Combining ODM and OCL in ontology verification

In order to automatically find and conveniently rectify the structural conflicts appearing in a new ontology model version after a series of ontology evolution,we propose an ontology model verification approach,in which the object constraint language (OCL) and an ontology definition meta-model (ODM) are used to complete the ontology verification.The ODM is composed of the ontology related elements and the definition rule related elements.The OCL is employed to describe the elements in the ontology definition meta-model:OCL extension for the ontology related elements,and OCL extension for the ontological design patterns.All of the above OCL rules will describe the constraint relationships between ontology elements.Associated with an example,the application of the ontology model verification approach based on OCL and ODM is introduced.Consequently,the conflicts happened in the ontology changing and evolution can be easily verified and rectified through this approach.

Ontology mapping approach based on set & relation theory and OCL

An ontology mapping approach based on set & relation theory and OCL is introduced,then an ontology mapping meta-model is established which is composed of ontology related elements,mapping related elements and definition rule related elements.This ontology mapping meta-model can be regarded as a unified mechanism to realize different kinds of ontology mappings.The powerful computation capability of set and relation theory and the flexible expressive capability of OCL can be used in the computation of ontology mapping meta-model to realize the unified mapping among different ontology models.Based on the mapping meta-model,a general mapping management framework is developed to provide a common mapping storage mechanism,some mapping APIs and mapping rule APIs.

Research on a randomized real-valued negative selection algorithm

A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.

Design and implementation of a large-scale multi-class text classifier

Although, researchers in the ATC field have done a wide range of work based on SVM, almost all existing approaches utilize an empirical model of selection algorithms. Their attempts to model automatic selection in practical, large-scale, text classification systems have been limited. In this paper, we propose a new model selection algorithm that utilizes the DDAG learning architecture. This architecture derives a new large-scale text classifier with very good performance. Experimental results show that the proposed algorithm has good efficiency and the necessary generalization capability while handling large-scale multi-class text classification tasks.

Generalized multi-layered granulations and approximations based on neighborhood systems under incomplete information systems

A generalized multi-layered granulation structure used by neighborhood systems is proposed. With granulated views, the concepts of approximations under incomplete information systems are studied, which are represented by covering of the universe. With respect to different levels of granulations, a pair of lower and upper approximations is defined and an approximation structure is investigated, which lead to a more general approximation structure. The generalized multi-layered granulation structure provides a basis of the proposed framework of granular computing. Using this framework, the interesting and useful results about information granulation and approximation reasoning can be obtained. This paper presents some useful explorations about the incomplete information systems from information views.

The specification and analysis of network embedded system

This paper proposes a formal method which is used to model and analyze network devices such as routers. It is based on an algebraic process called “ACSR-VP”, which enhances the original CCS algebraic process by incorporating the notions of time, resource requirements, dynamic prioritization, and synchronization. Therefore, although there are many formal methods to analyze the timed concurrency system, ACSR-VP, due to its prominent features, is best fit for analysis of a resource bounded real-time system. This paper extends ACSR-VP to EACSR-VP, which is more adaptive to the features of network devices and specializes in analyzing this kind of embedded system. EACSR-VP adds the notion of n-way communication which allows more than two processes to participate in synchronization. It also enhances value-passing capabilities which make for more flexible specifications. Finally, specifications, verification and analysis methods with EACSR-VP are introduced by a case study of router with multiple input queues.

A real-time fault-tolerant scheduling algorithm with low dependability cost in on-board computer system

To make the on-board computer system more dependable and real-time in a satellite, an algorithm of the fault-tolerant scheduling in the on-board computer system with high priority recovery is proposed in this paper. This algorithm can schedule the on-board fault-tolerant tasks in real time. Due to the use of dependability cost, the overhead of scheduling the fault-tolerant tasks can be reduced. The mechanism of the high priority recovery will improve the response to recovery tasks. The fault-tolerant scheduling model is presented simulation results validate the correctness and feasibility of the proposed algorithm.

Scale invariant features extraction for stereo vision

Stable local feature detection is a fundamental component of many stereo vision problems such as 3-D reconstruction, object localization, and object tracking. A robust method for extracting scale-invariant feature points is presented. First, the Harris corners in three-level pyramid are extracted. Then, the points detected at the highest level of the pyramid are correctly propagated to the lower level by pyramid based scale invariant （PBSI） method. The corners detected repeatedly in different levels are chosen as final feature points. Finally, the characteristic scale is obtained based on maximum entropy method. The experimental results show that the algorithm has low computation cost, strong antinoise capability, and excellent performance in the presence of significant scale changes.

Impact of small-world topologies on broadcasting for wireless sensor networks

Broadcasting is an important operation and been widely used in wireless sensor networks （WSNs）. These networks are power constrained as nodes operate with limited battery power. Wireless sensor networks are spatial graphs that have much more clustered and much high path-length characteristics. After considering energy- efficient broadcasting in such networks, by combining the small-world characteristic of WSNs and the properties of ant algorithm to quickly identify an optimal path, small-world power-aware broadcast algorithm is introduced and evaluated. Given different densities of network, simulation results show that our algorithm significantly improves life of networks and also reduces communication distances and power consumption.

A role based coordination model in agent systems

Coordination technology addresses the construction of open, flexible systems from active and independent software agents in concurrent and distributed systems. In most open distributed applications, multiple agents need interaction and communication to achieve their overall goal. Coordination technologies for the Internet typically are concerned with enabling interaction among agents and helping them cooperate with each other. At the same time, access control should also be considered to constrain interaction to make it harmless. Access control should be regarded as the security counterpart of coordination. At present, the combination of coordination and access control remains an open problem. Thus, we propose a role based coordination model with policy enforcement in agent application systems. In this model, coordination is combined with access control so as to fully characterize the interactions in agent systems. A set of agents interacting with each other for a common global system task constitutes a coordination group. Role based access control is applied in this model to prevent unauthorized accesses. Coordination policy is enforced in a distributed manner so that the model can be applied to the open distributed systems such as Internet. An Internet online auction system is presented as a case study to illustrate the proposed coordination model and finally the performance analysis of the model is introduced.

Detection of text in images using SUSAN edge detector

Text embedded in images is one of many important cues for indexing and retrieval of images and videos. In the paper, we present a novel method of detecting text aligned either horizontally or vertically, in which a pyramid structure is used to represent an image and the features of the text are extracted using SUSAN edge detector. Text regions at each level of the pyramid are identified according to the autocorrelation analysis. New techniques are introduced to split the text regions into basic ones and merge them into text lines. By evaluating the method on a set of images, we obtain a very good performance of text detection.

Configurable ontology mapping based on multi-feature

A configurable ontology mapping approach based on different kinds of concept feature information is introduced in this paper. In this approach, ontology concept feature information is classified as five kinds, which respectively corresponds to five kinds of concept similarity computation methods. Many existing ontology mapping approaches have adopted the multi-feature reasoning, whereas not all feature information can be computed in the real ontology mapping and only fractional feature information needs to be selected in the mapping computation. Consequently a configurable ontology mapping model is introduced, which is composed of CMT model, SMT model and related transformation model. Through the configurable model, users can conveniently select the most suitable features and configure the suitable weights. Simultaneously, a related 3-step ontology mapping approach is also introduced. Associated with the traditional name and instance learner-based ontology mapping approach, this approach is evaluated by an ontology mapping application example.

Community discovery method with uncertainty measure of overlapping nodes based on topological potential

Community discovery of complex networks,esp.of social networks,has been a hotly debated topic in academic circles in recent years.Since actual networks usually contain some overlapping nodes that are difficult to assign to a certain community,overlapping community discovery is under great demand in practical applications.However,at present network community discovery is mainly done by non-overlapping community discovery methods,overlapping discovery methods are not common.In this context,an overlapping community discovery method is proposed hereby based on topological potential and specific algorithms are also provided.This method not only considers the spread of the uncertainty of community identity of the overlapping nodes in the network,but also realizes a quantified representation,i.e.,uncertainty measure,of the community identity of the overlapping nodes.The experiment results show that this method yields the results that are consistent with those by the classic methods and are more reasonable.

A Heuristic Method of Scalar Multiplication of Elliptic Curve over OEF

Elliptic curve cryptosystem is the focus of public key cryptology nowadays, for it has many advantages RSA lacks. This paper introduced a new heuristic algorithm on computing multiple scalar multiplications of a given point. Based on this algorithm, a new method of computing scalar multiplication of elliptic curve over optimal extension field (OEF) using Frobenius map was presented. The new method is more efficient than the traditional ones. In the last part of this paper, the comparison was given in the end.

A self region based real-valued negative selection algorithm

Point-wise negative selection algorithms,which generate their detector sets based on point of self data,have lower training efficiency and detection rate.To solve this problem,a self region based real-valued negative selection algorithm is presented.In this new approach,the continuous self region is defined by the collection of self data,the partial training takes place at the training stage according to both the radius of self region and the cosine distance between gravity of the self region and detector candidate,and variable detectors in the self region are deployed.The algorithm is tested using the triangle shape of self region in the 2-D complement space and KDD CUP 1999 data set.Results show that,more information can be provided when the training self points are used together as a whole,and compared with the point-wise negative selection algorithm,the new approach can improve the training efficiency of system and the detection rate significantly.

Scan matching using line segment relationships for map-based localization of mobile robot

A new scan matching method for mobile robot localization is presented, which takes line segment as the feature and matches the real scans in the given reference map by relationships of the directional-defined line segments. The alignment was done by hierarchically identifying the multiple relationships and the result was recorded in a correspondence matrix, where the best match is defined and selected for localization. It is indicated that the searching algorithm of the best match can find the ambiguities and get rid of them. This method with less computational cost works well in occluded environment, and can correct the error in pose estimation without the need for the estimation itself. The efficiency, accuracy and robustness of this method were verified by experiments of localization in an occluded environment and a long-distance indoor navigation.

Recognition of vertical vowel graphemes of Korean characters based on combination of vowel graphemes

Korean characters consist of 2 dimensional distributed consonantal and vowel graphemes. The purpose of reducing the 2 dimensional characteristics of Korean characters to linear arrangements at early stage of character recognition is to decrease the complexity of following recognition task. By defining the identification codes for the vowel graphemes of Korean characters, the rules for combination of vowel graphemes are established, and a recognition algorithm based on the rules for combination of vowel graphemes, is therefore proposed for vertical vowel graphemes. The algorithm has been proved feasilbe through demonstrating simulations.

A modeling method for virtual scene based on multi-view image sequence

Through the analysis and comparison of shortcomings and advantages of existing technologies on object modeling in 3D applications,we propose a new modeling method for virtual scene based on multi-view image sequence to model irregular objects efficiently in 3D application.In 3D scene,this method can get better visual effect by tracking the viewer's real-time perspective position and projecting the photos from different perspectives dynamically.The philosophy of design,the steps of development and some other relevant topics are discussed in details,and the validity of the algorithm is analyzed.The results demonstrate that this method represents more superiority on simulating irregular objects by applying it to the modeling of virtual museum.

Information hiding scheme for vector maps based on fingerprint certification

An information hiding scheme for vector maps is presented to identify the source after the vector map is leaked in some key application areas. In this scheme, the fingerprint image of the map owner can be converted into a character string as the watermark, and then the watermark will be embedded into the coordinate descriptions of the attribute file by the "0-bit value" programming method. This programming algorithm ensures that the accuracy is lossless and the graphics is unchanged for any vector map. Experiments show that the presented hiding scheme has stable robustness, the average similarity rate is 97.2% for fingerprints matching and the false non-match rate is 1.38% in the blocking test. In the opening test, the former reaches 84.46% and the latter reaches 5.56%.

INFORMATION-CENTRIC NETWORKING

Information-Centric Networking(ICN)is an emerging direction in Future Internet architecture research,gaining significant tractions among academia and industry.Aiming to replace the conventional host-to-host communication model by a data-centric model,ICN treats data content as the first class entity in network architecture.