Advanced Materials Research Vols. 765-767

Abstract: In cognitive radio networks, cooperative sensing has been identified as an important technique in MAC-layer sensing. Intuitively, the more secondary users (SUs) are involved in sensing, the more sensing accuracy the SUs can achieve, whereas the more reporting delay the SUs consume, the less throughput the SUs can achieve. Thus, an important issue associated with SUs is how to maximize the SUs throughput under the constraint that the Primary Users (PUs) are sufficiently protected. To solve this issue, we develop a new sensing scheme to decrease the consumption of reporting delay and prove the unimodal characteristics of the SUs average throughput as a function of the fusion parameters based on the k-out-of-N fusion rule. Computer simulations show that, based on the developed sensing scheme and the proposed numerical optimization algorithm, significant improvement in the average throughput of SUs is achieved.

Abstract: This article studies the existence of motorist path dependence based on the path dependence theory and the motorist's route choice rule. Combining the path dependence theory, experiment was designed and conducted with the Second Wuhan Yangtze River Bridge and Erqi Yangtze River Bridge. It uses SPSS to analysis the survey data by Factorial ANOVA, which to prove motorists route choice has path dependence character. The motorists path dependence theory can be used to guide urban transportation demand management (TDM).

Abstract: Near-duplicate image retrieval is a classical research problem in computer vision, for which a large number of diverse approaches have been proposed. Recent studies have revealed that it can be used as an intermediate step to implement search-based celebrity identification given the existence of huge volume of user-tagged or text-surrounded celebrity images on the web. However, the effectiveness of existing near-duplicate image retrieval methods for such a task still remains unclear. To address this issue, this paper presents a comprehensive study of the existing near-duplicate image retrieval methods in a structural way. Four representatives of the existing methods, i.e. hash signature, mean SSIM, BoVW with SIFT features and ARG, are experimentally evaluated using a self-constructed dataset containing 24762 images of 15 top searched celebrities collected using 6 news search engines and the Google image search engine. The experimental results reveal that, compared with global feature based methods, local feature based ones are usually more appropriate for the task of celebrity identification in web images, as they can deal with partial duplicate and scene similar images better. In particular, BoVW with SIFT features is recommended as it provides the best trade-off between on-line speed and retrieval accuracy.

Abstract: We study web advertisements scheduling problem by fully considering the interaction of web users and web advertisements publishing system. We construct a Markov Decision Process (MDP) based web advertisements scheduling model and schedule advertisements publishing during the whole process of web surfing by the users, thus we make maximal use of personal behavior characteristics of every web user in the scheduling model. We also track the user habit with reinforcement learning, solve the MDP model by TD(λ) algorithm combing the function approximator, and obtain adaptive online scheduling policies for web advertisements publishing.

Abstract: Chinese microblogging texts are always short and casual, which bring some troubles to the traditional sentiment classification methods based on learning. To overcome this problem, we use a rule-based approach to classify the sentiment of Chinese microblogging texts. According to the characteristics of Chinese microblogging texts, we construct a thesaurus of subjective words for it, summarize the basic semantic rules expressing emotion and propose a rule-based approach to sentiment classification of Chinese microblogging texts. Finally, we compare our approach with a SVM-based approach. Our rule-based approach achieves an accuracy of 0.865, which is better than that of SVM-based approach.

Abstract: Data exchange and sharing is a key issue in the current informatization of rock engineering, and its advancement is constrained by various factors, particularly distributed heterogeneous environment and the lack of uniform data exchange formats. To address this need this paper presents the Rock Engineering data Exchange and sharing Framework (REEF), which adopts service-oriented architecture (SOA) and uses the rock engineering markup language (REML) as the standard exchange language. This REEF framework covers the main functions of data exchange and sharing in its domain, which can be used as the universal solution to reduce the difficulty of the industrial integration of rock engineering, and it provides support to the standardization and digitization in the field.

Abstract: Topic crawler is a tool for collecting electronic public opinion from the internet. The identification method of topics relevance identification directly affects the acquisition rate of topic crawler. To improve the low information acquisition rate of existing topic crawlers strategy, a modified SVM classifier algorithm which is based on online incremental learning is proposed. The idea of algorithm is to remove samples that affect the training set greatly in the historical training set, and then to re-train the historical set and the incremental set to obtain a complete training set. A framework of topic crawler is constructed on the basis of this algorithm. The results of experiments show that, this method can effectively improve the acquisition rate of the crawler.

Abstract: As the traditional implements of graph are complicated in data structure and hard to maintain, or short in storage utilization and low computational efficiency, this paper designs one storage model of graph based on variable collection according to the object oriented method, and implements it with the variable collection data type that high level programing languages process. Comparing with adjacency matrix and list, analysis and cases show that this model is comprehensible and extensible with high calculation efficiency.

Abstract: This paper presents a detection algorithm for anomaly network traffic, which is based on spectral kurtosis analysis. Firstly, we turn network traffic into time-frequency signals at different scales. These time-frequency signals hold the more detailed nature corresponding to different scales. Secondly, the time-frequency signals at different scales are transformed into a series of new time signals by time-frequency analysis theory. These new time signals hold obvious narrowband nature and embody the local properties of network traffic. Thirdly, we calculate the spectral kurtosis values of the new time signals and then perform the feature extractions. As a result, the abnormal network traffic can be correctly identified. Simulation results show that our algorithm is feasible and promising.

Abstract: Modern construction machinery is usually advanced and intelligent with complicated structure and operating principle. Therefore its fault diagnosis and maintenance are very difficult. In order to promote the maintenance capability and supply detailed as well as easy-to-use technical information, we propose the method of developing Interactive Electronic Technique Manual (IETM) based on PDA. The framework of the technical manual and key implementation techniques are addressed. The technical manual is developed with Visual Studio .Net 2008, along with the XML-based data access method on PDA. The IETM system of a type of construction machinery is accomplished. The practical results show that it can overcome the inconveniences of paper manuals, and navigate users through extensive amounts of information quickly and easily. It greatly improves the maintenance and support capability of the Engineering Corps.