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Li Mingjie of Guangdong University of Technology guided undergraduates to publish a research paper titled "Overcoming Cross-Sensitivity for the Accurate Identification of Acetone, Isopropanol, and Clinical Mixtures Using a MEMS Dual-Sensor Array and Multi-task Deep Learning" in ACS Sensors. According to Jiwei Net, the study integrated PdO-modified SnO2/ZnO and MoS2/ZIF-67 onto MEMS microheaters to construct a dual-sensor array, and combined a multi-task model of a one-dimensional convolutional neural network and a bidirectional long short-term memory network to simultaneously achieve identification and concentration prediction of acetone, isopropanol, and simulated clinical-ratio mixed gases. On data collected at 5–25 ppm gases and separately at 7 and 10 ppm, the model used a 15-second time window, achieving an overall classification accuracy of 95.4% and a mean absolute error of 0.625 ppm for concentration prediction, with results updated every 15 seconds. No subsequent progress has been seen to date.
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Oct 23
Jiwei.com
The team led by Li Mingjie at Guangdong University of Technology published in ACS Sensors the results on gas identification using a MEMS dual-sensor array and multi-task deep learning
The team led by Li Mingjie at Guangdong University of Technology published a study in ACS Sensors, integrating PdO-modified SnO2/ZnO and MoS2/ZIF-67 onto MEMS microheaters to construct a dual-sensor array, combined with a multi-task model of a one-dimensional convolutional neural network and a bidirectional long short-term memory network, simultaneously achieving identification and concentration prediction of acetone, isopropanol, and mixed gases with simulated clinical ratios. On data collected at 5–25 ppm gases and separately at 7 and 10 ppm, the model used a 15-second time window, achieving an overall classification accuracy of 95.4% and a mean absolute error of 0.625 ppm for concentration prediction, with results updated every 15 seconds.