scholarly journals Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network

2019 ◽  
Vol 9 (15) ◽  
pp. 2951 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen ◽  
Kanglin Cong ◽  
Shaolin Zhu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Forecast Accuracy ◽  
Short Term ◽  
Dynamic Displacement ◽  
Term Memory ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Lstm Network

In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy.

scholarly journals Compilation of Load Spectrum for 5MN Metal Extruder Based on Long Short-Term Memory Network

2021 ◽  
Vol 11 (20) ◽  
pp. 9708
Author(s):  
Xiaole Cheng ◽  
Te Han ◽  
Peilin Yang ◽  
Xugang Zhang
Keyword(s):  
Short Term Memory ◽  
Extrapolation Method ◽  
Load Prediction ◽  
Load Spectrum ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Lstm Network

As an important condition for fatigue analysis and life prediction, load spectrum is widely used in various engineering fields. The extrapolation of load samples is an important step in compiling load spectrum. It is of great significance to select an appropriate load extrapolation method. This paper proposes a load extrapolation method based on long short-term memory (LSTM) network, introduces the basic principle of the extrapolation method, and applies the method to the data set collected under the working state of 5MN metal extruder. The comparison between the extrapolated load data and the actual load shows that the trend of the extrapolated load data is basically consistent with the original tendency. In addition, this method is compared with the rain flow extrapolation method based on statistical distribution. Through the comparison of the short-term load spectrum compiled by the two extrapolation methods, it is found that the load spectrum extrapolation method based on LSTM network can better realize load prediction and optimize the compilation of load spectrum.

Modeling of the dynamic hysteresis in DEAP actuator using an empirical mode decomposition based long-short term memory network

2021 ◽  
pp. 1045389X2098699
Author(s):  
Zhaoguo Jiang ◽  
Yuan Li ◽  
Qinglin Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Hysteresis Phenomenon ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Hysteresis Nonlinearity ◽  
Memory Network ◽  
Long Short Term Memory

As a smart material-based actuator, the dielectric electro-active polymer (DEAP) actuator is widely considered to be a potential driving mechanism for many applications, especially in intelligent bio-inspired robotics. However, the DEAP actuator demonstrates rate-dependent and asymmetrical hysteresis phenomenon which leads to great tracking inaccuracy and even oscillatory response, severely limiting its further development. Feedforward Neural Network (FNN) model has already become a widely used method to describe this kind of strong hysteresis nonlinearity in recent years. However, the FNN has no ability to remember the historical state of long period of time which is also a very important factor to restrict hysteresis phenomenon. In this paper, a novel hybrid model, Long-Short Term Memory (LSTM) network combined with Empirical Mode Decomposition (EMD), is proposed to model the dynamic hysteresis nonlinearity in DEAP actuator. At first, the original control signal sequence is preprocessed into a series of sub-sequence by the EMD method and is reshaped by one-sided dead-zone operator. Then the input space of LSTM is conducted using the original control signal, the sub-sequence, and reshaped signal. Finally, the input space and the displacement signal are applied to train the long-short term memory network. In order to verify the performance of the proposed model, the traditional artificial back propagation neural network (BPNN) model, rate-dependent Prandtl-Ishlinskii (RPI) model, and nonlinear electromechanical (NEM) model are compared from prediction accuracy. The results demonstrate that: (1) the proposed model has a higher prediction accuracy than the traditional artificial BPNN, RPI, and NEM model; and (2) the prediction accuracy of LSTM network is significantly improved by using EMD. Therefore, the long-short term memory network combined with empirical mode decomposition is a competitive method compared to the existing state-of-the-art approach.

scholarly journals A Hybrid Model for Short-Term Traffic Flow Prediction Based on Variational Mode Decomposition, Wavelet Threshold Denoising, and Long Short-Term Memory Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Yang Yu ◽  
Qiang Shang ◽  
Tian Xie
Keyword(s):  
Traffic Flow ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Long Short Term Memory

Traffic flow prediction plays an important role in intelligent transportation system (ITS). However, due to the randomness and complex periodicity of traffic flow data, traditional prediction models often fail to achieve good results. On the other hand, external disturbances or abnormal detectors will cause the collected traffic flow data to contain noise components, resulting in a decrease in prediction accuracy. In order to improve the accuracy of traffic flow prediction, this study proposes a mixed traffic flow prediction model VMD-WD-LSTM using variational mode decomposition (VMD), wavelet threshold denoising (WD), and long short-term memory (LSTM) network. Firstly, we decompose the original traffic flow sequence into K components through VMD and determine the number of components K according to the sample entropy of different K values. Then, each component is denoised by wavelet threshold to obtain the denoised subsequence. Finally, LSTM is used to predict each subsequence, and the predicted values of each subsequence are combined into the final prediction results. In addition, the performance of the proposed model and the latest traffic flow prediction model is compared on the several well-known public datasets. The empirical analysis shows that the proposed model not only has good prediction accuracy but also has superior robustness.

scholarly journals Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions

2021 ◽  
Vol 23 (4) ◽  
pp. 612-618 ◽  
Author(s):  
Guoxiao Zheng ◽  
Weifang Sun ◽  
Hao Zhang ◽  
Yuqing Zhou ◽  
Chen Gao
Keyword(s):  
Tool Wear ◽  
Short Term Memory ◽  
Milling Process ◽  
Cutting Conditions ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Wear Condition

Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.

scholarly journals A Long Short-Term Memory (LSTM) Network for Hourly Estimation of PM2.5 Concentration in Two Cities of South Korea

2020 ◽  
Vol 10 (11) ◽  
pp. 3984 ◽  
Author(s):  
Khaula Qadeer ◽  
Wajih Ur Rehman ◽  
Ahmad Muqeem Sheri ◽  
Inyoung Park ◽  
Hong Kook Kim ◽  
...  
Keyword(s):  
Air Pollution ◽  
South Korea ◽  
Short Term Memory ◽  
Short Term ◽  
Deterministic Models ◽  
Term Memory ◽  
Pm 2.5 ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Lstm Network

Air pollution not only damages the environment but also leads to various illnesses such as respiratory tract and cardiovascular diseases. Nowadays, estimating air pollutants concentration is becoming very important so that people can prepare themselves for the hazardous impact of air pollution beforehand. Various deterministic models have been used to forecast air pollution. In this study, along with various pollutants and meteorological parameters, we also use the concentration of the pollutants predicted by the community multiscale air quality (CMAQ) model which are strongly related to PM 2.5 concentration. After combining these parameters, we implement various machine learning models to predict the hourly forecast of PM 2.5 concentration in two big cities of South Korea and compare their results. It has been shown that Long Short Term Memory network outperforms other well-known gradient tree boosting models, recurrent, and convolutional neural networks.

scholarly journals Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

Algorithms ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 173 ◽  
Author(s):  
Zhen Li ◽  
Tao Tang ◽  
Chunhai Gao
Keyword(s):  
Dynamic Model ◽  
Short Term Memory ◽  
Fundamental Problem ◽  
Learning Ability ◽  
Railway Transportation ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Memory Network ◽  
Long Short Term Memory

The automatic train operation system is a significant component of the intelligent railway transportation. As a fundamental problem, the construction of the train dynamic model has been extensively researched using parametric approaches. The parametric based models may have poor performances due to unrealistic assumptions and changeable environments. In this paper, a long short-term memory network is carefully developed to build the train dynamic model in a nonparametric way. By optimizing the hyperparameters of the proposed model, more accurate outputs can be obtained with the same inputs of the parametric approaches. The proposed model was compared with two parametric methods using actual data. Experimental results suggest that the model performance is better than those of traditional models due to the strong learning ability. By exploring a detailed feature engineering process, the proposed long short-term memory network based algorithm was extended to predict train speed for multiple steps ahead.

A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems

2020 ◽  
pp. 147592172093281
Author(s):  
Linchao Li ◽  
Haijun Zhou ◽  
Hanlin Liu ◽  
Chaodong Zhang ◽  
Junhui Liu
Keyword(s):  
Missing Data ◽  
Empirical Mode Decomposition ◽  
Hybrid Method ◽  
Short Term Memory ◽  
Measured Signal ◽  
Short Term ◽  
Term Memory ◽  
Mode Decomposition ◽  
Memory Network ◽  
Long Short Term Memory

Missing data, especially a block of missing data, inevitably occur in structural health monitoring systems. Because of their severe negative effects, many methods that use measured data to infer missing data have been proposed in previous research to solve the problem. However, capturing complex correlations from raw measured signal data remains a challenge. In this study, empirical mode decomposition is combined with a long short-term memory deep learning network for the recovery of the measured signal data. The proposed hybrid method converts the missing data imputation task as a time series prediction task, which is then solved by a “divide and conquer” strategy. The core concept of this strategy is the prediction of the subsequences of the raw measured signal data, which are decomposed by empirical mode decomposition rather than directly predicted, as the decomposition can assist in the modeling of the irregular periodic changes of the measured signal data. In addition, the long short-term memory network in the hybrid model can remember more long-range correlations of subsequences than can the traditional artificial neural network. Three widely used prediction models, namely, the autoregressive integrated moving average, support vector regression, and artificial neural network models, are also implemented as benchmark models. Raw acceleration data collected from a cable-stayed bridge are used to evaluate the performance of the proposed method for missing measured signal data imputation. The recovery results of the measured signal data demonstrate that the proposed hybrid method exhibits excellent performance from two perspectives. First, the decomposition by empirical mode decomposition can improve the accuracy of the core long short-term memory prediction model. Second, the long short-term memory model outperforms other benchmark models because it can fit more microscopic changes of measured values. The experiments conducted in this study also suggest that the change patterns of raw measured signal data are complex, and it is therefore important to extract the features before modeling.

scholarly journals Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4121
Author(s):  
Shaoqian Pei ◽  
Hui Qin ◽  
Liqiang Yao ◽  
Yongqi Liu ◽  
Chao Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Dew Point ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Short Term Load Forecasting ◽  
Memory Network ◽  
Long Short Term Memory

Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load.

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