一种面向分布式发电微电网的边缘计算架构与应用

随着边缘计算技术的快速发展,其在智能电网中的应用越来越广泛,但尚未应用于分布式发电微电网系统的运行控制中。本文首先提出了一种面向微电网的边缘计算架构,并介绍了边云协同的主要功能,其次阐述了该架构的构建方案,包括数据处理、网络通信及安全机制的实现,最后介绍了该架构在华南南海某海岛上的应用实践。     

[工程应用]智能制造场景的5G应用展望

为满足智能制造领域物联网当前发展的需求，研究了相应制造场景的5G应用技术。介绍了5G的关键技术，包括5G支持的应用场景、网络切片、网络功能虚拟化（NFV）/软件定义网络（SDN）、多接入边缘计算、设备对设备通信技术等；给出了基于扩展的信息物理系统5层框架，基于该框架分析了多个智能制造场景的5G应用（包括人机界面和生产信息技术、流程自动化、工厂自动化、物流和仓储、设备监控和维护等）；给出了以上应用的5G边缘计算开发框架，以集成上述场景；探讨了5G应用于智能制造场景所面临的多个挑战，以助于5G使能智能制造的落地应用。     

基于稳定连接的配电网边缘划分方法及应用研究

随着能源互联网的建设,为了应对配电主站所面临的数据接入和计算压力,文章提出了二级配电网边缘架构,以分布式边缘智能架构缓解主站压力。主要从边缘定义、划分方法、边缘功能描述与交互描述4个方面对所提边缘架构进行介绍。提出电网稳定连接区域概念及其生成方法,并结合南京地区电网实例,说明了以电网稳定连接区域作为边缘划分的合理性。最后,对所提边缘架构在配电网中的实际应用场景进行了设计和展望。     

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.     

High Accuracy Network Cardinalities Estimation by Step Sampling Revision on GPU

Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors, such as the random parameters inherent in linear estimator and the random functions used to map a host to different linear estimators. These random factors cannot be reduced by merging multiple estimators, and existing algorithms cannot remove the deviation caused by such factors. In this paper, we regard the estimation deviation as a random variable and proposed a sampling method, recorded as the linear estimator array step sampling algorithm (L2S), to reduce the influence of the random deviation. L2S improves the accuracy of the estimated cardinalities by evaluating and remove the expected value of random deviation. The cardinality estimation algorithm based on the estimator array is a computationally intensive algorithm, which takes a lot of time when processing high-speed network data in a serial environment. To solve this problem, a method is proposed to port the cardinality estimating algorithm based on the estimator array to the Graphics Processing Unit (GPU). Experiments on real-world highspeed network traffic show that L2S can reduce the absolute bias by more than 22% on average, and the extra time is less than 61 milliseconds on average.     

基于RSI的工业机器人开放式控制系统设计

为提高工业机器人的柔性化及智能化水平,同时满足数据的实时共享、监控,使控制系统具有可扩展性与可移植性,设计了一种基于x86平台和RSI的工业机器人开放式控制系统。引入RSI后可载入应用程序包,实现PC工控机与KUKA机器人系统的实时性数据交换。运用模块化设计思路,针对硬件系统,制定软件系统平台的各功能模块,配合Windows操作系统的数据处理能力,在保证工业机器人实时性的前提下,实现了功能的扩展性和增减性。该系统在KUKA工业机器人力反馈实验平台上进行了可行性验证,试验表明,此开放式控制系统的实时响应性良好,满足预期控制要求。     

Improving scenario decomposition algorithms for robust nonlinear model predictive control

This paper deals with the efficient computation of solutions of robust nonlinear model predictive control problems that are formulated using multi-stage stochastic programming via the generation of a scenario tree. Such a formulation makes it possible to consider explicitly the concept of recourse, which is inherent to any receding horizon approach, but it results in large-scale optimization problems. One possibility to solve these problems in an efficient manner is to decompose the large-scale optimization problem into several subproblems that are iteratively modified and repeatedly solved until a solution to the original problem is achieved. In this paper we review the most common methods used for such decomposition and apply them to solve robust nonlinear model predictive control problems in a distributed fashion. We also propose a novel method to reduce the number of iterations of the coordination algorithm needed for the decomposition methods to converge. The performance of the different approaches is evaluated in extensive simulation studies of two nonlinear case studies.     

美军全舰计算环境基础设施标准化设计方法探析磁

论文从硬件设备、软件服务、数据资源和任务调度等几个方面对美国海军在开发构建全舰计算环境基础设施过程中标准化的设计方法进行深入分析。通过采用标准化的计算、显示、存储等硬件资源并进行整合，提升硬件资源的利用效率和共用水平；开发各个层次的公共应用功能软件并形成标准化的服务，将软件和硬件解耦，达到应用与数据分离，使得基于TSCEI的信息化装备的构建更加快速灵活，开放性更好，可扩展性及升级能力大为增强。标准化在 TSCEI的推行和实施中起着至关重要的作用。标准化设计方法对构建我军新型信息基础设施具有较重要的借鉴作用。     

Simplifying the powder metallurgy manufacturing process using soft computing tools

The tools of soft computing will aid the knowledge mining in predicting and classifying the properties of various parameters while designing the composite preforms in the manufacturing of Powder Metallurgy (P/M) Lab. In this paper, an integrated PRNET (PCA-Radial basis functional neural NET) model is proposed in different versions to select the relevant parameters for preparing composite preforms and to predict the deformation and strain hardening properties of Al–Fe composites. It reveals that the predictability of this model has been increased by 67.89% relatively from the conventional models. A new PR-filter is proposed by slightly modifying the conventional filters of RBFNN, which improves the power of PRNET even though raw data are highly non-linear, interrelated and noisy. Moreover, fixing the range of input parameters for classifying the properties of composite preforms can be automated by the Fuzzy logic. These types of models will avoid expensive experimentation and risky environment while preparing sintered composite preforms. Thus the manufacturing process of composites in P/M Lab will be simplified with minimum energy by the support of these soft-computing tools.