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Networking alarm data types of coal mine safety monitoring and control system were analyzed. For coexisting problem of real alarm data and pseudo alarm data, a networking alarm data screening technology of coal mine safety monitoring and control system was proposed, and some key technologies were expounded including automatic identification of false alarm data of monitoring devices, automatic identification of sensor calibration data and breaking test data of three locking, automatic identification of sensor fault data, contrasting and analysis of real testing data by human and monitoring data, sensor identification in plane domain, etc. A networking alarm data screening platform of coal mine safety monitoring and control system was developed. The application result shows the platform can improve alarming accuracy rate of safety monitoring and control system.

Molecular identification of volatile organic compounds (VOCs) plays an important role in various applications including environmental monitoring and smart farming. Mid‐infrared (MIR) fingerprint absorption spectroscopy is a powerful tool to extract chemical‐specific features for gas identification. However, the detection and recognition of trace VOC gas mixtures remain challenging due to their intrinsic weak light–matter interaction and highly overlapped absorption spectra. Here, an artificial intelligence‐enhanced “photonic nose” for MIR spectroscopic analysis of trace VOC gas mixtures is proposed. To enhance the sensing performance by increasing bandwidth and sensitivity, the “photonic nose” is designed to employ coupled multi‐resonant plasmonic nanoantennas to cover MIR molecular fingerprints, coated with metal–organic frameworks as the gas enrichment layer. Low limits of detection are achieved (IPA: 1.99 ppm, ethanol: 3.43 ppm, and acetone: 9.82 ppm). With machine learning, a high classification accuracy of 100% is realized for 125 mixing ratios (IPA, ethanol: both 5 concentrations, 0–130 ppm; acetone: 5 concentrations, 0–201 ppm), and low‐deviation component concentration predictions of root‐mean‐squared error within 10 ppm are achieved for IPA and ethanol (both 0–130 ppm) under interference from 50 ppm acetone. The work paves the way for intelligent sensing platforms for environmental monitoring and smart framing. [ABSTRACT FROM AUTHOR]

Some problems are discussed about the electromagnetic radiation of mine communication cables, the interference of electromagnetic fields on cables and ports of mine communication cables. The signal crosstalk of capacitive coupling, inductive coupling between adjacent cables in underground coal mine and the influence of distribution parameters of mine communication cables on the electromagnetic compatibility is analyzed and studied in detail, and the solutions for these problems are proposed.

Bio/chemical mixture sensing in a water environment is of great importance in sensing applications. Relying on plentiful molecular fingerprints in mid‐infrared (MIR) and high integration potential, nanophotonic waveguide‐based MIR lab‐on‐a‐chip (LoC) provides a miniaturized and versatile solution for specific and label‐free bio/chemical detection. However, it is still challenging to implement an MIR LoC with on‐chip photodetection for chemical sensing in water, due to the strong MIR water absorption and limited MIR on‐chip photodetector scheme, let alone the spectral overlap issue in mixture analysis. Here, a MIR LoC integrating zero‐bias graphene photodetector is reported and the real‐time monitoring of three analytes in water leveraging the MIR LoC is demonstrated. Besides, using machine learning, the on‐chip collected spectra of the ternary mixture in water with 27 mixing ratios are successfully classified with an accuracy of 95.77%. Moreover, concentration prediction of individual analytes in a mixture is performed by developing a convolution regression network for mixture spectrum decomposition: 83.33% of the single‐component concentration predictions are within the 1 vol% error range, and an average root‐mean‐squared error of 1 vol% for mixture concentration predictions is achieved. The MIR LoC offers new opportunities for highly integrated intelligent sensing systems in various sensing scenarios in the Internet of Things era. [ABSTRACT FROM AUTHOR]