IaaS模式“云训练”资源预测-调度方法

基础设施即服务(infrastructure as a service,IaaS)模式"云训练"是基于IaaS云计算提出的武器装备系统模拟训练的模式,根据用户需求对训练资源进行预测调度是提高训练效果的重要保证。分析了"云训练"中用户任务、资源需求特点,采用阈值法进行预处理,通过动态权值系综模型得到预处理结果。在此基础上,提出基于减法-模糊聚类的模糊神经网络的资源需求预测方法(subtractive-fuzzy clustering based fuzzy neural network,SFCFNN),并引入自适应学习率和动量项以提升收敛速度和稳定性。调度器根据预测结果实现用户需求与资源之间的动态匹配。实验表明该方法可精确预测用户资源需求,实现资源动态调度,有效提高资源利用率与训练效果。

资源不确定环境下模具多项目预测—反应式调度算法

针对模具多项目执行过程中由于可再生资源发生故障而导致的调度计划变更问题,提出了一种预测-反应式调度算法。利用生灭过程理论对可再生资源的不确定性进行分析,通过大量基于启发式策略的仿真计算构建了一个相对稳定的基准调度计划,建立了以调度计划变更费用最小为优化目标的反应调度模型,并用混沌微粒群优化算法进行求解。通过仿真计算分析了所提算法的可行性与有效性。

Job Shop调度中重调度问题研究

描述重调度问题的框架,介绍重调环境、重调度策略和重调度方法,闸述车间重调度研究的重要性。

Application of predictive control scheduling method to real-time periodic control tasks overrun

Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.

Multi-UAV surveillance implementation under hierarchical dynamic task scheduling architecture

In this paper,we consider a multi-UAV surveillance scenario where a team of unmanned aerial vehicles(UAVs)synchronously covers an area for monitoring the ground conditions.In this scenario,we adopt the leader-follower control mode and propose a modified Lyapunov guidance vector field(LGVF)approach for improving the precision of surveillance trajectory tracking.Then,in order to adopt to poor communication conditions,we propose a prediction-based synchronization method for keeping the formation consistently.Moreover,in order to adapt the multi-UAV system to dynamic and uncertain environment,this paper proposes a hierarchical dynamic task scheduling architecture.In this architecture,we firstly classify all the algorithms that perform tasks according to their functions,and then modularize the algorithms based on plugin technology.Afterwards,integrating the behavior model and plugin technique,this paper designs a three-layer control flow,which can efficiently achieve dynamic task scheduling.In order to verify the effectiveness of our architecture,we consider a multi-UAV traffic monitoring scenario and design several cases to demonstrate the online adjustment from three levels,respectively.

An energy-saving scheduling scheme for streaming media storage systems

The rapid growth of streaming media applications on the Internet is proposing higher requirements on energy consumption and I/O performance of the storage systems.However,the optimized I/O requests from different initiators will be mixed disorderly when they are reaching the storage system concurrently,which leads to increasing energy consumption.This paper proposes an energy-saving scheduling scheme based on I/O Stream(ES-IOS).The ES-IOS scheme can take the advantage of the I/O characteristics of streaming media and reorganize the mixed and disordered I/O requests into "streams".Technically,The ES-IOS scheme includes two main points,a priority-based weighted stream scheduling algorithm(PWSS) and a regression-fitting-based popularity prediction algorithm(RFPP).The PWSS algorithm can schedule the I/O streams in weighted queue based on priority to limit energy consumption.The priority of each stream is determined by its popularity.According to the I/O access records over a period,the RFPP algorithm can predict the popularity of each stream via regression fitting.Based on the popularities,the PWSS algorithm assigns more continuous service time to the hot streams and reversely less service time to the cold ones.Trace-driven experiments show that the ES-IOS scheme can reduce the energy consumption by 38%and enhance the I/O throughput by 27%approximately.

STUDY ON OPTIMAL CONTROL OF MUNICIPAL WATER DISTRIBUTION NETWORK

A systematic investigation is made on the problems which are related to the optimal control of the municipal water distribution network.A mathematical model of forecasting the water short term demand is proposed using the time series trigonometric function analysis method;the service discharge based macroscopic model of network performance is established using the network structuring method;a relatively satisfactory mathematical model for the optimal control of water distribution network is put forward in view of security and economy,and solved by the constrained mixed discrete variable complex arithmetic.The model is applied in many examples and the results are satisfactory.

基于多智能体的分销链协同一体化运作建模与流程分析

预测-计划-调度是分销链运作研究的重要内容,协同一体化的运作模型构建及流程分析有助于优化分销链的管理。在比较了基于对象建模和基于Agent建模的基础上,给出了分销链运作与Agent的对应关系。根据多智能体技术的建模方法和流程,运用该技术对分销链协同一体化的运作模型进行了优化,并给出了基于多智能体技术的预测-计划-调度的智能运作模型,并对该模型的运作流程进行了分析。

Wavelet Analytical Forecasting Method of Water Consumption

A new method of short-term forecasting for water consumption in municipal supply water networks based on wavelet transformation is introduced. By wavelet decomposing commonly used in the signal field, water consumption per hour is decomposed into many series. Trend item, cycle item and random item are separated from the original time series in this way.Then by analyzing, building a model, forecasting every series and composing the results, the forecasting value of the original consumption is received. Simulation results show that this forecasting method is faster and more accurate, of which the error is less than 2%, indicating that the wavelet analytical method is practicable.

Regression Equations for Predicting the Effect of Tine Width on Draught and Soil Translocation in Moderately Fine Textured Soil

Experiments were conducted in an indoor soil bin filled with sandy clay loam soil. Tests were carried out with tillage tines to study the effect tool width on soil disturbance and draught. Depth of operation was held constant at 35 mm and then at 70 mm while speed was varied at three levels of 1.0, 3.6 and 9.0 km/h. The widths of the tines tested were 10, 20, 31, 40, 51, 88, 126, 163 and 200 mm. The cone penetration resistance of the soil varied from 400 to 600 kPa. Draught was measured with a load cell while soil disturbance was measured with a profile meter and meter rule. Draught increased at a decreasing rate with tine width. Quadratic models best fitted the data points with high R2 values. The increase in draught was affected by the forward speed since higher draught values were obtained at higher speed. Results show that the parameters of soil disturbance increased with increase in tine width, except height of ridge （hr）, which did not show any specific trend. The specific draught was highest （10.63 N/cm） with tine T20 while Tine T1 had the least specific draught of 5.2 N/cm.

Forecasting and optimal probabilistic scheduling of surplus gas systems in iron and steel industry

To make full use of the gas resource, stabilize the pipe network pressure, and obtain higher economic benefits in the iron and steel industry, the surplus gas prediction and scheduling models were proposed. Before applying the forecasting techniques, a support vector classifier was first used to classify the data, and then the filtering was used to create separate trend and volatility sequences. After forecasting, the Markov chain transition probability matrix was introduced to adjust the residual. Simulation results using surplus gas data from an iron and steel enterprise demonstrate that the constructed SVC-HP-ENN-LSSVM-MC prediction model prediction is accurate, and that the classification accuracy is high under different conditions. Based on this, the scheduling model was constructed for surplus gas operating, and it has been used to investigate the comprehensive measures for managing the operational probabilistic risk and optimize the economic benefit at various working conditions and implementations. It has extended the concepts of traditional surplus gas dispatching systems, and provides a method for enterprises to determine optimal schedules.

New Approach to the Flexible Planning of Learning Process in High Education System

Expediency of this work is conditioned by the inconsistency between the market requirement of the specialists and the planning process of high educational system. For solving this problem it is important to make consulting or expect system for flexible planning of teaching modules of every specialty. We make an attempt to consider this problem in two aspects： the prediction of market demand for planning taking into consideration of studies duration and scheduling of educational process. The prediction task consists in data acquisition of market requirement for each profession in discrete time interval to predict dynamic evolution of every specialty. The solution of the prediction task will be using to determination of prognostic quantity of students for each specialty. As regards the second aspect, it consists in finding a schedule of the teaching modules, i.e. the distribution of subjects in the semesters, keeping the total limits of credits, to update and adapt syllabus. In this paper, we present a genetic algorithm as a solution method for the modular scheduling problem. Genetic algorithms （GAs） allow a more general approach to the scheduling problem, which is rated using a fitness function. GA can be successfully applied to find optimized sequential schedules.

Observation System Experiments for Typhoon Nida(2004)Using the CNOP Method and DOTSTAR Data

This study investigated the influence of dropwindsonde observations on typhoon forecasts. The study also evaluated the feasibility of the conditional nonlinear optimal perturbation (CNOP) method as a basis for sensitivity analysis of such forecasts. This sensitivity analysis could furnish guidance in the selection of targeted observations. The study was performed by conducting observation system experiments (OSEs). This research used the fifth-generation Mesoscale Model (MM5), the Weather Research and Forecasting (WRF) model, and dropsonde observations of Typhoon Nida at 1200 UTC 17 May 2004. The dropsondes were collected under the operational Dropsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) program. In this research, five kinds of experiments were designed and conducted:(1) no observations were assimilated; (2) all observations were assimilated;(3) observations in the sensitive area revealed by the CNOP method were assimilated;(4) the same as in (3), but for the region revealed by the first singular vector (FSV) method;and (5) observations within a randomly selected area were assimilated. The OSEs showed that (1) the DOTSTAR data had a positive impact on the forecast of Nida's track;(2) dropsondes in the sensitive areas identified by the MM5 CNOP and FSV remained effective for improving the track forecast for Nida on the WRF platform;and (3) the greatest improvement in the track forecast resulted from the CNOP-based (third) simulation, which indicated that the CNOP method would be useful in decision making about dropsonde deployments.

Gain-scheduling model predictive control for unmanned airship with LPV system description

This paper presents a gain-scheduling model predictive control(MPC) for linear parameter varying(LPV) systems subject to actuator saturation. The proposed gain-scheduling MPC algorithm is then applied to the lateral control of unmanned airship.The unmanned airship is modeled by an LPV-type system and transformed into a polytopic uncertain description with actuator saturation. By introducing a parameter-dependent state feedback law, the set invariance condition of the polytopic uncertain system is identified. Based on the invariant set, the gain-scheduling MPC controller is presented by solving a linear matrix inequality(LMI) optimization problem. The proposed gain-scheduling MPC algorithm is demonstrated by simulating on the unmanned airship system.