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In order to overcome the problems of poor real-time performance, high false alarm rate and underreport alarm rate of mine external fire monitoring, a method of fire monitoring using the fusion of visible visual features is proposed.Firstly, the visual features corresponding to the video images of fire sources in different monitoring environments are analyzed, and the extraction methods of fire source texture, sharp corners, similarity coefficient and flicker frequency are designed.Then, an improved seed region growth algorithm is used to segment the suspected fire area, and different feature extraction methods are used to calculate the dynamic and static characteristics of the suspected fire area.Secondly, the extracted dynamic and static features are used to construct fire feature vectors.Finally, a fire monitoring model using BP neural network is constructed, and monitoring model is verified.The results show that the proposed fire monitoring method can effectively detect mine external fire in different scenes and distances.The correct rate and detection rate are 98.60% and 99.06%, respectively, the false detection rate is as low as 2.00%.It has strong anti-interference ability and robustness.

The complex working environment of mine leads to the degradation of the infrared image. The existing infrared image enhancement algorithm is easy to lose the scene details or causes the target edge blur while improving the signal-to-noise ratio and contrast. In order to solve the above problems, a mine infrared image enhancement algorithm based on dual domain decomposition coupling improved Gaussian Laplacian (ILoG) factor and contrast limited adaptive histogram equalization (CLAHE) (ILoG-CLAHE) is proposed. Firstly, the dual domain decomposition model is used to decompose the mine infrared image into a detailed sub-images containing high-frequency information and a basic sub-images containing low-frequency information. Secondly, the CLAHE algorithm is used to improve the brightness, contrast and definition of the basic sub-images to highlight the general features of the monitoring scene. The constructed ILoG operator is used to suppress noise and sharpen edges of detail sub-images and eliminate gradient inversion. Thirdly, the reconstructed image with improved image quality is obtained through the basic sub-image and detail sub-image after reconstruction processing. Finally, a Gamma correction function of gray level redistribution is designed to adjust the brightness of the reconstructed image. The mine infrared-enhanced image is obtained. The performance of the algorithm is analyzed by subjective vision and objective indicators. The results show that the overall visual effect and objective index of the mine infrared image enhanced by the mine infrared image enhancement algorithm based on dual domain and ILoG-CLAHE have been greatly improved. The comprehensive enhancement performance and robustness are better. Compared with the original mine infrared image and the six comparison algorithms, the comprehensive evaluation index values of this algorithm are increased by 0.28, 0.11, 0.23, 0.38, 0.57, 0.04, and 0.10 respectively. The six algorithms include CLAHE algorithm, bilateral filtering(BF) decomposition and CLAHE enhancement of basic sub-images (BF-CLAHE) algorithm, BF decomposition and Gamma transform (BF-Gamma) algorithm, guided filtering and Gamma transform (GF-Gamma) algorithm, adaptive histogram equalization(AHE) coupled Laplacian transform (AHE-LP) algorithm, and un-sharp mask(UM) based layer fusion (LF-UM) algorithm. The brightness, clarity and contrast of images are greatly improved, and noise suppression and edge sharpening are realized. It shows that the algorithm is suitable for the enhancement of infrared images in the complex working environment of mine.

Mine exogenous fire monitoring methods were analyzed, and a mine exogenous fire identification method based on visible light and infrared image was proposed. ① Mine exogenous fire monitoring methods based on sensors for temperature, smoke and others need to arrange multiple points, so cost is high, and maintenance workload is large. ② Mine exogenous fire monitoring method based on visible light image has advantages of large monitoring range, simple use, low cost, small influence by distance, high resolution ratio, rich color information, clear edge texture information, easy extraction of flame structure information, etc. But it is influenced greatly by cap-lamp lights, roadway lights and red objects. ③ Mine exogenous fire monitoring method based on infrared image has advantages of large monitoring range and simple use, and can monitor fire in case of low visibility with smoke or others. But monitoring distance, mine dust, ambient temperature or humidity and emissivity of object surface all affect temperature measurement. Close-range workers, incandescent lamps and electromechanical equipment will affect fire monitoring. ④ The mine exogenous fire identification method based on visible light and infrared image improves accuracy of fire identification. When both visible light image and infrared image identify fire, it is determined to be a fire, and a fire alarm signal is issued. When there is only a single visible light image or infrared image to identify fire, it is not determined to be a fire, and a suspected fire alarm signal is issued. ⑤ The feasibility of the mine exogenous fire identification method based on visible light and infrared image is verified by experiments.

Video image characteristics of gas and coal dust explosion are analyzed: Explosive fireball is usually red with high brightness, high temperature and strong radiation of infrared and ultraviolet rays, and area of the fireballs expands rapidly; Area, brightness, color, shape and radiation intensity of flame front are constantly changing; Area, color and shape of smoke and dust are constantly changing; There are objects that move or deform rapidly. Based on visible light video image, near infrared video image, far infrared video image and ultraviolet video image, a method of gas and coal dust explosion perception alarm and explosion source judgment is proposed: According to brightness and change rate of highlight area, area and change rate of highlight area, image shape and its change, image color and its change, gas and coal dust explosion are identified; Explosion source is determined according to image changes and time sequence of the changes and camera damage at different positions. A gas and coal dust explosion perception alarm and explosion source judgment method is proposed based on multi-information fusion of visible light video image, near infrared video image, far infrared video image, ultraviolet video image, O2, CO2, CO, temperature, sound, vibration, air pressure, wind speed, wind direction, smoke, dust, etc. The method reduces impact of fire, roadway lamp, miner's lamp, car lamp, red clothing, electromechanical equipment heat, fault discharge of electrical equipment and cable, coal and gas outburst, rock burst, large area roof fall, flood, blasting operation, coal falling on working face, coal transfer and transportation on gas and coal dust explosion identification.

Application status of key technologies of gas telemetry based on tunable diode laser absorption spectroscopy were introduced including tunable diode laser technology, signal noise reduction technology and trace gas detection technology. Some problems of the key technologies of gas telemetry were pointed out, such as unstable performance of tunable diode laser, short distance of gas telemetry, poor signal noise reduction effect and big error of trace gas detection. Development directions of the key technologies of gas telemetry were proposed, which were developing tunable diode laser with high performance, studying nonlinear and unstable signal noise reduction algorithm, and designing digital lock-in amplifier for underground.

Chinese cabbage (Brassica rapa L. ssp. pekinensis) is an important food crop. However, its growth and development are commonly impacted by black spot disease. To examine the response mechanisms of Chinese cabbage to black spot disease, transcriptome and metabolome sequencing were performed on the leaves of Chinese cabbage genotypes J405 (resistant) and B214 (susceptible), 48 h post-infection (hpi) with Alternaria brassicicola. Expression of essential genes in the jasmonic acid, cytokinin, and auxin signaling pathways of both Chinese cabbage genotypes was inhibited. The expression of the pathogenesis-related protein 1 (PR1) gene mediated by the salicylic acid pathway is inhibited in the Chinese cabbage genotype B214. The basic endochitase B (CHIB) gene in the ethylene pathway of both Chinese cabbage genotypes was upregulated. The accumulation of reactive oxygen species in the disease spots of Chinese cabbage genotype J405 was greater than in genotype B214. The respiratory burst oxidase (RBOH) gene in the reactive oxygen species metabolic pathway was significantly upregulated in genotype J405, while no change was observed in genotype B214. We found that oxidation-reduction-related genes such as type-2 peroxiredoxin genes, NADPH-dependent thioredoxin reductase genes, glutathione peroxidase genes, and glutathione S-transfer genes were differentially expressed across both Chinese cabbage genotypes at 48 hpi. Metabolomics demonstrated that delta-tocopherol and S-hexyl glutathione were all downregulated in genotype J405, while they were upregulated in genotype B214. This approach also identified differential expression of genes in the carotenoid biosynthesis pathway, the glycinebetaine biosynthesis pathway, as well as in the specific sulfur glycoside metabolism pathway. These findings indicate that ethylene signaling is important in the hormone signaling regulatory network-mediated disease resistance and defense in Chinese cabbage. When facing pathogen infection, hormone transduction pathways associated with growth and development in Chinese cabbage are inhibited. The accumulation of reactive oxygen species and the outbreak of various secondary metabolites may endow the Chinese cabbage genotype J405 with increased resistance to black spot disease. [ABSTRACT FROM AUTHOR]

Herein, a CuPc/Bi-MOF cascade heterojunction is synthesized exhibiting an excellent NH3 yield (7.13 μg h−1 cm−2) and stability. Characterization studies show that the cascade heterostructure with a unique morphology and oxygen vacancies offers new insights into future photoelectrocatalytic material design. [ABSTRACT FROM AUTHOR]