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  • Open Access


    Production Dynamic Prediction Method of Waterflooding Reservoir Based on Deep Convolution Generative Adversarial Network (DC-GAN)

    Liyuan Xin1,2,3, Xiang Rao1,2,3,*, Xiaoyin Peng1,2,3, Yunfeng Xu1,2,3, Jiating Chen1,2,3

    Energy Engineering, Vol.119, No.5, pp. 1905-1922, 2022, DOI:10.32604/ee.2022.019556

    Abstract The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs. Considering that the construction of geological models with traditional numerical simulation software is complicated, the computational efficiency of the simulation calculation is often low, and the numerical simulation tools need to be repeated iteratively in the process of model optimization, machine learning methods have been used for fast reservoir simulation. However, traditional artificial neural network (ANN) has large degrees of freedom, slow convergence speed, and complex network model. This paper aims to… More >

  • Open Access


    Conveyor Belt Detection Based on Deep Convolution GANs

    Xiaoli Hao1,*, Xiaojuan Meng1, Yueqin Zhang1, Jindong Xue2, Jinyue Xia3

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 601-613, 2021, DOI:10.32604/iasc.2021.017963

    Abstract The belt conveyor is essential in coal mine underground transportation. The belt properties directly affect the safety of the conveyor. It is essential to monitor that the belt works well. Traditional non-contact detection methods are usually time-consuming, and they only identify a single instance of damage. In this paper, a new belt-tear detection method is developed, characterized by two time-scale update rules for a multi-class deep convolution generative adversarial network. To use this method, only a small amount of image data needs to be labeled, and batch normalization in the generator must be removed to… More >

  • Open Access


    Conveyor-Belt Detection of Conditional Deep Convolutional Generative Adversarial Network

    Xiaoli Hao1,*, Xiaojuan Meng1, Yueqin Zhang1, JinDong Xue2, Jinyue Xia3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2671-2685, 2021, DOI:10.32604/cmc.2021.016856

    Abstract In underground mining, the belt is a critical component, as its state directly affects the safe and stable operation of the conveyor. Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations. This tends to cause a large amount of calculation and low detection precision. To solve these problems, in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network (CDCGAN) was designed. In the traditional DCGAN, the image generated… More >

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