磁耦合谐振式无线电能传输系统传输特性研究

针对磁耦合谐振式无线电能传输(MCR-WPT)系统中线圈参数和负载电阻改变对系统传输性能造成的影响,利用两线圈结构的MCR-WPT等效电路模型,推导了系统输出功率和效率表达式,分析了线圈互感、负载电阻与系统输出功率和传输效率的关系,以及线圈线径、匝数与互感的关系;借助COMSOL有限元仿真软件建立线圈三维模型,并搭建多组两线圈MCR-WPT实验系统对理论分析结果进行了验证.研究结果表明:通过增大收发线圈线径和匝数,可以提高系统输出功率和传输效率,但与线圈线径相比,线圈匝数对传输效率的影响更为明显,而且随着匝数的增加,系统会在更远的传输距离处取得输出功率最大值;随着负载电阻不断增大,系统输出功率和传输效率都呈现先增加后减小的趋势,证明了系统的输出功率和传输效率均存在最大值,但系统输出功率和传输效率达到最大值时的最佳负载不同,即不存在可使输出功率和传输效率同时取最大值的最优负载电阻.     

磁耦合谐振式无线能量传输系统的软件设计

为了解决特定要求的磁耦合谐振式无线能量传输系统的实验参数选取问题,首先利用线圈的互感模型分析了该系统的工作原理,然后通过理论分析得到系统线圈尺寸、负载阻值、谐振频率等各个参数与负载功率和传输效率的关系。在此基础上设计了磁耦合谐振式无线能量传输系统的软件,该软件能够计算出给定距离下负载功率最大时的系统参数,为设计实验装置提供理论依据。最后,通过实验验证了该设计的实用性。     

无线电能传输系统谐振线圈优化设计

以磁耦合谐振式无线电能传输系统两线圈等效电路模型为例,详细分析了谐振线圈的线圈半径、导线半径和线圈匝数等参数对谐振线圈电感和空载时的品质因数的影响;在电源频率为13.56MHz时,得出谐振线圈的优化参数如下:电感值为2.634×10-5 H,分布电容值为5.229×10-6μF,线圈半径为50mm,导线半径为1.05mm,线圈匝数为10匝。该优化结果对磁耦合谐振式无线电能传输系统的研究具有一定的参考价值。     

基于阻抗特性分析的电磁层析成像传感器优化

设计一种与电磁层析成像(MIT)系统的工作频率相匹配的传感器阵列对于提升图像重建的精度至关重要.针对MIT传感线圈结构,研究了频域内线圈结构参数的变化与其阻抗的对应关系,包括线圈匝数和线圈直径.实验结果表明:增加线圈匝数或扩大线圈直径均会导致线圈阻抗响应曲线向着频率较低的区域平移.在设计MIT传感器时,为了使得优化后的线圈结构与MIT系统的工作频率能够有效匹配,需要协同考虑线圈不同结构参数引起的效应.     

无线电能传输系统谐振线圈优化设计

以磁耦合谐振式无线电能传输系统两线圈等效电路模型为例,详细分析了谐振线圈的线圈半径、导线半径和线圈匝数等参数对谐振线圈电感和空载时的品质因数的影响;在电源频率为13.56MHz时,得出谐振线圈的优化参数如下:电感值为2.634×10-5 H,分布电容值为5.229×10-6μF,线圈半径为50mm,导线半径为1.05mm,线圈匝数为10匝。该优化结果对磁耦合谐振式无线电能传输系统的研究具有一定的参考价值。     

基于罚函数算法的感应式磁力仪优化设计

低等效磁场噪声、轻小型感应式磁力仪是空间、深地磁测等应用的必然要求。本文提出了一种在感应式磁力仪参数及等效磁场噪声、轻小化约束下，求解最优感应线圈匝数、线径参数配置的方法。文中首先建立了感应式磁力仪等效磁场噪声、重量指标数学模型，然后基于罚函数算法求解其数值解，从而得到感应线圈匝数、线径参数最优值。为验证理论分析结果，根据优化参数制作感应式磁力仪并进行指标测试，测试结果与理论设计相吻合，验证了基于罚函数算法求解感应式磁力仪最优参数的准确性。     

基于GA-NN的复杂工艺生产过程多目标优化研究

在复杂非线性多目标优化问题求解中,非线性模型结构很难事先给定,需要检验的参数也非常繁多,应用传统的建模方法和优化模型已难以解决更为复杂的现实问题。人工神经网络技术为解决复杂非线性系统建模问题提供了一条新的途径。将神经网络响应面作为目标函数或者约束条件,加上其他常规约束条件进行系统模型的建立,再应用遗传算法进行优化,从而实现设计分析与设计优化的分离。以某化工企业的生产过程优化问题为例,利用BP神经网络建立了工艺参数与性能目标之间的模型,然后利用遗传算法搜索最优工艺参数,获取了用于指导生产的样本点数据。研究结果表明,该方法能够获得高精度的多目标优化模型,从而使优化效率大为提高。     

基于线圈互感的阵列涡流传感器优化设计

线圈单元间互感一直是影响接收/发射式阵列涡流传感器准确性的干扰因素,工程实际中采用多种方法减弱线圈单元间互感对检测线圈单元的影响,但线圈单元间互感中包含缺陷信息,有效利用线圈单元间互感可以获得缺陷更多的信息,有利于缺陷的评估.运用ANSYS软件进行三维有限元仿真,利用线圈单元间互感研究了线圈单元相关参数对阵列涡流传感器检测灵敏度和空间分辨率的影响,理论上为基于线圈单元间互感的阵列涡流传感器的优化设计提供了参考.     

谐振式无线电能传输系统的研究

阐述了磁耦合谐振式无线电能传输的工作原理,在对发射与接收电路几种拓扑结构分析的基础上设计了一个收发电路均采用串联补偿结构、谐振频率为1 MHz、负载为10Ω的模型。基于线圈的互感耦合等效模型,从电路角度分析得出系统的输出功率和效率与线圈、负载、互感等参数之间的关系。针对磁耦合谐振系统满足最大输出功率时效率比较低的状况,提出了功效积指标。在满足系统输出功率的基础上,该指标实现了较高的传输效率。最后,通过Matlab仿真验证了所提功效积指标的可行性。     

MEMS电子罗盘三维补偿线圈参数优化设计

为解决MEMS电子罗盘易受强磁干扰影响导致其航向解算失准的问题,结合两种多目标优化算法与COMSOL有限元仿真,设计了一种优化的三维补偿线圈结构.首先根据Helmholtz线圈工作原理,在COMSOL软件环境中进行3D建模,设计正交试验初步设定了各组线圈参数,并简单分析了三维线圈的磁场梯度分布特性.通过COMSOL和MATLAB联合仿真,设置了线圈优化的目标函数和约束条件,分别运用NSGA-II与MOPSO两种算法进行求解,并确定了线圈的实际结构参数.仿真结果表明该三维线圈能满足强磁补偿需求,具有较高的补偿效率.     

Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization

Global derivative-free deterministic algorithms are particularly suitable for simulation-based optimization, where often the existence of multiple local optima cannot be excluded a priori, the derivatives of the objective functions are not available, and the evaluation of the objectives is computationally expensive, thus a statistical analysis of the optimization outcomes is not practicable. Among these algorithms, particle swarm optimization (PSO) is advantageous for the ease of implementation and the capability of providing good approximate solutions to the optimization problem at a reasonable computational cost. PSO has been introduced for single-objective problems and several extension to multi-objective optimization are available in the literature. The objective of the present work is the systematic assessment and selection of the most promising formulation and setup parameters of multi-objective deterministic particle swarm optimization (MODPSO) for simulation-based problems. A comparative study of six formulations (varying the definition of cognitive and social attractors) and three setting parameters (number of particles, initialization method, and coefficient set) is performed using 66 analytical test problems. The number of objective functions range from two to three and the number of variables from two to eight, as often encountered in simulation-based engineering problems. The desired Pareto fronts are convex, concave, continuous, and discontinuous. A full-factorial combination of formulations and parameters is investigated, leading to more than 60,000 optimization runs, and assessed by three performance metrics. The most promising MODPSO formulation/parameter is identified and applied to the hull-form optimization of a high-speed catamaran in realistic ocean conditions. Its performance is finally compared with four stochastic algorithms, namely three versions of multi-objective PSO and the genetic algorithm NSGA-II.     

Real-time dynamic optimization of batch systems

In this paper a methodology for designing and implementing a real-time optimizing controller for batch processes is proposed. The controller is used to optimize a user-defined cost function subject to a parameterization of the input trajectories, a nominal model of the process and general state and input constraints. An interior point method with penalty function is used to incorporate constraints into a modified cost functional, and a Lyapunov based extremum seeking approach is used to compute the trajectory parameters. The technique is applicable to general nonlinear systems. A precise statement of the numerical implementation of the optimization routine is provided. It is shown how one can take into account the effect of sampling and discretization of the parameter update law in practical situations. A simulation example demonstrates the applicability of the technique.     

Multiobjective optimization of trusses using genetic algorithms

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GA-based approaches, proving that this technique generates better trade-offs and that the genetic algorithm can be used as a reliable numerical optimization tool.     

An integrated approach to topology,sizing, and shape optimization

Topology optimization has become very popular in industrial applications, and most FEM codes have implemented certain capabilities of topology optimization. However, most codes do not allow simultaneous treatment of sizing and shape optimization during the topology optimization phase. This poses a limitation on the design space and therefore prevents finding possible better designs since the interaction of sizing and shape variables with topology modification is excluded. In this paper, an integrated approach is developed to provide the user with the freedom of combining sizing, shape, and topology optimization in a single process.     

多元插值全局优化算法及其应用

本文介绍一种多元插值逼近和动态搜索轨迹相结合的全局优化算法.该算法大大减少了目标函数计算次数,寻优收敛速度快,算法稳定,且可获得全局极小,有效地解决了大规模非线性复杂动态系统的参数优化问题.一个具有8个控制参数的电力系统优化控制问题,采用该算法仅访问目标函数78次,便可求得最优控制器参数。     

Effects of dominant wolves in grey wolf optimization algorithm

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.     

云搜索优化算法

本文将云的生成、动态运动、降雨和再生成等自然现象与智能优化算法的思想融合,建立了一种新的智能优化算法-云搜索优化算法(CSO)。生成与移动的云可以弥漫于整个搜索空间,这使得新算法具有较强的全局搜索能力;收缩与扩张的云团在形态上会有千奇百态的变化,这使得算法具有较强的局部搜索能力;降雨后产生新的云团可以保持云团的多样性,这也是使搜索避免陷入局优的有效手段。实验表明,基于这三点建立的新算法具有优异的性能,benchmark函数最优值的计算结果以及与已有智能优化算法的比较展现了新算法精确的、稳定的全局求解能力。     

Value Creation in a Network: The Role of Pricing and Revenue Optimization and Enterprise Profit OptimizationTM

The Internet has created a virtual upheaval in the structural features of the supply and demand chains for most businesses. New agents and marketplaces have surfaced. The potential to create value and enhance profitable opportunities has attracted both buyers and sellers to the Internet. Yet, the Internet has proven to be more complex than originally thought. With information comes complexity: the more the information in real time, the greater the difficulty in interpretation and absorption. How can the value-creating potential of the Internet still be realized, its complexity notwithstanding? This paper argues that with the emergence of innovative tools, the expectations of the Internet as a medium for enhanced profit opportunities can still be realized. Creating value on a continuing basis is central to sustaining profitable opportunities. This paper provides an overview of the value creation process in electronic networks, the emergence of the Internet as a viable business communication and collaboration medium, the proclamation by many that the future of the Internet resides in “embedded intelligence”, and the perspectives of pragmatists who point out the other facet of the Internet—its complexity. The paper then reviews some recent new tools that have emerged to address this complexity. In particular, the promise of Pricing and Revenue Optimization (PRO) and Enterprise Profit Optimization^TM (EPO) tools is discussed. The paper suggests that as buyers and sellers adopt EPO, the market will see the emergence of a truly intelligent network—a virtual network—of private and semi-public profitable communities.     

粒子群算法在组合优化问题上的研究与发展

粒子群优化算法是一种新兴的基于群智能搜索的优化技术。该算法简单、易实现、参数少,具有较强的全局优化能力,可有效应用于科学与工程实践中。介绍了算法的基本原理和算法在组合优化上一些改进方法的主要应用形式。最后,对粒子群算法作了一些深入分析并在此基础上对粒子群算法应用于组合优化问题做了一些总结。     

SEO技术研究

为了利用搜索引擎优化SEO（Search Engine Optimization）技术给网站带来高质量的流量并将其转化为商业利益,理解搜索引擎的算法和排名原理十分必要。通过对网站的结构优化、关键词优化、单页优化、防止被搜索引擎惩罚和挽救被惩罚网站等技术的研究,达到提高网站排名,实现网站的价值目的。