A Novel Mine Cage Safety Monitoring Algorithm Utilizing Visible Light
Abstract
:1. Introduction
- We propose a novel mine cage safety monitoring algorithm. To the best of our knowledge, we are the first to propose the use of visible light technology to count miners with miner’s lamps. Besides, this method can also be applied to miner’s attendance in the future.
- We designed a special safe area monitoring method to minimize the probability of danger in the mine cage. To the best of our knowledge, we are the first to propose the use of visible light technology to perceive the range of human activity in underground mines. It can effectively reduce the probability of hidden risks only through the modulated LED lamp and a small number of PDs.
- We implemented our algorithm with a simulated coal mine cage. We also designed the special transmitter (including Xilinx ZYNQ-7020 FPGA and commercial LED lamps) and receiver (including the PD Honeywell SD5421-002 and AN706 digital-to-analog converter (ADC)). The experimental results show that the accuracy of personnel overload judgment and safe area monitoring of our algorithm can reach 99%, and the accuracy of limb extension monitoring is more than 96%.
2. Algorithm Overview
- Step 1: We use pulse width modulation (PWM) to assign unique PWM frequencies to each miner’s headlamp (i.e., LED-1 in Figure 1) as their identity (ID) tags according to the frequency allocation law (Section 3.1.1) and then store the correspondence between miner i and frequency in the preset database.
- Step 2: When the miner enters the mine cage with his/her modulated headlamp on, the j-th PD deployed on the top of the cage (i.e., PD-1 in Figure 1) can perceive the modulated optical signals illuminating within its field of vision (FoV). Next, the data sampling module sends the sampled data of the voltage value of all the PDs to the server for processing.
- Step 3: After receiving the sampled data, the server separates all visible light frequencies of the mixed optical signals perceived by , and the correct light frequencies can be matched by comparing with the preset dataset. Finally, we can accurately calculate the number of people in the mine cage combining all the PDs.
- Step 1: First, we modulate the miner’s lamp deployed on the top of the cage door (i.e., LED-2 in Figure 1) to broadcast the light beacon with unique PWM frequency, and we represent its flashing frequency as .
- Step 2: Then we first collect fingerprints (i.e., the frequency power of perceived by the k-th PD when no limbs extend out of the mine cage) on a series of PDs deployed at the bottom of the cage door (i.e., PD-2 in Figure 1), and store these data into the preset dataset. Meanwhile we represent it as .
- Step 3: Finally, when the mine cage is running, the server calculates the real-time change ratio of the frequency power of obtained by each PD compared with the frequency power of the preset data (i.e., ), and we regard the change ratio of the frequency power as the basis for limb extension monitoring.
3. Specific Process
3.1. Personnel Counting
3.1.1. Frequency Allocation
3.1.2. Frequency Separation and Personnel Counting
3.2. Limb Extension Monitoring
- (1)
- We first collect the frequency power of the miner’s lamp on the cage door as perceived by the k-th PD deployed at the bottom of the cage door without the obstruction of external objects and then store it in the preset dataset.
- (2)
- Assuming that when a person enters the cage, the frequency power of the k-th PD at time t is , then the change ratio of the frequency power of the k-th PD can be expressed as:Since the fluctuation of the light intensity, the inherent noise of the PD, and the incomplete waveform factors generated by the LED, the frequency power fluctuation rate is less than 20% when the light is not blocked [32]. In order to maximize the accuracy of the limb extension monitoring of our algorithm, we think that when any PD receives a frequency power change rate of is greater than 20%, it indicates that the limb has extended out of the cage.
- (3)
- Due to the limitation of the sensing angle (i.e., FoV) of the PD, in theory, the more PDs are deployed, the higher the accuracy of the limb extension monitoring. On the other hand, it is not practical to deploy a large number of PDs in a harsh mine cage environment. Thus we need to give the best number of deployed PDs according to the actual situation of the mine cage. The conditions for the deployment of PDs are as follows.
- Condition 1: As shown in Figure 7, in the case where the miner’s lamp can fill the bottom of the entire cage door (see Step 3), assume that the minimum height we need to monitor is h, the width is l, and the FoV of the PD is , then the number N of PD we choose is at least:
- Condition 2: The larger the FoV of the PD, the smaller the blind zone for sensing light, so the fewer PDs are needed. Half of the FoV of the PD should be greater than the angle between the connection between the PD and the LED and the plane normal to the LED to ensure the PD’s effective reception of the modulated light signal. We express the height of the mine cage as H, and the distance from the deployed PD to the mine cage as S, therefore we give the selection range:
- Condition 3: Since most of the miner’s lamps are spotlights with a certain spotlight angle, we need to ensure that the illumination range of the LED lamp deployed on the top of the cage door can fully cover the PDs deployed at the bottom of the cage door. We mark the concentration of the LED lamp as , the width of the mine cage is l, and the height of the mine cage as H. Thereby the condition that the range of the LED light irradiating the bottom of the cage door needs to be met is
3.3. Safe Area Delineation
4. Experimental Evaluation
4.1. Experimental Equipment and Environment
4.2. Personnel Counting
4.3. Limb Extension Monitoring
4.4. Safe Area Monitoring
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Miner | ||||||
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Frequency (Hz) | 1770 | 2300 | 2710 | 3125 | 3600 | 4260 |
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Yang, X.; Pang, M.; Li, P.; Chen, P.; Niu, Q. A Novel Mine Cage Safety Monitoring Algorithm Utilizing Visible Light. Sensors 2020, 20, 3920. https://doi.org/10.3390/s20143920
Yang X, Pang M, Li P, Chen P, Niu Q. A Novel Mine Cage Safety Monitoring Algorithm Utilizing Visible Light. Sensors. 2020; 20(14):3920. https://doi.org/10.3390/s20143920
Chicago/Turabian StyleYang, Xu, Mingzhi Pang, Peihao Li, Pengpeng Chen, and Qiang Niu. 2020. "A Novel Mine Cage Safety Monitoring Algorithm Utilizing Visible Light" Sensors 20, no. 14: 3920. https://doi.org/10.3390/s20143920
APA StyleYang, X., Pang, M., Li, P., Chen, P., & Niu, Q. (2020). A Novel Mine Cage Safety Monitoring Algorithm Utilizing Visible Light. Sensors, 20(14), 3920. https://doi.org/10.3390/s20143920