模式识别(心理学)
卷积神经网络
计算机科学
人工智能
支持向量机
卷积(计算机科学)
维数(图论)
组分(热力学)
特征(语言学)
特征提取
人工神经网络
二进制数
数学
算术
热力学
物理
哲学
语言学
纯数学
作者
Xiaojin Zhao,Zhihuang Wen,Xiaofang Pan,Michael Fraser,Amine Bermak
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2019-01-01
卷期号:7: 12630-12637
被引量:38
标识
DOI:10.1109/access.2019.2892754
摘要
In this paper, we present a novel one-dimensional deep convolutional neural network (1D-DCNN) with a multi-label-way-based algorithm for comprehensively and automatically extracting features and classifying mixture gases. Although a number of pattern recognition methods have been used to analyze the mixed gases, the performances of these methods highly depend on the hand-crafted feature engineering. By contrast, the proposed implementation, based on one-dimensional convolution, is capable of automatically extracting features and distinguishing the individual component of binary mixture gases composed of ethylene, CO, and methane. To the best of our knowledge, the proposed 1D-DCNN algorithm is first applied in the mixture gases' recognition. In addition, the proposed 1D-DCNN with multi-label way not only significantly reduces the label dimension but also quantifies the probability of each component in mixed gases. Compared with the conventional pattern recognition algorithms including support vector machine, artificial neural network, k-nearest neighbor, and random forest, the proposed 1D-DCNN exhibits a higher recognition accuracy (96.30%) based on our extensive experimental results using ten-fold cross validation.
科研通智能强力驱动
Strongly Powered by AbleSci AI