Passive monitoring method for underground personnel violation entry
1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China
2.
Internet of Things(Perception Mine) Research Center, China University of Mining and Technology, Xuzhou 221008, China
More Information
Abstract
In view of problems of poor practicability, poor stability and low accuracy of existing monitoring methods for underground personnel violation entry, a passive monitoring method for underground personnel violation entry based on channel state information of WiFi network was proposed. The method includes training phase and testing phase. In the training phase, channel state information data under conditions of somebody entry and nobody entry is collected respectively, and the collected data is preprocessed through outlier elimination and filtering. Then the preprocessed data is constructed into eigenvalue to establish discrimination model. In the testing phase, the collected data is preprocessed to construct eigenvalue, which is input into the discrimination model established in the training phase, so as to realize judgment of personnel violation entry. The experimental results show that accuracy of the method is 99.31%.
Related Articles
[1] LIU Hai, WANG Qiyao, GAO Peng, WANG Xinyan, FENG Xingyu, CUI Hongzhong, GAO Pengfei. Design of terahertz metasurface methane sensor based on bound states in the continuum [J]. Journal of Mine Automation, 2025, 51(2): 48-56. DOI: 10.13272/j.issn.1671-251x.18220
[2] WEI Chunxian, LI Tao, LIAN Changjin. The application of time sensitive network in coal mine [J]. Journal of Mine Automation, 2024, 50(S1): 65-68,99.
[3] LIU Hai, ZHOU Tong, CHEN Cong, GAO Peng, DAI Yaowei, WANG Xiaolin, DUAN Senhao, GAO Zongyang. Design of all dielectric metasurface methane sensor based on Fano resonance [J]. Journal of Mine Automation, 2023, 49(9): 106-114. DOI: 10.13272/j.issn.1671-251x.18108
[4] TANG Shoufeng, SHI Jingcan, ZHOU Nan, ZHAO Renci, TONG Guangming, HUANG Jie. Digital recognition method of methane sensor based on improved CNN-SVM [J]. Journal of Mine Automation, 2022, 48(1): 53-57. DOI: 10.13272/j.issn.1671-251x.2021070033
[5] WANG Haibo. Research progress of low-power methane sensor [J]. Journal of Mine Automation, 2021, 47(5): 16-23. DOI: 10.13272/j.issn.1671-251x.17754
[6] LIU Changyi, ZHANG Jingyuan, HUANG Xiangdong, ZHANG Ni, LI Jingbo, LIU Jie. Research on gas sensitive mechanism of low concentration methane threshold based on micro-nano ionization sensor [J]. Journal of Mine Automation, 2021, 47(3): 34-40. DOI: 10.13272/j.issn.1671-251x.2020110067
[7] SHEN Guojie. Research on pulse power supply of MEMS low power consumption catalytic methane sensor [J]. Journal of Mine Automation, 2018, 44(7): 27-31. DOI: 10.13272/j.issn.1671-251x.2018020033
[8] LI Si-guang. Design of a Novel Phase-sensitive Protector [J]. Journal of Mine Automation, 2012, 38(9): 23-26.
[9] JIANG Lei, LIU Fang-hua. The Research of Bridge Circuit with Constant Temperature Detection of Catalyst Combustion Methane Sensor [J]. Journal of Mine Automation, 2006, 32(6): 14-16.
[10] LIU Zhi-cun~, SUN Lin-feng~. Study on Automatic Adjustment of Zero and Correction of Sensitivity of Mine Intelligent Methane Sensor [J]. Journal of Mine Automation, 2005, 31(3): 4-6.
Cited by
Periodical cited type(12)
1.
赵亚东,马腾飞,思旺斗,王猛. 煤矿井下移动机器人同步定位关键技术研究. 煤矿机械. 2024(02): 48-51 .
2.
司垒,王忠宾,魏东,顾进恒,闫海峰,谭超,朱远胜. 基于IMU-LiDAR紧耦合的煤矿防冲钻孔机器人定位导航方法. 煤炭学报. 2024(04): 2179-2194 .
3.
高毅楠,姚顽强,蔺小虎,郑俊良,马柏林,冯玮,高康洲. 煤矿井下多重约束的视觉SLAM关键帧选取方法. 煤炭学报. 2024(S1): 472-482 .
4.
刘敬东,李旭,于凤启,苟丙荣,贺国庆,巩泽文. 激光SLAM技术在巷道精细建模的应用研究. 煤矿机械. 2024(10): 199-202 .
5.
黄晨烜,常健,王雷. 基于激光雷达的井下带式输送机边缘提取方法. 工矿自动化. 2024(09): 115-123 .
本站查看
6.
胡青松,李敬雯,张元生,李世银,孙彦景. 面向矿井无人驾驶的IMU与激光雷达融合SLAM技术. 工矿自动化. 2024(10): 21-28 .
本站查看
7.
崔邵云,鲍久圣,胡德平,袁晓明,张可琨,阴妍,王茂森,朱晨钟. SLAM技术及其在矿山无人驾驶领域的研究现状与发展趋势. 工矿自动化. 2024(10): 38-52 .
本站查看
8.
马亮,高亮,廉博翔,张琦,蔺小虎,姜之跃. 基于已知点约束的高精度煤矿巷道三维点云建模方法. 工矿自动化. 2024(11): 78-83+151 .
本站查看
9.
夏建超,周亮亮,陈仁. 恶劣环境下钢包脱挂钩状态自动识别技术研究. 重型机械. 2023(04): 62-67 .
10.
高海跃,王凯,王保兵,王丹丹. 基于全局点云地图的煤矿井下无人机定位方法. 工矿自动化. 2023(08): 81-87+133 .
本站查看
11.
程健,李昊,马昆,刘斌,孙大智,马永壮,殷罡,王广福,李和平. 矿井视觉计算体系架构与关键技术. 煤炭科学技术. 2023(09): 202-218 .
12.
李少安,刘欣,郭长鑫,王博,丁浩然,李晓健. 基于激光雷达自主定位导航的多功能机器人. 无线互联科技. 2023(17): 54-57 .
Other cited types(6)