基于三次样条的动态GM(1,1)模型在智能电网用电量预测中的应用

Electricity demand forecasting plays an important role in smart grid expansion planning. In this paper, we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle. Using piecewise polynomial interpolation thought, this model can dynamically predict the general trend of time series data. Combined with low-order polynomial, the cubic spline interpolation has smaller error, avoids the Runge phenomenon of high-order polynomial, and has better approximation effect. Meanwhile, prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment. Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.     

一种基于免疫克隆的偏好多维QoS云资源调度优化算法

针对云计算环境中高效资源调度问题,首先从理论上对云资源调度进行了建模,对用户应用偏好和多维QoS中的用户效用进行了量化,给出了多维QoS优化的目标函数.结合具有快速多目标优化能力的免疫克隆算法,提出了一种云资源调度优化算法.根据应用偏好信息为抗体分配偏好优先级,据此进行抗体的免疫克隆操作,提高抗体免疫基因操作的预见性,...     

基于家用电器耗电量的广东省城乡居民用电需求分析

居民生活用电一直是电力市场中最具潜力的增长点,而对城乡居民生活用电需求的分析也一直是各界人士倍受关注的热点问题之一。本文基于leap模型从家用电器耗电量的角度分析广东省城乡居民未来5年的用电需求。结果发现,2016-2020年城镇空调、电冰箱的使用引起的用电量年均增长率分别为8.21%和8.33%;而农村空调、电冰箱的使用引起的用电量年均增长率分别为11.41%和7.31%。城乡居民用电潜在需求较大。     

基于5G边缘计算的智能电网高性价比任务调度

针对智能电网环境中电力数据量庞大且对处理时效性要求高的问题,将5G边缘计算引入智能电网系统.研究了基于5G边缘计算的智能电网任务调度问题,在满足电网任务完成需求的同时,最大限度地降低成本.基于此提出了一种基于贪心策略的启发式任务调度算法,通过与两种算法在包括输入任务数、传输数据大小和延迟要求等参数下的比较,验证了所提算...     

基于数据挖掘的电力云资源规划调度

随着虚拟化技术的应用,数据中心的资源利用率已经得到一定程度的提高,但是云资源通常还是根据用户提出的需求预先分配,资源利用率仍然有待提高.为了进一步改进资源利用率,云中心的实际资源需求可分为“周期资源”及“峰期资源”.“周期资源”可以通过历史规律,利用递阶成分负载模型进行分析,预测出数据中心大部分时间的资源需求;“峰期资源”主要是满足短时间内的各种高峰资源需求,这种需求应用了排队论及随机均衡算法模型并根据实际应用的需要动态启用、分配和收回资源.通过使用资源收集与分配守护进程对云中心资源需求进行规划调度实验,效果明显,从而为云中心资源利用率的提升及节能减排提供了一种有效途径.     

基于有效性的逆荷兰式拍卖网格资源调度

提出了基于有效的逆荷兰式拍卖算法，用于解决网格计算环境下的网格资源管理和调度，阐述了利用GridSim建立基于有效的逆荷兰式拍卖算法的模拟平台的主要步骤。最后模拟实验表明此算法具有优良的性能。     

三维QoS约束下效益最优的多需求网格资源调度

文中在搭建的三维Qos模型空间下研究基于效益最优的资源调度算法,采用经济模型等关注系统与用户交互的管理方式,向用户提供服务质量保证.文中提出的资源调度策略研究在三维QoS约束下如何才能最大的满足用户和资源调度者的需求,从而使整个系统的效益值最大.文中提出的可以被反复调用的算法用来最优化基于三维的Qos资源调度.实验显示了最优化基于多维QoS的资源调度算法会得到更少的运行费用和更高的调度效率.     

安全和能量关键的分布式协作任务调度

传统分布式任务调度严重地忽略了任务的能耗和安全因素,不适用于安全关键分布式嵌入式系统,针对安全和能量关键的分布式协作应用,提出了一种安全感知和能量感知的任务映射调度算法SEATMS.作为一种多项式复杂度的启发式算法,SEATMS能够在满足协作应用的实时需求和能耗约束前提下,最大程度地降低系统安全风险.和同类算法相比,所...     

校园计算网格作业自适应调度的研究和实现

由于网格资源具有分布、异构、动态、自治等特性。使得网格作业调度成为网格计算中具有挑战性的任务之一。针对校园高性能计算资源负载不均衡、管理模式不统一的问题，提出了校园计算网格自适应调度系统的解决方案。详细研究了网格作业的白适应调度技术，设计了含有负载均衡能力的资源动态信息收集模型，并给出自适应调度算法。实现了校园范围内高性能计算资源的负载均衡和统一的资源管理。     

改进的多任务分配与调度遗传算法

提出了一种改进的混沌遗传算法,定义了问题模型,采用一任务序列加一主机序列的编码表示,并由约束关系阶段随机产生合法调度。为确保生成新调度,改进了交叉机制;为保持解的多样性和均衡负载,设计了任务均衡变异算子。为利用混沌优化技术,交叉、变异频率都由混沌序列动态控制。仿真结果表明,该算法总能得到最佳解。     

Analysis of Consumers' Choices and Time‐Consumption Behaviors for Various Broadcasting and Telecommunication Convergence Services

In this study, we analyzed consumers' choices of various broadcasting and telecommunication convergence services and time consumption for chosen services by using survey data. A multivariate probit model was used to model consumers' choices of various broadcasting and telecommunication convergence services, and an ordered probit model was used to model consumers' time consumption for chosen services. Factors affecting consumers' choices and time‐consumption behavior were identified, and simulation results of market competition and substitution were obtained. Based on these results, it was found that for the time being, consumers are highly locked into existing broadcasting services and are likely to become more price‐sensitive to the new broadcasting and telecommunication convergence services. Also, the ways in which individual characteristics affect choices and time consumption were found to be very diverse service by service.     

一种认知无线电系统的传输调度方案

将认知无线电系统中的传输调度方案建模为一个约束马尔科夫决策过程（CMDP）,即在满足缓存器内包数约束的情况下最小化发送数据包消耗的平均功率。因为在认知无线电系统中,环境参数无法预先得知,为此利用R学习来自适应地获取CMDP的近似最优策略。在仿真结果中,对基于R学习的传榆调度方案的性能进行了比较和分析,结果显示该方案能适用于参数未知的环境且有效地降低平均功率。     

多用户多天线系统中基于THP的低复杂度调度排序算法

本文提出了下行多用户多天线系统中一种新的基于Tomlinson-Harashima预编码(THP)的低复杂度调度排序算法.该算法根据THP与误符号率的特点,将调度和排序结合在一起,有效降低了复杂度.仿真结果表明,本文提出的算法在平均误符号率性能上逼近贪婪调度best-first排序,而复杂度更低,在实际系统中有很好的应用价值.     

Applying Hierarchy Model to Routing and Scheduling Operations in Postal Logistics

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光突发交换网络的突发包组装和调度

光突发交换(OBS)由于结合了光线路交换和光分组交换的优点并且改进了两者的不足之处,成为实现IP over WDM的重要手段。主要研究实现光突发包在边缘路由器的组装生成和调度机制。通过模拟的方法研究边缘路由器的输出性能,包括输出流量的形状和时延特性。结果显示,不同服务等级的时延要求可以通过组装控制输出流的最大时延得以保证。同时边缘路由器组装和分配信道缓冲区设置和优先分配机制可以影响到输出光突发包的长度和突发度。     

Scheduling Algorithms for Star-Coupled WDM Networks with Tunable Transmitter and Tunable Receiver Architecture

This paper presents the design and analysis of two scheduling algorithms for a reservation-based medium access control (MAC) protocol for wavelength division multiplexed (WDM) multi-channel optical networks. The network architecture is based on a passive star topology with one tunable transmitter and receiver (TT-TR) per node. The main objective of scheduling algorithm design is to reduce the computation time while maximizing the utilization of the network resources. In this paper, we propose two scheduling schemes called SEQSAM (SEQuential Scheduling AlgorithM) and BALSAM (BALanced Scheduling AlgorithM). Let M denote the number of nodes, C the number of channels, and K the maximum number of packets transmitted by one node to another. SEQSAM uses the M × M traffic demand matrix--obtained during the reservation phase of the MAC protocol--to compute a collision-free schedule for the nodes of the network. BALSAM uses the modified MULTI-FIT algorithm (MMFT) [1] to convert the M × M matrix into a corresponding M × C matrix, which is input to the IBS (Interval Based Scheduling) algorithm [2] that schedules the requests of the nodes. The overall time complexity of SEQSAM is O(M ³) compared to BALSAM algorithm's time complexity of O(M ² CK + M ² + MlogM). Note that the lower bound for any scheduling algorithm operating on a M × M matrix is O(M ²). A simulation-based performance study that considers network utilization, computation time, tuning latency, average packet latency and throughput for 1.2 Gbps and 2.4 Gbps data streams is presented.     

无线传感器网络中保障生命期和有效延时的数据融合调度方法

In scenarios of real-time data collection of long-term deployed Wireless Sensor Networks (WSNs), low-latency data collection with long network lifetime becomes a key issue. In this paper, we present a data aggregation scheduling with guaranteed lifetime and efficient latency in WSNs. We first construct a Guaranteed Lifetime Minimum Radius Data Aggregation Tree (GLMRDAT) which is conducive to reduce scheduling latency while providing a guaranteed network lifetime, and then design a Greedy Scheduling algorithM (GSM) based on finding the maximum independent set in conflict graph to schedule the transmission of nodes in the aggregation tree. Finally, simulations show that our proposed approach not only outperforms the state-of-the-art solutions in terms of schedule latency, but also provides longer and guaranteed network lifetime.     

天津高校建筑能耗现状分析及节能对策

在天津市建筑能耗统计调查基础上,对天津市高校建筑的能耗现状及特点、存在问题和节能改造潜力进行了分析,指出了该类建筑进行节能改造以及节能运行的对策及措施。     

一种基于马尔可夫决策过程的认知无线电网络传输调度方案

该文提出了一种适用于认知无线电网络的跨层传输调度方案,即满足掉包率约束的前提下最小化平均功率消耗。此方案被建模为约束马尔可夫决策过程(MDP)。采用拉格朗日乘子法求解此MDP,并且提出了一种黄金分割乘子搜索法。提出两种简化方法,即状态聚合以及行动集缩减来解决维灾问题。仿真结果显示简化方法对该方案的性能影响很小,且该方案的平均功耗最低。     

基于蚁群优化的时间Petri网及其在柔性制造系统调度优化中的应用

Petri网的优化调度求解主要采用基于状态空间的智能搜索方法,执行效率往往不高.提出基于蚁群优化的时间Petri网,通过在Petri网的变迁和进化规则中引入蚁群优化机制,改变Petri网原有的非确定性选择行为,使之具备蚁群寻优选择智能行为,克服了需要基于状态空间进行启发式搜索的缺陷,提高了调度优化的计算效率.最后应用蚁群优化时间Petri网模型,对柔性制造系统的区间Job Shop调度问题进行建模和优化求解.实验结果进一步验证了所提模型和方法的有效性.