Means of Data Mining Techniques and Finite Element Method

 An experience is presented using the finite element method (FEM) and data mining (DM) techniques to develop models that can be used to optimize the skin-pass rolling process based on its operating conditions. A FE model based on a real skin-pass process is built and validated. Based on this model, a group of FE models is simulated with different adjustment parameters and with different materials for the sheet; both variables are chosen from pre-set ranges. From all FE model simulations, a database is generated; this database is made up of the above mentioned adjustment parameters, sheet properties and the variables of the process arising from the simulation of the model. Various types of data mining algorithms are used to develop predictive models for each of the variables of the process. The best predictive models can be used to predict experimentally hard-to-measure variables (internal stresses, internal strains, etc) which are useful in the optimal design of the process or to be applied in real time control systems of a skin-pass process in-plant.     

铁矿石烧结工艺参数优化模型的设计与应用

在分析配矿方案、工艺参数、产、质量指标三者相互关系的基础上，提出了两种优化烧结工艺参数的数学逻辑模型，即网络输出参数优化模型和网络输入参数优化模型．通过神经网络建模，比较建模结果，网络输入参数优化模型的效果更好．在神经网络模型的基础上结合遗传算法求解网络输入参数优化模型，计算出最佳的工艺参数．模型通过应用，不仅降低了烧结能耗，而且提高了烧结矿的产、质量，验证了模型的有效性和实用性．     

穿孔效率与能耗的建模与综合优化

 对于无缝钢管穿孔生产,穿孔效率和穿孔能耗是衡量生产的两个重要指标。但由于其影响因素复杂,难以建立机制模型,故难以实现对其模型的综合优化。为实现穿孔效率和穿孔能耗的综合优化,根据穿孔生产工艺将穿孔过程分成3个部分,提出了基于均值子时段MICR方法的穿孔效率和穿孔能耗的预报模型。在预报模型的基础上,采用遗传算法对各段工艺参数的平均值进行寻优,得到穿孔效率和穿孔能耗综合优化结果。仿真和试验结果表明,基于穿孔效率和穿孔能耗预报模型的优化方法可有效取得最优工艺参数,为穿孔效率的提高和穿孔能耗的降低提供依据。     

Substructural Identification Method without Interface Measurement

Substructural identification provides a novel means by which to reduce a large problem to smaller problems of manageable size, thereby improving numerical convergence and accuracy. Various methods proposed by several researchers thus far require interface response measurements, which are then treated as input to the substructures of concern. In practice, however, it is not always possible to obtain interface measurements, particularly if rotational response is required for beam/frame structures. In this paper, a method for parameter identification of substructures without the need of interface measurements is proposed. On the basis of receptance theory, an inverse problem is formulated in the frequency domain. Interface forces are eliminated by using different sets of measurements in the substructure concerned under the same dynamic excitation. The genetic algorithms approach is employed to determine the unknown parameters, and the fitness function is defined to minimize the difference between the estimates of interface forces obtained using different sets of response measurements. Three numerical examples are presented to illustrate the proposed method, and account for effects of measurement noise.     

Axisymmetric Shape Analysis of Sessile Droplets: A Metrology for High-Temperature Wetting and Nonwetting Systems

An optimization process for the solution of the Laplace equation of capillarity by numerical analysis is described. The approach is based on the image analysis of droplets that provides the “best” initial graphical estimate from parameters of the system, which are then optimized. This optimization process yields the contact angle, the surface tension, and the work of adhesion for any experimental droplet that has an equilibrium axisymmetric shape (i.e., a sessile or pendant drop). For the optimization, the Levenberg-Marquardt (LM) and the Gauss-Newton (GN) methods are compared through an objective function. The LM has the lowest number of iterations and produces the smallest errors for the optimization. It can converge to a solution even when different combinations of initial parameters are chosen for optimization. The best results are obtained when all the parameters are optimized to generate a solution. To illustrate the process, sessile drops of molten aluminum resting on sapphire single crystal (0001) are described and analyzed.     

连铸二冷参数的优化和控制研究

近些年连铸过程模拟成为研究的重点,模拟的根本目的在于优化连铸过程.为在保证连铸坯质量的前提下进一步提高连铸生产率,基于数学上的子问题接近优化技术建立了连铸过程二冷参数优化系统,可对不同钢种、不同拉速、不同冶金限制准则下的二冷各段配水进行最优化.运用优化后的这些过程参数,将使铸机以最大生产率、最小消耗运行,同时又保证了生产无缺陷铸坯.整个优化系统包括两个模块,一个是热过程模拟模块,另一个是设计优化模块,系统靠在两个模块之间反复自动循环调用而使参数得到优化.     

Identification of Manning’s Roughness Coefficients in Shallow Water Flows

A numerical method based on optimal control theories for identifying Manning’s roughness coefficients (Manning’s n) in modeling of shallow water flows is presented. The coefficients are difficult to be determined especially when the spatial variation is significant, and are usually estimated empirically. The present methodology is applied to determine the optimal values of the spatially distributed parameters, which give least overall discrepancies between simulations and measurements. Through a series of systematic studies to identify the n values in both a hypothetical open channel and a natural stream stretch, several identification procedures based on unconstrained and constrained minimizations are analyzed. It is found that the limited-memory quasi-Newton method has the advantages of higher rate of convergence, numerical stability and computational efficiency. Although the identification of Manning’s n is chosen as an example, the identification methods can be applied to numerical simulations of various flow problems.