直流伺服系统模型及其辨识

伺服系统又称为随动系统,是构成自动化体系的一种基本环节,是由若干元件和部件组成的具有功率放大作用的一种自动控制系统。系统辨识是指当用户无法从物理上得出所研究系统的数学模型,但可以通过适当的实验手段测试出系统的某种响应信息时,就可以根据它来获得系统的数学模型。本文完成了对直流伺服系统的建模以及辨识,尤其是利用系统的阶跃响应数据对直流伺服系统的模型进行了直接辨识,即由系统阶跃响应采样数据构造线性方程组,通过对方程组求解估计出系统参数。最后,对辨识出的系统进行了仿真,将仿真结果与原阶跃响应数据比较,表明所采用的辨识方法取得了良好的效果。     

基于动态时间规整的手势加速度信号识别

为了提高基于加速度传感器的动态手势识别算法的性能,本文采用了动态时间规整(DTW)识别算法。通过该算法计算测试模板和参考模板的相似度,从而得出识别结果。为了验证该方法,建立了一套手势加速度无线采集系统,并采集了41个志愿者的手势信息。实验结果表明,该方法手势平均识别率在97%以上。与HMM识别算法相比,DTW识别算法在识别的准确率上比HMM识别算法更具优势。     

中小型软件质量度量应用与研究

目前国内对软件质量评估还没有明确的准则和衡量标准,难以实现定量评价,运用软件度量的最新研究成果,针对中小型软件进行质量指标分析,确定度量指标体系,建立定量度量模型,最终开发软件系统,进行案例分析。     

基于LMDI和STIRPAT模型的区域用水影响因素定量分析研究

区域用水影响因素分析对落实最严格水资源管理制度、实现节水型社会建设具有重要意义。为定量表征区域用水变化和影响因素之间的关系,通过LMDI因素分解识别人口规模、经济发展水平、节水技术水平对用水变化的影响程度,采用STIRPAT模型解析用水总量对人口规模、经济发展水平、节水技术水平变化的变动响应关系,以榆林市2000—2017年用水及影响因素变化为例开展了实证研究。结果显示:人口规模和经济发展水平对用水增长起到正向促进作用,节水技术水平起到负向抑制作用。人口规模、经济发展水平、节水技术水平效应依次为8 163.124万m~3、144 238.924万m~3、-126 161.049万m~3;人口规模、经济发展水平、节水技术水平每变化1%,用水将分别变化1.208%、0.061%和0.037%;经济发展水平和节水技术水平是用水变化的主要影响因素,但用水对人口规模变化敏感程度最高。最后,结合榆林市水资源特点提出针对性措施和建议,以期为榆林市水资源规划与管理提供参考。     

Digital twin modeling method based on biomimicry for machining aerospace components

High-performance aerospace component manufacturing requires stringent in-process geometrical and performance-based quality control. Real-time observation, understanding and control of machining processes are integral to optimizing the machining strategies of aerospace component manufacturing. Digital Twin can be used to model, monitor and control the machining process by fusing multi-dimensional in-context machining process data, such as changes in geometry, material properties and machining parameters. However, there is a lack of systematic and efficient Digital Twin modeling method that can adaptively develop high-fidelity multi-scale and multi-dimensional Digital Twins of machining processes. Aiming at addressing this challenge, we proposed a Digital Twin modeling method based on biomimicry principles that can adaptively construct a multi-physics digital twin of the machining process. With this approach, we developed multiple Digital Twin sub-models, e.g., geometry model, behavior model and process model. These Digital Twin sub-models can interact with each other and compose an integrated true representation of the physical machining process. To demonstrate the effectiveness of the proposed biomimicry-based Digital Twin modeling method, we tested the method in monitoring and controlling the machining process of an air rudder.     

Building a right digital twin with model engineering

In recent years, the concept of digital twin (DT) is attracting more and more attention from researchers and engineers. But there is still no consensus on what a right DT is. On one hand, some common models are renamed as DTs. On the other hand, some DTs extremely pursue ‘the same’ as physical objects, which bring unnecessary complexities to them. In this paper, we try to answer two questions from the point of view of model engineering: how to define a right digital twin, and how to build a right digital twin. The concept and related technologies of model engineering are introduced. Some basic principles and a set of metrics for a right DT are given. An evolutionary concurrent modeling method for DT (ECoM4DT) is proposed not only inheriting the theory from classic M&S methods but also highlighting the characteristics of DT compared with traditional models to systemically guide the DT modeling process.     

Input‐to‐state stability of min‐max MPC scheme for nonlinear time‐varying delay systems

This paper studies the robustness problem of the min–max model predictive control (MPC) scheme for constrained nonlinear time‐varying delay systems subject to bounded disturbances. The notion of the input‐to‐state stability (ISS) of nonlinear time‐delay systems is introduced. Then by using the Lyapunov–Krasovskii method, a delay‐dependent sufficient condition is derived to guarantee input‐to‐state practical stability (ISpS) of the closed‐loop system by way of nonlinear matrix inequalities (NLMI). In order to lessen the online computational demand, the non‐convex min‐max optimization problem is then converted to a minimization problem with linear matrix inequality (LMI) constraints and a suboptimal MPC algorithm is provided. Finally, an example of a truck‐trailer is used to illustrate the effectiveness of the proposed results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

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.     

多媒体课件改变了教师的“主讲”地位

随着高等教育教学改革步伐和数字化教学的不断推进和发展,越来越多的教师热衷于多媒体教学课件的使用和研究,传统的教学模式被逐渐取代,教师教学的主导地位越来越不明显,数字教学的时代悄然而至。     

领域驱动模型的WEB软件系统设计研究

首先分析了J2EE3层架构的特性及局限,说明了领域驱动设计模型中的充血模型的优势。通过运用最新JDKI．7及Tomcat的相应改造,设计出能实现以充血模型结构为基础的新型WEB系统开发框架,并通过一简单实例说明架构如何支持系统开发。