In order to minimize the expected logistics cost of the supplier alliance, the Inventory Routing Problem (IRP) of multiple suppliers and multiple products under random fluctuations of demand was studied. Based on the horizontal collaboration strategy, a reasonable share method of vehicle distribution costs among the members of the supplier alliance was designed. By considering the retailer's distribution soft and hard time windows and inventory service level requirements, a heterogeneous vehicle inventory routing mixed-integer stochastic programming model of multiple suppliers and multiple products was established, and the inverse function of demand cumulative distribution was employed to transform this model into a deterministic programming model. Then an improved genetic algorithm was designed to solve the programming model. The results of example analysis show that the use of heterogeneous vehicles for distribution can reduce the total cost of supplier alliance by 8.3% and 11.92% respectively and increase the loading rate of distribution vehicles by 24% and 17% respectively, compared with the use of homogeneous heavy-duty and light-duty vehicles. The sensitivity analysis results indicate that no matter how the proportion of suppliers' supply to the total supply of the alliance and the variation coefficient of retailers' commodity demand change, the total cost of the supplier alliance can be effectively reduced by using heterogeneous vehicles for distribution; and the greater the demand variation coefficient is, the more obvious the advantage of using heterogeneous vehicles for distribution has.

The sugarcane node is difficult to recognize due to the diversity and complexity of surface that different types of sugarcane have. To solve the problem, a sugarcane node recognition method suitable for different types of sugarcane was proposed based on machine vision. Firstly, by the iterative linear fitting algorithm, the target region was extracted from the original image and its slope angle to horizontal axis was estimated. According to the angle, the target was rotated to being nearly parallel to the horizontal axis. Secondly, Double-Density Dual Tree Complex Wavelet Transform (DD-DTCWT) was used to decompose the image, and the image was reconstructed by using the wavelet coefficients that were perpendicular or approximately perpendicular to the horizontal axis. Finally, the line detection algorithm was used to detect the image, and the lines near the sugarcane node were obtained. The recognition was realized by further verifying the density, length and mutual distances of the edge lines. Experimental results show that the complete recognition rate reaches 92%, the localization accuracy of about 80% of nodes is less than 16 pixels, and the localization accuracy of 95% nodes is less than 32 pixels. The proposed method realizes node recognition for different types of sugarcane under different background with high position accuracy.

Focusing on the repetitiveness of geomagnetic fingerprints and accumulated error in Pedestrian Dead Reckoning (PDR), a fusion indoor localization method was proposed by using multiple sensors of smartphone. Firstly, WiFi and RANdom SAmple Consensus (RANSAC) algorithm were used to find initial positions of users. Then the step length was calculated by using the accelerometer of smartphone, and the conclusion of turn was given by gyroscope. Finally, the localization of PDR was corrected by means of geomagnetic field using map-constrained adaptive Particle Filter (PF), which is a high precision indoor localization method. The simulation results show that the proposed method can effectively overcome the accumulated error of the PDR and the repetitiveness of geomagnetic values, improve the positioning accuracy and reduce the energy consumption.

In view of the problem of computational redundancy due to the repeated computation of resource mapping positions in the traditional de-resource mapping method of Long Term Evolution-Advanced (LTE-A) physical layer, a new architecture of Physical Downlink Shared channel (PDSCH) de-resource mapping method was proposed, which provides support for the related physical layer processing of the LTE-A air interface analyzer. Firstly, before to the mapping of the physical downlink signal and the channel de-resource, the resource indexes of each signal and channel in single antenna port 0 mode, transmit diversity mode, single-stream beamforming, and dual-stream beamforming were generated; and then, the time-frequency location of the resource was directly located according to the resource index; finally, the PDSCH de-resource mapping module was put in the entire LTE-A link level simulation platform, and the simulations were given in four transmission modes, and the corresponding bit error rate and throughput comparison chart was obtained, which provides a theoretical reference to final hardware implementation. At the same time, compared with the de-resource mapping module under the traditional architecture, it shows that the de-resource mapping module under the new architecture costs 33.33% less time than the traditional computation mapping simulation, which reduces the de-resources and device resource consumption when de-resources mapping.

Focusing on the issue that the saliency detection algorithm via graph-based manifold ranking (MR algorithm) is over dependent on background features extracted from boundary nodes, an improved saliency detection algorithm combined with foreground and background features was proposed. Firstly, an image was divided into several super-pixels and a close-loop model was constructed. Secondly, the foreground and background seeds were obtained by using manifold ranking algorithm according to foreground and background features. Then these two kinds of seed nodes were combined through brightness and color characteristics, resulting in more accurate query nodes. Finally, a saliency map of the image was obtained by computing the saliency value via manifold ranking algorithm. Experimental results show that compared with MR algorithm, the precision rate, the recall rate and the F-measure of the proposed algorithm are significantly improved, and the obtained saliency maps are much more close to the true value.

Aiming at the problem of the large amount of data and the complicated algorithm in 3D scene feature recognition process, an feature extraction and reconstruction of environmental plane algorithm based on Kinect was proposed. Firstly, a method of RANdom SAmple Consensus (RANSAC) environment segmentation with the combination of geometrical and color information was proposed, which overcame the over segmentation and lack-segmentation based on geometric characteristic and improved the accuracy. Secondly, according to the principle of perspective projection, the three-dimensional transformation matrix was derived, which guided the 3D environment mapped into a plane. The extraction of contour points in two-dimensional space was realized by searching object boundary information which used convex hull concept. Finally, the 3D information of the contour points was recovered by the rotating inverse transform and the reconstruction of environment features was completed. Three groups of scene data were used to verify the algorithm and the experimental results show the proposed algorithm gains more precise segmentation, reduces over segmentation phenomena, and also has better reconstruction effect for objects with different shape features.

The service consumers lack trust in cloud service providers in the interactive process. Aiming at the problem, a trust model of cloud computing based on Service Level Agreement (SLA) was proposed. In the model, when registering to a third-party trust platform which called service center, a cloud service provider must submit its strength evaluation report on its strength, operations, technologies and service attributes, etc. According to the relevant criteria, service center made an evaluation of the cloud service provider and got the system trust. Then, the system trust was combined to traditional reputation. Thus, direct trust, indirect trust and system trust were made as three important factors for evaluating a cloud service provider, and the last trust value was calculated. Finally, the service consumer could make the SLA negotiation with the service provider according to the service and the last trust value, was used to determine the selection from multi-service providers. Thus, the dishonest or less reputable cloud service providers were excluded. The experimental results show that the last trust value is obtained more comprehensively and accurately due to the introduction of system trust and service consumer can select the cloud service provider with high credibility, which can effectively prevent the dishonest behaviors of cloud service providers and improve the success rate of interaction in the proposed trust model.

In a time flooded with massive spam messages, clearing them waste a huge amount of effort and time. The mining sent feature of spam messages is the key to solving this problem. On the basis of analyzing current text-message filtering mechanisms, an effective interaction period is proposed by combining the discrete interaction units of the message sender into a consecutive interaction unit according to the essence of median filter. Utilizing the ratio of input to output and Effective Interaction Period (EIP), a general filtering algorism of spam message is built. Experimenting on 20 millions real messages, the recall ratio of the proposed algorithm is 99.51% and the precision ratio is 49.90%. The experimental results indicate that the novel algorism greatly enhances the efficiency and velocity of detection, which can be applied to spam messages real-time intercepted technology.

Real-time moving vehicle modeling and dynamic behavior simulation is an important research in virtual reality game engine. In this paper, a real-time vehicle game engine was designed and presented. Auther provided a method of real-time vehicle rendering based-on multi-body dynamics in engine, and could simulate different of dynamic behaviors as real as in real world. The other techniques such as collision detection, 3D music rendering, real-time virtual environment rendering were also realized. Thus,it can enhance the immersion when user walk-through virtual environment.