Study of a Jacobian-free approach in the simulation of compressible fluid flows in porous media using a derivative-free spectral method

The development of Jacobian-free software for solving problems formulated by nonlinear partial differential equations is of increasing interest to simulate practical engineering processes. For the first time, this work uses the so-called derivative-free spectral algorithm for nonlinear equations in the simulation of flows in porous media. The model considered here is the one employed to describe the displacement of miscible compressible fluid in porous media with point sources and sinks, where the density of the fluid mixture varies exponentially with the pressure. This spectral algorithm is a modern method for solving large-scale nonlinear systems, which does not use any explicit information associated with the Jacobin matrix of the considered system, being a Jacobian-free approach. Two dimensional problems are presented, along with numerical results comparing the spectral algorithm to a well-developed Jacobian-free inexact Newton method. The results of this paper show that this modern spectral algorithm is a reliable and efficient method for simulation of compressible flows in porous media.     

MTConnect compliant monitoring for finishing assembly interfaces of large-scale components: A vertical tail section application

Monitoring is a significant issue for finishing the assembly interfaces of large-scale components before final assembly. Acquisition and supervision of the pivotal data is essential to ensure the security and reliability for machining the large and complicated components with high-value. This process is generally cumbersome and time-consuming because there are various types of data coming from different components and sensors. The problem becomes more serious when considering the whole shop floor. Recently, MTConnect has been proven to be an effective method to realize standardized data collection and monitoring process. However, MTConnect is still under development and cannot cover the whole finishing process such as on-machining measuring (OMM) and fixturing. To address the issue, an MTConnect compliant method with extended data models is proposed in this paper to implement a standardized monitoring system. Firstly, a finishing system for the assembly interfaces is introduced, including the framework, workflow and key procedures and data. Then extended MTConnect data models are proposed to represent the finishing system including on-machine touch-trigger probe and sensor-based intelligent fixturing related information. Based on the extended MTConnect data models, a web-based monitoring system is developed for data collection and monitoring by combining an MTConnect agent and an OPC adapter. The proposed approach is validated by collecting and monitoring the key process data using an airplane vertical tail as an application. The advantages of using MTConnect would be more significant when extended to the entire factory and implemented in cloud manufacturing in the future.     

改进牛顿法大规模电力系统潮流计算

电网互联导致电力系统规模不断扩大,对牛顿法进行潮流计算提出了更高的要求。探讨5种改进牛顿法应用到大规模电力系统潮流计算中。经IEEE 300、Poland多个互联的大规模电力系统共6个算例分析表明,算法1和算法2改善了初值范围,同样的迭代次数下,收敛精度较经典牛顿法高,但计算时间较经典的牛顿法并未明显提高;算法3和算法4提高潮流计算的速度和收敛精度。经UCTE 1254病态系统测试,算法3较算法5能高效地处理病态潮流问题,因而更适合于大规模电力系统潮流计算。     

基于免疫原理的大规模分布式入侵检测模型

将生物免疫原理运用在入侵检测中,以适应入侵检测向大规模分布式的方向发展的要求。提出一种新的检测模型,将异常检测与误用检测有机地联系在一起,并充分考虑主机所面临的实际网络环境,对IDS检测主机进行精简,以提高主机的检测效率。     

Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method

Large-scale plant-wide processes have become more common and monitoring of such processes is imperative. This work focuses on establishing a distributed monitoring scheme incorporating multivariate statistical analysis and Bayesian method for large-scale plant-wide processes. First, the necessity of distributed monitoring is demonstrated by theoretical analysis on the impact of process decomposition on multivariate statistical process monitoring performance. Second, a stochastic optimization algorithm-based performance-driven process decomposition method is proposed which aims to achieve the best possible monitoring performance from process decomposition aspect. Based on the obtained sub-blocks, local monitors are established to characterize local process behaviors, and then a Bayesian fault diagnosis system is established to identify the underlying process status of the entire process. The proposed distributed monitoring scheme is applied on a numerical example and the Tennessee Eastman benchmark process. Comparison results to some state-of-the-art methods indicate the efficiency and feasibility.     

Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification

Analysis of high dimensional data in modern applications, such as neuroscience, text mining, spectral analysis, chemometrices naturally requires tensor decomposition methods. The Tucker decompositions allow us to extract hidden factors (component matrices) with different dimension in each mode, and investigate interactions among various modalities. The alternating least squares (ALS) algorithms have been confirmed effective and efficient in most of tensor decompositions, especially Tucker with orthogonality constraints. However, for nonnegative Tucker decomposition (NTD), standard ALS algorithms suffer from unstable convergence properties, demand high computational cost for large scale problems due to matrix inverse, and often return suboptimal solutions. Moreover they are quite sensitive with respect to noise, and can be relatively slow in the special case when data are nearly collinear. In this paper, we propose a new algorithm for nonnegative Tucker decomposition based on constrained minimization of a set of local cost functions and hierarchical alternating least squares (HALS). The developed NTD-HALS algorithm sequentially updates components, hence avoids matrix inverse, and is suitable for large-scale problems. The proposed algorithm is also regularized with additional constraint terms such as sparseness, orthogonality, smoothness, and especially discriminant. Extensive experiments confirm the validity and higher performance of the developed algorithm in comparison with other existing algorithms.     

Adaptive atomic capture of multiple molecules

Facing the scale, heterogeneity and dynamics of the global computing platform emerging on top of the Internet, autonomic computing has been raised recently as one of the top challenges of computer science research. Such a paradigm calls for alternative programming abstractions, able to express autonomic behaviours. In this quest, nature-inspired analogies regained a lot of interest. More specifically, the chemical programming paradigm, which envisions a program’s execution as a succession of reactions between molecules representing data to produce a result, has been shown to provide some adequate abstractions for the high-level specification of autonomic systems.     

利用UID的唯一性解决大型服务器及多客户端用户的设置问题

石油行业对大型服务器的应用一般是将大型石油应用软件安装到服务器上,如果所有研究人员通过其多个网络客户端对其登录。这就需要很多并不固定的用户供他们使用,常规的用户管理理念无法满足这样的用户设置。本文以油田勘探解释系统为例,阐述如何利用UID的唯一性来解决大型服务器及多客户端用户的设置问题。这样既保证了各用户数据的安全与共享,又满足了用户对建立个性化用户的需要。     

基于贝叶斯理论框架的传感器选择算法

针对大规模传感器网络(Large-scale sensor networks)的目标跟踪问题, 本文在贝叶斯(Bayes)框架下, 提出了一种全新的基于传感器选择的多传感器目标跟踪算法.算法的具体思路为:首先基于Bayes框架, 根据不同的管理目标, 推导出传感器选择的目标函数; 然后根据目标函数, 计算出相应的传感器选择方案; 最后将选择的传感器进行数据融合, 求得传感器网络的目标跟踪结果.相比传统的基于量测野值点剔除思想的目标跟踪算法以及基于系统偏差估计的传感器配准算法, 本文提出的基于传感器选择的多传感器目标跟踪算法不仅目标跟踪精度更高, 且跟踪性能更稳定.同时本文提出的传感器选择算法还可以适用于杂波数目较少的目标跟踪场景.仿真结果说明了本文所提算法的有效性.     

大规模分布式入侵检测系统的研究

现在的入侵检测系统大多存在系统可扩展性差、入侵检测技术单一、缺乏对入侵事件有效的响应机制等问题,难以满足企业对入侵检测系统在规模及有效性上的的需求。文章设计了一种大规模分布式入侵检测系统的整体模型,并在系统中综合运用了多种入侵检测方法和响应机制,使整个系统具有好的规模扩展性、高的入侵检测性能和有效的响应机制。