Applied Mechanics and Materials Vols. 380-384

Abstract: The subwavelength focusing lens usually can not focus on the preset focus. There are many factors leading to the errors, and one of the most important factors is the choosing of the dispersion relation. In this paper, different dispersion relations are studied and the applicable conditions are found for both the period characteristic equation and the MIM characteristic equation in designing the focusing lens. A focusing lens with long focal depth is designed according the applicable condition and the accuracy of the design is verified by the simulation of FDTD method. It is believed that this study will provide useful information for further designing of the subwavelength lens.

Abstract: According to the problem that the sensor nodes are highly energy-constrained, a novel dynamic clustering algorithm for WSN, which is in application to object tracking, is proposed. The cluster head selection mechanism takes not only the distance between the cluster head and the sink node but also the received signal strength and the residual energy of nodes into consideration. The analysis result indicates that, the energy consumption of network communication is reduced and the cluster size is optimized. The simulation results show that the novel algorithm can efficiently prolong the lifetime of WSN, especially when the sink node is far from the network.

Abstract: A fast remote sensing scene matching method, taking airports, oil depots, harbors and so on as research objects, is proposed in this article which is based on the SR saliency detection and frequency segmentation. Saliency detection is used to determine the candidate region where the target may exist to reduce the searching range effectively. And then, frequency segmentation is used to eliminate the frequency component except the frequency of the target to reduce the redundant information, thereby saving the computation of SIFT feature extraction and matching. A variety of experiments under different interference factors are carried out in this paper. Experimental results show that the fast matching algorithm proposed in this paper can not only maintain the validity of SIFT features under the condition of rotation, scale, illumination and viewpoint changes, but also shorten the matching time largely and improve the matching efficiency, laying the foundation for further practical application.

Abstract: Bounded Model Checking is an efficient method of finding bugs in system designs. LTL is one of the most frequently used specification languages in model checking. In this paper, We present an linearization encoding for LTL bounded model checking. We use the incremental SAT technology to solve the BMC problem. We implement the new encoding in NuSMV model checker.

Abstract: Considering the control characteristics of the variable air volume air-conditioning system and the deficiencies of BFO algorithm, this paper presents improved BFO algorithm, and applies it to the optimal adjustment of PID controller parameters of VAV air conditioning control system. The identification results show that the new PID control strategy improves the performance of the system by digital simulation in the circumstances of MATLAB/Simulink.

Abstract: Children custody disputes are complicated for many judges owing to so many dynamic factors should to be considered. This paper proposes a novel model for legal expert system Based on LVQ Neural Network. Firstly, all clauses and discretionary factors, extracted from theories and judicial practice, involved with custody disputes in divorce were quantified to obtain related set elements. Then the LVQ neural network was applied to construct a model for children custody disputes weight vector analysis on the set elements. Ultimately, the weight vector close to decision boundaries between classes could promote the classification performance. Accordingly, the performance of the LVQ by generating weight vectors close to decision boundaries is higher. By testing, custody disputes legal expert system could consider all relevant circumstances from a good all-around point of view and effectively.

Abstract: Analyze the traffic flow in multi-scale time window of a freeway by using the nonlinear analysis method such as Correlation dimension , recursive map and so on, we find that chaos and fractal still exist in wide observation scales. Traffic flow correlation dimension reduces when the length of time window increases, in the observation scale of minutes. However, traffic flow correlation dimension reduces when the length of time window reduces, in the observation scale of seconds, instead of fractal property disappearance as predicted before. We present that, from the view of prediction, the recording point which is 10 times of the correlation dimension is an essential length of the data to predict. The simple model we present, which includes speed difference between vehicles and observation scales of traffic flow, can explain some of the reasons of the traffic flow chaos.

Abstract: We discuss the problem of nonsingularity of complex partitioned matrices and give the new criterion for determining nonsigularity of complex partitioned matrices and irreducible partitioned matrices by the additive approach. And we obtain the new condition for a matrix to be a positive stable matrix.

Abstract: According to the problem that the traditional search algorithms dont consider the needs of individuals, various recommender systems employing different data representations and recommendation methods are currently used to cope with these challenges. In this paper, inspired by the network-based user-item rating matrix, we introduce an improved algorithm which combines the similarity of items with a dynamic resource allocation process. To demonstrate its accuracy and usefulness, this paper compares the proposed algorithm with collaborative filtering algorithm using data from MovieLens. The evaluation shows that, the improved recommendation algorithm based on graph model achieves more accurate predictions and more reasonable recommendation than collaborative filtering algorithm or the basic graph model algorithm does.

Abstract: How to evaluate vocation education performance is one of the difficulties and hot research fields for the researchers related. The paper presents a new model for evaluating vocation education performance based on the principle of analytic hierarchy process and fuzzy comprehensive evaluation. First an evaluation indicator system of vocation education performance is designed through analyzing the characteristics of vocation learners behavior; Secondly in constructing the comprehensive evaluation model for vocation education performance, analytic hierarchy process and fuzzy comprehensive evaluation are combined and two level fuzzy evaluation is adopted to satisfy the dynamics, subjective and transitional characteristics of indicators and improve evaluation accuracy. Thirdly datum from three vocational colleges are taken for examples to verify the validity and feasibility of the model and the experimental results show that the model can evaluate vocation education performance practically and can help vocation education service providers take corresponding concrete measures to enhance its education performance.