位移预测是边坡地质灾害监测预警的关键,本文以温州绕城高速公路边坡为例,提出了一种新的基于图深度学习与北斗监测的边坡多因子位移预测方法。首先基于北斗高精度监测点位的空间位置对整体监测体系的图结构进行建模,构建图节点之间的...位移预测是边坡地质灾害监测预警的关键,本文以温州绕城高速公路边坡为例,提出了一种新的基于图深度学习与北斗监测的边坡多因子位移预测方法。首先基于北斗高精度监测点位的空间位置对整体监测体系的图结构进行建模,构建图节点之间的邻接矩阵。再对北斗高精度位移、降雨量、地下水位与土壤含水率多因子监测数据进行去粗差、插值与归一化等时序数据处理,并进行时空相关性分析,结果表明位移主要受连续两个月的降雨量、三级边坡的地下水位与土壤含水率的影响。将最先进的基于图深度学习的GTS(Graph for Time Series)预测模型引入边坡位移预测中,提出适用于北斗高精度边坡变形监测的GTS-BDS位移预测模型。当预测时长为1 h时,其均方根误差(RMSE)、平均绝对误差(MAE)与平均绝对百分比误差(MAPE)指标评价分别达到0.301、0.154与3.5%,均优于LSTM与T-GCN等模型。本文所提出的位移预测方法充分利用了北斗高精度及其他传感器监测点位之间的空间拓扑与监测数据的时序特征,从整体监测体系的角度提升边坡位移预测的准确率与可靠性,在边坡安全预警中具有良好的应用前景。展开更多
Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for position...Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.展开更多
文摘位移预测是边坡地质灾害监测预警的关键,本文以温州绕城高速公路边坡为例,提出了一种新的基于图深度学习与北斗监测的边坡多因子位移预测方法。首先基于北斗高精度监测点位的空间位置对整体监测体系的图结构进行建模,构建图节点之间的邻接矩阵。再对北斗高精度位移、降雨量、地下水位与土壤含水率多因子监测数据进行去粗差、插值与归一化等时序数据处理,并进行时空相关性分析,结果表明位移主要受连续两个月的降雨量、三级边坡的地下水位与土壤含水率的影响。将最先进的基于图深度学习的GTS(Graph for Time Series)预测模型引入边坡位移预测中,提出适用于北斗高精度边坡变形监测的GTS-BDS位移预测模型。当预测时长为1 h时,其均方根误差(RMSE)、平均绝对误差(MAE)与平均绝对百分比误差(MAPE)指标评价分别达到0.301、0.154与3.5%,均优于LSTM与T-GCN等模型。本文所提出的位移预测方法充分利用了北斗高精度及其他传感器监测点位之间的空间拓扑与监测数据的时序特征,从整体监测体系的角度提升边坡位移预测的准确率与可靠性,在边坡安全预警中具有良好的应用前景。
基金supported by the National Key R&D Program of China(No.2018AAA0100804)the Talent Project of Revitalization Liaoning(No.XLYC1907022)+5 种基金the Key R&D Projects of Liaoning Province(No.2020JH2/10100045)the Capacity Building of Civil Aviation Safety(No.TMSA1614)the Natural Science Foundation of Liaoning Province(No.2019-MS-251)the Scientific Research Project of Liaoning Provincial Department of Education(Nos.L201705,L201716)the High-Level Innovation Talent Project of Shenyang(No.RC190030)the Second Young and Middle-Aged Talents Support Program of Shenyang Aerospace University.
文摘Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.
