基于离散小波变换算法和Inception卷积模块一维卷积神经网络的石油类污染物红外光谱定性分析OA北大核心CSTPCD
Classification Method for Petroleum Pollutants Based on Inception-One-Dimensional Convolutional Neural Network and Infrared Spectroscopy
红外光谱技术具有高效和无损等优点,在石油类污染物分类检测领域中具有重要的研究与应用价值.本研究提出了一种结合离散小波变换(DWT)算法和基于Inception卷积模块的一维卷积神经网络(Incep-tion-1D-CNN)的石油类污染物分类方法,首先使用DWT算法对原始红外光谱数据进行去噪处理,消除因实验环境、仪器误差和人工操作等因素产生的干扰信息;再通过Inception-1D-CNN模型获取多尺度的红外光谱特征信息,并基于此模型对石油类污染物…查看全部>>
Infrared spectroscopy technology has many advantages such as high efficiency and non-destructiveness,and has an important research and application value in the field of petroleum pollutant classification and detection.In this study,a petroleum pollutant classification method by combing the discrete wavelet transform(DWT)algorithm and a one-dimensional convolutional neural network based on the Inception module(Inception-1D-CNN)was proposed.Firstly,the DWT alg…查看全部>>
孔德明;何绍炜;李心怡;赵珺瑜;宁晓东
燕山大学电气工程学院,秦皇岛 066000燕山大学电气工程学院,秦皇岛 066000燕山大学电气工程学院,秦皇岛 066000燕山大学电气工程学院,秦皇岛 066000燕山大学信息科学与工程学院,秦皇岛 066000
红外光谱石油类污染物Inception模块卷积神经网络离散小波变换
Infrared spectrumPetroleum pollutantsInception moduleConvolution neural networkDiscrete wavelet transform
《分析化学》 2024 (9)
1287-1297,11
国家自然科学基金项目(No.62173289)资助. Supported by the National Natural Science Foundation of China(No.62173289).
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