Fast, large-scale model predictive control by partial enumeration

Partial enumeration (PE) is presented as a method for treating large, linear model predictive control applications that are out of reach with available MPC methods. PE uses both a table storage method and online optimization to achieve this goal. Versions of PE are shown to be closed-loop stable. PE is applied to an industrial example with more than 250 states, 32 inputs, and a 25-sample control horizon. The performance is less than 0.01% suboptimal, with average speedup factors in the range of 80-220, and worst-case speedups in the range of 4.9-39.2, compared to an existing MPC method. Small tables with only 25-200 entries were used to obtain this performance, while full enumeration is intractable for this example.     

基于ARMAX模型车辆行驶动态过程的空燃比自校正控制的构建与设计

本文基于自回归滑动平均（ARMAX）控制模型的简要介绍，利用混合信号ISPFLASH微控制器技术，详细介绍了车辆行驶动态过程的空燃比自校正控制（STC）的硬件结构和软件配置及设计方法。     

Using information-systems constructs to study online- and telephone-banking technologies

The research literature has seen a number of studies aimed at understanding customer attitudes towards banking technologies, and thereby providing implications for developmental planning and marketing. However, even though banking technologies have a significant amount of information systems (IS) component to them, there is a paucity of research that considers them from the IS perspective. In this paper, we fill this gap by considering three constructs, namely usefulness, ease of use and usage, that are all rooted in the IS literature. We study user’s perceptions of these constructs, as well as the relationships among them, for both online and telephone banking.     

ANALYSIS OF EXPERIMENTAL AND NUMERICAL RESULTS OF A TURBULENT SWIRLING FLOW IN A TUBE

In many applications swirling flow is used to enhance heat and mass transfer. One of the problems of modelling a turbulent swirling flow is the choice of the turbulent closure model which is acceptable for engineering purposes. To evaluate which model performs best, numerical results are compared with experimental data. Local velocity measurements are carried out on a swirling flow in a circular tube. The measurement method is hot-wire anemometry combined with visualization techniques. The numerical analysis is carried out using the k-ε model and the Algebraic Stress Model. In the experiment a symmetrical swirl was observed, Comparing the experimental data with the numerical results shows that the Algebraic Stress Model represents the experimental data quite well, whereas the k-ε model fails.     

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。     

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.     

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。