Applied Mechanics and Materials Vols. 543-547

Abstract: Back-propagation (BP) neural network algorithm is currently used most widely and grows fastest for its powful nonlinear simulation capability. However BP neural network is so easy to fall into local minima that it cant find the global optimum which limits its application in many fields. The paper, taking tax innovation teaching evaluation for example, advances a new evaluation algorithm based on improved BP neural network algorithm. Firstly an evaluation indicator system of tax major innovation teaching is designed through analyzing the specific characteristics of innovation teaching requirements. Secondly, in order to overcome the shortages of low convergence speed of original BP neural network algorithm, the paper improves BP algorithm through integrating BP algorithm and ant colony algorithm, ant improving the overall search method of integrated algorithm. Thirdly data from three universities are taken for examples to verify the validity and feasibility of the model and the experimental results show that the model can evaluate university innovation teaching practically.

Abstract: K-means algorithm has powerful ability to cluster large data sets due to its high efficiency in data mining but its calculation instability limits the application of the algorithm, so the research of intelligent optimization of K-means algorithm has become a hot research field for the researchers related. First the calculation instability of the original K-means algorithm is analyzed with more details; Second, the improvement of cluster seed selection methods and the calculation flow of K-means algorithm are redesigned to speed up the calculation and enhance the stability of the improved model; Third, the paper realizes and conducts the analysis in customer classification practice of the improved algorithm which show that the improved K-means algorithm has better performance in classification accuracy and calculation stability and can be used in customer classification for network trade enterprises practically.

Abstract: It is an important work for modern libraries to predict reader flow. With the help of reader flow, library staff can grasp the change regulation of readers, allocate tasks rationally and take steps ahead of time in high-risk period. Because of reader flows typical non-linear characteristics, evolutionary neural network technology is introduced in this research so as to improve the accuracy of reader flow prediction. A prediction method for library reader flow based on evolutionary neural network is proposed. Genetic algorithm is used to optimize and design BP neural network firstly, then evolutionary neural network is used to predict reader flow. The experimental results show that evolutionary neural network is an effective tool for us to predict library reader flow. We can realize an accurate prediction for library reader flow by this method.

Abstract: This paper puts forward a kind of evolutionary algorithm and the neural network combining with the new method of optimization of hidden layer nodes number of particle swarm algorithm of neural network. The BP neural network technology is a kind of more mature neural network method, but there are easy to fall into local minimum value, unable to accurately determine the number of hidden layer nodes of the network, the disadvantages such as slow convergence speed. This paper puts forward the optimization with hidden node number of particle swarm neural network (HPSO neural network) is the hidden layer of BP network node number as a particle swarm optimization (PSO) algorithm is an important optimization goal, network of hidden layer nodes and the number of each BP network weights and closed value together, common as particle swarm algorithm optimization goal.

Abstract: The three-dimensional modeling of the maxillofacial soft and hard tissue has a great significance for the study of facial growth and development, diagnosis and treatment of facial deformity, postoperative face prediction and treatment evaluation. The key technology of the maxillofacial soft and hard tissue reconstruction is described.

Abstract: Cotton Coolmax weft-varied fabric comfortability was discussed. Nine kinds of Coolmax weft-varied fabric were selected as example, JC9.7tex yarn was used as warp. Alkali peeling Coolmax 7. 6tex yarn, common Coolmax 8.3tex yarn and JC 9.7tex yarn were combined and used as weft. Property of the nine fabrics were tested including air permeability, moisture permeability and capillary effect. Method of grey clustering analysis was adopted to analyze test data, advantages and disadvantages orders of the nine fabrics comprehensive comfortability can be got. It is considered that cotton Coolmax weft-varied fabric comprehensive comfortability can be improved by alkali peeling.

Abstract: Retracted paper: Many computational biologists would agree that, had it not been for the essential unification of evolutionary programming and DNS, the emulation of e-business might never have occurred. After years of significant research into operating systems, we disprove the improvement of e-business, which embodies the key principles of hardware and architecture. In order to realize this intent, we use virtual theory to verify that journaling file systems and congestion control are rarely incompatible.

Abstract: Over the past decade, there has been a great demand of Unmanned Aerial Vehicles (UAVs) in numerous industrial and military operations around the world. This paper is focused on low fixed-wing UAV remote sensing system, put remote sensing technology and UAV technology closely to fixed-wing unmanned aircraft as a platform, which is equipped with high-resolution digital remote sensing sensors, it has easy transition since the airport does not depend on landing site, it is a new low-speed high-resolution remote sensing data acquisition system. It has capability of a survey of real-time quick monitoring, and has been an effective complement to conventional means for satellite remote sensing and aerial photography.

Abstract: False alarm and missing alarm are two of the most important performances for multiple bits watermarking systems. In this paper, we study false alarm and missing alarm probability models when multiple watermarks or multiple bits watermarks embedded. We derive the false alarm and missing alarm probability models for dither modulation from the detection principle of the detectors. The theoretical results are compared with the experimental results obtained in the case of random work and watermark, and the comparison validates the accuracy of the models, and it also shows that random work and watermark have little influence on the false alarm and missing alarm probabilities, and this is the same with the situation when only one bit watermark is embedded by DM.

Abstract: Online digital image trading is a new tendency in digital stage, and multiple watermarking is one of the most important techniques that can be used to protect copyright and content security. In this paper a dynamic multiple watermarking method based on spread transform is introduced into the digital work trading frame developed from the frame proposed by European Union in order to make sure that the frame can make a good balance among the rights and interests of the work originators, the work publishers and the buyers really and truly.