Applied Mechanics and Materials Vols. 644-650

Abstract: Vehicle networking is an important part of intelligent traffic system. It combines wireless sensor network and mobile autonomous network, which is better for drivers to obtain road condition information to ensure driving safety. This paper focuses on the analysis of subtractive clustering and proposed clustering routing algorithm based on subtractive clustering in order to reduce communication of wireless sensor network and redundant flooding and routing expanse. In the algorithm, cluster head selection adopts subtractive clustering to produce cluster node in node intensive place. Cluster forms by adopting current non cluster head node mechanism which reduced energy consumption. Then the maintenance method of cluster is proposed. At last, it introduces routing protocol of cluster which is easier for clustering structure to manage and synchronize network.

Abstract: With the constant development of the securities market, securities investment is the core topic, and how to choose from a wide range of securities, is always a difficult problem in front of investors. This paper introduces the basic concept of genetic algorithm, to all parts of the genetic algorithm and genetic algorithm based model has made the detailed introduction. Combining genetic algorithm and portfolio, this paper puts forward the model based on genetic algorithm, the securities investment, through carried on the thorough research of securities investment model, and considering the modern portfolio theory in our country is practical, which is based on short-selling restrictions, transaction cost limit and the smallest unit of trading limit proposed portfolio selection model with investment restrictions.

Abstract: In order to control and dispatch the traffic efficiently, predicting the traffic-flow has become more and more important. The artificial neural network technology has a great mapping ability on non-linear, it can deal with the complex problems through modeling analysis. This paper apply wavelet neural network to predict traffic-flow based on fuzzy clustering. From experience result, this approach predicted traffic-flow with high accuracy.

Abstract: In this pape, we propose a matrix Iterative Hard thresholding pursuit algorithm for low-rank minimization that extends Foucart's Hard Thresholding Pursuit (HTP) algorithm from the sparse vector to the low-rank matrix case. The performance guarantee is given in terms of the rank-restricted isometry property and a low-rank solotion is presented. The numerical experiments empirically demonstrate that, although the affine constraints does not satisfy the restricted isometry property in matrix completion, our algorithm also recovers the low-rank matrix from a number of uniformly sampled entries and is more efficient compared with SVT and ADMiRA.

Abstract: For meeting the needs of equipment maintenance support, all kinds of maintenance support data are widely used and studied. By analyzing these data, information models are put forward. XML is used to describe the models. The information models built by XML have good maintainability and portability. The unified information models will play an important role in data exchange in equipment maintenance support areas.

Abstract: This work aims at presenting a new method for reproducing oil paintings via 3D printing. We used a laser scanner to detect the surface morphology of an oil painting and found it was capable to print the stereo brushstrokes through layered printing after analyzing the UV ink thickness of type UJF-3024 ink-jet printer. Base on the 3D model we established, a slicing strategy was proposed for 3D printing. An experiment was conducted to validate feasibility of the method. Ultimately, the method has proven to be comparatively effective.

Abstract: Aiming at the insufficient passenger evacuation ability of Xizhimen subway station, related facilities are improved and optimized combining pedestrian characteristics with calculation methods of evacuation time and dynamical model, at the same time Matlab and other related technologies are used. The results indicate that the improvement of passenger evacuation on existing lines can shorten evacuation time and increase the service level of Xizhimen subway station.

Abstract: The development of Internet and digital library has triggered a lot of text categorization methods. How to find desired information accurately and timely is becoming more and more important and automatic text categorization can help us achieve this goal. In general, text classifier is implemented by using some traditional classification methods such as Naive-Bayes (NB). ARC-BC (Associative Rule-based Classifier by Category) can be used for text categorization by dividing text documents into subsets in which all documents belong to the same category and generate associative classification rules for each subset. This classifier differs from previous methods in that it consists of discovered association rules between words and categories extracted from the training set. In order to train and test this classifier, we constructed training data and testing data respectively by selecting documents from Yahoo. The experimental result shows that the performance of ARC-BC based text categorization is very pretty efficient and effective and it is comparable to Naïve Bayesian algorithm based text categorization.

Abstract: In evaluation and decision-making, for an object system with multiple attributes and indicators, it is a typical pattern to calculate an assessment value on each object using some methods to get the ranking of objects, so as to choose the best according to sorting result of the values.The paper establishes the evaluation ranking model on the basis of multi-user attribute comprehensive evaluation method which focuses on how to give a comprehensive evaluation on objects taking the users’ preferences on different attributes in an evaluation group into account,and thereby produce evaluation values and sorting results of objects.Simulation verifies the ranking model in evaluation and decision-making.

Abstract: This paper dose the theoretical analysis for solving the algorithm complexity of a neural network using rational cubic spline weight functions with linear denominator. It concludes that the time complexity of the training algorithm has linear relations with the neural network’s input dimensions, output dimensions as well as the number of samples.