Network selection policy based on effective capacity in heterogeneous wireless communication systems

A great deal of previous research was based on the concept of Shannon theory without considering the delay characteristic. However, in reality, different services have different delay constraints. In this paper, two new network selection policies are proposed for heterogeneous wireless communication systems using effective capacity, which incorporate delay in the transmission rate. Users can access the proper network after considering the delay demands of different services. Our proposed policies aim to maximize the entire throughput with different delay constraints. Both the mathematicM analysis and simulations show that the proposed network selection policies can improve network throughput while providing quality-of-service guarantees.     

Performance analysis of three multi-radio access control policies in heterogeneous wireless networks

Access control policy in wireless networks has a significant impact on QoS satisfaction and resource utilization efficiency. The design of access control policy in heterogeneous wireless networks （HWNs） becomes more challenging especially for the heterogeneous multiple access protocols of each radio network. In this paper, a Markov model is proposed to analyze the performance of three access control policies for HWNs. The first policy is the optimal radio access technology （O-RAT） selection, where the incoming traffic always tries to access one network with the maximum service rate before admission. The second policy intends to allocate the same data to all networks. And the traffic will leave the system if it is accomplished first by one of these networks, which is formulated as the aggregated multi-radio access （A-MRA） technology. The third policy is named the parallel multi-radio access （P-MRA） transmission, in which the incoming traffic is split into different networks. The traffic is served with the sum of the service rates provided by overall networks. Numerical and simulate results show the effectiveness of our analytical framework and the performance gain of the three access control policies. As illustrated with some representative results, the P-MRA policy shows superior performance gain to the other two policies independent on the specific parameters of the different multiple access protocols due to the multiplexing gain.     

Framework for distributed video surveillance in heterogeneous environment

In order to overcome the disadvantages of traditional centralized video monitoring system or distributed systems based on wired network, we propose a framework for distributed video surveillance in heterogeneous environment. Video flows are compressed with the scalable video encoding standard MPEG-4 and transmitted over lnternet or wireless network. Video surveillance can be performed wherever there is Internet or mobile telephone signal. The feasibility of this framework has been demonstrated with a prototype implementation. The system is cheaper and easier to achieve with simple equipments, so it can be widely used in practice.     

A novel Data-Driven framework based on BIM and knowledge graph for automatic model auditing and Quantity Take-off

Model auditing is a critical step before conducting Building Information Modeling (BIM)-based Quantity Take-off (QTO) because these models may contain various human errors and mistakes, leading to insufficient semantic information and inconsistent modeling style in BIM models. The traditional object-oriented approach has difficulties in representing unstructured BIM data (e.g., interrelationships), while rule-based methods involve tremendous human efforts to develop rule sets, lacking flexibility for different requirements. Therefore, this study aims to establish a novel data-driven framework based on BIM and knowledge graph (KG) to represent unstructured BIM data for automatic inferences of auditing results of BIM model mistakes. It starts by establishing a BIM-KG data model via identifying required information for auditing purposes. Subsequently, BIM data is automatically transformed into the BIM-KG representations, the embeddings of which are trained using a knowledge graph embedding model. Automatic mechanisms are then developed to utilize the computable embeddings to effectively identify mistake BIM elements. The framework is validated using illustrative examples and the results show that 100% mistake elements can be identified successfully without human intervention.     

An Intra-Server Interconnect Fabric for Heterogeneous Computing

With the increasing diversity of application needs and computing units, the server with heterogeneous pro- cessors is more and more widespread. However, conventional SMP/ccNUMA server architecture introduces communication bottleneck between heterogeneous processors and only uses heterogeneous processors as coprocessors, which limits the efficiency and flexibility of using heterogeneous processors. To solve this problem, this paper proposes an intra-server inter- connect fabric that supports both intra-server peer-to-peer interconnection and I/O resource sharing among heterogeneous processors. By connecting processors and I/O devices with the proposed fabric, heterogeneous processors can perform direct communication with each other and run in stand-alone mode with shared intra-server resources. We design the proposed fabric by extending the de-facto system I/O bus protocol PCIe （Peripheral Computer Interconnect Express） and implement it with a single chip cZodiac. By making full use of PCIe＇s original advantages, the interconnection and the I/O sharing mechanism are light weight and efficient. Evaluations that have been carried out on both the FPGA （Field Programmable Gate Array） prototype and the cycle-accurate simulator demonstrate that our design is feasible and scalable. In addition, our design is suitable for not only the heterogeneous server but also the high density server.     

Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network

Algorithms for numeric data classification have been applied for text classification. Usually the vector space model is used to represent text collections. The characteristics of this representation such as sparsity and high dimensionality sometimes impair the quality of general-purpose classifiers. Networks can be used to represent text collections, avoiding the high sparsity and allowing to model relationships among different objects that compose a text collection. Such network- based representations can improve the quality of the classification results. One of the simplest ways to represent textual collections by a network is through a bipartite heterogeneous network, which is composed of objects that represent the documents connected to objects that represent the terms. Heterogeneous bipartite networks do not require computation of similarities or relations among the objects and can be used to model any type of text collection. Due to the advantages of representing text collections through bipartite heterogeneous networks, in this article we present a text classifier which builds a classification model using the structure of a bipartite heterogeneous network. Such an algorithm, referred to as IMBHN （Inductive Model Based on Bipartite Heterogeneous Network）, induces a classification model assigning weights to objects that represent the terms for each class of the text collection. An empirical evaluation using a large amount of text collections from different domains shows that the proposed IMBHN algorithm produces significantly better results than k-NN, C4.5, SVM, and Naive Bayes algorithms.     

Research on radial investigation depth characteristics of induction logging on heterogeneous stratum

In induction logging, using geometrical factor to analyze radial investigation depth is a useful way to estimate the radial investigation ability of a logging tool. But radial investigation depth analysis based on DOLL geometrical factor often leads to substantial inaccurate estimates of the true apparent conductivity σa in inhomogeneous or invaded cases. Although Propagation Effect Geometrical Factor （PEGF） includes both the mutual-effect of eddy flows and the reflection and transmission on the interface of heterogeneous media, it＇s too complicated to use. The Advanced Geometrical Factor （AGF） discussed in this paper combines the merits of DOLL geometrical factor and propagation effect geometrical factor. The AGF considers the mutual-effect of eddy flows in nonhomogeneous medium and gives good predictions of two kinds of relations in an axisymmetric inhomogeneous two-layer media in a permissive error scale. One kind of the relations is between radial investigation depth and emission signal frequency. The other is the relation between radial investigation depth and electrical resistivity ratio of invaded zone and original stratum. Different from usual way of making conclusions from graphs of experimental data, proof formulas and simulations are given in this paper. The conclusions referred to the radial investigation depth characteristics using the Doll GF are compensated by using AGF.     

OpenMC： Towards Simplifying Programming for TianHe Supercomputers

Modern petascale and future exascale systems are massively heterogeneous architectures. Developing productive intra-node programming models is crucial toward addressing their programming challenge. We introduce a directive- based intra-node programming model, OpenMC, and show that this new model can achieve ease of programming, high performance, and the degree of portability desired for heterogeneous nodes, especially those in TianHe supercomputers. While existing models are geared towards oifloading computations to accelerators （typically one）, OpenMC alms to more uniformly and adequately exploit the potential offered by multiple CPUs and accelerators in a compute node. OpenMC achieves this by providing a unified abstraction of hardware resources as workers and facilitating the exploitation of asynchronous task parallelism on the workers. We present an overview of OpenMC, a prototyping implementation, and results from some initial comparisons with OpenMP and hand-written code in developing six applications on two types of nodes from TianHe supercomputers.     

PandaDB: Intelligent Management System for Heterogeneous Data

With the development of big data application, the demand of large-scale structured/unstructured data fusion management and analysis is becoming increasingly prominent. However, the differences in management, process, retrieval of structured/unstructured data brings challenges for fusion management and analysis. This study proposes an extended property graph model for heterogeneous data fusion management and semantic computing, and defines related property operators and query syntax. Based on the intelligent property graph model, this study implements PandaDB, an intelligent fusion management system for heterogeneous data. This study depicts the architecture, storage mechanism, query mechanism, property co-storage, AI algorithm scheduling, and distributed architecture of PandaDB. Test experiments and cases show that the co-storage mechanism and distributed architecture of PandaDB have good performance acceleration effects, and can be applied in some scenarios of fusion data intelligent management such as entity disambiguation of academic knowledge graph.     

PandaDB:一种异构数据智能融合管理系统

随着大数据应用的不断深入,对大规模结构化/非结构化数据进行融合管理和分析的需求日益凸显.然而,结构化/非结构化数据在存储管理方式、信息获取方式、检索方式方面的差异给融合管理和分析带来了技术挑战.本文提出了适用于异构数据融合管理和语义计算的属性图扩展模型,并定义了相关属性操作符和查询语法.接着,基于智能属性图模型提出异构数据智能融合管理系统PandaDB,并详细介绍了PandaDB的总体架构、存储机制、查询机制、属性协存和AI算法集成机制.性能测试和应用案例证明,PandaDB的协存机制、分布式架构和语义索引机制对大规模异构数据的即席查询和分析具有较好的性能表现,该系统可实际应用于学术图谱实体消歧与可视化等融合数据管理场景.     

Correction to: PolyGraph: a data flow model with frequency arithmetic

International Journal on Software Tools for Technology Transfer - The publication of this article unfortunately contained a mistake.     

Cognition-Driven Traffic Simulation for Unstructured Road Networks

Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers, which brings various heterogeneous traffic behaviors. Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation. Most existing traffic methods generate traffic behaviors by adjusting parameters and cannot describe those heterogeneous traffic flows in detail. In this paper, a cognition-driven trafficsimulation method inspired by the theory of cognitive psychology is introduced. We first present a visual-filtering model and a perceptual-information fusion model to describe drivers’ heterogeneous cognitive processes. Then, logistic regression is used to model drivers’ heuristic decision-making processes based on the above cognitive results. Lastly, we apply the high-level cognitive decision-making results to low-level traffic simulation. The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.

     

Correction to: Buckling of magneto-electro-hygro-thermal piezoelectric nanoplates system embedded in a visco-Pasternak medium based on nonlocal theory

Microsystem Technologies - The original version of this article unfortunately contained a mistake. Farzad Ebrahimi was not listed among the authors.     

Correction to: On fusing the latent deep CNN feature for image classification

World Wide Web - The original version of this article unfortunately contained a mistake. The spelling of Guangcan Liu’s name was incorrect.     

Correction to: Network Effects of International Shocks and Spillovers

Networks and Spatial Economics - The original version of the article unfortunately contained a mistake. The Annex table was distorted and some columns were out of order. The corrected version is...     

基于元数据的油气信息管理系统

非结构化数据在信息总量中所占的比例远大于结构化数据,是油气信息管理过程中的宝贵数据资产,如何对这些非结构化数据有效管理和应用是新型油气信息管理系统建设的重要内容之一。为了实现对油气信息资源中包括非结构化数据在内的综合数据高效管理与集成应用,从应用元数据技术角度,一方面对非结构化数据进行定义和详细描述,另一方面以数据库元数据为核心对系统框架进行构建,从而设计了一种新型的油气信息管理系统。系统实现了油气信息综合数据的存储与管理,而且可以对异构数据源进行有效集成应用,有良好的灵活性和扩展性。     

Integrating Concepts and Knowledge in Large Content Networks

Large content networks like the World Wide Web contain huge amounts of information that have the potential of being integrated because their components fit within common concepts and/or are connected through hidden, implicit relationships. One attempt at such an integration is the program called the “Web of Data,” which is an evolution of the Semantic Web. It targets semi-structured information sources such as Wikipedia and turns them into fully structured ones in the form of Web-based databases like DBpedia and then integrates them with other public databases such as Geonames. On the other hand, the vast majority of the information residing on the Web is still totally unstructured, which is the starting point for our approach that aims to integrate unstructured information sources. For this purpose, we exploit techniques from Probabilistic Topic Modeling, in order to cluster Web pages into concepts (topics), which are then related through higher-level concept networks; we also make implicit semantic relationships emerge between single Web pages. The approach has been tested through a number of case studies that are here described. While the applicative focus of the research reported here is on knowledge integration on the specific and relevant case of the WWW, the wider aim is to provide a framework for integration generally applicable to all complex content networks where information propagates from multiple sources.     

Progress in database search strategies

Retrieval speed and precision ultimately determine the success of any database system. This article outlines the challenges posed by distributed and heterogeneous database systems, including those that store unstructured data, and surveys recent work. Much work remains to help users retrieve information with ease and efficiency from a heterogeneous environment in which relational, object-oriented, textual, and pictorial databases coexist. The article outlines the progress that has been made in query processing in distributed relational database systems and heterogeneous and multidatabase systems     

基于PCA-SVM准则改进区域生长的非结构化道路识别

针对智能车辆在非结构化道路识别中需要采用众多的特征参数,增加了特征融合识别难度与计算复杂度,并且部分背景与道路区域存在相似性会产生道路识别的误分、误判的问题,提出了一种基于主成分分析的支持向量机（PCA-SVM）准则改进区域生长的非结构化道路识别算法。首先,对非结构化道路颜色、纹理等复杂特征信息进行提取,采用PCA对提取的特征信息进行降维;然后,利用降维后的主元特征对SVM进行训练后作为复杂道路单元格的分类器。利用道路位置、起始单元格等先验知识以及道路边界单元格统计特征改进区域生长方法,在单元格生长时利用分类器判别,排除误判区域。实际道路检测结果表明,所提算法具有较好的鲁棒性,能够有效识别非结构化路面区域。对比结果表明,所提算法在保证准确率的同时,将10余维复杂特征信息压缩为3维主元特征,相比传统算法可缩短计算时间一半以上。针对背景与道路相似区域造成的传统算法10%左右的误判问题,所提算法能够有效排除。在野外环境下基于视觉的局部路径规划与导航方面,所提算法为缩短识别时间、排除背景干扰提供了可行途径。     

非结构化P2P系统复本研究

针对非结构化P2P系统中的复本放置问题，提出了一种新的放置策略，该策略充分考虑非结构化P2P系统中节点的异质性，将受欢迎程度高的文件的复本放置在高性能的节点上，增加了受欢迎程度高的文件的可用性，同时由于系统中受欢迎程度高的文件的复本增加，减少了系统查询文件时所要搜索的节点数．仿真实验和分析表明，该复本放置策略能有效地提高文件的可用性，降低查询带来的网络流量．