基于Delphi的计算机辅助检定系统设计与实现

介绍了计算机在计量领域方面的一个成功应用案例。该系统能根据计量检定的要求,精确地对各种仪表进行检定计算、数据保存、报表打印等。     

Human error identification in human reliability assessment. Part 2: Detailed comparison of techniques

This is the second part of a two-part review of human error identification (HEI) approaches in human reliability assessment (HRA). Part 1 reviewed the probabilistic risk assessment (PRA) context in which HRA occurs, and then detailed 12 HEI techniques which have evolved in the field of HRA. Part 2 attempts to compare the way these techniques perform against a range of criteria relevant to HEI theoretical and empirical validity, and practical usefulness in applied HRA. It is hoped that these comparisons will help assessors in the selection of techniques for practical applications. The comparisons also point to research and development needs in the area of applied HEI.     

一种新的修正恒模算法

阐述了恒模算法(CMA)、修正恒模算法(MCMA)和判决引导(DD)算法的基本原理。针对CMA和MCMA收敛速度慢,固定步长条件下收敛速度和剩余误差之间存在矛盾的缺陷,在分析CMA误差特性的基础上,利用DD算法误差函数的模值和判决器输出构造新的误差函数,提出了一种新的变步长MCMA。新算法在加快收敛速度的同时保持小的剩余误差。仿真结果表明,新算法比CMA和MCMA收敛速度快,均衡输出剩余码间干扰(ISI)小而且能克服相位偏移,具有很好的实用性。     

生产线试运行过程工艺分析软件的应用

介绍了生产线试运行过程中，用工艺分析软件解决所遇到的工艺问题，为工艺分析软件开拓了一个新的应用领域。     

结合误差变换的Bouc-Wen迟滞非线性系统反步控制器设计

针对智能材料执行器中非平滑、多映射的迟滞非线性,采用Bouc-Wen模型描述迟滞,并提出了一种基于误差变换的反步控制器设计方案.首先利用Bouc-Wen模型中的变量特性,通过预设性能函数,将误差约束在预设范围内.然后通过误差变换,将一个对输出误差存在约束的跟踪问题转化为一个无约束的镇定问题.最后利用反步控制法设计迟滞系统的控制器,该控制方法保证期望的跟踪精度,并能将误差限定在设定范围内且满足预设性能,提高了系统的暂态和稳态性能.仿真结果表明设计方法的有效性.     

六维腕力传感器非线性正解耦方法设计

多维力传感器利用其多个转换单元完成测量加载于其结构上未知负载的作用效果,解耦是其设计的重要组成部分。针对传统静态线性解耦方法的不足,试图将传统线性解耦方程扩展为多项式结构,受其多元高次方式通解形式的启发,构造了一种多项式非线性静态正解耦方程,该方法无需传统线性解耦方法中的曲线拟合、逆解,不依赖以系统是线性为前提,且方程可以扩展成任意结构的多项式。实验结果表明:该方法能降低输出耦合误差。     

Telerobot-enabled HUB-CI model for collaborative lifecycle management of design and prototyping

The HUB-CI model is investigated in a telerobotic system in a client/server network to manage the lifecycle of engineering design and prototyping. The purpose of this platform is to support collaborative engineering design and proof of concept to enhance distributed team collaboration and resource utilization. The suggested platform is exemplified in two collaboration support tools and a physical prototyping platform. Structured Co-Insight Management is developed to support innovative idea exchanges and the consensus decision-making during the design process. Conflict/error detection management helps preventing conflicts and errors during the lifecycle of design and development. Physical collaboration over the network occurs when a team controls the telerobot operation during prototyping and testing in design cycles. A pilot system is implemented with a group project for the design of an electronic circuit (including both hardware and software designs). The functional assessment method is used to compare this platform to other collaborative design tools. The system presented offers unique qualitative advantages as an integrated collaboration support system.     

Explicit solution of relative entropy weighted control

We consider the minimization over probability measures of the expected value of a random variable, regularized by relative entropy with respect to a given probability distribution. In the general setting we provide a complete characterization of the situations in which a finite optimal value exists and the situations in which a minimizing probability distribution exists. Specializing to the case where the underlying probability distribution is Wiener measure, we characterize finite relative entropy changes of measure in terms of square integrability of the corresponding change of drift. For the optimal change of measure for the relative entropy weighted optimization, an expression involving the Malliavin derivative of the cost random variable is derived. The theory is illustrated by its application to several examples, including the case where the cost variable is the maximum of a standard Brownian motion over a finite time horizon. For this example we obtain an exact optimal drift, as well as an approximation of the optimal drift through a Monte-Carlo algorithm.     

Exploring approaches to improve the performance of autonomous monitoring with imperfect data in location-aware wireless sensor networks

In recent years, information and sensing technologies have been applied to the construction industry to collect and provide rich information to facilitate decision making processes. One of the applications is using location data to support autonomous crane safety monitoring (e.g., collision avoidance and dangerous areas control). Several location-aware wireless technologies such as GPS (Global Positioning System), RFID (Radio-frequency identification), and Ultra-Wide Band sensors, have been proposed to provide location information for autonomous safety monitoring. However, previous studies indicated that imperfections (errors, uncertainty, and inconsistency) exist in the data collected from those sensors and the data imperfections have great impacts on autonomous safety monitoring system performance. This paper explores five computationally light-weight approaches to deal with the data imperfections, aiming to improve the system performance. The authors built a scaled autonomous crane safety monitoring testbed with a mounted localization system to collect location data and developed five representative test cases based on a live construction jobsite. Seven hundred and sixty location readings were collected at thirty-eight test points from the sensors. Those location data was fed into the reasoning mechanisms with five approaches to generate the safety decisions at those thirty-eight test points and evaluate system performance in terms of precision, recall and accuracy. The results indicate that system performance can be improved if at least ten position readings from sensors can be collected at small intervals at any location along the moving path. However, by including additional data such as velocity and acceleration that may be read from devices mounted on workers, localization error may be significantly reduced. These findings represent a path forward to improve localization accuracy by mixing imperfect data from the sensed environment with supplemental input.