文献类型：期刊文献

英文题名：Multi-task deep learning model for quantitative volatile organic compounds analysis by feature fusion of electronic nose sensing

作者：Ni, Wangze[1,2];Wang, Tao[3];Wu, Yu[4];Liu, Xue[1,2];Li, Zhuoheng[1,2];Yang, Rui[1,2];Zhang, Kai[1,2];Yang, Jianhua[1,2];Zeng, Min[1];Hu, Nantao[1,2];Li, Bin[5];Yang, Zhi[1]

机构：[1]Shanghai Jiao Tong Univ, Natl Key Lab Adv Micro & Nano Manufacture Technol, Shanghai 200240, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Micro Nano Elect, Shanghai 200240, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[4]Shanghai Marine Diesel Engine Res Inst, Shanghai 201108, Peoples R China;[5]Zhengzhou Univ Light Ind, Sch Elect & Informat, Zhengzhou 450002, Peoples R China

年份：2024

卷号：417

外文期刊名：SENSORS AND ACTUATORS B-CHEMICAL

收录：;EI(收录号：20242716601106);WOS:【SCI-EXPANDED(收录号:WOS:001263123100001)】；

基金：This work was supported by the National Key Research and Development Program of China (2022YFB3205500) , the National Natural Science Foundation of China (62371299, 62301314, and 62101329) , the China Postdoctoral Science Foundation (2023M732198) , and the Natural Science Foundation of Shanghai (23ZR1430100) . We also acknowledge analysis support from the Instrumental Analysis Center of Shanghai Jiao Tong University and the Center for Advanced Electronic Materials and Devices of Shanghai Jiao Tong University. The computations in this paper were run on the pi 2.0 cluster supported by the Center for High Performance Computing at Shanghai Jiao Tong University.

语种：英文

外文关键词：Electronic nose; Image conversion; Convolutional neural network; Long short-term memory network; Feature fusion

摘要：In exploring pattern recognition for electronic noses via deep neural networks, traditional networks encounter key challenges, such as low training efficiency, and neglect of spatial-temporal attributes of gas sensor response sequences. In this study, an unmanned gas-sensing test system is used to generate a large dataset to ensure robust model training. The Gramian angular field-Markov transition field is utilized to convert time sequences into images. Using advanced image processing tools, the images are then subsequently compressed with data augmentation. This method fully preserves temporal and spatial features within the sequences, thus enhancing model performance. Furthermore, the proposed multi-task learning (MTL) framework competently performs simultaneous classification and regression tasks. The primary component of the MTL network, majorly constituted of convolutional neural networks, emphasizes the spatial features of the sequences. The integration of a long short-term memory layer ensures the preservation of temporal feature analysis of the input data, thereby enhancing predictive performance. When images are compressed to only 3.9 % of the original data, substantial information can still be preserved. Subsequently, the model trained by such compressed images attains an accuracy of 95.31 % and an R2 score of 0.9510 for classification and regression tasks, respectively. Our work reveals the remarkable potential of integrating temporal and spatial features in pattern recognition, promoting the potency of multi-tasking deep learning networks in electronic nose technology.