Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e....Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.展开更多
An indoor positioning method for robots is presented to improve the precision of displacement measurement using only low-cost inertial measurement units(IMUs).Firstly,a high-fidelity displacement estimation for linear...An indoor positioning method for robots is presented to improve the precision of displacement measurement using only low-cost inertial measurement units(IMUs).Firstly,a high-fidelity displacement estimation for linear motion is proposed.A new robot motion model is designed as well as an axis alignment that only uses a single axis of the accelerometer.The integral error of velocity is eliminated by a new subsection calculation method.Two complementary IMUs are combined by assigning them different weights to obtain high accuracy displacement results.Secondly,an orientation estimation based on a fusion filter for the steering motion is proposed.Experiments show that the proposed method significantly improves the accuracy of linear motion measurement and is effective for the indoor positioning of a robot.展开更多
On the basis of the comparison data of Stage II of the tunnel site leveling project at Hutubi seismic station and the observation data of Stage IV of the site cross fault leveling project at Hutubi and the level obser...On the basis of the comparison data of Stage II of the tunnel site leveling project at Hutubi seismic station and the observation data of Stage IV of the site cross fault leveling project at Hutubi and the level observation data from the cross fault survey lines in Dafeng from 1987 to 2012,this paper analyses the variation rates of the tunnel site leveling observation results and the difference of annual change rates of the cross fault level observations at Hongshan seismic station in Hutubi. This paper concludes the reliability of the Ni004 optical level used by the station and puts forward a proposal based on the analysis. This paper also explores the cross fault leveling research on the ground deformation in the region concerned on the basis of the historical observation of the cross fault level at Dafeng and the comparison results of the tunnel site leveling observation in Hutubi.展开更多
基金The authors appreciate the financial support provided by the Natural Science Foundation of China(No.41807294)This study was also financially supported by China Geological Survey Project(Nos.DD20190716 and 0001212020CC60002)。
文摘Landslide displacement prediction can enhance the efficacy of landslide monitoring system,and the prediction of the periodic displacement is particularly challenging.In the previous studies,static regression models(e.g.,support vector machine(SVM))were mostly used for predicting the periodic displacement.These models may have bad performances,when the dynamic features of landslide triggers are incorporated.This paper proposes a method for predicting the landslide displacement in a dynamic manner,based on the gated recurrent unit(GRU)neural network and complete ensemble empirical decomposition with adaptive noise(CEEMDAN).The CEEMDAN is used to decompose the training data,and the GRU is subsequently used for predicting the periodic displacement.Implementation procedures of the proposed method were illustrated by a case study in the Caojiatuo landslide area,and SVM was also adopted for the periodic displacement prediction.This case study shows that the predictors obtained by SVM are inaccurate,as the landslide displacement is in a pronouncedly step-wise manner.By contrast,the accuracy can be significantly improved using the dynamic predictive method.This paper reveals the significance of capturing the dynamic features of the inputs in the training process,when the machine learning models are adopted to predict the landslide displacement.
基金National Natural Science Foundation of China(61375103,61533004,61320106012,and 61321002)the 863 Program of China(2014AA041602,2015AA042305 and 2015AA043202)+2 种基金the Key Technologies Research and Development Program(2015BAF13B01 and 2015BAK35B01)the Beijing Municipal Science and Technology Project(D161100003016002)the "111" Project under Grant B08043
文摘An indoor positioning method for robots is presented to improve the precision of displacement measurement using only low-cost inertial measurement units(IMUs).Firstly,a high-fidelity displacement estimation for linear motion is proposed.A new robot motion model is designed as well as an axis alignment that only uses a single axis of the accelerometer.The integral error of velocity is eliminated by a new subsection calculation method.Two complementary IMUs are combined by assigning them different weights to obtain high accuracy displacement results.Secondly,an orientation estimation based on a fusion filter for the steering motion is proposed.Experiments show that the proposed method significantly improves the accuracy of linear motion measurement and is effective for the indoor positioning of a robot.
基金sponsored by the Natural Science Foundation of Xinjiang Uighur Autonomous Region2012211B56)the Natural Science Foundation of China(41374031)the Earthquake Science and Technology Spark Plan(XH1030),and the Earthquake Science and Technology Spark Progam XH14054Y)
文摘On the basis of the comparison data of Stage II of the tunnel site leveling project at Hutubi seismic station and the observation data of Stage IV of the site cross fault leveling project at Hutubi and the level observation data from the cross fault survey lines in Dafeng from 1987 to 2012,this paper analyses the variation rates of the tunnel site leveling observation results and the difference of annual change rates of the cross fault level observations at Hongshan seismic station in Hutubi. This paper concludes the reliability of the Ni004 optical level used by the station and puts forward a proposal based on the analysis. This paper also explores the cross fault leveling research on the ground deformation in the region concerned on the basis of the historical observation of the cross fault level at Dafeng and the comparison results of the tunnel site leveling observation in Hutubi.
