In this work, we report a rational design of graphene sheets-wrapped anatase TiO(2) hollow particles. This unique hybrid structure demonstrates significantly enhanced lithium storage capabilities compared to the pure TiO(2) counterpart, which clearly uncovers the merit of structural design and rational integration with graphene sheets.

Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM.

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In the open pit mine production systems, a certain number of trucks transport mine and rock between the power shovel and the unloading point. Due to the mining truck has characteristics of high height, long width and big size, it has a large blind zone and a long braking distance. Therefore, the probability of accidents in mining trucks is high, which results in huge loss of manpower, material resources and financial resources. In this paper, tiny-yolov3 is used to detect obstacles in the mine, its real-time performance is high enough, but the detection accuracy is not ideal. Therefore, this paper proposes an improved target detection model based on tiny-yolov3. The residual network structure based on convolutional neural network is added to the tiny-yolov3 structure, and the accuracy of obstacle detection is improved under the condition of real-time detection. The experimental results show that compared with tiny-yolov3, the detect precision of tiny-yolov3 with residual structure is improved, and the detection speed is reduced slightly, there is no particular impact on the real-time nature of the entire algorithm.INDEX TERMS Convolutional neural network, real-time, residual network, target detection, tiny-yolov3.

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