基于扩展卡尔曼滤波的测量系统数据分析

为消除误差和随机干扰对测量数据的影响。利用扩展卡尔曼滤波算法对测量数据进行处理,并提出基于扩展卡尔曼滤波算法的不可信点判断方法对测量数据进行野值剔除。两种方法结合运用,可以在保留数据特征的基础上,提高数据的精度,更好地反映测量目标的性能。     

Safe and intuitive manual guidance of a robot manipulator using adaptive admittance control towards robot agility

Key areas of robot agility include methods that increase capability and flexibility of industrial robots and facilitate robot re-tasking. Manual guidance can achieve robot agility effectively, provided that a safe and smooth interaction is guaranteed when the user exerts an external force on the end effector. We approach this by designing an adaptive admittance law that can adjust its parameters to modify the robot compliance in critical areas of the workspace, such as near and on configuration singularities, joint limits, and workspace limits, for a smooth and safe operation. Experimental validation was done with two tests: a constraint activation test and a 3D shape tracing task. In the first one, we validate the proper response to constraints and in the second one, we compare the proposed approach with different admittance parameter tuning strategies using a drawing task where the user is asked to guide the robot to trace a 3D profile with an accuracy or speed directive and evaluate performance considering path length error and execution time as metrics, and a questionnaire for user perception. Results show that appropriate response to individual and simultaneous activation of the aforementioned constraints for a safe and intuitive manual guidance interaction is achieved and that the proposed parameter tuning strategy has better performance in terms of accuracy, execution time, and subjective evaluation of users.     

A balanced solution of a fuzzy soft set based decision making problem in medical science

The purpose of this paper is two folded. Firstly, the concept of mean potentiality approach (MPA) has been developed and an algorithm based on this new approach has been proposed to get a balanced solution of a fuzzy soft set based decision making problem. Secondly, a parameter reduction procedure based on relational algebra with the help of the balanced algorithm of mean potentiality approach has been used to reduce the choice parameter set in the parlance of fuzzy soft set theory and it is justified to the problems of diagnosis of a disease from the myriad of symptoms from medical science. Moreover the feasibility of this proposed method is demonstrated by comparing with Analytical Hierarchy Process (AHP), Naive Bayes classification method and Feng's method.     

A hybrid stock selection model using genetic algorithms and support vector regression

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.     

The interprocedural analysis and automatic parallelization of Scheme programs

Lisp and its descendants are among the most important and widely used of programming languages. At the same time, parallelism in the architecture of computer systems is becoming commonplace. There is a pressing need to extend the technology of automatic parallelization that has become available to Fortran programmers of parallel machines, to the realm of Lisp programs and symbolic computing. In this paper we present a comprehensive approach to the compilation of Scheme programs for shared-memory multiprocessors. Our strategy has two principal components:interprocedural analysis andprogram restructuring. We introduceprocedure strings andstack configurations as a framework in which to reason about interprocedural side-effects and object lifetimes, and develop a system of interprocedural analysis, using abstract interpretation, that is used in the dependence analysis and memory management of Scheme programs. We introduce the transformations ofexit-loop translation andrecursion splitting to treat the control structures of iteration and recursion that arise commonly in Scheme programs. We propose an alternative representation for s-expressions that facilitates the parallel creation and access of lists. We have implemented these ideas in a parallelizing Scheme compiler and run-time system, and we complement the theory of our work with snapshots of programs during the restructuring process, and some preliminary performance results of the execution of object codes produced by the compiler.This work was supported in part by the National Science Foundation under Grant No. NSF MIP-8410110, the U.S. Department of Energy under Grant No. DE-FG02-85ER25001, the Office of Naval Research under Grant No. ONR N00014-88-K-0686, the U.S. Air Force Office of Scientific Research under Grant No. AFOSR-F49620-86-C-0136, and by a donation from the IBM Corportation.     

平流层验证飞艇横侧向模型辨识研究

研究采用系统辨识方法建立平流层验证飞艇横侧向模型,针对飞行试验中风的扰动和测量噪声明显,对辨识参数的精度有影响。为解决上述问题,提出了采用滤波误差法,辨识稳定微分和控制微分系数的策略,获得飞艇较精确的平飞横侧向模型。首先考虑飞艇为浮力体,质量和惯量受环境影响较大,很难精确测量的问题,将飞艇非线性模型转化为具有稳定性微分和控制微分的飞艇横侧向线性模型,辨识参数不受质量、惯量变化的影响,并采用滤波误差方法,辨识模型的稳定微分和控制微分,可以减小风的扰动误差对辨识精度的影响,最后用辨识模型进行飞行仿真,将仿真得到的数据和实际飞行试验数据进行对比分析,结果表明,辨识得到的模型可以较好地反映飞艇的动力学特性,可为飞艇偏航控制器的设计提供参考。     

关于Photoshop插件的SAR图像处理探究

SAR图像处理主要包括相干斑抑制、特征提取等,如果在使用Photoshop插件SAR图像处理图片,将主要精力集中于算法的设计和优化方面,可以尽量减少在用户界面、内存管理和基本的图像操作方面的编程,既可以实现多种图像处理,又容易实现功能的充分扩大与填充,从而在最短的时间内做做多的事。     

基于FLUENT的烘干机参数优化

为了解决烘干机烘室内气流分布不均匀,导致烘干成品质量参差不齐的问题,采用FLUENT软件和正交试验相结合的方法,以风速为评价指标,对不同参数下烘干房内的气流进行模拟分析。同时用极差分析法和标准差相结合的方法对评价指标进行分析,得出最佳的参数组合,并对其进行仿真模拟分析。结果表明:烘干房烤盘间距为10 mm、出风口大小1 000 mm×230 mm、进风流速为4.5 m/s时,烘干效果最好,为优化烘房设计参数的研究提供了理论依据。     

冷轧管机轧辊加工及加工工具设计

冶钢集团钢管公司的冷轧线装备有德国米尔公司生产的SKW75VMR、SKW125VMR型超长行程冷轧管机,其轧辊孔型加工也采用德国米尔公司生产的KB-47孔型开槽机。介绍了KB-47孔型开槽机的加工范围和工作原理。结合冷轧辊孔型设计计算,阐述了加工环型轧辊的专用工具准备过程和孔型凸轮样板的设计加工方法。     

铸造工艺方案的评价模型及其应用

利用系统工程理论和模糊数学的法则提出了一个定量评价铸造工艺方案的方法。详细介绍了铸造工艺方案评价模型和铸造工艺方案评价指数的计算式，并结合铸造工艺设计的程序和长期积累的经验详细讨论了评价的方法步骤、指标体系的确定、评价标准的制定、评价框图、数据处理方法以及结果分析。