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Li S.,University of Swansea | Xu L.D.,CAS Institute of Computing Technology | Xu L.D.,Old Dominion University | Wang X.,University of Swansea
IEEE Transactions on Industrial Informatics | Year: 2013

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment. © 2013 IEEE. Source


Sun X.,CAS Institute of Computing Technology
Future Generation Computer Systems | Year: 2010

As the semantic data grows rapidly on the Web, we need flexible and powerful tools to describe and manage complex data, information and knowledge structures on the Web. Basic structural semantic information of classes, instances, properties and relationships can be described using Semantic Web languages. More and more applications need to describe and manage objects with complex structures, operations and interactions on the Web. In this paper, we introduce an Object-Oriented Semantic Link Network language OSLN that can be used to define complex objects with rich object-oriented semantics on the Web. In OSLN, objects are the basic semantic elements with internal members and functions that are declared to express attributes and semantic processes. Semantic links are defined to describe semantic relationships among objects. Many important features from traditional object-oriented programming languages are incorporated into OSLN, allowing users to write semantic programs for not only describing complex structures of objects but also defining object operations and manipulations. OSLN enables users to write semantic scripts like using traditional programming languages, which will improve both user experiences and application areas of the Semantic Web technologies. © 2009 Elsevier B.V. All rights reserved. Source


Tian R.,CAS Institute of Computing Technology
Computer Methods in Applied Mechanics and Engineering | Year: 2013

A GFEM without extra dof is developed. Without the extra dof, the resulting linear system becomes independent-in size-of the order of local approximation, the resulting PU approximation becomes free of linear dependence, and the resulting stiffness matrix becomes good conditioned. Numerical studies show the new GFEM's excellent convergence properties and excellent stability. © 2013 Elsevier B.V. Source


He W.,Old Dominion University | Xu L.D.,Old Dominion University | Xu L.D.,CAS Institute of Computing Technology
IEEE Transactions on Industrial Informatics | Year: 2014

Many industrial enterprises acquire disparate systems and applications over the years. The need to integrate these different systems and applications is often prominent for satisfying business requirements and needs. In an effort to help researchers in industrial informatics understand the state-of-the-art of the enterprise application integration, we examined the architectures and technologies for integrating distributed enterprise applications, illustrated their strengths and weaknesses, and identified research trends and opportunities in this increasingly important area. © 2005-2012 IEEE. Source


Meng Z.,Guangxi University | Shi Z.,CAS Institute of Computing Technology
Information Sciences | Year: 2012

A systematic study of attribute reduction in inconsistent incomplete decision systems (IIDSs) has not yet been performed, and no complete methodology of attribute reduction has been developed for IIDSs to date. In an IIDS, there are various ways to handle missing values. In this paper, a missing attribute value may be replaced with any known value of a corresponding attribute (such a missing attribute value is called a "do not care" condition). In this way, this paper establishes reduction concepts specifically for IIDSs, mainly by extending related reduction concepts from other types of decision systems into IIDSs, and then derives their relationships and properties. With these derived properties, the extended reducts are divided into two distinct types: heritable reducts and nonheritable reducts, and algorithms for computing them are presented. Using the relationships derived here, the eight types of extended reducts established for IIDSs can be converted to five equivalent types. Then five discernibility function-based approaches are proposed, each for a particular kind of reduct. Each approach can find all reducts of its associated type. The theoretical analysis of the proposed approaches is described in detail. Finally, numerical experiments have shown that the proposed approaches are effective and suitable for handling both numerical and categorical attributes, but that they have different application conditions. The proposed approaches can provide a solution to the reduction problem for IIDSs. © 2012 Elsevier Inc. All rights reserved. Source

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