介观压阻效应在硅微加速度计中的应用

为了突破传统机电转换局限，提高加速度计灵敏度，提出以介观压阻效应为工作原理制作高灵敏度的硅微加速度计，基于这种原理设计一种四梁岛结构，通过理论分析与仿真计算，得出该结构在lgn输入下的输出，并对介观压阻灵敏度和压阻灵敏度的量级作出了比较，从理论上验证了采用介观压阻效应制作高灵敏度传感器的可行性，为此类加速度计的设计提供参考。     

压阻式微加速度计灵敏度的温度特性研究

以压阻式微加速度计为研究对象,定量研究了环境温度对其灵敏度的影响。理论研究结果表明,压阻式微加速度计灵敏度的相对误差随着温度偏差绝对值的增大而增大;其中灵敏度误差在温度偏差为 20℃时达到-4.943%,在温度偏差为-20℃时达到4.982%。设计了压阻式微加速度计压敏电阻的温度特性测试系统,测试结果表明,在温度偏差为 20℃时,灵敏度误差为-4.303%,温度偏差为-20℃时,实验值为4.251%。理论计算值与实验测试值相吻合。     

基于介观压阻效应的硅微加速度计研究

为了突破传统机电转换局限,提高加速度计灵敏度,提出以介观压阻效应为工作原理制作高灵敏度的硅微加速度计,基于这种原理设计并制备了GaAs基加速度计,通过理论分析计算与试验测试,得出该结构在0.1 gn输入下的输出,并对介观压阻灵敏度和压阻灵敏度的量级作出了比较,验证了采用介观压阻效应制作高灵敏度传感器的可行性,为此类加速度计的设计提供参考.     

基于共振隧穿的位移传感器设计

运用了一种新效应一介观压阻效应,以AIAs／GaAs／AIAs共振隧穿双势垒（DBRT）结构薄膜作为力敏元件,设计了一种压阻式微位移传感器。通过分析、计算和模拟得到了它的输入、输出特性和灵敏度,把它与同类传感器做了比较,结果显示：DBRT结构可以提高灵敏度、调节灵敏度。为设计介观压阻式微位移传感器提供了理论依据。     

HEMT微加速度计的结构优化设计

为满足HEMT微加速度计的高结构灵敏度和输出灵敏度的要求,对微加速度计的弹性结构进行了优化设计,得到一种全新的微加速度计折梁结构.利用ANSYS有限元分析软件对加速度计原结构和新结构进行仿真,通过计算和分析比较,结果表明:优化后加速度计结构合理,满足设计目标,新结构的结构灵敏度提高了1个数量级,输出灵敏度比之前增大了3倍.     

基于三梁结构的电热驱动谐振式微加速度传感器研究

为了提高电热激励/压阻拾振谐振式微加速度传感器的品质因数和消除交叉灵敏度,提出一种基于三梁结构的新设计.依据有限元模拟得到的固有振型来选择三梁结构参数,以减少能量耗散,提高品质因数.分析本设计消除交叉灵敏度的机制和独特的三梁结构的激振方式并对其进行有限元模拟.模拟结果证实这种谐振式微加速度传感器的灵敏度高于1000 Hz/g.     

复合量程微加速度计中阻尼的分析与设计

为了避免微加速度计在工作过程中因为共振导致结构损坏,需要在结构中合理设计阻尼.设计了一个复合量程压阻式微加速度计,为了使结构中各个传感器具有较好的阻尼参数,通过静电键合在硅结构层下制作一玻璃层.根据Reynolds方程,可知当硅-玻璃静电键合间距d=2.25μm时,复合量程微加速度计中各个传感器可得到较好的阻尼比.     

基于MXT9030的微电容加速度计系统搭建

阐述了三明治式电容微加速度计的工作原理,并在此基础上介绍了一种具体的三明治式微加速度计的设计:采用一种通用的电容读取电路MXT9030实现对电容式微加速度传感器的信号检测,通过微控制器的控制,调节MXT9030电路的内部参数,使加速度计系统具有良好的线性度及灵敏度。实验结果表明,该设计可满足较大范围内的电容差分信号输入,并具有良好的检测灵敏度和线性度。     

基于圆片级阳极键合封装的高g_n值压阻式微加速度计

设计了一种适合于高gn值压阻式微加速度计圆片级封装的结构,解决了芯片制造工艺过程中电极通道建立、焊盘保护、精确划片等关键技术。采用玻璃—硅—玻璃三层阳极键合的方式进行圆片级封装,较好地解决了芯片密封性、小型化和批量化等生产难题。在4 in生产线上制作的高gn值压阻式微加速度计样品,尺寸仅为1 mm×1 mm×0.8 mm;对传感器进行的校准与抗冲击性能测试,结果表明:样品具备105gn的抗冲击能力、0.15μV/gn/V的灵敏度以及200 kHz的谐振频率。     

一种新颖的微杆杠结构的理论建模及其在谐振式微加速度传感器中的应用

为了提高谐振式微加速度传感器的灵敏度,提出一种新颖的微杠杆结构.分析该结构的工作原理,推导该结构的理论模型,得到这种微杆杠放大倍数的解析表达式.在这个理论模型的基础上,为了进一步提高微杆杠的放大倍数,对其参数进行优化,分析微杆杠结构中各参数对于放大倍数的影响,优化后该结构的放大倍数高达200.基于这种微杠杆结构设计两种分别为静电驱动/电容检测和电热驱动/压阻拾振的谐振式微加速度传感器,分别介绍这两种传感器的工作原理及其特点,并对这两种结构进行有限元模拟.模拟结果证实这两种微加速度传感器的灵敏度均高于1 000 Hz/gn,进而验证这种新提出的微杠杆结构的有效性.     

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.     

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.     

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。     

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。     

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。     

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.     

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。