Aiming at the optimal choice of sales channel structure in the closed-loop supply chain, considering the uncertainty of market demand and quality level of recycled products, four average gross profit models for the closed-loop supply chain system with four sales channel structures under the government differentially weighted subsidy were constructed with the objective of maximizing the gross profit. Firstly, Fuzzy Chance Constrained Programming (FCCP) method was used to transform the fuzzy constraints into clear corresponding expressions equivalently. Then, Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) were used to solve numerical examples of the model comparatively. Finally, sensitivity analysis was performed on the parameters. The results show that the maximum difference ratio of the above two algorithms is 0.018%, indicating that both algorithms do not fall into the local optimal solution, which verifies the validity of the algorithms and the confidence of the models. Enterprises can formulate optimal recycling, production and sales strategies according to different confidence levels of the potential demands, choose the optimal channel structure and increase the gross profit gradually.

The problem of influence maximization is to select a group of effective seed users in social networks, through which information can reach the largest scope of spread. Traditional researches on influence maximization rely on the specific network structures and diffusion models, however, the manually processed simplified networks and the diffusion models based on assumptions have great limitations on assessing the real influence of users. To solve this problem, an Influence Maximization algorithm based on User Interactive Representation (IMUIR) was proposed. First, the context pairs were constructed through random sampling according to users' interaction traces, and the vector representations of the users were obtained by the SkipGram model training. Then, the greedy strategy was used to select the best seed set according to the activity degrees of the source users and the interaction degrees between these users with other users. To verify the effectiveness of IMUIR, experiments were conducted to compare it with Random, Average Cascade (AC), Kcore and Imfector algorithms on two social networks with real interactive information. The results show that IMUIR selects the seed set with higher quality, produces a wider scope of influence spread, and performs stablely on the two datasets.

In order to solve the intelligence extraction of information in the era of remote sensing big data,it is important to build the model and method of intelligent information analysis fitting the intrinsic characteristics of remote sensing data. To meet the demand of universal remote sensing intelligent acquisition of large-scale water information,an intelligent extraction method of remote sensing water information based on visual selective attention mechanism and AdaBoost algorithm was proposed. Firstly,by the optimization design of RGB color scheme of remote sensing multi-feature index,the enhancement and visual representation of the water information image features were realized. Then,in HSV color space,the key node information of the chromatic aberration distance image was used to construct the classification feature set,and AdaBoost algorithm was used to construct the water recognition classifier. On this basis,the category that the water belongs to was automatically recognized from the image color clustering result,so as to realize the intelligent extraction of water information. Experimental results show that the proposed method has the water information extraction results improved on Leak Rate(LR)and Composite Classification Accuracy(CCA). At the same time,the proposed method not only effectively reduces the dependence on high quality training samples,but also has good performance on the recognition of temporary water areas such as water with high sediment concentration at wet season and submerged area caused by flooding.

In the Device-to-Device (D2D) caching network where mobile user terminals are all cache-enabled, the spatial distributions of all users were modeled as the Homogeneous Poisson Point Processes (HPPP). On this basis, combined with the randomness of content caching and requesting, the network interference was analyzed exactly and then approximately under specific scenarios. Considering that the D2D caching technique has the dual characteristics of both caching at terminals and D2D communications, which means that the content offloadeding includes self-offloading way and D2D-offloading way, and the content transmission has to meet the constraints of received Signal-to-Interference Ratio (SIR) and D2D maximal communication distance simultaneously, the closed-form expressions of Successful Offloading Probability (SOP) of the random D2D caching network was deducted. Simulation results show that the proposed SOP is a general metric and can be reduced to the special cases of existing research results. For example, when the users are distributed densely and the D2D maximal communication distance is relatively large, the SOP is reduced to the Successful Transmission Probability (STP) without considering the D2D distance constraint.

Aiming at the problems of network security such as information disclosure and tampering in the process of information exchange for railway bridge monitoring systems under open Internet environment, a data transmission protocol based on short signature scheme was proposed for railway bridge monitoring. Firstly, an identity-based short signature scheme was designed on the basis of Boneh's short signature. Then the scheme was proved to be safe under the random oracle model and the Inverse Computational Diffie-Hellman Problem (Inv-CDHP), and it was further applied to the data transmission protocol for railway bridge monitoring. Finally, the key code of the scheme was given and compared with several schemes. The experimental results and analysis show that the average time consumption of the proposed scheme is close to the classical Boneh's scheme, but 6% and 22% lower than that of Fangguo Zhang's scheme and Leyou Zhang's scheme. Therefore, the proposed scheme has more advantages in terms of signature length and efficiency, and can effectively solve the problem of lack of integrity protection and identity reliability authentication of monitoring data.

In the bridge crane payload space positioning system based on monocular vision, in order to solve the problem of matching performance degradation caused by the rotation and tilt of the target template, a real-time bridge crane payloadspace positioning method based on circle detection with vertical gradient direction lines and linear pre-interpolation was proposed. Aspherical target was attached to the top of the payload, and the spherical target was not sensitive to the rotation and tilt when it was detected. First, the spherical target in the Region of Interest (ROI) was detected accurately and fastly by the circle detection method based on vertical gradient direction lines. Secondly, the space coordinates of the spherical target center was confirmed by the space geometry method. And then, the space coordinates were fed back by the method of the linear pre-interpolation. In the contrast experiment with traditional method, space coordinates were transformed to the cable lengths and the payload-swing angles. The experimental results show that the payload-swing angles transformed by this method are more accurate than that of traditional method, and the method can meet the real-time requirement. Moreover, the maximum length measurement error between this method and traditional method is 2.49%, which meets the accuracy requirement.

In order to improve the accuracy of rating prediction and resolve cold-start problem in recommended systems, on the basis of the TrustWalker model, a random walk model based on user preferences, named PtTrustWalker, was proposed. Firstly, the similarities of users and items were calculated in social networks through matrix factorization method. And then, the items were clustered and the preference of user to items and the user similarity in different categories were calculated through user's scores. Finally, by making use of authority score and user preference, the credibility was detailed into user's credit in different categories, and the score was predicted by the item score of trusted user's highest preference which was similar to the target item in the process of migration. The proposed model decreases the influence of noisy data and improves the stability of the recommendation. The experimental results show that, the PtTrustWalker model has some improvements in the quality and speed of recommendation compared with the existing random walk models.

Pre-positioning facilities for collecting tents held by communities is helpful to improve the efficacy of tents collection in post-earthquake relief. A problem for pre-positioning facilities that collect the tents held by commnities considering various earthquake scenarios was studied. A bi-stage stochastic programming model was formulated for the facility location problem. The model aimed at minimizing economic costs and time-related costs. Considering the effects of weights and other parameters on the solutions, five experiments were performed and analyzed. A case was used to verify the practicability of the developed model that located the facilities of tents collection from communities in Pudong New District, Shanghai. Finally, a scenario based on Ya'an earthquake was used to analyze the influence of parameters on the solution under such a certain scenario.