农药残毒快速检测智能化系统

针对农药残毒传统检测系统体积大、灵敏度差和检测速度较慢等问题,设计出一种基于"光转化—化学发光法"的农药残毒快速检测智能化系统。介绍了系统的整体结构、功能以及软硬件实现。系统将农药残毒的浓度转化为光强,进而转化为电压,通过微处理器的A/D转换等实现检测与显示。与目前常用的气相色谱、液相色谱等离线检测技术相比,该系统可以对多种常见农药的种类和溶度实现快速的在线检测,是一种全新的检测方法。     

数据融合算法在农药残毒检测系统中的应用

农药残毒检测在食品安全监管的过程中具有重要意义。通过自行设计的微型农药残毒快速检测仪对不同浓度的敌百虫溶液进行了多组测量。试验结果表明,大于最低检出值的溶液浓度与发光强度之间呈线性关系。在概率融合意义下定义模糊支持区间和模糊支持概率,将一种适合于单传感器重复采样的数据融合方法应用于农药残毒检测数据的处理中,克服了传统数字滤波方法在抗干扰方面的不稳定性,达到快速、准确检测的目的。     

基于USB的有机磷农药检测系统设计

为实现在农业生产中对有机磷农药的残留量进行快速有效地检测,设计了一种基于USB的有机磷农药检测系统。首先简单介绍了有机磷农药检测系统的工作原理,然后利用ARM微控制器实现数据采集,并通过USB与上位机PC机通信,实现下位机与上位机数据的实时传输,最终实现有机磷农药检测系统的功能,该系统可以有准确有效地检测出溶液中有机磷农药的残留量。     

基于光纤锥成像的光电指纹采集方法研究

为了提取更精确的指纹图像,在分析指纹提取技术的基础上研究和设计了一种基于新型光纤锥与电荷耦合器件(charge coupled device,CCD)耦合技术的光电式指纹采集方法与系统.该系统根据手指透光特性和光纤锥的传光原理给出了光源布局及光强分析,分析了该系统的硬件组成原理,给出了实验结果,并分别与非接触式和接触式光电指纹采集方法进行了对比.该指纹采集系统具有体积小、快速采集、分辨率高、可靠性强等优点.     

基于机器视觉的农药残留速测卡结果判读方法

针对人工判读大批量农药残留速测卡结果存在的误判率高、效率低等问题,提出基于机器视觉的农药残留速测卡结果准确快速判读方法,根据判读原理设计图像采集系统,利用边缘检测技术结合特征提取对速测卡图像处理分析。实验结果表明,该方法在保证准确性的前提下,判读效率达到约500次判读每分钟,能够满足蔬菜采摘前和销售前大批量农药残留检测需求,有助于绿色蔬菜溯源系统的建设和管理。     

光纤耦合输出半导体激光器的可靠性检测系统

介绍了一种光纤耦合输出半导体激光器的可靠性测试系统.该系统基于电导数原理,能够对光纤耦台输出半导体激光器的主要光电参数、电导数曲线以及电导数参数进行快速无损检测,具有体积小、成本低等优点.     

一种新型DNA自组装磁珠光电检测系统及其在DNA计算机研制中的应用

文中提出了一种基于硅芯片集成自组装磁珠颗粒的新型DNA光电检测系统,该系统利用普通照射光源及光电二极管进行光电信号转换,通过比较DNA杂交反应前后的光电流值,来识别DNA杂交信号.该系统是一种首次将磁珠和光电二极管相结合的新型DNA杂交检测系统,具有成本低廉、快速检测及高精度的特点.这种检测方法不需要信号增强步骤,就能够有效区分DNA单碱基错配及完全杂交的情况;由于采用了磁珠颗粒,易于在DNA计算中删除问题的非解.文中给出了求解图的最小顶点覆盖问题DNA计算模型实例,该实例证实了文中所提出的检测系统较传统检测系统具有明显的优势,有利于实现DNA计算机检测系统中解的自动化检测.     

光电型综合毒性检测仪的设计

针对饮用水的安全检测,给出了一种光电型综合毒性检测仪的设计方案。荧光素—荧光素酶反应体系能发出荧光,毒性物质的存在则会抑制荧光的发光强度。毒性的强度可通过计算毒性物质对发光强度的抑制程度得出。本文所涉毒性检测仪以光电倍增管作为光强检测元件。论文介绍了该仪器的设计原理和系统结构。实验结果显示,样机可以实现快速准确的生物毒性检测。     

一种采用TMS320LF2407A的农药残留快速检测仪

提出了一种利用光电接收器的接收功率的变化率与抑制率的关系设计农药残留快速检测仪的方法，并采用全新的光路设计和性能稳定可靠的DSP处理器，能同时实现15种样品的在线检测。实验结果表明该仪器具有检测精度高、检测速度快、检测样品种类多、可靠性好、成本低和便于携带等特点。     

基于ARM9的动基座光电测量仿真检测系统

针对目前动基座光电测量仿真检测系统成本高、人机交互性差、携带不方便的问题,设计出了一种可视化、便携的动基座光电测量仿真栓测系统.该系统采用了三星ARM9处理器、嵌入式Linux操作系统以及Qt/Embedded技术,使用Joystick摇杆和终端控制命令操纵光电平台,由串口通信对平台的各种性能指标进行实时检测.文章介绍了系统组成和软件设计流程,重点时设计中的关键问题--Linux下Joystick编程和串口通信的解决过程进行了阐述.测试表明,该系统具有功能丰富、可靠性高、操作便捷等优点.     

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。