Hierarchical planning for a surface mounting machine placement

For a surface mounting machine (SMM) in printed circuit board (PCB) assembly line, there are four problems, e.g. CAD data conversion, nozzle selection, feeder assignment and placement sequence determination. A hierarchical planning for them to maximize the throughput rate of an SMM is presented here. To minimize set-up time, a CAD data conversion system was first applied that could automatically generate the data for machine placement from CAD design data files. Then an effective nozzle selection approach was implemented to minimize the time of nozzle changing. And then, to minimize picking time, an algorithm for feeder assignment was used to make picking multiple components simultaneously as much as possible. Finally, in order to shorten pick-and-place time, a heuristic algorithm was used to determine optimal component placement sequence according to the decided feeder positions. Experiments were conducted on a four head SMM. The experimental results were used to analyse the assembly line performance.

An Improved Virtual Machine Placement Algorithm Based on Traffic Bandwidth Optimization in Data Center

The Virtual Machine(VM) placement is a serious problem to limit the improvement of resource utilization of data center. The VM traffic bandwidth demand is a Non zero-sum resource that the global traffic sum is relative with each VM placement position. In this paper, we introduce a new improved traffic constant algorithm in the data center, called Degree and Weighted Maximum Traffic Ratio(DWMTR). The proposal DWMTR algorithm defines a new weighted ratio parameter in this paper. The main body of the parameter is constructed with the ratio, current overall intra-cluster traffic divided by current overall inter-cluster traffic, when a new VM places in the data center. The DWMTR algorithm has the ability to constraint the inter-cluster traffic incensement more strictly than the current VM placement algorithms based on traffic bandwidth allocation. For this algorithm based on the theoretical analysis and simulation, it confirms the proposed DWMTR possesses smaller global interactive traffic cost than the control group algorithms in the appointed VM placement in the three-layer data center model.

A Virtual Machine Placement Strategy Based on Virtual Machine Selection and Integration

Cloud data centers face the largest energy consumption.In order to save energy consumption in cloud data centers,cloud service providers adopt a virtual machine migration strategy.In this paper,we propose an efficient virtual machine placement strategy(VMP-SI)based on virtual machine selection and integration.Our proposed VMP-SI strategy divides the migration process into three phases:physical host state detection,virtual machine selection and virtual machine placement.The local regression robust(LRR)algorithm and minimum migration time(MMT)policy are individual used in the first and section phase,respectively.Then we design a virtual machine migration strategy that integrates the process of virtual machine selection and placement,which can ensure a satisfactory utilization efficiency of the hardware resources of the active physical host.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.

Torque Sharing Function Control of Switched Reluctance Machines with Reduced Current Sensors

This paper presents a Torque Sharing Function(TSF)control of Switched Reluctance Machines(SRMs)with different current sensor placements to reconstruct the phase currents.TSF requires precise phase current information to ensure accurate torque control.Two proposed methods with different chopping transistors or a new PWM implementation require four or two current sensors to replace the current sensors on each phase regardless of the phase number.For both approaches,the actual phase current can be easily extracted during the single phase conducting region.However,how to separate the incoming and outgoing phase current values during the commutation region is the difficult issue to deal with.In order to derive these two adjacent currents,the explanations and comparisons of two proposed methods are described.Their effectiveness is verified by experimental results on a four-phase 8/6 SRM.Finally,the approach with a new PWM implementation is selected,which requires only two current sensors for reducing the number of sensors.The control system can be more compact and cheaper.

Coordinated power system stabilizers design of a nine-machine system

In our earlier paper,power system stabilizers (PSSs) are designed for a nine-machine system,a new pole-placement tech-nique is developed for the design,and participation factors are used to decide how many stabilizers are required and where they shall be.Eachmachine being represented by a low-order linear model,there is some reservation of the results.In this paper,extensive transient simulationsare performed and each machine is represented by a high-order nonlinear model.Coherent groups are found.A weighted speed deviationindex (SDI) is defined to find out the most unstable machines in the system.PSSs are designed after the decisions of PSS number and sites.Transient simulations are carried out again for the closed-loop system.A system stability index (SSI) is used to evaluate the stability of theclosed-loop system.It is found that three PSSs are sufficient to ensure the stability of the nine-machine system.

基于分组遗传算法的数据中心虚拟机节能映射

近年来,随着人们对云计算业务需求持续增长,数据中心能耗日益增加,由此不仅增加了运营成本,巨大的碳排放对生态环境也产生严重的影响,数据中心节能已成为当前亟须解决的重要难题。对云数据中心的虚拟机放置(Virtual Machine Placement,VMP)进行优化能有效地提高资源利用率,同时,VMP也是减少数据中心能耗的重要技术之一;针对数据中心的能耗感知VMP问题,提出一种基于分组遗传算法的节能算法EEGGA(Energy-Efficient Grouping Genetic Algorithm),算法将节能VMP问题视为装箱问题(Bin Packing Problem,BPP),并应用基于分组编码的遗传算法对其进行求解,通过减少活动物理主机的数量(装箱数量)以实现降低数据中心能耗的目标;在算法迭代过程的交叉和变异等阶段,设计了多种启发优化策略提升子代染色体的适应度,从而提高算法的节能性能和加快迭代收敛的速度;通过仿真实验,在收敛速度和求解性能等方面将提出的算法与传统的节能遗传算法进行对比,实验结果表明:提出的算法能够有效地减少数据中心的能耗,在节能性能和求解收敛速度方面均优于其他算法。

机器视觉技术在LED贴片控制系统研发中的应用研究

随着科技的飞速发展,机器视觉技术在各行各业的应用逐渐成为关注焦点。在LED贴片控制系统研发领域,机器视觉技术的引入为提高贴片生产效率、确保贴片质量、降低人工操作成本提供了全新的解决方案。传统的LED贴片控制系统往往依赖于人工操作,但由于贴片尺寸小、精度要求高等特点,人工操作容易受到限制,且容易引发质量波动。机器视觉技术的应用通过高精度的图像识别和处理,能够实时准确地捕捉贴片的位置、方向和质量等关键信息,为后续的控制系统提供精准的输入数据。基于此,本文对机器视觉技术在LED贴片控制系统研发中的应用展开了研究。

基于数据中心的虚拟机放置优化节能策略研究

近年来,随着5G、人工智能等新兴技术的发展,算力为千行百业的数字化转型注入强心剂。社会各行各业对算力需求的增长日益明显,运营商数据中心的服务器为保证高可用性,数据中心的高能耗俨然成为制约数据中心发展的一大阻碍,“节能增效”是数据中心的刚需。由于大量资源碎片造成的资源利用率低下和能源浪费问题。为了解决这个问题,提出了一种基于线性整数规划结合装箱的多目标优化算法。旨在通过优化虚拟机放置,进行虚拟机的二次调度,提高资源利用率,腾挪出更多的空闲主机,执行物理机下电等绿色节能操作,以达到“节能增效”目的。

Optimal Data Placement and Replication Approach for SIoT with Edge

Social networks(SNs)are sources with extreme number of users around the world who are all sharing data like images,audio,and video to their friends using IoT devices.This concept is the so-called Social Internet of Things(SIot).The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources,and this task demands an efficient storage procedure.For this kind of large volume of data storage,the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low latency.The major issue is the way to store the data and replicate these large data items optimally and allocate the request from the data center efficiently.For efficient storage of these data,we use edge server,which is part of the cloud server,in this study.Thus,the data are distributed and stored with quick access,which will reduce the latency with response.The proposed data placement approach learns with machine learning(ML)algorithm called radial basis kernel function assisted with support vector machine(RBF-SVM)to classify the data center for storing the user and friend’s data from the SIoT devices.These learning algorithms will be used to predict the workload of the data stored in the data center as either edge or cloud depending on the existing time slots.The data placement with dynamic nature is also optimized using the proposed dynamic graph partitioning(GP)method to meet the individual user’s demand of low latency with minimum costs.This way will keep the SIoT data placement efficient and effective over time.Accordingly,this proposed data placement and replication approach introduces three kinds of innovations compared with the existing data placement approach.(i)Rather than storing the user data in a single cloud,this study uses the edge server closest to the SIoT devices for faster access with reduced response time.(ii)The classification algorithm called RBF-SVM is used to find storage for user for reducing data replication.(iii)Dynamic GP is introduced for data placement with reduced latency and minimum cost to fulfil the dynamic nature of the SN.The simulation result of this approach obtains reduced latency of 130 ms and minimum cost compared with those of the existing data placement approaches.Therefore,our proposed data placement with ML-based learning on edge provides promising results in terms of efficiency,effectiveness,and performance with reduced latency and minimum cost.

基于LightGBM的超大沉井下沉状态预测及传感器优化布置

沉井下沉状态预测及传感器优化布置有利于确保沉井安全平稳下沉、降低监测成本。基于机器学习中的LightGBM框架建立超大沉井下沉状态预测模型,利用沉井底部结构应力传感器监测数据,准确预测沉井下沉速度、横桥向高差和顺桥向高差,并通过传感器重要程度分析,提出可满足下沉状态预测精度的传感器优化布置方案。将提出的沉井下沉状态预测模型和传感器优化布置方法应用于常泰长江大桥主塔超大沉井下沉工程,结果表明:沉井下沉预测时,3个预测指标的R2均大于0.94,下沉状态预测精度高;对下沉状态预测较为重要的传感器主要集中在沉井外圈和横纵轴线附近区域;在满足下沉状态预测精度的条件下,传感器优化布置方案可减少传感器数量达45.5%。优化布置方案包含的传感器数量相同时,提出的优化布置方案在下沉状态预测精度方面整体优于基于特征变量相关性分析的优化布置方案。

基于容器放置优化的云数据中心节能技术

为解决容器服务过程中带来的容器重复放置和“虚拟机-物理机”操作系统类型限制问题,提出一种离散白鼬优化容器放置方法(discrete stoat optimization container placement,DSO-CP)。以最小化能耗和最大化资源利用率为优化目标,建立多目标约束的容器放置优化模型,利用离散索引减少解向量结构维数,降低适应度函数复杂度,将容器放置和虚拟机放置两个阶段定义为一个问题,参考白鼬环状收缩捕猎模式构建DSO-CP算法进行联合优化,迭代获得最优解。仿真对虚拟机资源利用率和物理机激活数量进行分析,讨论3种不同环境下的能耗情况。仿真结果表明,该算法在降低数据中心能耗及提升其计算能力方面具有一定优势。

OpenPARF:基于深度学习工具包的大规模异构FPGA开源布局布线框架

该文提出一个面向大规模可编辑逻辑门阵列(FPGA)的开源布局布线框架OpenPARF。该框架基于深度学习工具包PyTorch实现,支持GPU大规模并行计算求解。在布局算法方面,该文设计了一种新型非对称多静电场系统,对FPGA布局问题进行建模。在布线算法方面,该文支持对FPGA可编程逻辑块(CLB)内部布线资源进行准确建模,并在大规模不规则布线资源图上进行布线,提高了异构FPGA芯片布线器的性能和效率。该文在ISPD 2016和2017 FPGA竞赛数据集和工业标准级FPGA数据集上进行了实验,结果表明该框架可减少0.4%~12.7%的布线线长,并实现两倍以上布局效率提升。

基于虚拟扩展网络的虚拟机及路由路径联合部署

为响应租户的虚拟机使用请求,云数据中心通常从物理服务器中选择虚拟机放置服务器,随后计算租户与所选择的虚拟机放置服务器之间的路由路径以承载两者之间的流量。但是这种顺序的处理方式使得虚拟机放置服务器的计算过程难以考虑其对网络造成的影响,可能导致网络利用率降低甚至出现网络拥塞。为解决该问题,提出一种虚拟机及路由路径联合部署算法VENet。VENet通过添加虚拟交换机及虚拟链路将原网络拓扑扩展为虚拟网络拓扑,基于该虚拟扩展网络拓扑,将虚拟机及路由路径联合部署问题近似为商品流问题,即将虚拟机请求近似为从相应接入起点交换机到虚拟目的交换机之间的商品流,对该商品流问题进行建模,通过线性规划的方式求解该模型,即可同时获得虚拟机放置位置及相应的路由路径。实验结果表明,VENet算法能够提高数据中心可接受的虚拟机请求数上限,缩短租户与虚拟机部署位置之间的路由路径长度同时降低数据中心的网络负载率,路由路径长度和网络负载率相比WLC-GA算法分别降低42%和30%。

Virtual machine placement optimizing to improve network performance in cloud data centers

With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine （VM） is becoming a major issue for cloud providers. The existing virtual machine placement （VMP） solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization （MLU）. On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.

VirtCO:Joint Coflow Scheduling and Virtual Machine Placement in Cloud Data Centers

Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines(VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations.However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.

先进复合材料自动铺丝设备研究现状与发展趋势

先进的复合材料由于其高比强度、比模量、大幅减重和良好的耐腐蚀性,已被广泛用于航空航天工业及其他领域。自动铺丝机是实现自动铺丝的必要设备,是影响铺放质量和生产效率的重要环节。因此,研发和改进自动铺丝机,成为制造复合材料复杂构件的最关键的课题之一。本文针对近年来国内外的自动铺丝设备,重点围绕铺丝机机床主体结构、纱架形式、铺丝头压力系统、加热系统与张力系统等方面,对当前自动铺丝机的研究与应用现状进行论述与分析,总结了国内外自动铺丝设备的研究成果,分析了国内外铺丝设备的优势与不足,最后探讨并总结了自动铺丝设备未来的发展趋势,为铺丝机的设计与优化提供了一个参考方向。

A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems

Virtualization is the most important technology in the unified resource layer of cloud computing systems.Static placement and dynamic management are two types of Virtual Machine(VM)management methods.VM dynamic management is based on the structure of the initial VM placement,and this initial structure will affect the efficiency of VM dynamic management.When a VM fails,cloud applications deployed on the faulty VM will crash if fault tolerance is not considered.In this study,a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors,including the service-level agreement violation rate,resource remaining rate,power consumption rate,failure rate,and fault tolerance cost.Then,a heuristic ant colony algorithm is proposed to solve the model.The service-providing VMs are placed by the ant colony algorithms,and the redundant VMs are placed by the conventional heuristic algorithms.The experimental results obtained from the simulation,real cluster,and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.

Base placement optimization of a mobile hybrid machining robot by stiffness analysis considering reachability and nonsingularity constraints

The mobile hybrid machining robot has a very bright application prospect in the field of high-efficiency and high-precision machining of large aerospace structures.However,an inappropriate base placement may make the robot encounter a singular configuration,or even fail to complete the entire machining task due to unreachability.In addition to considering the two constraints of reachability and non-singularity,this paper also optimizes the robot base placement with stiffness as the goal to improve the machining quality.First of all,starting from the structure of the robot,the reachability and nonsingularity constraints are transformed into a simple geometric constraint imposed on the base placement:feasible base placement area.Then,genetic algorithm is used to search for the base placement with near optimal stiffness(near optimal base placement for short)in the feasible base placement area.Finally,multiple controlled experiments were carried out by taking the milling of a protuberance on the spacecraft cabin as an example.It is found that the calculated optimal base placement meets all the constraints and that the machining quality was indeed improved.In addition,compared with simple genetic algorithm,it is proved that the feasible base placement area method can shorten the running time of the whole program.

基于Kriging修正模型的贴片机横梁优化设计

贴片机横梁是承载贴片机完成整个贴片运动的关键部位,若横梁运动过程变形过大会损坏元件,因此通过提高贴片机横梁的静刚度和模态频率,进而提高贴片机的贴装精度对横梁进行结构优化。采用Kriging修正模型的方法对贴片机横梁进行参数优化,通过插入全局检验点来提高响应面的精度,利用多目标遗传算法得到代理模型的Pareto最优解,将优化前后的横梁进行了对比分析,横梁的最大变形降低了17.7%,一阶固有频率提高了3.7%,对横梁优化有一定效果。

国内外复合材料工艺设备发展述评之四——自动铺放成型

本文分析了自动铺放成型工艺所用设备的发展历程,指出其优缺点,并对今后发展提出建议。