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有色金属(选矿部分):2023,(4):129-133
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一维卷积神经网络在磁铁矿分选中的应用
吴敏, 潘春荣
(江西理工大学)
Application of One-dimensional Convolutional Neural Network in Magnetite Separation
wumin, panchunrong
(Jiangxi University of Science and Technology)
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本文已被:浏览 454次   下载 320
投稿时间:2022-06-13    修订日期:2022-06-28
中文摘要: 传统磁铁矿磁选方法由于磁感应强度受限,导致大粒度矿石的分选精度低。本文针对粒度大于20mm的磁铁矿石,对其磁感应信号进行分析,建立了一种改进的一维卷积神经网络(1D-CNN)磁铁矿石识别分类模型。首先,将采集的磁感应信号去除直流分量;其次,将该信号作为1D-CNN模型的输入,通过卷积层与池化层自适应地提取信号特征;最后,输出层利用Softmax逻辑回归实现磁铁矿石磁感应信号的分类。将该模型与经典1D-CNN进行了对比研究,并采用混淆矩阵评估该模型的特征识别效果;实验结果表明,该模型识别准确率达到了94%、收敛速度提升约28%。
Abstract:Due to the limitation of magnetic induction intensity of the traditional magnetite separation method, the separation accuracy of large-grained ore is low. In this paper, the magnetic induction signal of magnetite with a grain size greater than 20mm is analyzed, and an improved one-dimensional convolutional neural network (1D-CNN) magnetite recognition classification model is established. First, the DC component of magnetic induction signal is removed. Then, the signal is used as an input to the 1D-CNN model, and the signal characteristics are extracted adaptively through the convolution layer and the pooling layer. Finally, the output layer uses Softmax logic regression to realize the classification of magnetite magnetic induction signals. The model is compared with the classic 1D-CNN, and the confusion matrix is used to evaluate the feature recognition effect of the model. The experimental results show that the recognition accuracy of the model has reached 94%, and the convergence speed has increased by about 28%.
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基金项目:江西省研究生创新专项资金(YC2021-S577)
引用文本:
吴敏,潘春荣.一维卷积神经网络在磁铁矿分选中的应用[J].有色金属(选矿部分),2023(4):129-133.
wumin,panchunrong.Application of One-dimensional Convolutional Neural Network in Magnetite Separation[J].Nonferrous Metals(Mineral Processing Section),2023(4):129-133.

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