基于CAN总线网络控制系统的混合调度算法研究

文章以CAN总线为研究对象,在网络控制系统中,经过对原有动、静态调度算法及采用分级调度机制的混合调度算法(MTS)的分析和研究,提出了一种改进的混合调度算法IMTS;在IMTS算法中,采用动态调度算法LLF或EDF来调度硬实时性消息;采用静态调度算法DM,并分别结合RM算法或先来先服务策略来调度软实时性消息和非周期非实时性消息;最后利用基于Matlab/Simulink仿真环境下的TrueTime-1.5工具箱分别对一般的MTS算法和改进的IMTS算法进行仿真比较,分析了IMTS算法对网络控制系统性能的影响,该算法取得了较理想的调度效果。     

基于遗传算法的云计算资源调度策略研究

对云计算环境中的资源调度问题进行了研究,鉴于当前云计算环境中资源利用率不高,节点负载不均衡的问题,提出了一种新的基于遗传算法的关于虚拟机负载均衡的调度策略。根据历史数据和系统的当前状态以及通过遗传算法,该策略能够达到最佳负载均衡和减少或避免动态迁移,同时还引入了平均负载来衡量该算法的全局负载均衡效果。最后通过在CloudSim平台进行仿真实验,结果表明,该策略具有相当好的全局收敛性和效率,当系统虚拟机被调度之后,算法在很大程度上能够解决负载不均衡和高迁移成本问题,并且极大地提高了资源利用率。     

基于多维评价模型及改进蚁群优化算法的云计算资源调度策略

针对云计算环境下资源调度模型未充分考虑资源评价的问题,为更好适应不同节点计算性能和大规模数据环境的处理需求,提出了一种基于多维评价模型的虚拟机资源调度策略。首先,在云计算环境下建立包括网络性能在内的多维资源评价模型,在此基础上提出一种改进的蚁群优化算法实现资源调度策略;然后在云计算仿真平台CloudSim上进行实现。实验结果表明,该算法可以更好适应不同网络性能的计算环境,显著提高了资源调度的性能,同时降低了虚拟机负载均衡离差,满足了云计算环境下的虚拟机资源负载均衡需求。     

基于免疫蚁群优化的无线传感器网络节点调度研究

提出一种基于免疫优化蚁群算法(QIACO)的无线传感器网络节点调度策略方法;针对传统的蚁群算法在寻优过程中存在的收敛速度慢、容易出现停滞现象等缺陷,借鉴免疫系统的自我调节机制,提出了一种新的疫苗选取策略及疫苗接种方法,设计了基于免疫优化的蚁群算法,达到提高算法的收敛速度和避免停滞现象的发生;通过对MESA算法、蚁群算法、量子遗传算法和QIACO算法在负载均衡性分析、能耗均衡性分析和网络寿命分析进行仿真验证,提出的算法在无线传感器网络节点调度策略中效果最好,能有效地提高网络寿命。     

基于DAG任务依赖和Q-Learning蚁群算法的在线网格集群资源调度

网格集群资源调度是一个NP难题,而现有的调度方法通常具有任务调度效率低和负载不均衡的问题,由此设计了一种基于Q-Learning算法和蚁群算法融合的协同依赖型任务调度方法。首先对基于DAG的网格集群协同调度数学模型进行了定义,然后,采用改进的一步TD算法即Q-Learning算法实现集群资源的初始分配,从而得到最优调度方案以及对应的Q值,在此基础上提出一种改进的蚁群算法实现网格集群资源到任务分配的进一步优化,将Q-Learning算法得到的分配方案的Q值用于初始化蚁群路径中的信息素,以避免蚁群的盲目搜索,同时将Q值引入路径概率函数中使得蚂蚁具有启发式的搜索能力,从而获得协同依赖多任务集群调度的最终方案。在Gridsim环境下进行仿真试验,结果表明文中方法能有效地实现网格集群调度,且较其它方法具有任务调度效率高、CPU利用率高和负载均衡的优点,具有较大的优越性。     

一种用于云计算资源调度的双向蚁群优化算法

对云计算环境中的资源调度问题进行了研究,针对蚁群优化算法(ACO)在处理大规模组合优化问题时易陷入搜索速度慢和局部最优解的缺陷,提出了一种实现云计算负载均衡的双向蚁群优化算法(BACO)用于资源调度;该算法考虑到了每个虚拟机的负载和计算能力,同时在云环境中引入了蚂蚁的向前移动和向后移动;最后通过在CloudSim平台进行仿真实验,结果表明该算法的总任务完成时间较短,具有较好的寻优能力,并且能够实现负载均衡,是一种有效的资源调度算法。     

基于强化蚁群算法的任务DAG在线网格集群资源调度

网格集群资源调度是一个NP难题,而现有的调度方法通常具有任务调度效率低和负载不均衡的问题,由此设计了一种基于强化学习算法和蚁群算法融合的协同依赖型任务调度方法;首先对基于DAG的网格集群协同调度数学模型进行了定义,然后,采用改进的一步TD算法即Q-Learning算法实现集群资源的初始分配,从而得到最优调度方案以及对应的Q值,在此基础上提出一种改进的蚁群算法实现网格集群资源到任务分配的进一步优化,将Q-Learning算法得到的分配方案的Q值用于初始化蚁群路径中的信息素,以避免蚁群的盲目搜索,同时将Q值引入路径概率函数中使得蚂蚁具有启发式的搜索能力,从而获得协同依赖多任务集群调度的最终方案;在Gridsim环境下进行仿真试验,结果表明文中方法能有效地实现网格集群调度,且较其它方法具有任务调度效率高、CPU利用率高和负载均衡的优点,具有较大的优越性。     

基于Wi-Fi与Web的云计算资源调度算法研究

为了缩短云计算执行时间,改善云计算性能,在一定程度上加强云计算资源节点完成任务成功率,需要对云计算资源进行调度。当前的云计算资源调度算法在进行调度时,通过选择合适的调度参数并利用CloudSim仿真工具,完成对云计算资源的调度。该算法在运行时有效地进行平衡负载,导致云计算资源调度的均衡性能较差,存在云计算资源调度结果误差大的问题。为此,提出一种基于Wi-Fi与Web的云计算资源调度算法。该算法首先利用自适应级联滤波算法对云计算资源数据流进行滤波降噪,然后以降噪结果为基础,采用本体论对云计算资源进行预处理操作,最后通过人工蜂群算法完成对云计算资源的调度。实验结果证明,所提算法可以良好地应用于云计算资源调度中,有效提高了云计算资源利用率,具有实用性以及可实践性,为该领域的后续研究发展提供了可靠支撑。     

基于改进蚁群算法的无线传感器网络路由的优化

为了延长无线传感器网络(Wireless Sensor Network ,WSN)的生命周期,均衡各个节点间能量消耗,针对现有的WSN路由优化算法存在的问题,提出了一种基于改进蚁群算法的路由优化算法。首先通过对蚁群算法和遗传算法的优劣性比较,在蚁群算法的基础上,结合遗传算法的选择、交叉和变异的操作,从而提高蚁群算法的搜索速度和寻优能力。最优路径评价函数综合考虑节点能耗及节点的剩余能量,使剩余能量多的节点优先参与数据转发,均衡节点间的能量消耗。通过与经典蚁群算法及遗传算法的对比实验表明,随着数据转发轮数增加,改进的蚁群算法能耗小,剩余能量多,网络生命周期明显延长;随着整个网络运行时间的增长,改进的蚁群算法,节点均衡能耗性好,最优路径搜索的成功率也明显优于其他两种算法。     

云环境下基于节能和负载均衡的混沌粒子群资源优化调度

针对传统云计算资源调度方法仅关注任务的最大完成时间,没有考虑到节能和资源负载均衡的问题,提出了一种基于混沌粒子群算法实现云资源优化调度的方法;首先,定义了以节能和负载均衡为目标的多目标数学模型,然后设计了一组靠近最优Pareto 前沿的解作为初始种群,采用改进的粒子群算法来搜索最优调度方案,当最优解连续两代未发生变化时,通过混沌遍历法对粒子进行局部寻优,以加快获取全局最优解;在CloudSim仿真环境下结合Matlab工具进行实验,结果表明:文中方法负载均衡离差平均值为0.156,且较其它方法,具有较好的负载均衡能力和较低的能耗,具有很强的可行性。     

On resource scheduling and interference mitigation in distributed massive-MIMO wireless orientations via NOMA transmission

The goal of this paper is to evaluate the performance of a proposed resource management approach that mitigates the co-channel interference (CCI) in non-orthogonal multiple access (NOMA) scenarios and maintains their enhanced spectral efficiency in distributed massive multiple input multiple output (d-massive MIMO) configurations as well. In this context, dedicated resource scheduling algorithms (RSAs) in power domain (PD) and frequency domain (FD) are studied in terms of resources’ orthogonality. Specifically, in PD case, adjusted power levels (denoted as PD-NOMA) per subcarrier and mobile terminal (MT) are assigned, while in FD case, the subcarriers are either orthogonal (FD orthogonal multiple access, FD-OMA) or non-orthogonal (FD-NOMA) to each other. The response of the d-massive MIMO system is evaluated statistically via independent Monte Carlo (MC) simulations considering a multicellular multi-user network topology and compared to typical multicellular MIMO configurations. In this framework, a simulation platform is implemented that integrates both the PD and FD RSAs. In PD, both intercell co-channel interference (Inter-CCI) and intracell co-channel interference (Intra-CCI) are modelled analytically in order to estimate the assigned power per subcarrier and MT. In the latter case (Intra-CCI), the worst-case scenario is assumed: a subcarrier can be assigned to multiple MTs (full spectral overlapping) leading to intense Intra-CCI. In FD, two subcarrier allocation approaches are considered: Pseudo-Random or maximum signal-to-noise ratio (MSNR). The simulations in both FD implementations (FD-NOMA, FD-OMA) show that, thanks to the proposed PD-NOMA scheme, each MT requires 1/4 of the maximum available power for downlink transmission. Moreover, in any of the investigated NOMA schemes, despite the intense Intra-CCI, roughly the same number of MTs as in the OMA case can access the network. Therefore, it is straightforward that, even in worst-case scenarios, the NOMA RSAs (i) are wisely exploiting the available resources; (ii) can inherently combat intense Intra-CCI and, in this way, maintain the system’s performance (number of MTs, power savings, resilience against CCI, computation complexity). Finally, it is worth noting that in contrary to typical MIMO configurations, the d-massive MIMO architecture alone can lead up to a 13.63% increase in the system’s capacity (10% maximum allowed blocking probability, 1 tier, 1 subcarrier per MT). In this case, the increased spatial separation that is achieved, along with the exploitation of NOMA RSAs, lead to a decreased CCI (both Intra- and Inter-); hence, SNR is improved and consequently the number of accepted MTs as well.     

A new fog computing resource management (FRM) model based on hybrid load balancing and scheduling for critical healthcare applications

Critical healthcare application tasks require a real-time response because it affects patients’ life. Fog computing is the best solution to get a fast response and less energy consumption in healthcare. However, current solutions face difficulties in scheduling the tasks to the correct computing devices based on their priorities and capacity to meet the tasks’ deadlines and resource limitations with minimal latency. Furthermore, challenges of load balancing and prioritization are raised when dealing with inadequate computing resources and telecommunication networks while obtaining the best scheduling of emergency healthcare tasks. In this study, a fog computing resource management (FRM) model is proposed, which the proposed model has three main solutions. Firstly, resource availability is calculated according to the average execution time of each task. Secondly, load balancing is enhanced by proposing a hybrid approach that combines the multi-agent load balancing algorithm and the throttled load balancing algorithm. Thirdly, task scheduling is done based on priority, resource availability, and load balancing. The results have been acquired using the iFogSim toolkit. Two datasets are used in this study, the blood pressure dataset was acquired from the UTeM clinic, and the ECG dataset was acquired from the University of California at Irvine. Both datasets are integrated to enlarge the attributes and get accurate results. The results demonstrate the effectiveness of managing resources and optimizing task scheduling and balancing in a fog computing environment. In comparison with other research studies, the FRM model outperforms delay by 55%, response time by 72%, cost by 72%, and energy consumption by 70%.     

VXI总线多通道压力传感器仿真模块设计与实现

某型发动机系统主要由综控器、传感器、电机泵、电磁阀等部件组成,综控器是整个系统的核心控制部件,为测试综控器的性能是否符合要求,需要为综控器提供各种工况下的传感器（主要为压力传感器）数据进行仿真试验。采用真实的压力传感器开展上述工作,一方面需要构建复杂的工作环境,难度大、数据精确度难以保证,另一方面地面环境下的传感器信号变化难以实时调整,难以满足数据快速变化的需要。基于以上因素考虑,给出了一种基于VXI总线的多通道压力传感器仿真模块的设计方法,模块共有24路压力传感器电路,每路独立配置输出。模块以“VXI总线接口电路 VXI总线功能电路”的结构进行布局,VXI总线接口电路主要包括“CPLD 双口RAM”,对VXI总线指令进行解释、译码;VXI总线功能电路主要包括“96系列单片机 DA输出电路 两线压力传感器电路”,完成对真实传感器输出信号的电气特性仿真模拟。模块的驱动程序在Lab Windows/CVI6.0的环境下编制,供用户进行程序调用和快速操作使用。该模块已在VXI总线测试系统中得到应用,实际情况表明,模块的输出精度高、信号调整方便、响应快,满足了综控器仿真深度测试的要求。     

IIC总线和LVDS在高速数据传输接口电路中的应用研究

当前在高速数据系统中,LVDS接口已被广泛应用,为实现同系列设备之间的智能识别、自动握手以及LVDS高速数据链路通信质量的检测,利用FPGA的IO电路结构,设计一种模拟IIC总线协议电路,该IIC总线高效地实现设备之间的信息双向传递;同时利用FPGA内部丰富寄存器资源设计PRBS码型电路来检测LVDS接口芯片电路误码率。实际测试表明该多通道LVDS传输方式在2米长电缆连接能够实现数据的稳定、低误码率传输,并且在时钟频率为100MHz时,数据传输速率高达4.68Gb/s。     

Study on the method of automatic measurement of flexible material processing path based on computer vision and wavelet

Straightness, roundness, and primitive angle error of contour are important indicators of evaluating path precision during flexible material path processing. As processing path is composed of small arc or small line segment primitives, also the deformation of the flexible material during the processing path, making the captured image of processing path not clear, the edge of processing image over local uneven gray, the pixels of boundaries between the processing path image edge and background organizations not obvious. In order to extract the flexible material path contour effectively, mosaic method for flexible material processing path image is studied, next fast positioning strategy is introduced, and then we puts forward the search algorithm which taking processing path corner search as the cut-in-point, designing slope angle curve of starting and terminal point of each primitive and conducting slope angle curve for multiple scales wavelet transform by regarding DB(4) as wavelet operator based on wavelet edge modulus maxima extract principle. By judging whether one point of the curve appears at wavelet transform extremum, it can be determined whether the point is a corner one. In order to accelerate wavelet transform computing speed, FPGA IP core is 8-tap transpose is used to design the decomposition and reconfigurable of DB(4). The total time consumed by IP core wavelet decomposition increased only 2.802% compared to the PC computation time; path angle relative error is 8%, and the average measurement time is 198.22 ms.     

基于云计算和物联网的网络大数据技术研究

为了使网络大数据应用的范围更广,更大程度地提高网络数据存储与管理精度,减少网络数据处理与控制的时间,需要对网络大数据进行研究。当前的网络大数据研究方法多是采用Hadoop基础架构对网络大数据进行研究,在数据存储中没有设定具体的安全存储指标,无法得到数据安全存储指标权重,存在数据存储安全性能低,网络大数据研究精度偏差大等问题。为此,提出一种基于云计算和物联网的网络大数据研究方法。该方法首先利用分级网络编码对网络数据进行传输,以传输的数据为基础,采用CRC算法实现网络数据的计算,然后依据分组存储的方式将数据进行存储,最后利用分层逆序叠加定位法对网络数据进行高精度查询,由此完成对网络大数据的研究。实验结果表明,所提方法可以全面具体地对网络大数据进行研究,提高了数据处理精度和网络数据计算速度,增加了网络数据存储空间容量和查询效率,减少了网络数据运行时的丢失率,扩展了网络数据的运作范围,为后续网络大数据的研究提供了强有力的依据。     

A novel algorithm for solving optimal path planning problems based on parametrization method and fuzzy aggregation

In this Letter a new approach for solving optimal path planning problems for a single rigid and free moving object in a two and three dimensional space in the presence of stationary or moving obstacles is presented. In this approach the path planning problems have some incompatible objectives such as the length of path that must be minimized, the distance between the path and obstacles that must be maximized and etc., then a multi-objective dynamic optimization problem (MODOP) is achieved. Considering the imprecise nature of decision maker's (DM) judgment, these multiple objectives are viewed as fuzzy variables. By determining intervals for the values of these fuzzy variables, flexible monotonic decreasing or increasing membership functions are determined as the degrees of satisfaction of these fuzzy variables on their intervals. Then, the optimal path planning policy is searched by maximizing the aggregated fuzzy decision values, resulting in a fuzzy multi-objective dynamic optimization problem (FMODOP). Using a suitable t-norm, the FMODOP is converted into a non-linear dynamic optimization problem (NLDOP). By using parametrization method and some calculations, the NLDOP is converted into the sequence of conventional non-linear programming problems (NLPP). It is proved that the solution of this sequence of the NLPPs tends to a Pareto optimal solution which, among other Pareto optimal solutions, has the best satisfaction of DM for the MODOP. Finally, the above procedure as a novel algorithm integrating parametrization method and fuzzy aggregation to solve the MODOP is proposed. Efficiency of our approach is confirmed by some numerical examples.     

基于质心理念的无线传感网络路由协议的研究与改进*

在无线传感器网络中存在诸如节点能量损耗以及数据传输时所存在的时延等问题,因此在深入研究PEGASIS以及相关改进算法的基础上,并结合力学中质心的概念而提出一种基于质心理念的链式算法CMC-PEGASIS(Center of mass concept-PEGASIS)。该算法将整个传感器区域分成等宽的五个子区域,通过计算每个子区域内节点至基站的距离并按照距离与基站的远近来成链。其次引入质心的概念找到每个区域节点的能量中心,同时结合节点自身的能耗以及每个节点与基站的距离关系,得到每个子区域中的最佳的簇头节点,最后每个区域的簇头节点直接与基站完成信息的传输。经过理论分析和仿真结果得知：CMC-PEGASIS算法降低了全局能耗、减少了信息延迟率同时延长了网络的生命周期。     

基于MDARM和VxWorks的小型无人机执行器故障检测方法

由于目前在小型无人机执行器故障诊断中存在着智能化程度较低,容易受到人为因素干扰,从而出现故障漏检等问题,难以满足小型无人机对飞行安全的要求。为此,本文提出一种基于多维数据关联规则挖掘(Multidimensional Data Association Rules Mining: MDARM)和VxWorks操作系统的小型无人机执行器故障诊断方法,通过建立执行器内部传感器测量的温度、压力、流速、力矩等相关变量的历史数据库,并对这些数据进行预处理,以避免带来噪声污染,并利用可测量参数与不可测量参数之间的关联性,建立故障诊断知识库,避免了诊断过程中的人为因素干扰,实现小型无人机执行器故障的精准测量。实验结果证明,这种方法能够有效地提高故障准确率64.7%,对小型无人机执行器的智能诊提供有效指导,应用前景广阔。