一种改进的特征匹配算法在碎片云测量建模中的应用

为满足超高速撞击典型Whipple防护构型的损伤评估需求,利用图像处理技术对碎片云序列阴影图像进行深入研究。使用超高速序列激光阴影成像仪得到三组不同实验条件下碎片云发展过程的高清阴影图像,分别对每组最具代表性的2帧进行图像处理分析;根据碎片云图像特点以及碎片运动特性,提出了一种改进的碎片二次特征匹配算法,该方法包含碎片粗定位、特征定义及初匹配和精确匹配三步策略;通过运用改进的匹配算法,对选取的相邻两帧图片完成碎片高效匹配,并提取匹配碎片的运动参数,进而分析碎片的速度分布和飞行角度分布,获取二次碎片云相关运动特性;得到三组实验各自的轨迹模拟图。根据得到的轨迹分析结果分别对三组实验的后板损伤进行估计,并通过与防护构型的实际损伤结果进行比较,验证了该方法的有效性。     

计及RSC控制的DFIG短路电流直流分量解析表达

现有文献针对计及转子变流器(RSC)控制的双馈感应风电机组(DFIG)定子短路电流解析表达,将定子磁链当作一阶直流衰减分量或忽略功率外环控制。基于DFIG电压、磁链和RSC控制方程,得到定子电流关于定子电压和定子功率的传递函数,提出定子电流的精确解析表达式。基于RSC内、外环PI参数关系,推导直流分量衰减时间常数和角频率关于PI参数的表达式。分析了RSC内外环PI参数对定子电流直流衰减分量的影响。仿真结果验证了解析表达式的准确性,为PI参数选取和保护装置测量、整定提供依据。     

Multi-objective layout optimization for an orbital propellant depot

Structural and Multidisciplinary Optimization - The overall layout optimization design of an orbital propellant depot involves the optimization of shape, size, and positions of propellant tanks in...     

Evaluating performance of daylight-linked building controls during preliminary design

Thanks to the spread of new light sources and of smart dynamic control systems, automation sector has begun to play a fundamental role in lighting design. In this regard, daylight-linked control systems (DLCSs) represent a particularly interesting research field, since they offer great opportunities both in obtaining energy savings and in improving visual comfort conditions. However, their use is not so spread, because of the difficulties in predicting their functioning during the design process and in evaluating their effective energetic and economic advantages: available technical solutions are so many that design choices can be very hard for specialists. To overcome these obstacles, a precise assessment methodology is needed. Given these premises, the goal of the paper is to show the effectiveness of new performance parameters (Daylight Integration Adequacy, Percentage Intrinsic Light Excess, Percentage Light Waste and Percentage Light Deficit) in order to evaluate DLCs performance and to underline which factors mostly affect their functioning.     

Laplacian regularized robust principal component analysis for process monitoring

Principal component analysis (PCA) is one of the most widely used techniques for process monitoring. However, it is highly sensitive to sparse errors because of the assumption that data only contains an underlying low-rank structure. To improve classical PCA in this regard, a novel Laplacian regularized robust principal component analysis (LRPCA) framework is proposed, where the “robust” comes from the introduction of a sparse term. By taking advantage of the hypergraph Laplacian, LRPCA not only can represent the global low-dimensional structures, but also capture the intrinsic non-linear geometric information. An efficient alternating direction method of multipliers is designed with convergence guarantee. The resulting subproblems either have closed-form solutions or can be solved by fast solvers. Numerical experiments, including a simulation example and the Tennessee Eastman process, are conducted to illustrate the improved process monitoring performance of the proposed LRPCA.     

A Bernoulli Optimal Kalman Filter for a Multi-sensor System with Random Data Packet Dropouts and Delays

Wireless Personal Communications - A distributed filtering method is proposed to solve the packet dropouts and delays in a multi-sensor wireless sensor network. For an asynchronous multi-sensor...     

Logistics service scheduling with manufacturing provider selection in cloud manufacturing

In service-oriented manufacturing models, manufacturing resources in different enterprises are integrated and shared through network, cloud platforms, and logistics. On cloud manufacturing platforms, service providers offer on-demand manufacturing services to service demanders according to supply-demand matching results. As a special type of manufacturing services, logistics services provide transportation capabilities for production services and demanders. It is a critical issue to schedule logistics services efficiently, especially when manufacturer selections have been planned. This research focuses on the logistics scheduling problem in cloud manufacturing with pre-selected manufacturers. We analyze this optimization problem from aspects of tasks, production services, logistics services, and optimization objectives. Then a logistics scheduling method is proposed to reduce the average delivery time from manufacturers to customers. In the proposed method, the total time from start points of logistics to demanders is considered to reduce the average delivery time of all tasks. Based on four different scenarios, we build their scheduling models and run simulations to verify the effectiveness of the proposed method. Results show that the average task delivery time of the proposed method is shorter than three typical strategies.     

Foreground extraction via dual-side cameras on a mobile device using long short-term trajectory analysis

This paper presents a foreground extraction method for live-streaming videos using dual-side cameras on mobile devices. Compared to conventional methods, which estimate both foreground and background models from the front camera, the proposed method uses the rear camera to infer the reference background model. To this end, the short-term trajectory analysis is first performed to cluster point trajectories of the front camera, and then the long-term trajectory analysis is performed to compare the paths of the clustered trajectories with the reference path obtained from the rear camera. In particular, clusters having high correlation are classified as background using the Gaussian mixture model. Additionally, a pixel-wise segmentation map is obtained via graph-based segmentation. Experimental results show that the proposed method is robust under a variety of camera motion, outperforming state-of-the-art methods. Code and dataset can be found at https://github.com/YCL92/dualCamSeg.     

Cloud manufacturing ecosystem analysis and design

The manufacturing industry is facing the impact of a dynamic market and intensive competition. Many companies are looking for a new approach to improve their business activities in a collaborative business ecosystem with other stakeholders. Cloud computing enables the sharing of manufacturing resources and capabilities between different stakeholders to support business and physical production. The purpose of this research is to explore approaches moving towards a cloud manufacturing ecosystem and present possible implications for practice. To fulfil the research objectives, a multiple-case study was conducted within sheet metal manufacturing companies. Business and technology related requirements for cloud-based collaborative manufacturing portals were collected through interviews with industrial practitioners from the sheet metal manufacturing perspective. Based on analysis a prototype model of a CloudEcosystem was presented to demonstrate the essential features of the portals. This research found that there are three different portal types for cloud manufacturing ecosystems depending on the value chain configuration. Close to real-time information provided by cloud-based platforms can create manufacturing ecosystems where machine owners, product designers and customers may collaborate and compete simultaneously. The CloudEcosystem designed in this research can provide a practical tool and technical solution to help manufacturers think about moving towards cloud manufacturing ecosystems.