反渗透阻垢剂的合成、应用及机理研究进展

投加阻垢剂是解决反渗透(Reverse osmosis,RO)膜结垢问题简单有效的方法。综述了RO系统中阻垢剂的合成应用、阻垢特征、机理及存在问题。含磷类阻垢剂因导致水体磷污染已经基本被其它几类阻垢剂替代,绿色环保类阻垢剂在阻垢效果和可降解性方面均比聚合物类好。但任何阻垢剂单独使用都难于满足(RO)系统阻垢的实际需求,阻垢剂的复配可相互弥补单一阻垢剂的缺陷,同时发挥其协同效应,而阻垢剂的特征改性具有更强的针对性和适用性。因此,阻垢剂的复配和改性是其研究的趋势和重点。     

A relativistic extension of the complex scaling method using oscillator basis functions

We develop the complex scaling method within the relativistic framework by expanding the Dirac spinors in the complete set of eigensolutions of a harmonic oscillator potential, and present the theoretical formalism of describing the discrete bound and resonant states on the same footing. Based on a well established and frequently used model, we demonstrate the utility and applicability of the extended method and examine the stability of the results with respect to the variations of the parameters of the model. Satisfactory agreements are found for all the calculated results in comparison with some other calculations in references. Especially, the present calculation in the nonrelativistic limit gives a consistent result with that in the nonrelativistic calculation.     

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

A Modern Graphics Processing unit (GPU) is able to perform massively parallel scientific computations at low cost. We extend our implementation of the checkerboard algorithm for the two-dimensional Ising model [T. Preis et al., Journal of Chemical Physics 228 (2009) 4468-4477] in order to overcome the memory limitations of a single GPU which enables us to simulate significantly larger systems. Using multi-spin coding techniques, we are able to accelerate simulations on a single GPU by factors up to 35 compared to an optimized single Central Processor Unit (CPU) core implementation which employs multi-spin coding. By combining the Compute Unified Device Architecture (CUDA) with the Message Parsing Interface (MPI) on the CPU level, a single Ising lattice can be updated by a cluster of GPUs in parallel. For large systems, the computation time scales nearly linearly with the number of GPUs used. As proof of concept we reproduce the critical temperature of the 2D Ising model using finite size scaling techniques.     

Missile target automatic recognition from its decoys based on image time-series

As the development of the technology for radar target recognition, missile target automatic recognition has received considerable attention in recent years. Missile target, compared with the plane target, is hard to recognize for its smallness, feebleness and maneuver. In this paper, a new recognition method based on radar image time-series, which can significantly reduce the recognition time and classification error, is proposed. The image time-series are produced by range instantaneous Doppler imaging algorithm firstly, and then cross-range scaling of the images is processed. In particular, the inertia ratio, extracted from the obtained image time-series, is introduced to distinguish the missile from decoys. Furthermore, the effectiveness of this method is demonstrated by application to simulated data and it has been shown that this method has the potential to be used in a number of real-time applications.     

Global and robust formation-shape stabilization of relative sensing networks

This paper proposes a simple, distributed algorithm that achieves global stabilization of formations for relative sensing networks in arbitrary dimensions with fixed topology. Assuming the network runs an initialization procedure to equally orient all agent reference frames, convergence to the desired formation shape is guaranteed even in partially asynchronous settings. We characterize the algorithm robustness against several sources of errors: link failures, measurement errors, and frame initialization errors. The technical approach combines algebraic graph theory, multidimensional scaling, and distributed linear iterations.     

Landmark MDS ensemble

Landmark multidimensional scaling (LMDS) uses a subset of data (landmark points) to solve classical multidimensional scaling (MDS), where the scalability is increased but the approximation is noise-sensitive. In this paper we present an LMDS ensemble where we use a portion of the input in a piecewise manner to solve classical MDS, combining individual LMDS solutions which operate on different partitions of the input. Ground control points (GCPs) that are shared by partitions considered in the ensemble, allow us to align individual LMDS solutions in a common coordinate system through affine transformations. We incorporate priors into combining multiple LMDS solutions such that the weighted averaging by priors improves the noise-robustness of our method. Our LMDS ensemble is much less noise-sensitive while maintaining the scalability and the speed of LMDS. Experiments on synthetic data (noisy grid) and real-world data (similar image retrieval) confirm the high performance of the proposed LMDS ensemble.     

SMS云网关负载感知弹性伸缩控制算法

SMS短信网关（Short Message Service,SMS）具有并发量大、峰值高、难预测以及消耗资源量大的特点,如何保障系统高效、稳定运行一直是电信运营商致力解决的关键问题之一.本文提出了一种能够通过云计算平台动态资源控制能力,实现负载感知的弹性SMS云网关设计方案及弹性控制算法,提升了SMS短信网关服务质量目标保障效率,以及资源利用率.仿真环境下测试结果显示相比现有算法,弹性控制算法执行效率更高,集群规模随资源利用率抖动性更小.     

Extending metric multidimensional scaling with Bregman divergences

Sum of weighted square distance errors has been a popular way of defining stress function for metric multidimensional scaling (MMDS) like the Sammon mapping. In this paper we generalise this popular MMDS with Bregman divergences, as an example we show that the Sammon mapping can be thought of as a truncated Bregman MMDS (BMMDS) and we show that the full BMMDS improves upon the Sammon mapping on some standard data sets and investigate the reasons underlying this improvement. We then extend a well known family of MMDS, that deploy a strategy of focusing on small distances, with BMMDS and investigate limitations of the strategy empirically. Then an opposite strategy is introduced to create another family of BMMDS that gives increasing mapping quality. A data preprocessing method and a distance matrix preprocessing are introduced.     

Dynamic demultiplexing in network calculus—Theory and application

During the last two decades, starting with the seminal work by Cruz, network calculus has evolved as a new theory for the performance analysis of networked systems. In contrast to classical queueing theory, it deals with performance bounds instead of average values and thus has been the theoretical basis of quality of service proposals such as the IETF’s Integrated and Differentiated Services architectures. Besides these it has, however, recently seen many other application scenarios as, for example, wireless sensor networks, switched Ethernets, avionic networks, Systems-on-Chip, or even to speed-up simulations, to name a few.In this article, we extend network calculus by adding a new versatile modeling element: a demultiplexer. Conventionally, demultiplexing has been either neglected or assumed to be static, i.e., fixed at the setup time of a network. This is restrictive for many potential applications of network calculus. For example, a load balancing based on current link loads in a network could not be modeled with conventional network calculus means. Our demultiplexing element is based on stochastic scaling. Stochastic scaling allows one to put probabilistic bounds on how a flow is split inside the network. Fundamental results on network calculus with stochastic scaling are therefore derived in this work. We illustrate the benefits of the demultiplexer in a sample application of uncertain load balancing.