Automatically modeling, simulating and explaining physical systems

This paper presents an interactive framework for constructing models from high-level specifications of real physical systems, deriving low-level simulations from the constructed models and then generating explanations of the simulated behavior to help engineers interpret and control the real physical systems. To demonstrate our method, we show an example of modeling river networks, which are eco-environmental engineering systems, to provide a computer-based solution to problems of simulating and controlling concentrations of water quality constituents in river networks[1].     

Uniform modeling of parameter dependent nonlinear systems

This paper addresses the problem of approximating parameter dependent nonlinear systems in a unified framework. This modeling has been presented for the first time in the form of parameter dependent piecewise affine systems. In this model, the matrices and vectors defining piecewise affine systems are affine functions of parameters. Modeling of the system is done based on distinct spaces of state and parameter, and the operating regions are partitioned into the sections that we call ’multiplied simplices’. It is proven that this method of partitioning leads to less complexity of the approximated model compared with the few existing methods for modeling of parameter dependent nonlinear systems. It is also proven that the approximation is continuous for continuous functions and can be arbitrarily close to the original one. Next, the approximation error is calculated for a special class of parameter dependent nonlinear systems. For this class of systems, by solving an optimization problem, the operating regions can be partitioned into the minimum number of hyper-rectangles such that the modeling error does not exceed a specified value. This modeling method can be the first step towards analyzing the parameter dependent nonlinear systems with a uniform method.     

计算机专业CAD课程的深化实践

在现行的社会技术人才的高度需求下,CAD课程的教学任务更显得意义重大。探讨如何提高教学效率．培养学生的技术应用能力、职业实践能力和创新能力,缩短计算机专业学生cAI）技术应用与单位需求的差距而进行相应的有效实用教学。     

Performance of lot-level low impact development technologies under historical and climate change scenarios

Low impact development (LID) systems have potential to make urban cities more sustainable and resilient, particularly under challenging climate conditions. To quantify performance capabilities, modeling results for an array of combinations of LIDs are described using PCSWMM at lot-level to examine performance of individual LIDs on volume and peak flow reductions. Among the four LIDs studied: rain barrel (RB), vegetative swale (VS), bioretention cell (BC), and permeable pavement (PP), PP at lot-level demonstrated the best capability for reducing surface runoff volumes and peak runoff rates under historical weather conditions, while BC showed similar capability for reduction of runoff volumes but minimal peak flow reduction. With PP as the controlling method at lot-level, the maximum percentage reduction of runoff volume for a 2-year storm is 58% whereas for a 100-year storm, the runoff volume reduction is 20%. These results mean the extent of flooding that may arise from the 100-year storm is reduced, but not eliminated. Effectively, the 100-year storm volumes with LID are devolved to have flooding equivalent to a 25-year storm. Under climate change scenarios, performance for all LIDs declined at various levels, where BC was the most resilient LID for a climate change scenario, such that projected 2-year or 5-year storms with climate change will have its impact devolved with LID in place, to result in similar volumes and peaks without LID under historical conditions. Furthermore, even with an assembly of lot-level LIDs distributed throughout the community, there is not attenuation to substantial degrees of flooding for major events, but there can be effective control for water quantity for small (2- to 5-years in particular) storm events.     

Digital Twin Enhanced Dynamic Job-Shop Scheduling

For dynamic scheduling, which is daily decision-making in a job-shop, machine availability prediction, disturbance detection and performance evaluation are always common bottlenecks. Previous research efforts on addressing the bottlenecks primarily emphasize on the analysis of data from the physical job-shop, but with little connection and convergence with its virtual models and simulated data. By introducing digital twin (DT), further convergence between physical and virtual spaces of the job-shop can be achieved, which greatly enables dynamic scheduling. DT fuses both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed. It also enables comprehensive performance evaluation for rescheduling using multiple-dimension models, which can describe geometric properties, physics parameters and behaviors of the machines. In the paper, a five-dimension DT for a machine in the job-shop is introduced first, then the DT-based machine availability prediction, disturbance detection and performance evaluation methods are explored. Based on this, a DT-enhanced dynamic scheduling methodology is proposed. A scheduling process of making hydraulic valves in a machining job-shop is taken as a case study to illustrate the effectiveness and advantages of the proposed method.     

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.     

浅谈金属着色在现代首饰设计中的应用

金属着色工艺在现代工业技术的条件下已经发展到了很高的水平,有了技术的支撑,金属的固有色可以轻易得到改变,这就为现代首饰设计中各种金属的运用提供了自由空间,从此,色彩在现代首饰设计中真正扮演了主角,真正成为了现代首饰艺术中的关键造型因素,极大地丰富了现代首饰艺术的造型语言和表现力。     

四旋翼无人机的系统模型与辨识

研究无人机系统优化控制问题,为实现四旋翼微型飞行器的自动控制飞行,需要得到正确的系统模型参数,实现精确控制。子空间辨识法可以直接地反应出多输出多输出系统模型,算法比较容易实现。首先从四旋翼的力学模型出发,建立了在小角度假设下的线性系统模型,并首次将子空间辨识方法运用到四旋翼飞行器的辨识中。用仿真实例实验,假设飞行器在缓慢上升过程,采用由子空间辨识方法获得的系统模型输出与原系统在同样输入条件下的输出进行了比较。结果表明在假设条件下的辨识具有可行性,为中旋翼无人机优化控制提供了依据。     

神经网络在工业污水处理建模中的应用

研究工业污水指标在线准确检测问题,工业污水处理是一种复杂的动态生物反应工程系统,具有多变量、非线性等特点,难以建立精确的数学模型,且在线实时检测仪表价格昂贵。从应用工程角度出发,提出将遗传神经网络方法应用于污水指标实时检测,建立污水指标软测量模型。利用一些可测指标与水质指标间的关联关系,通过神经网络进行拟合,并采用遗传算法优化神经网络参数。仿真结果表明,建立的神经网络污水估计模型,可以对污水指标进行实时检测和估计,并为污水在线实时检测提供了新的思路。     

云计算联盟体系结构建模研究

云计算联盟的体系结构对云计算联盟的设计、实现和资源管理效率产生重要的影响。由于云计算联盟的构成和运行过程具有典型的复杂系统特性,基于涌现理论和复杂网络理论对云计算联盟的体系结构进行建模,提出了一种基于域和复杂网络理论的云计算联盟体系结构模型,并对该模型中资源的发布和发现机制进行了详细的研究。