QR code (quick response code) is used due to its beneficial properties, especially in the mobile payment field. However, there exists an inevitable risk in the transaction process. It is not easily perceived that the attacker tampers with or replaces the QR code that contains merchant’s beneficiary account. Thus, it is of great urgency to conduct authentication of QR code. In this study, we propose a novel mechanism based on visual cryptography scheme (VCS) and aesthetic QR code, which contains three primary schemes for different concealment levels. The main steps of these schemes are as follows. Firstly, one original QR code is split into two shadows using VC multiple rules; secondly, the two shadows are embedded into the same background image, respectively, and the embedded results are fused with the same carrier QR code, respectively, using XOR mechanism of RS and QR code error correction mechanism. Finally, the two aesthetic QR codes can be stacked precisely and the original QR code is restored according to the defined VCS. Experiments corresponding to three proposed schemes are conducted and demonstrate the feasibility and security of the mobile payment authentication, the significant improvement of the concealment for the shadows in QR code, and the diversity of mobile payment authentication.

In the big data background, the accuracy of fault diagnosis and recognition has been difficult to be improved. The deep neural network was used to recognize the diagnosis rate of the bearing with four kinds of conditions and compared with traditional BP neural network, genetic neural network and particle swarm neural network. Results showed that the diagnosis accuracy and convergence rate of the deep neural network were obviously higher than those of other models. Fault diagnosis rates with different sample sizes and training sample proportions were then studied to compare with the latest reported methods. Results showed that fault diagnosis had a good stability using deep neural networks. Vibration accelerations of the bearing with different fault diameters and excitation loads were extracted. The deep neural network was used to recognize these faults. Diagnosis accuracy was very high. In particular, the fault diagnosis rate was 98 % when signal features of vibration accelerations were very obvious, which indicated that using deep neural network was effective in diagnosing and recognizing different types of faults. Finally, the deep neural network was used to conduct fault diagnosis for the gearbox of wind turbines and compared with the other models to present that it would work well in the industrial environment.

Rapid rescue response has the highest priority in case of emergency randomly happening on the freeway network, which allows rescue vehicles to have many trajectory options. Searching for the fastest way is not easy within a short time after traffic accident happens especially for the mountainous area with special characteristics such as limited traffic capacity, enclosed internal space and so on. Here, road segment model is proposed to determine smallest road segment covering possible rescue ways. Other than traditional optimal search methods, modified reinforcement-leaning is introduced to find the optimal road trajectory. The proposed methods are tested in the freeway of Qinling Tunnel group, Xihan Freeway of Shaanxi province, China as a case study. Compared with traditional shortest path method, the rescue vehicle arrival time to the accident location is shortened from 22.9 to 6.5 min and dissipation time is also shortened from 52.4 to 25.6 min. Both of them show the proposed road trajectory could improve the rescue effectiveness and reduce the influence to road network. Successful application of these case study shows they could probably extend to use to other scenarios and contribute to improve the intelligence transportation system.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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This article presents a novel evolutionary strategy for multi-objective optimization in which a population's evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve towards optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multi-objective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness and speed of convergence.