一种新型排烟冷却塔的探讨

首先,根据结构参数在GAMBIT2．3中建立新型排烟冷却塔的三维模型并设置边界条件。在完成塔内汽水换热,抽力及阻力计算的基础上。应用FLUENT6．3计算平台,结合自编程序,模拟发现：新型排烟冷却塔能有效避免因常规烟塔烟道布置产生的射流卷吸负压;降低烟气对烟塔的腐蚀作用;使烟气与热湿空气组成的混合气体抬升性能得到改善。对优化排烟冷却塔结构,进行合理的烟道布置提供了指导性建议。     

可重构复杂制造系统的设施布置优化方法研究

针对可重构复杂制造系统可模块化和可集成化等特点,结合层次分析的思想和QAP数学模型,通过布置分类把信息集成的设施模块分为独立型模块和依赖型模块,提出了先后对独立型模块和依赖型模块进行布置的二次设施布置方法。阐述了二次设施布置方法的思想,构建了其模型,并用蚁群算法和重心法求解了这个模型。运用此布置方法建立了某车间的设施布局模型,简化了设施布置过程,实现了对可重构复杂制造系统设施布置的优化。     

裂隙介质两相渗流数值模拟研究

以离散裂隙概念为基础建立了离散裂隙型模型,该模型通过对裂隙的降维处理提高了计算效率并适用于所有类型裂隙介质的渗流研究。详细阐述了离散裂隙型模型的基本原理及其两相渗流数学模型,基于Galerkin加权残量法建立了该模型的有限元数值计算格式。通过单裂隙中的Buckley–Leverett两相渗流算例验证了数值算法的正确性。以裂缝性油藏注水开发为例,应用离散裂隙型模型分别对2种不同类型的裂隙介质进行了实例研究。数值计算结果表明：裂隙的存在导致了岩体的强烈非均质性和各向异性,其中裂隙的方位、大小、连通性及其类别对裂隙介质两相渗流均有着重要的影响;正确认识裂隙的分布及其类别对于裂缝性油藏的开发动态研究及其注采井网的布置有着重要的实际意义 。     

一种基于PIO改进强化学习的航天器复杂多约束姿态机动规划新方法

本文针对多个姿态约束条件下的航天器姿态机动规划问题进行了研究,提出了一种基于鸽群算法的改进的策略梯度强化学习算法(PIOPGRL).首先,针对强制指向约束和禁止指向约束,建立了基于角度的姿态约束模型,根据约束模型建立了强化学习的回报函数.然后,使用适应度函数替代策略评价函数,将鸽群算法与强化学习相融合.针对策略梯度强化学习算法计算量大、收敛速度慢的问题,使用鸽群算法求解策略梯度,极大减少了计算量.仿真结果表明,相比于策略梯度强化学习算法,基于自PIO改进强化学习的航天器姿态机动规划算法(PIOPGRL)在极大减少计算量的同时,有更优的规划结果,更小的机动代价,适用于微小航天器解决多个姿态约束条件下的姿态机动规划问题.     

复杂裂隙网络无压渗流分析的变分不等式提法

通过对仅适用于湿区裂隙渗流的Darcy定理进行延拓处理,并将潜在溢出面归纳为Signorini型互补边界条件,建立了定义在裂隙网络全域上无压渗流问题的偏微分方程（PDE）提法.为了减小试探函数的选取难度和消除溢出点的奇异性,进一步发展了与PDE提法等价的变分不等式（VI）提法,并给出了离散型变分不等式提法的有限元数值求解格式、迭代算法与计算流程.由于使用了连续型的Heaviside函数代替阶跃型函数,避免了对位于过渡区域裂隙单元进行积分时形成的数值跳跃,从而显著提高了有限元数值求解过程中的稳定性.典型算例的计算结果验证了该方法的有效性和可靠性,并表明基于离散裂隙网络模型的无压渗流分析不但可以较好模拟裂隙岩体渗流的非均质性和优势水流方向,还可以准确预测坡体内部排水结构的渗流量,为裂隙岩体边坡防渗排水系统的优化布置提供科学依据.     

基于共识度水平的研究项目评估决策研究

运用交互式群决策方法考虑大型科研项目评估决策问题，使用共识度的概念描述交互式决策的结果。给出了基于共识度水平的项目评估决策流程；针对现有共识度计算使用专家意见最大距离的局限性，提出一种使用专家平均距离来描述共识度的模型。最后用一个算例来说明模型的改进效果。     

基于政策引导的废旧轮胎逆向物流网络优化设计模型研究

分析国内外废旧轮胎的利用现状,借鉴发达国家先进经验,提出建设区域性废旧轮胎逆向物流网络的具体模式.在市场调查、合理预测的基础上设定目标,构建基于政策引导下的废旧轮胎回收利用混合整数线性规划模型.模型中设立了废旧轮胎回收下限指标,目的是促进系统回收率的提高;同时设立了不同再利用形式使用废旧胎的比例,以引导网络体系科学有序发展.最后以算例进行实际分析,合理规划回收中心、集散中心和再利用场所,使整个回收利用体系成本达到最小     

一种改进的前飞时倾转旋翼机非定常气弹动力学模型

倾转旋翼机前飞时,旋翼会处于复杂的非定常气动力环境中,但现有的倾转旋翼机前飞气弹稳定性分析模型主要采用准定常片条理论对旋翼的空气动力载荷进行气弹动力学建模,气弹稳定性分析结果与实验值存在明显误差.本文采用ONERA非定常气动力模型进行旋翼气动力建模,并考虑翼型压缩性和失速的影响,建立了倾转旋翼机前飞时的非定常气弹动力学模型.根据NASA试验模型参数,本文计算了系统各模态频率和阻尼比随前飞速度的变化,计算结果相对于现有理论模型更接近试验数据,表明本文建立的非定常气弹动力学模型可以比现有模型更准确地描述倾转旋翼机的前飞气弹稳定性.     

无线传感器网络最小中继节点布置问题的近似算法

为了消除传感器节点路由负载的不平衡,可在无线传感器网络中布置少量功能较强的中继节点作为路由节点,最小化中继节点数是其主要优化目标.文中证明了有界平面区域上的中继节点布置问题是P问题,但一般情况下的计算复杂度相当巨大.从中继节点布置问题的几何覆盖特征出发,提出了一种O(n~2 log n)时间的贪心近似算法,其中n为传感器节点数目.在该算法迭代过程的每一阶段,先从未被覆盖的传感器节点中选出一个关键节点,为了阻止孤立节点的产生,再按照"优先覆盖与关键节点距离较近的传感器节点"的原则来确定中继节点的位置.实验结果表明该算法可在很短的时间内生成一个接近最优的可行中继节点布置,且在中继节点布置的尺寸以及执行时间方面都要优于现有算法.     

基于改进型粒子群理论的大承载车辆盘式制动器优化设计

为了解决大承载盘式制动器产品设计水平较低和可靠性较差等问题,以大承载车辆盘式制动器为研究对象,建立将制动距离与制动温升作为优化目标的优化设计模型,使用改进型粒子群理论对重载车辆盘式制动器进行优化设计,最后通过有限元计算方法对优化后的模型进行验证。分析结果表明,随着车辆制动距离的增加,盘式制动器温升整体呈现下降的趋势;通过对优化设计后盘式制动器进行有限元验证,优化后盘式制动器模型的强度、温度及制动效能特性均满足使用要求,制动器设计方法得到丰富、可靠性得到提高。     

Dynamics of vehicle-pavement coupled system based on a revised flexible roller contact tire model

A revised flexible roller contact tire model (RFRC tire model) is proposed,which considers not only the geometric and flexible filtering effect,but also tire damping and pavement displacement.A vehicle-pavement coupled system is modeled as a two DOF oscillator moving along a simply supported beam on a linear viscoelastic foundation.By using the Galerkin's and Direct Integral method,dynamical responses of the vehicle-pavement coupled system are obtained based on the RFRC tire model and the traditional single...     

Market timing using combined forecasts and machine learning

Successful market timing strategies depend on superior forecasting ability. We use a sentiment index model, a kitchen sink logistic regression model, and a machine learning model (least absolute shrinkage and selection operator, LASSO) to forecast 1‐month‐ahead S&P 500 Index returns. In order to determine how successful each strategy is at forecasting the market direction, a “beta optimization” strategy is implemented. We find that the LASSO model outperforms the other models with consistently higher annual returns and lower monthly drawdowns.     

An Inhomogeneous Hidden Markov Model for Efficient Virtual Machine Placement in Cloud Computing Environments

In a cloud environment virtual machines are created with different purposes, such as providing users with computers or handling web traffic. A virtual machine is created in such a way that a user will not notice any difference from working on a physical computer. A challenging problem in cloud computing is how to distribute the virtual machines on a set of physical servers. An optimal solution will provide each virtual machine with enough resources and at the same time not using more physical services (energy/electricity) than necessary to achieve this. In this paper we investigate how forecasting of future resource requirements (CPU consumption) for each virtual machine can be used to improve the virtual machine placement on the physical servers. We demonstrate that a time‐dependent hidden Markov model with an autoregressive observation process replicates the properties of the CPU consumption data in a realistic way and forecasts future CPU consumption efficiently. Copyright © 2016 John Wiley & Sons, Ltd.     

基于交通流特征的可变信息板布局优化方法

为了引导交通流的合理分布并确定城市路网中可变信息板的布局方案,本文提出了一种基于路段交通流特征信息量评估的可变信息板布局优化方法。利用图论对城市路网进行描述,建立了基于信息熵的路段交通流特征信息量评估模型。并结合实际路网的拓扑结构特性,设计了可变信息板布局方案的初步生成方法。12节点的网络实例验证显示:该布局方法是有效的,且便于交通管理者所理解。同时,提出的交通流特征提取方法具有较强的应用性,可为道路规划与安全评估提供基础信息。     

Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels

For improving forecasting accuracy and trading performance, this paper proposes a new multi-objective least squares support vector machine with mixture kernels to forecast asset prices. First, a mixture kernel function is introduced into taking full use of global and local kernel functions, which is adaptively determined following a data-driven procedure. Second, a multi-objective fitness function is proposed by incorporating level forecasting and trading performance, and particle swarm optimization is used to synchronously search the optimal model selections of least squares support vector machine with mixture kernels. Taking CO₂ assets as examples, the results obtained show that compared with the popular models, the proposed model can achieve higher forecasting accuracy and higher trading performance. The advantages of the mixture kernel function and the multi-objective fitness function can improve the forecasting ability of the asset price. The findings also show that the models with a high-level forecasting accuracy cannot always have a high trading performance of asset price forecasting. In contrast, high directional forecasting usually means a high trading performance.     

A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

Air pollution has received more attention from many countries and scientists due to its high threat to human health. However, air pollution prediction remains a challenging task because of its nonstationarity, randomness, and nonlinearity. In this research, a novel hybrid system is successfully developed for PM_2.5 concentration prediction and its application in health effects and economic loss assessment. First, an efficient data mining method is adopted to capture and extract the primary characteristic of PM_2.5 dataset and alleviate the noises' adverse effects. Second, Harris hawks optimization algorithm is introduced to tune the extreme learning machine model with high prediction accuracy, then the optimized extreme learning machine can be established to obtain the forecasting values of PM_2.5 series. Next, PM_2.5-related health effects and economic costs was estimated based on the predicted PM_2.5 values, the related health effects, and environmental value assessment methods. Several experiments are designed using three daily PM_2.5 datasets from Beijing, Tianjin, and Shijiazhuang. Lastly, the corresponding experimental results showed that this proposed system can not only provide early warning information for environmental management, assist in the formulation of effective measures to reduce air pollutant emissions, and prevent health problems but also help for further research and application in different fields, such as health issues due to PM_2.5 pollutant.     

钢丝绳芯胶带芯绳断裂问题以及计算机模拟

钢丝绳芯胶带是钢丝绳芯带式输送机的主要部件，断带是带式输送机经常出现的重大事故之一。根据复合材料结构力学理论，建立了部分芯绳断裂的张力分配的力学和数学模型，运用微机对此问题做了模拟研究。并得出了相关结论。     

A deep residual compensation extreme learning machine and applications

The extreme learning machine (ELM) is a type of machine learning algorithm for training a single hidden layer feedforward neural network. Randomly initializing the weight between the input layer and the hidden layer and the threshold of each hidden layer neuron, the weight matrix of the hidden layer can be calculated by the least squares method. The efficient learning ability in ELM makes it widely applicable in classification, regression, and more. However, owing to some unutilized information in the residual, there are relatively huge prediction errors involving ELM. In this paper, a deep residual compensation extreme learning machine model (DRC-ELM) of multilayer structures applied to regression is presented. The first layer is the basic ELM layer, which helps in obtaining an approximation of the objective function by learning the characteristics of the sample. The other layers are the residual compensation layers in which the learned residual is corrected layer by layer to the predicted value obtained in the previous layer by constructing a feature mapping between the input layer and the output of the upper layer. This model is applied to two practical problems: gold price forecasting and airfoil self-noise prediction. We used the DRC-ELM with 50, 100, and 200 residual compensation layers respectively for experiments, which show that DRC-ELM does better in generalization and robustness than classical ELM, improved ELM models such as GA-RELM and OS-ELM, and other traditional machine learning algorithms such as support vector machine (SVM) and back-propagation neural network (BPNN).     

汽车爆胎报警及控制系统的研究现状及发展趋势

本文分析了汽车爆胎发生的原因及爆胎后车辆的运动特征,综述了防爆胎报警系统和爆胎控制系统的研究现状。在总结上述两种系统优缺点的基础上,对兼有爆胎报警和控制功能的系统的发展趋势进行了展望。