Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise t...Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise the adverse effects of surface displacement, it is vital to monitor and accurately predict them and unravel their mechanisms. In recent years, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have proven effective in predicting complex problems. However, CNN neglects the dynamic dependency of the input in the temporal dimension, which affects surface displacement features. The Convolutional-LSTM model can dynamically learn the temporal dependency among input features via the feedback connections in the LSTM to improve accurate captures of surface displacement features. This study focused on evaluating the C-LSTM model in predicting surface displacement of underground mines and assessed the predictive capabilities and generalisation strength of using hybridised ANN models. Geodetic and geotechnical data gathered from a Gold Mine in Ghana was used. The three models were tested on experimental data collected at Monitoring Scan Point 3. It was observed from the prediction output that all the methods could provide applicable and practical results. However, using indicators like root mean square error (RMSE) and correlation coefficient (R) in assessing the output of the prediction, the C-LSTM had the best prediction output. This study contributes to the advancement of accurate and efficient prediction of surface displacement of underground mines, ultimately enhancing and assisting safety operations.展开更多
在传感器网络中,有监督的异常数据检测算法的检测准确率以及鲁棒性受限于有标注数据集的构建,无监督异常数据检测算法往往导致较高的误报率(FPR)。为解决上述问题,针对到达服务器端的传感器数据流提出了一种基于卷积神经网络(CNN)和长...在传感器网络中,有监督的异常数据检测算法的检测准确率以及鲁棒性受限于有标注数据集的构建,无监督异常数据检测算法往往导致较高的误报率(FPR)。为解决上述问题,针对到达服务器端的传感器数据流提出了一种基于卷积神经网络(CNN)和长短时记忆网络(LSTM)的半监督在线异常检测算法。本算法利用K-means判别检测误差,并在检测中利用新数据重新训练机器学习模型,从而提高模型在长时间范围内的异常检测准确度。为了评估本算法的性能,使用因特尔伯克利实验室数据集IBRL(Intel Berkeley Research Lab)完成仿真实验,并与同类算法进行对比。实验结果表明,与同类算法相比,本算法对各个数据集都具有较高的召回率和F1-Score;应用K-means聚类的半监督模型,其异常检测结果更稳定。展开更多
文摘Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise the adverse effects of surface displacement, it is vital to monitor and accurately predict them and unravel their mechanisms. In recent years, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have proven effective in predicting complex problems. However, CNN neglects the dynamic dependency of the input in the temporal dimension, which affects surface displacement features. The Convolutional-LSTM model can dynamically learn the temporal dependency among input features via the feedback connections in the LSTM to improve accurate captures of surface displacement features. This study focused on evaluating the C-LSTM model in predicting surface displacement of underground mines and assessed the predictive capabilities and generalisation strength of using hybridised ANN models. Geodetic and geotechnical data gathered from a Gold Mine in Ghana was used. The three models were tested on experimental data collected at Monitoring Scan Point 3. It was observed from the prediction output that all the methods could provide applicable and practical results. However, using indicators like root mean square error (RMSE) and correlation coefficient (R) in assessing the output of the prediction, the C-LSTM had the best prediction output. This study contributes to the advancement of accurate and efficient prediction of surface displacement of underground mines, ultimately enhancing and assisting safety operations.
文摘在传感器网络中,有监督的异常数据检测算法的检测准确率以及鲁棒性受限于有标注数据集的构建,无监督异常数据检测算法往往导致较高的误报率(FPR)。为解决上述问题,针对到达服务器端的传感器数据流提出了一种基于卷积神经网络(CNN)和长短时记忆网络(LSTM)的半监督在线异常检测算法。本算法利用K-means判别检测误差,并在检测中利用新数据重新训练机器学习模型,从而提高模型在长时间范围内的异常检测准确度。为了评估本算法的性能,使用因特尔伯克利实验室数据集IBRL(Intel Berkeley Research Lab)完成仿真实验,并与同类算法进行对比。实验结果表明,与同类算法相比,本算法对各个数据集都具有较高的召回率和F1-Score;应用K-means聚类的半监督模型,其异常检测结果更稳定。
