冰冻圈水文过程对黑河上游径流的影响分析

黑河上游处于高寒山区,开展冰冻圈水文过程对径流的影响研究,对于判断该地区径流变化趋势及其可持续性具有重要意义。本文应用基流分割、逐步多元回归等方法,分析了1960—2013年黑河上游出山径流量的变化及其原因,重点估计了融雪、冰川融化对径流的贡献,探讨了土壤冻融过程对径流变化的可能影响。分析结果表明,黑河上游山区河川径流在近54年间呈现上升趋势,其中以基流部分的上升为主。降水与气温变化对河川径流上升均有较大贡献。导致春季径流增加的主要因素为温度、降水次之;导致秋季径流增加的主要因素为降水、温度次之。夏季黑河上游东支的雨强有显著增大趋势(日雨量增幅为0.9 mm/10 a),对于河川径流贡献的比例约为15%。黑河山区的逐年降雪量变化不显著,冰川融化对径流增加的贡献小于10%。由此推断气温上升导致基流增加的主要原因是:气温升高导致高寒山区冻土活动层增厚,增加了土壤蓄水容量,从而导致降雨下渗量增加和基流量增大。由于黑河上游冻土分布广泛,未来气温持续上升的情况下,这种产流机制变化导致的基流增加具有可持续性。     

A resource oriented integration architecture for the Internet of Things: A business process perspective

The vision of the Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. These services when integrated with the traditional enterprise level services form the creation of ambient intelligence for a wide range of applications. To facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT resources, service oriented computing and resource oriented approaches have been widely used as promising technologies. However, a reference architecture integrating IoT services into either of these two technologies is still an open research challenge. In this article, we adopt the resource oriented approach to provide an end-to-end integration architecture of front-end IoT devices with the back-end business process applications. The proposed architecture promises a programmer friendly access to IoT services, an event management mechanism to propagate context information of IoT devices, a service replacement facility upon service failure, and a decentralized execution of the IoT aware business processes.     

南水北调中线工程冬季输水冰情的数值模拟

南水北调中线工程总干渠是由明渠和各种过水建筑物组成的树状系统,特点是线路长、过水建筑物类型多、冰水动力学响应过程复杂。本文将冰情发展模型与树状明渠系统复杂内边界条件下的渠道非恒定流模型进行集成耦合,开发了大型长距离调水工程冬季输水冰情数值模拟平台,提出了树状明渠系统复杂内边界条件的等效变换方法和计算冰盖前缘动态发展的虚拟流动法。根据实测的沿线气象资料和设计资料,利用该平台系统模拟了在寒冷气温条件下总干渠初冰、冰盖形成、发展和消融过程。分析了冬季输水的冰情特性及冰期输水能力,提出了冰期安全运行的水力控制条件,通过对典型年总干渠敞流期、冰盖形成期、稳封期、调度期以及开河全过程的模拟,给出了安全运行输水调度方案及建议。     

A minimum Hellinger distance estimator for stochastic differential equations: An application to statistical inference for continuous time interest rate models

A minimum disparity estimator minimizes a φ-divergence between the marginal density of a parametric model and its non-parametric estimate. This principle is applied to the estimation of stochastic differential equation models, choosing the Hellinger distance as particular φ-divergence. Under an hypothesis of stationarity, the parametric marginal density is provided by solving the Kolmogorov forward equation. A particular emphasis is put on the non-parametric estimation of the sample marginal density which has to take into account sample dependence and kurtosis. A new window size determination is provided. The classical estimator is presented alternatively as a distance minimizer and as a pseudo-likelihood maximizer. The latter presentation opens the way to Bayesian inference. The method is applied to continuous time models of the interest rate. In particular, various models are tested using alternatively tests and their results are discussed.     

Application of reinforcement learning to the game of Othello

Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such complex problems are associated with some difficulties. As we discuss in this article, these methods are plagued by the so-called curse of dimensionality and the curse of modelling. In this article, we discuss reinforcement learning, a machine learning technique for solving sequential decision making problems with large state spaces. We describe how reinforcement learning can be combined with a function approximation method to avoid both the curse of dimensionality and the curse of modelling. To illustrate the usefulness of this approach, we apply it to a problem with a huge state space—learning to play the game of Othello. We describe experiments in which reinforcement learning agents learn to play the game of Othello without the use of any knowledge provided by human experts. It turns out that the reinforcement learning agents learn to play the game of Othello better than players that use basic strategies.     

Queueing analysis of a server node in transaction processing middleware systems

Quantitative performance modeling of complex information systems is of immense importance for designing enterprise e-business infrastructures and applications. In this paper, we present a traffic model of a server node in a typical transaction processing middleware system as well as a quantitative framework to model and analyze its performance. A multi-class open queueing network model is presented in which multi-class jobs are admitted to a number of server processes sharing hardware resources including the CPU and the disk. We have developed a viable approximation method, which decomposes the dependent components into their independent counterparts while preserving their relevant characteristics. We have conducted queueing-theoretic delay analyses and verified the approach using simulation. Results demonstrate the strength of our approach in predicting delays, elapsed times and other system performance measures.     

Querying business processes with BP-QL

We present in this paper BP-QL, a novel query language for querying business processes. The BP-QL language is based on an intuitive model of business processes, an abstraction of the emerging BPEL (business process execution language) standard. It allows users to query business processes visually, in a manner very analogous to how such processes are typically specified, and can be employed in a distributed setting, where process components may be provided by distinct providers.     

线性离散时滞重复过程的H∞控制

首先给出了线性离散时滞重复过程的2D(两维)Roesser模型, 采用线性矩阵不等式方法导出了过程稳定并具有H_∞扰动抑制度的一个充分条件. 通过求一个线性矩阵不等式的可行解来构造系统的一个状态反馈H_∞控制器. 进一步, 通过求解一个线性矩阵不等式凸优化问题得到该过程的最优状态反馈H_∞控制器.     

一种工业过程稳态优化控制算法

针对带有输出关联约束的工业过程,提出一种确定其稳态优化控制的算法.首先通过对数变换将原问题转化为一个等价而且可在对数空间求解的优化控制问题;然后为避免事先选择一个合适罚系数的困难,在算法中引入了目标函数的线性化形式.该优化算法不仅能收敛到正确的系统最优解,而且可用现有的二次规划算法计算.应用简单的滤波技术,改善了算法在有量测噪声情况下的性能.仿真结果表明,所提出的优化算法是有效的.