As a new method to detect vital signs, Ultra-wideband (UWB) radar could continuously monitor human respiratory signs without contact. Aimed at addressing the problem of large interference and weak acquisition signal in radar echo signals from complex scenes, this paper adopts a UWB radar echo signal processing method that combines strong physical sign information extraction at P time and Variational Mode Decomposition (VMD) to carry out theoretical derivation. Using this novel processing scheme, respiration and heartbeat signals can be quickly reconstructed according to the selection of the appropriate intrinsic mode functions (IMFs), and the real-time detection accuracy of human respiratory signs is greatly improved. Based on an experimental platform, the data collected by the UWB radar module were first verified against the measured values obtained at the actual scene. The results of a validation test proved that our UWB radar echo signal processing method effectively eliminated the respiratory clutter signal and realized the accurate measurement of respiratory and heartbeat signals, which would prove the existence of life and further improve the quality of respiration and heartbeat signal and the robustness of detection.

Due to high gas content and a low permeability coefficient in deep coal seam mining, the spontaneous combustion of coal around the wellbore can easily occur, leading to difficulties in extracting gas during the mining process. To determine the dangerous area around the borehole and conduct advanced prevention and control measures are the keys to preventing spontaneous combustion in boreholes. However, the dangerous area around the borehole is not clear, the sealing parameters lack scientific basis, and the key prevention and control measures are not clear, which have caused great harm to coal mines. This study took the 24,130 working face of Pingdingshan No. 10 Mine as an example, using numerical simulation, theoretical analysis, and field tests to classify the risks of studying the surrounding area of the wellbore. The dangerous area variations under different lengths of shotcrete in the roadway were analyzed, the optimal plugging parameters were studied, and the current “two plugs and one injection” plugging device was optimized. Based on the oxygen concentration and air leakage rate, a method was proposed to divide the dangerous area of fissure coal spontaneous combustion around the borehole induced by gas extraction. The dangerous area of spontaneous combustion around the borehole was defined as having an oxygen concentration larger than 7% and an air leakage rate less than 0.004 m/s. The comprehensive control measures of the grouting length at 2–4 m, hole-sealing parameter at 20-13 (hole-sealing depth 20 m, hole-sealing length 13 m) and the “two plugs, one injection and one row” device were determined.

To address the problem of fault branch recognition in mine ventilation systems, a one-class classification algorithm is introduced to construct the MC-OCSVM ventilation system fault diagnosis model, which is integrated with multiple OCSVMs. This model adopts uniform hyperparameters and transforms the ventilation system fault diagnosis problem into a maximum decision distance problem, to realize the effective use of mine monitoring wind speed data. The experiments on public KEEL datasets verify that the one-class classification integration model can solve the multiclassification problem and that the MC-OCSVM model has better generalizability than other one-class classification integration models do. The experiment is carried out in the Buertai coal mine, and the results show that the proposed algorithm can identify fault branches quickly and accurately, with an accuracy of 93.2% and a single fault diagnosis time is 1.2 s, highlighting its strong robustness.

The main objective of this paper was to assess the capability of multisource remote sensing imagery fusion for coastal zone classification. Five scenes of Gaofen- (GF-) 1 optic imagery and four scenes of synthetic aperture radar (SAR) (C-band Sentinel-1 and L-band ALOS-2) imagery were collected and matched. Note that GF-1 is the first satellite of the China high-resolution earth observation system, which acquires multispectral data with decametric spatial resolution, high temporal resolution, and wide coverage. The results showed that based on the comparison of C- and L-band SAR for coastal coverage, it is verified that C band is superior to L band and parameter subsets of σvv0, σvh0, and Dcross can be effectively used for coastal classification. A new fusion method based on the wavelet transform (WT) was also proposed and used for imagery fusion. Statistical values for the mean, entropy, gradient, and correlation coefficient of the proposed method were 67.526, 7.321, 6.440, and 0.955, respectively. We therefore conclude that the result of our proposed method is superior to GF-1 imagery and traditional HIS fusion results. Finally, the classification output was determined along with an assessment of classification accuracy and kappa coefficient. The kappa coefficient and overall accuracy of the classification were 0.8236 and 85.9774%, respectively, so the proposed fusion method had a satisfying performance for coastal coverage mapping.

This study addresses the limitations of traditional fire monitoring systems in underground electromechanical chambers, including narrow coverage, information silos, and delayed response. It proposes and validates a complete closed‑loop multi‑information fusion and graded warning system specifically tailored to this confined scenario. The system integrates multi‑parameter sensing, deep learning‑based flame detection, and optimized sensor layout strategies to achieve real‑time monitoring, early warning, and collaborative control. A portable multi‑parameter sensor was developed for synchronous detection of temperature, smoke, and hazardous gases , offering enhanced portability and extended battery life . Flame detection was improved using an optimized YOLOv5 algorithm that incorporates attention mechanisms and an additional detection layer, enabling accurate recognition of small‑scale flames in complex underground environments. Sensor layout was optimized through 3D spatial modeling and multi‑objective intelligent algorithms, markedly improving monitoring coverage while reducing redundancy. Furthermore, a hybrid fire risk assessment model combining Fuzzy Neural Networks (FNN) and Grey Relational Analysis (GRA) was established. A graded early warning mechanism based on the EPI (Early Warning Index) model was also developed. Experimental results demonstrate that the proposed system enhances fire perception accuracy, response speed, and decision‑making reliability, providing a robust technical foundation for intelligent fire safety management in underground mines.