一种基于软阈值方法的改进A1/A2极化干涉分类算法

在分析了极化干涉技术的发展现状和发展趋势的基础上,从极化干涉的特殊性入手,分析了干涉相干系数在极化干涉分类中的作用,并且系统分析了基于相干最优优化后的相干系数进行极化干涉分类过程的原理,总结了现有极化干涉分类算法在阈值选择方面存在的不足,阐述了硬阈值方法和软阈值方法的基本原理和它们之间的区别,最后提出基于软阈值方法的改进A1/A2非监督极化干涉分类算法,解决了极化干涉分类现有的分类空间中的阈值问题。实验表明这种方法能够得到更好的分类结果,做出了改进。     

多目标进化算法在物流配送中心选址中的应用

传统的物流配送中心选址模型过于单一地追求物流成本的最小化,而没有考虑服务的质量与效率．本文将顾客时间满意度作为度量物流服务水平的一个标准,提出了物流配送中顾客时间满意度的计算方法,建立了以物流成本最小化和时间满意度最大化为目标的物流配送中心选址多目标优化模型．采用一种基于NSGA一Ⅱ的多目标进化算法来求解,通过选择合适的编码方法和遗传算子可以得到模型的最优解,并通过实际算例说明了模型和算法的有效性．该模型能一次得到多组有效解,从而可以为物流配送中心选址提供更加全面的决策支持．     

6m焦炉推焦机的结构特点及优化设计

介绍了6 m焦炉推焦机的结构特点。通过规范主动齿轮与齿条的啮合、规范走行轨道和推焦杆支辊的标高、调节滑履高度和在推焦杆两侧加装耐热材料可以解决推焦杆振动和推焦杆中前部立板产生裂纹的问题。     

基于熵相关系数的关联性自动判别方法——COCA

数据库自管理、自调优中查询计划的自动优化是目前的关注热点。为保证优化器估值精度,用统计学方法,给出了一种基于熵相关系数的对字段关联性的自动判别的新算法——COCA。该算法有下列特点：（1）限制少,没有卡方检验的频数限制,卡方检验只有在列联表中至少有80%的格子频数大于5的情况下才可信; （2）结果多,卡方检验（CORDS）只判断字段之间是否有关联,新方法可计算字段之间双向的关联程度。实验表明, 新方法更坚固,产生更多的统计信息,可以支持后面更高效、准确地建立直方图。     

Efficient shape optimization for certain and uncertain aerodynamic design

In this paper, we present novel developments in aerodynamic shape optimization based on shape calculus as well as the proper treatment of aleatoric uncertainties in the field of aerodynamic design.     

Cell design in bacteria as a convex optimization problem

In this paper, we investigate the prediction of the cell composition of bacteria with respect to their medium. By modeling the bacterium as an interconnection of subsystems, the problem is written as a non-smooth convex optimization problem equivalent to a Linear Programming feasibility problem. We then obtain a new method, called Resource Balance Analysis (RBA), predicting the distribution of the available resources in the medium among the various cellular subsystems. Beyond its predictive capability, the proposed approach grasps some fundamental aspects of the bacterium physiology by including a refined model. This method reveals the existence of an intrinsic bottleneck in the system resource distribution of the bacterium, leading to the existence of a structural limitation of its growth rate which can be predicted. RBA is also able to predict the configuration of the metabolic network for a given medium at steady-state regimen which nicely fits the available experimental results for the gram-positive model bacterium Bacillus subtilis.     

Optimality and convergence of adaptive optimal control by reinforcement synthesis

Adaptive Optimal Control (AOC) by reinforcement synthesis is proposed to facilitate the application of optimal control theory in feedback controls. Reinforcement synthesis uses the critic–actor architecture of reinforcement learning to carry out sequential optimization. Optimality conditions for AOC are formulated using the discrete minimum principle. A proof of the convergence conditions for the reinforcement synthesis algorithm is presented. As the final time extends to infinity, the reinforcement synthesis algorithm is equivalent to the Dual Heuristic dynamic Programming (DHP) algorithm, a version of approximate dynamic programming. Thus, formulating DHP with the AOC approach has rigorous proofs of optimality and convergence. The efficacy of AOC by reinforcement synthesis is demonstrated by solving a linear quadratic regulator problem.     

模拟退火法在耦合电容器优化设计中的应用

耦合电容器作为线路载波装置及电容式电压互感器的主要组成部分,已在电力系统中被广泛应用。研究如何合理的进行耦合电容器设计,对缩小产品体积、降低成本、提高产品性能是十分必要的。笔者将耦合电容器的设计问题与现代优化方法相结合,应用模拟退火优化策略进行优化设计,改进了产品的设计技术。     

Seeker optimization algorithm for global optimization: A case study on optimal modelling of proton exchange membrane fuel cell (PEMFC)

In order to optimize the proton exchange membrane fuel cell (PEMFC) model parameters, a novel approach based on seeker optimization algorithm (SOA) is proposed. The SOA is based on the concept of simulating human searching behaviors, where the choice of search direction is based on the empirical gradient by evaluating the response to the position changes and the decision of step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, after evaluated on benchmark function optimization, the SOA is applied to optimal modelling of the PEMFC by using a fuel cell test system in Fuel Cell Application Centre (FAC) at the Temasek Polytechnic, and compared with several state-of-the-art versions of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The simulation results show that the proposed approach is superior to other compared algorithms, and the PEMFC model with optimized parameters by SOA fitted experimental data well. Hence, SOA is an effective and reliable technique for optimizing the parameters of PEMFC model, and can be helpful for system analysis, optimization design and real-time control of the PEMFCs.     

Optimal power system operation using parallel processing system and PSO algorithm

In recent studies, PSO algorithm is applied to solve OPF problem. However, population based optimization method requires higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallelization of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC-cluster system with 8 Intel Pentium IV 2 GHz processors. The proposed approach has been tested on the test systems. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.