基于改进人工鱼群-蛙跳算法优化LSSVM参数短期负荷预测

短期电力负荷预测在电力系统安全调度、经济运行方面起到关键作用。在用最小二乘支持向量机进行负荷预测时,参数选择将直接影响预测精度。为了提高LSSVM负荷预测精度,文中提出一种基于Levy变异自适应视野人工鱼群-蛙跳算法对LSSVM进行参数优化的方法。以某县负荷、天气等历史数据对LSSVM进行训练,建立LAVAFSA-SFLA-LSSVM、AFSA-LSSVM、LAFSA-SFLA-LSSVM共3种预测模型,对该地区某日24 h的电力负荷进行预测。算例结果表明,LAVAFSA-SFLA-LSSVM预测精度比AFSA-LSSVM和LAFSA-SFLA-LSSVM更高,预测误差更小。     

本地WDM网络节点上波分复用设备的配置

本文对本地WDM网络节点上波分复用设备的配置进行了分析 ,认为在城域WDM网中一般只配前置放大器 ,而大本地网需在收端配前置放大器 ,发端配功率放大器 ,对于节点距离超过100km的情况下 ,需加线路放大器     

Z箍缩初级实验平台安全联锁系统

正在调试的Z箍缩实验装置（Z－pinch Primary Test Stand）由24个基于Marx发生器和水线的性能、结构相同的模块组成,运行控制节点众多,控制规模大,试验过程复杂,因此要求建立z箍缩初级实验平台安全联锁系统,具备较高的实验安全性能,以保障人身和设备安全。安全联锁系统包括现场单元、安全联锁下位机系统及远程腔制系统。其中现场单元包括各种开关、指示灯、报警装置等,安全联锁下位机选用可编程逻辑控制器实现现场单元的统一监控,远程控制系统实现网络总控及统一管理。系统通信信号全部采用光纤传输,有效隔离电磁干扰。C＋＋Builder为系统上位机及网络总控的软件开发平台,CORBA中间件技术实现总控与安全联锁的通信,中心数据库完成与安全联锁系统的数据交换。     

A new fuzzy‐based method for load balancing in the cloud‐based Internet of things using a grey wolf optimization algorithm

Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud‐based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud‐based IoT. Since the load balancing issue is one of the NP‐hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well‐known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB‐LB and EBCA‐LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.     

Queue length‐based load balancing in data center networks

Traffic load balancing in data centers is an important requirement. Traffic dynamics and possibilities of changes in the topology (e.g., failures and asymmetries) make load balancing a challenging task. Existing end‐host–based schemes either employ the predominantly used ECN or combine it with RTT to get congestion information of paths. Both congestion signals, ECN and RTT, have limitations; ECN only tells whether the queue length is above or below a threshold value but does not inform about the extent of congestion; similarly, RTT in data center networks is on the scale of up to few hundreds of microseconds, and current data center operating systems lack fine‐grained microsecond‐level timers. Therefore, there is a need of a new congestion signal which should give accurate information of congestion along the path. Furthermore, in end‐host–based schemes, detecting asymmetries in the topology is challenging due to the inability to accurately measure RTT on the scale of microseconds. This paper presents QLLB, an end‐host–based, queue length–based load balancing scheme. QLLB employs a new queue length–based congestion signal that gives an exact measure of congestion along the paths. Furthermore, QLLB uses relative‐RTT to detect asymmetries in the topology. QLLB is implemented in ns‐3 and compared with ECMP, CONGA, and Hermes. The results show that QLLB significantly improves performance of short flows over the other schemes and performs within acceptable level, of CONGA and Hermes, for long flows. In addition, QLLB effectively detects asymmetric paths and performs better than Hermes under high loads.     

通信系统中数字上变频技术的研究与设计

为了将通信系统中数字基带信号调制到中频信号上,采用数字上变频技术,通过对数字I、Q两路基带信号进行FIR成形滤波、半带插值滤波、数字混频处理得到正交调制后的中频信号,最后经MATLAB仿真分析得到相应的时域和频域图,来验证电路设计的有效性。     

基于PCI局部总线的1553B总线接口卡设计

根据1553B数据总线协议及其接口技术要求,设计了一种基于PCI局部总线的1553B总线接口卡。系统使用PLX公司的PCI9052和DDC公司的1553B协议芯片BU-61580,通过FPGA芯片EP1C12B进行PCI协议和1553B协议的转换,使用DSP控制器TMS320F2812作为下位机的主控单元,并编制了接口卡驱动程序,实现了1553B总线和PCI总线的转换。     

一种改进的Linux集群系统负载均衡算法

提出一种基于改进的Linux集群系统负载均衡算法.首先通过修改负载权值计算指标,然后通过动态反馈机制收集各个各服务器的实时负载,负载均衡器根据收集的负载信息动态调整服务器权值,最后根据权值选择最优服务器去处理新的任务.仿真测试结果表明,改进负载均衡算法提高了整个系统的响应能力和吞吐量,较大幅度地提高系统的综合性能.     

基于云模型的负载均衡问题研究

云环境是一个复杂多变的环境,其不同层次提供不同的服务,在处理服务请求时其服务类型与服务器节点供给都是一个动态,随机的变化过程,如何高效的组织以及利用这些节点的处理能力,是该环境中负载技术应该考虑的关键问题.本文对云计算中私有云模型做了概括总结;并对负载均衡调度问题在不同平台中的研究现状做了相关描述,最后、指出了负载均衡调度问题在私有云中未来的研究方向.     

云环境下基于虚拟机动态迁移的调度策略研究

针对目前云环境资源调度采用静态负载均衡策略易于导致资源浪费的问题,提出了一种双限定值的虚拟机动态迁移的调度策略.该策略将当前负载状况与负载过重或过轻时两个限定值比较,选择介于二者之间能耗较低的虚拟机迁移至目标节点.仿真实验表明,该策略能够减少迁移次数,降低虚拟机迁移能耗,从而尽可能达到负载均衡和满足服务等级协议的需求.