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Aiming at the problem of low quality in image reconstruction of traditional image reconstruction algorithm of electromagnetic tomography(EMT), an EMT image reconstruction algorithm based on autoencoder neural network of Restricted Boltzmann Machine (RBM) is proposed. Firstly, the basic principles of EMT system and autoencoder neural network are analyzed. Autoencoder neural network is a deep learning model, which contains two parts: encoder and decoder. The encoding process of the encoder is equivalent to the object field detection process in the EMT system; the decoding process of the decoder is equivalent to the image reconstruction process. On this basis, an autoencoder neural network model is built. In this model, the RBM is used for layer by layer pre-training to obtain the initial weight and offset, and the global weight and offset are adjusted by BP algorithm. The parameter file generated in the trained autoencoder neural network is used to construct a decoder. Finally, the detected voltage value output by the EMT system is input into the decoder network to obtain the reconstructed image of the EMT. Furthermore, data with Gaussian noise and data regarding flow pattern not in training dataset are used to test the generalization ability and practicability of the network, respectively. The experimental results show that the method in this paper is a kind of EMT image reconstruction method with higher accuracy, which also provides a new means for EMT image reconstruction.

Safe operation of transport system depends on right examination of their technical conditions and right operating measures thereof. The conventional methods based on magnetic test for steel-cord conveyor belt inspection have many limitations. So a new detection method which is based on both magnetic method and X-ray digital imaging technology is used. This paper introduces the key techniques and control scheme of the detection system. Five main steps, including self-diagnosis, hardware test, magnetic inspection, radiographic inspection and diagnosis, are discussed in detail. The system provides a preventive maintenance program which can achieve high precision for it uses image analysis techniques to process digital radiography images to identify gravity of defects.

The roundness error is an important index of mechanical part and its interchangeability, it is the key to quality of product. A new method for calculating the roundness error based on artificial bee colony algorithm has been proposed in this paper. Artificial bee colony algorithm is an evolutionary computation. It has the character of simple technique, easy digital realization and few controlling parameters. Firstly, the basic principle of artificial bee colony algorithm is concisely introduced in this paper. The detailed steps for calculating the roundness error based on artificial bee colony algorithm have been described. Finally experiment results are given. These results have shown that the proposed method can correctly and effectively evaluate roundness error. The proposed method can overcome the local convergence in evaluation of roundness error based on least square method (LSM).