基于图像处理的片式元件缺陷检测算法

表面缺陷检测是电子元器件生产过程中的关键步骤,通过分析表面缺陷检测的现状问题,结合片式元件图像的自身特征,提出一种基于改进的自适应阈值canny算子的片式元件缺陷检测算法;该算法把片式元件灰度图像进行区域分割,得到不同值域范围内的灰度区域,利用带有敏感度的自适应双阈值canny算子进行边缘检测;算法实现结果表明,该算法能高效地检测并处理不同缺陷类型的片式元件,并且可以达到工业生产的检测速度。     

通用视觉检测设备的专家在线平台设计

针对视觉检测设备在复杂的生产环境下存在图像处理参数设置问题,设计了一套B/S架构的通用的专家在线平台,通过在原有的视觉检测平台上增加一个普通的USB摄像头及相应的应用软件,并通过网络将出现的新问题的图像实时传输给生产厂家或外部专家,由专家通过浏览器设置合理的图像处理参数,平台综合了数据协同、远程培训等功能,达到了远程解决问题的目的。     

数字化图书馆、博物馆导航系统研究

文章介绍了数字化图书馆、博物馆的智能化传播方式的意义,发展现状,实现的可行性方案和工作原理与基本运行流程,对认识数字化图书馆、博物馆新的信息传播形态和发展趋势有积极的意义。     

Towards a theoretical framework of strategic decision, supporting capability and information sharing under the context of Internet of Things

The effective strategy of Internet of Things (IoT) can help firms to grasp the emerging opportunities from the IoT and then improve their competitive advantage. In this article, based on organizational capability perspective, we provide a theoretical framework which classifies IoT strategies into four archetypes from two dimensions of managers’ strategic intent and industrial driving force, and propose that market-based exploratory capabilities play a more important role for firms adopting get-ahead strategy, and market-based exploitative capabilities play a more important role for firms adopting catch-up strategy in market. The technology-based exploratory capabilities play a more important role for firms adopting get-ahead strategy in technology, and technology-based exploitative capabilities play a more important role for firms adopting catch-up strategy in technology. Especially, external industry information sharing more efficiently contributes to the enhancement of both market-based and technology-based exploratory capabilities, and internal industry information sharing more efficiently contributes to the enhancement of both market-based and technology-based exploitative capabilities.     

Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.     

Optimal Performance in Tracking Stochastic Signal Under Disturbance Rejection

The analysis method of optimal tracking performance is proposed for multiple‐input multiple‐output (MIMO) linear time‐invariant (LTI) systems under disturbance rejection. An H₂ criterion of the error signal between the output of the plant and the reference signal is used as a measure for the tracking performance. Spectral factorization is applied to obtain the optimal solution of the system tracking error. The explicit expressions are derived for this minimal tracking error with respect to random reference signals under disturbance rejection. It is shown that the nonminimum phase zeros, the zero direction, the unstable poles, the pole direction of a given plant, statistical characteristics of the reference input signal, and disturbance signal have a negative effect on a feedback system's ability to reduce the system error with disturbance rejection. The results show that the optimal tracking performance will further be damaged because of disturbance rejection. Some typical examples are given to illustrate the theoretical results.     

曝光数据流优化问题的蒙特卡罗算法

集成工艺中控制图形发生器产生掩模图形的数据流文件的数据排列顺序优化,是一个其结点数可达数千个之多的大规模旅行商问题(从镜头架的位置,即每个矩形的矩心为结点),常规算法早已不适用。本文采用多阶段蒙特卡罗算法并加以改进,补充了“分层优化模式”,邻接结点的预合并和在各个阶段末提取共同子路径并删去相关结点等一系列逐步缩小问题规模的措施,形成了一个实用的优化算法。相应的软件已成功地应用于薄膜磁头的数据流文件的优化。     

基于分形扫描的高灰度平板显示的硬件实现

本文将分形算法应用于2048级高灰度平板显示的扫描映射,使用平板显示系统灰度图像的最优扫描结构及分形模型,以解决平板显示系统扫描形成灰度图像过程中的时间冗余问题。由分形扫描核心模块,得到了相应的每个扫描时间对应的子空间码和位码序列进行扫描,然后通过检测模块验证了2048级高灰度分形扫描方法的准确性,最终由硬件系统验证了2048级高灰度分形扫描的可实现性。     

以1,3-二(4-邻羟基苯亚胺次甲基)苯氧基丙烷为载体的铁离子(Ⅲ)电极的研究

以1,3-二(4-邻羟基苯亚胺次甲基)苯氧基丙烷为中性载体制备了PVC膜Fe3+离子选择性电极。该电极对Fe3+呈现出良好的选择性和近Nernst电位响应性能。电极斜率为21mV/dec,线性范围为3.0×10-5～1.0×10-1mol/L,检出限为1.0×10-5mol/L。采用交流阻抗技术研究了电极的响应机理,并将电极作为指示电极初步用于EDTA的电位滴定。     

基于2,4-二羟基苯甲酸铜(Ⅱ)为载体的硫氰酸根电极的电位响应

制备了基于一种简单的金属铜配合物2,4-二羟基苯甲酸铜(Ⅱ)(Cu(Ⅱ)DHBA)为载体的PVC膜硫氰酸根离子(SCN-)选择性电极。该电极在1.0×10-1～1.0×10-6mol/LSCN-浓度范围内呈现斜率为-59.5mV/dec的近Nernst电位响应,检测下限为9.1×10-7mol/L。利用紫外可见光谱及交流阻抗技术初步探讨了电极对SCN-呈现的选择性电位响应机理。该电极作为直接电位分析法的指示电极,成功运用于实验室废水中硫氰酸盐含量的测定。