Volume 44 Issue 12
Turn off MathJax
Article Contents
Abstract
Related Articles
ZHANG Liya. Design of mine rescuing support system[J]. Journal of Mine Automation, 2018, 44(12): 19-23. DOI: 10.13272/j.issn.1671-251x.17372
Citation: ZHANG Liya. Design of mine rescuing support system[J]. Journal of Mine Automation, 2018, 44(12): 19-23. DOI: 10.13272/j.issn.1671-251x.17372
shu

Design of mine rescuing support system

  • 1.

    China Coal Research Institute, Beijing 100013, China

  • 2.

    Coal Mine Emergency Rescue Technology Equipment Research Center, Beijing 100013, China

  • 3.

    Beijing Mine Safety Engineering Technology Research Center, Beijing 100013, China

More Information
    Graphical Abstract
    • Abstract
  • For rapid and highly efficient requirements of mine emergency handling and rescue, a mine rescuing support system was designed by analyzing environmental information in disaster scene and vital sign of rescuer. Composition of the system was introduced as well as design schemes of two detectors namely ahead underground environment detector and wearable vital sign equipment for rescuer. Rescuing support detecting method was expounded which was based on fusion analysis of vital sign of rescuer and underground environmental information. The test results show that the system realizes real-time monitoring and warning of rescuer status in rescue process and environmental condition in underground disaster scene, which ensures rescuer safety and improves rescue efficiency.
    • FullText(HTML)
    • References (0)
    • Related Articles

  • Related Articles

    [1]GAI Yonggang. A defogging algorithm for coal mine underground images based on dark channel guided filtering and lighting correction[J]. Journal of Mine Automation, 2024, 50(6): 89-95. DOI: 10.13272/j.issn.1671-251x.2024030048
    [2]MU Qi, GE Xiangfu, WANG Xinyue, LI Lei, LI Zhanli. A coal mine underground image enhancement method based on multi-scale gradient domain guided image filtering[J]. Journal of Mine Automation, 2024, 50(6): 79-88, 111. DOI: 10.13272/j.issn.1671-251x.2023080126
    [3]MIAO Zuohua, ZHAO Chengcheng, ZHU Liangjian, LIU Daiwen, CHEN Aoguang. Image enhancement algorithm for non-uniform illumination in underground mines[J]. Journal of Mine Automation, 2023, 49(11): 92-99. DOI: 10.13272/j.issn.1671-251x.2023060032
    [4]HONG Yan, ZHU Danping, GONG Pingshun. Retinex mine image enhancement algorithm based on TopHat weighted guided filtering[J]. Journal of Mine Automation, 2022, 48(8): 43-49. DOI: 10.13272/j.issn.1671-251x.2022020029
    [5]FAN Zhanwen, LIU Bo. Research on adaptive enhancement technology of low illumination image based on improved Retinex[J]. Journal of Mine Automation, 2021, 47(S1): 126-130.
    [6]TANG Shoufeng, SHI Ke, TONG Guangming, SHI Jingcan, LI Huashuo. A mine low illumination image enhancement algorithm[J]. Journal of Mine Automation, 2021, 47(10): 32-36. DOI: 10.13272/j.issn.1671-251x.2021060052
    [7]LIU Xiaoyang, QIAO Tong, QIAO Zhi. Image enhancement method of mine based on bilateral filtering and Retinex algorithm[J]. Journal of Mine Automation, 2017, 43(2): 49-54. DOI: 10.13272/j.issn.1671-251x.2017.02.011
    [8]LI Xinnia. Improved non-local means filtering algorithm for video monitoring image of coal mine[J]. Journal of Mine Automation, 2015, 41(6): 66-70. DOI: 10.13272/j.issn.1671-251x.2015.06.016
    [9]ZHANG Qia. A filtering method for infrared image based on improved pseudo median filtering and non-local means filtering[J]. Journal of Mine Automation, 2014, 40(12): 57-60. DOI: 10.13272/j.issn.1671-251x.2014.12.015
    [10]LIU Yi, JIA Xu-fen, TIAN Zi-jia. A processing method for underground image of uneven illumination based on homomorphic filtering theory[J]. Journal of Mine Automation, 2013, 39(1): 9-12.

  • Cited by

    Periodical cited type(14)

    1. 汪学明，刘峰. 基于RV1126的井下运输煤流感应传感器设计. 煤矿机电. 2025(01): 31-36 .
    2. 闫明伟. 煤矿井下巷道行驶车辆车前障碍物检测方法研究. 矿业安全与环保. 2024(01): 168-174 .
    3. 郭永辉. 煤矿井下图像增强算法研究. 矿山机械. 2024(06): 53-57 .
    4. 张延军，夏黎明. 基于改进加权引导滤波的煤矿井下图像除雾算法研究. 矿业研究与开发. 2023(05): 203-210 .
    5. 姚超修，蒋泽，胡亚磊. 基于改进EnlightenGAN的煤矿井下图像增强算法. 煤炭技术. 2023(09): 219-222 .
    6. 苏波，李超，王莉. 基于多权重融合策略的Retinex矿井图像增强算法. 煤炭学报. 2023(S2): 813-822 .
    7. 乔佳伟，贾运红. Retinex算法在煤矿井下图像增强的应用研究. 煤炭技术. 2022(03): 193-195 .
    8. 陈晨，梁霄. 低照度下平面图像舒适色度范围测定方法. 吉林化工学院学报. 2022(05): 83-88 .
    9. 樊占文，刘波. 基于改进的Retinex低照度图像自适应增强技术研究. 工矿自动化. 2021(S1): 126-130 . 本站查看
    10. 王永杰. 基于FPGA的低照度环境下激光图像增强研究. 激光杂志. 2021(05): 68-72 .
    11. 杨帅，田益民，李璐瑶，郑美俊，高雪，宋方方. 局部相关的非线性采矿图像增强算法. 科技通报. 2021(10): 44-47 .
    12. 魏峰. 基于情景感知驱动的煤矿监察装备及平台设计. 煤炭工程. 2020(07): 157-160 .
    13. 蔡改贫，汪龙，罗小燕，姜志宏. 基于分块处理的矿石图像多阈值二值化算法. 矿业研究与开发. 2020(12): 153-157 .
    14. 范伟强，刘毅. 基于自适应小波变换的煤矿降质图像模糊增强算法. 煤炭学报. 2020(12): 4248-4260 .

    Other cited types(17)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (125) PDF downloads (18) Cited by(31)
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1172663    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return