基于稀疏子空间的类星体光谱异常特征并行提取与分析

类星体是人类所观测到的最遥远天体,对于了解早期宇宙的演化具有重要科学意义。由于类星体距离地球较远,其红移一般较大,导致在光学观测窗口中只有很少的特征（发射线）,且难以识别。类星体光谱的异常特征提取与分析可对未知类星体的识别,提供有效的判别依据。离群检测作为数据挖掘领域的一个主要研究内容,旨在发现那些稀有、特殊数据对象及异常特征,可作为从海量类星体光谱数据中,发现特殊、未知类星体的一种有效途径和手段。Spark作为新一代大数据分布式处理框架,可为海量天体光谱的有效分析和处理,提供一个高效且可靠的并行编程平台。本文充分利用集群系统和Spark编程模型的强大数据处理能力,提出一种基于稀疏子空间的类星体光谱异常特征并行提取与分析方法,其工作由三个模块组成,即类星体光谱特征约减、类星体光谱的稀疏子空间构造和搜索、类星体光谱异常特征提取并行算法设计与分析。类星体光谱特征约减模块,通过属性相关性分析来识别呈现聚类结构的类星体光谱特征线,这些特征线通常会聚集在稠密区域且对类星体光谱异常特征检测毫无意义。光谱特征约减旨在运行异常特征检测算法之前剪枝类星体光谱的冗余特征线,缩小光谱数据检测范围。类星体光谱的稀疏子空间构造和搜索模块,通过设定的稀疏系数阈值来测量类星体光谱的子空间密度,并采用粒子群优化方法作为稀疏子空间的搜索策略,从而快速、高效地获取类星体的异常特征。在第三个模块中,提出了一种MapReduce框架下的类星体光谱异常数据并行检测算法,该算法由并行化数据约减策略、稀疏子空间并行搜索技术两个MapReduce构成,达到适应海量光谱数据的处理目标。最后对检测出的部分类星体异常特征进行了理论分析、测量及人眼证认,充分说明稀疏子空间可为识别特殊、未知类星体候选源,提供有效支持和有力证据。     

A Dynamic Anomaly Detection Approach Based on Permutation Entropy for Predicting Aging-Related Failures

Software aging is a phenomenon referring to the performance degradation of a long-running software system. This phenomenon is an accumulative process during execution, which will gradually lead the system from a normal state to a failure-prone state. It is a crucial challenge for system reliability to predict the Aging-Related Failures (ARFs) accurately. In this paper, permutation entropy (PE) is modified to Multidimensional Multi-scale Permutation Entropy (MMPE) as a novel aging indicator to detect performance anomalies, since MMPE is sensitive to dynamic state changes. An experiment is set on the distributed database system Voldemort, and MMPE is calculated based on the collected performance metrics during execution. Finally, based on MMPE, a failure prediction model using the machine learning method to reveal the anomalies is presented, which can predict failures with high accuracy.     

基于云服务的统计测试非监督故障诊断预测

为了对系统中的潜在故障进行有效地预测,提出一种基于统计测试的非监督故障预测方法。首先,将云服务系统定义为运行在相同的软/硬件环境下,具有相同输入数据的并行系统。在数据预处理过程中,对性能计数器中的数据进行标准化,并选取了一定分位数下的计数器数据信息。最后根据具有相同软/硬件环境和输入数据的节点将产生相同的输出这一原则提出了一种统计测试方法用于系统故障的预测。实验表明,本文提出的基于统计测试的故障预测方法与其它相关算法相比,具有预测准确性高和执行效率快等优点。     

A Coupling Framework for Multi-Domain Modelling and Multi-Physics Simulations

This paper describes a coupling framework for parallel execution of different solvers for multi-physics and multi-domain simulations with an arbitrary number of adjacent zones connected by different physical or overlapping interfaces. The coupling architecture is based on the execution of several instances of the same coupling code and relies on the use of smart edges (i.e., separate processes) dedicated to managing the exchange of information between two adjacent regions. The collection of solvers and coupling sessions forms a flexible and modular system, where the data exchange is handled by independent servers that are dedicated to a single interface connecting two solvers’ sessions. Accuracy and performance of the strategy is considered for turbomachinery applications involving Conjugate Heat Transfer (CHT) analysis and Sliding Plane (SP) interfaces.     

BOTDA光纤传感技术在光纤环圈质量评估中的应用

在分析光纤环对陀螺整体性能影响的基础上,提出将BOTDA光纤传感技术用于光纤环质量检测。通过对光纤环的在线检测、热应力检测和骨架性能影响测试,能及时发现并消除光纤绕制中的应力尖峰及换层现象,消除由骨架、热应力引起的光纤应力曲线反转现象。     

Quantifying the chiral magnetic effect from anomalous-viscous fluid dynamics

The Chiral Magnetic Effect(CME) is a macroscopic manifestation of fundamental chiral anomaly in a many-body system of chiral fermions, and emerges as an anomalous transport current in the fluid dynamics framework. Experimental observation of the CME is of great interest and has been reported in Dirac and Weyl semimetals. Significant efforts have also been made to look for the CME in heavy ion collisions. Critically needed for such a search is the theoretical prediction for the CME signal. In this paper we report a first quantitative modeling framework, Anomalous Viscous Fluid Dynamics(AVFD), which computes the evolution of fermion currents on top of realistic bulk evolution in heavy ion collisions and simultaneously accounts for both anomalous and normal viscous transport effects. AVFD allows a quantitative understanding of the generation and evolution of CME-induced charge separation during the hydrodynamic stage, as well as its dependence on theoretical ingredients. With reasonable estimates of key parameters, the AVFD simulations provide the first phenomenologically successful explanation of the measured signal in 200 AGe V Au Au collisions.     

Unsupervised Anomaly Detection with Distillated Teacher-Student Network Ensemble

We address the problem of unsupervised anomaly detection for multivariate data. Traditional machine learning based anomaly detection algorithms rely on specific assumptions of normal patterns and fail to model complex feature interactions and relations. Recently, existing deep learning based methods are promising for extracting representations from complex features. These methods train an auxiliary task, e.g., reconstruction and prediction, on normal samples. They further assume that anomalies fail to perform well on the auxiliary task since they are never trained during the model optimization. However, the assumption does not always hold in practice. Deep models may also perform the auxiliary task well on anomalous samples, leading to the failure detection of anomalies. To effectively detect anomalies for multivariate data, this paper introduces a teacher-student distillation based framework Distillated Teacher-Student Network Ensemble (DTSNE). The paradigm of the teacher-student distillation is able to deal with high-dimensional complex features. In addition, an ensemble of student networks provides a better capability to avoid generalizing the auxiliary task performance on anomalous samples. To validate the effectiveness of our model, we conduct extensive experiments on real-world datasets. Experimental results show superior performance of DTSNE over competing methods. Analysis and discussion towards the behavior of our model are also provided in the experiment section.     

Computational analysis of the roles of biochemical reactions in anomalous diffusion dynamics

Most biochemical processes in cells are usually modeled by reaction–diffusion(RD) equations. In these RD models,the diffusive process is assumed to be Gaussian. However, a growing number of studies have noted that intracellular diffusion is anomalous at some or all times, which may result from a crowded environment and chemical kinetics. This work aims to computationally study the effects of chemical reactions on the diffusive dynamics of RD systems by using both stochastic and deterministic algorithms. Numerical method to estimate the mean-square displacement(MSD) from a deterministic algorithm is also investigated. Our computational results show that anomalous diffusion can be solely due to chemical reactions. The chemical reactions alone can cause anomalous sub-diffusion in the RD system at some or all times.The time-dependent anomalous diffusion exponent is found to depend on many parameters, including chemical reaction rates, reaction orders, and chemical concentrations.     

An algorithm for the automatic detection of anomalous temperature variations at ground level: its application to the METEOSAT-2 satellite

Summary The algorithm, starting from techniques typical of image processing and information theory, is an instrument for the detection of anomalous variations in any physical parameter measured by remote sensing. Within a software system capable of checking the validity of such a measurement, comparing it with extant historical data and reporting it, the algorithm acts as a filter able to amplify the anomaly. The aim of this work is to see if it possible to detect anomalous temperature variations in some way connected with fire at ground level by means of the sensors in the infrared heat band of the METEOSAT-2 satellite. The choice of this geostationary satellite was made because, despite low spatial resolution (at the latitude of Sardinia pixels are about 7 km square), it has good time resolution (one slot every 30 minutes).     

脑卒中信息管理系统权限控制的设计与实现

随着医疗信息技术和互联网的飞速发展,医疗信息资源的安全越来越备受关注,权限管理为解决信息系统安全性问题提供了重要保障,为防止非法获得或破坏信息起着重要的作用。脑卒中信息管理系统存放大量脑卒中患者的治疗信息,为医生发现其潜在的疾病提供依据。针对脑卒中信息管理系统复杂的权限控制需求,本文提出了一种权限控制方法,它采用SpringMVC+Hibernate后台框架,Bootstrap前台框架技术,以及B/S结构软件设计中的精粒度权限管理思路,实现了软件系统中的权限管理。实践证明系统能够满足权限控制需求,具有良好的可操作性和灵活性。     

A bit-level image encryption algorithm based on spatiotemporal chaotic system and self-adaptive

This paper proposes a bit-level image encryption algorithm based on spatiotemporal chaotic system which is self-adaptive. We use a bit-level encryption scheme to reduce the volume of data during encryption and decryption in order to reduce the execution time. We also use the adaptive encryption scheme to make the ciphered image dependent on the plain image to improve performance. Simulation results show that the performance and security of the proposed encryption algorithm can encrypt plaintext effectively and resist various typical attacks.     

CSNS加速器真空控制系统的设计与实现

中国散裂中子源(CSNS)加速器真空控制系统负责真空数据采集、设备监控和闸板阀控制与联锁,是设备运行和故障诊断以及超高真空保持的重要保障。本文介绍了加速器真空需求,基于实验物理及工业控制系统EPICS软件框架的真空控制系统设计与实现,使用横河可编程逻辑控制器PLC控制与联锁设备,摩莎MOXA工控机监测真空状态,EPICS PV数据直接进入声音报警系统和历史数据库系统,为工作人员及时发现和处理问题、进行后续数据分析和机器研究等提供了便捷途径和可靠保障。目前,该系统已完成现场安装和调试,并已正式投入运行。运行结果表明,该系统具有稳定性好、可靠性高、人机交互友好的特点,很好地满足了加速器真空控制系统运行的需要。     

Study on generation mechanism of anomalous acoustic-gravity waves before the 2011 Beijing earthquake(M_L=3.0)

Observation results of abnormal acoustic-gravity waves before a Beijing earthquake(M_L=3.0) are presented.During this period,abnormalities of earth surface tilt variations were also recorded.The cross-correlations between the both values are high,which reach maximal values of 0.5 in the area close to the epicenter.The correlations decrease with increasing distances from the epicenter.It was proposed that generation of the anomalous waves may be associated with the pressure and wind perturbations in the air flow caused by slowly shaking mountains during slow surface motion preceding the earthquake in Beijing.Based on the wind velocity data taken from a 350 m meteorological tower in Beijing,the propagation of ducted acoustic-gravity waves in a two-layer model of the atmosphere was numerically simulated.It is shown that characteristic periods,amplitudes and velocities of the simulated phases which were assumed from non-stationary air flow relative to mountains are approximate to the observed phases.A consistency between the simulated results and observation data indicates that a slow surface motion may be a possible source of the anomalous acoustic-gravity waves observed prior to the earthquake.     

北京一次小震级地震前异常声-重力波产生机理的研究

观测到北京一次小震级地震(ML=3.0)前的异常声-重力波,并同时记录到异常地表倾斜信号。二者相关系数比较高,在接近震中的地区相关系数达到最大值0.5,相关系数随着远离震中的距离增大而减小。经过研究表明,异常声-重力波的产生与震前的地表缓慢活动引发的山体缓慢晃动导致的气流风速波动变化有关。基于350 m高的气象铁塔观测的风速数据,对两层大气模型传播的声-重力波进行了数值计算,得到的相对山体非平稳气流引起的声-重力波与观测信号在特征周期、幅度和速度三个参数上近似。以地表倾斜为例的模型与观测数据的一致性表明该震前声-重力波与地震存在关联性。      

Temperature dependence of elastic moduli of submicrocrystalline copper

The possibility of applying the two-component model of single-phase hybrid materials to the explanation of the anomalous temperature dependence of elastic moduli of copper with a submicrocrystalline structure has been discussed. An analysis has been performed based on new experimental data. A twin mechanism of changes in the crystallite orientation has been proposed. These changes cause the anomalous behavior of the elastic moduli of copper during deformation and subsequent heatings.     

基于GPIB通信的大冷量风冷冷水机组性能测试系统设计

大冷量风冷冷水机组性能测试系统是针对研制的500RT风冷冷水机组性能测试台运行过程中所需要的参数采集、监测以及处理的专门软件。软件开发依据GB/T 18430.1-2007和AHRI 551/591所规定的风冷冷水机组性能测试要求进行,应用最常用的Visual Basic面向对象编程语言为开发平台,构建高效的采集、检测与数据处理测试程序,能够实现对风冷冷水机组性能测试过程中实验数据自动保存与分析处理。测试软件系统通过数据采集仪以通用GPIB接口通信方式把各种传感器获取的信号传输至计算机,实现在不同测试工况下对风冷冷水机组各项性能测试参数的自动采集与监控。测试软件系统能够根据测试所得数据进行测试系统状态判定,有效实验数据分析处理、存档,绘制和打印实时曲线,并输出测试报告。     

A framework for the automated real-time detection of short tonal sounds from ocean observatories

Acquisition of acoustic data from ocean observatories is expected to play a key role for the long-term monitoring of marine mammals and anthropogenic noise. It typically requires processing of a large volume of acoustic data and it must rely on automated identification of signals. We present an algorithmic framework for the detection of short tonal sounds (e.g. cetacean calls, anthropogenic pings) intended to act as a first stage in a system for the automated real-time detection, classification, and localisation of acoustic sources. The algorithm was validated under a diversity of scenarios expected at ocean observatories. Using simulated signals that emulate a variety of cetacean call-types, perfect identification of signal position was obtained for signal to noise ratios of ?15 to ?5 dB, depending on the signal-type. Separation of real-world data segments with short tonal sounds (mainly cetacean calls) from segments with other sounds or noise resulted in Area Under the ROC Curve values between 0.96 and 0.98. The algorithm can be used to automatically identify cetacean calls and anthropogenic short tonal sounds much faster than in real-time, thereby reducing the burden put on data transmission, storage, or processing by classification and localisation algorithms.     

Chaos in computer performance

Modern computer microprocessors are composed of hundreds of millions of transistors that interact through intricate protocols. Their performance during program execution may be highly variable and present aperiodic oscillations. In this paper, we apply current nonlinear time series analysis techniques to the performances of modern microprocessors during the execution of prototypical programs. Our results present pieces of evidence strongly supporting that the high variability of the performance dynamics during the execution of several programs display low-dimensional deterministic chaos, with sensitivity to initial conditions comparable to textbook models. Taken together, these results show that the instantaneous performances of modern microprocessors constitute a complex (or at least complicated) system and would benefit from analysis with modern tools of nonlinear and complexity science.     

等离激元增强拉曼光谱食用合成色素快速检测系统设计

基于等离激元增强拉曼光谱技术,研制出适合现场分析检测的食用合成色素快速检测系统,适用于饮料、肉制品、蜜饯等食品中合成色素的快速检测。检测系统的硬件部分主要由双CPU(ARM和FPGA)主控板、样品前处理模块(含有增强粒子施加装置)、半导体激光光源、光谱数据采集模块构成;软件部分控制样品前处理模块自动运行,并可读取被测样品的拉曼谱图。利用快速检测系统对三种实际样品(蜜饯、汽水、火腿肠)中的食用合成色素胭脂红、柠檬黄、诱惑红进行检测,以验证该系统在食用合成色素检测中的性能。样品检测结果的相对标准偏差小于±5%,表明此检测系统具有良好的灵敏度和重现性,且检测时间短,能够满足食品中合成色素现场快速检测的要求。     

基于故障矩阵的贝叶斯故障定位方法

故障的准确诊断和定位是云计算系统提供持续服务的前提条件。为了提高系统故障诊断和定位的性能,本文提出了一种基于故障矩阵的贝叶斯故障定位方法。首先,对云计算系统的软件结构进行了抽象,对事物进行了定义,并描述了事务的执行路径。其次,将系统运行的多个执行路径表示为故障矩阵,并给出了组件健康状态的逻辑命题表达式。最后,应用贝叶斯概率分析了系统故障的概率。实验表明,本文提出的方法与其它相关方法相比,故障识别的准确性更高,所用的执行时间更短。