Assessment of Rib Spalling Hazard Degree in Mining Face Based on Background Subtraction Algorithm and Support Vector Machine

Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Anon, Rongxin

doi:10.18520/cs/v116/i12/2001-2012

Cited by 13 publications

(5 citation statements)

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“…Combined with the freedom information of fully mechanized mining face in coal mine, the equipment spatial coordinate system was constructed [9], and the coordinate data of fully mechanized mining equipment was set on the coordinate system to characterize the attitude Angle relationship of equipment. Considering the environmental conditions of coal mine comprehensively, the straightness measurement model of fully mechanized mining equipment is set as follows:…”

Section: Design Of Collision Risk Warning Methods Of Fully Mechanized...mentioning

confidence: 99%

The collision risk warning method for fully mechanized mining equipment in coal mine under multi-sensor

2023

Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023)

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The geological environment of fully mechanized mining in coal mines is complex, and the requirements for the performance of fully mechanized mining equipment are relatively high. Under the influence of various natural factors and geological environmental factors, there are certain hidden dangers of collision risks in the operation of fully mechanized mining equipment in coal mines. In order to solve this problem, a new collision risk warning method for fully mechanized mining equipment for coal mines is proposed by introducing multi-sensors. By establishing the pose measurement model of the fully mechanized mining equipment, the operation rules and pose changes of the fully mechanized mining equipment can be obtained; sensors are deployed to obtain the pose data of the fully mechanized mining equipment, and the pose change parameters of the fully mechanized mining equipment can be calculated to predict the overall usage and potential of the fully mechanized mining equipment at the current moment. risk hidden dangers; predict collision risk, build alert indicators, and issue alerts. Through the experimental analysis, it can be seen that the designed alarm method, the accuracy of the collision risk alarm results of the fully mechanized mining equipment is above 96.16%, which is highly feasible.

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Section: Design Of Collision Risk Warning Methods Of Fully Mechanized...mentioning

confidence: 99%

The collision risk warning method for fully mechanized mining equipment in coal mine under multi-sensor

2023

Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023)

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“…In China, the conditions for coal seam occurrence are complex, and with the widespread use of fully mechanized working face with great mining height (FGH), coal wall rib-spalling influence on the coal wall stability of FGH has become a key technical problem influencing safe and efficient mining of FGH [1][2][3][4][5][6][7][8][9][10][11]. Coal wall stability is influenced by the main roof movement of the working face, coal body strength, and support, among which the internal joints fissures of the coal body have a crucial influence on the coal body strength.…”

Section: Introductionmentioning

confidence: 99%

The Instability Characteristics and Displacement Law of Coal Wall Containing Joint Fissures in the Fully Mechanized Working Face with Great Mining Height

Guo

Wang

2022

Energies

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Coal wall rib-spalling is regarded as a key technical problem influencing safe and efficient mining of fully mechanized working face with great mining height (FGH) while the coal wall stability is influenced by the strength of the coal body, of which the internal joint fissures have a crucial impact on the strength of the coal body. This research attempted to explore how the coal wall stability of FGH is influenced by the occurrence of joint fissures. This paper uses physical and numerical simulations to systematically analyze the instability characteristics and displacement law of FGH. Research results show that the form and scope of the instability of coal wall rib-spalling depend on the state of the coal seam joint fissures development area, and the development state of coal seam joint fissures is related to the combination of the coal seam joints; under the condition of hard coal, the coal wall stability is better at the inclination angle of 90°, and less stable at 45° and 135°; under the condition of medium-hard coal and joint surface inclination angle of 45° and 135°, the smaller the spacing of joint surface, the larger the area of the working face rib-spalling, and the less stable the coal wall.

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“…Hence, if we can extract useful signals from each other's eyes and design a human-computer interaction system, we can assist human beings in realising the good wishes of human-computer [2]. Eye movement signals have the benefits of large amplitude, easy waveform identification, and easy processing when compared to other bioelectric signals, providing more reliable and convenient circumstances for collecting eye movement information [3].…”

Section: Introductionmentioning

confidence: 99%

Eye Movement Prediction Based on Adaptive BP Neural Network

Tang

2021

Scientific Programming

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This paper uses adaptive BP neural networks to conduct an in-depth examination of eye movements during reading and to predict reading effects. An important component for the implementation of visual tracking systems is the correct detection of eye movement using the actual data or real-world datasets. We propose the identification of three typical types of eye movements, namely, gaze, leap, and smooth navigation, using an adaptive BP neural network-based recognition algorithm for eye movement. This study assesses the BP neural network algorithm using the eye movement tracking sensors. For the experimental environment, four types of eye movement signals were acquired from 10 subjects to perform preliminary processing of the acquired signals. The experimental results demonstrate that the recognition rate of the algorithm provided in this paper can reach up to 97%, which is superior to the commonly used CNN algorithm.

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Assessment of Rib Spalling Hazard Degree in Mining Face Based on Background Subtraction Algorithm and Support Vector Machine

Cited by 13 publications

References 0 publications

The collision risk warning method for fully mechanized mining equipment in coal mine under multi-sensor

The collision risk warning method for fully mechanized mining equipment in coal mine under multi-sensor

The Instability Characteristics and Displacement Law of Coal Wall Containing Joint Fissures in the Fully Mechanized Working Face with Great Mining Height

Eye Movement Prediction Based on Adaptive BP Neural Network

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