Aiming at the scheduling problem of underground mining equipment in shot mining, this paper proposes an improved cultural gene algorithm (MA). The global search applies the genetic algorithm, and some adjustments are made in its crossover and mutation operations; the local search uses the simulated annealing algorithm. The global search applies the genetic algorithm, and some adjustments are made in its crossover and mutation operations; the local search uses the simulated annealing algorithm, considering that the algorithm will have a certain probability to jump out of the optimal solution range, so on the basis of the original algorithm, the Gaussian function is replaced by the Cauchy function to avoid this problem. The algorithm is applied to the scenario of 5S15J for simulation experiments. After that, compared with the results of the genetic algorithm, it shows that the improved MA algorithm is obviously better in total time and total interval time, and can obtain high-quality solutions and an ideal cooperative scheduling strategy.
Deep neural networks (DNNs), which have high accuracy prediction and stable network performance, have been widely deployed in various fields. However, the adversarial example, a sample of input data which has been modified very slightly in a way, may easily cause a DNN to maximize loss. Instead of white box attack being able to obtain gradient information, most DNN based systems in actual use can only be attacked by multiple queries. In this paper, we regard face recognition (FR) system as target, and propose a new method named SA-Attack to generate adversarial samples which cannot be distinguished by human within very limited queries. Experiments show that SA-Attack can successfully attack advanced face recognition models, including public and commercial solutions, which proves the practicability of our method.
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