Coal Thickness Prediction Method Based on VMD and LSTM

The change in coal seam thickness has an important influence on coal mine safety and efficient mining. It is very important to predict coal thickness accurately. However, the accuracy of borehole interpolation and BP neural network is not high. Variational mode decomposition (VMD) has strong denoising ability, and the long short-term memory neural network (LSTM) is especially suitable for the prediction of complex sequences. This paper presents a method of coal thickness prediction using VMD and LSTM. Firstly, empirical mode decomposition (EMD) and VMD methods are used to denoise simple signals, and the denoising effect of the VMD method is verified. Then, the wedge-shaped coal thickness model is constructed, and the seismic forward modeling and analysis are carried out. The results show that the coal thickness prediction based on seismic attributes is feasible. On the basis of VMD denoising of the original 3D seismic data, VMD-LSTM is used to predict coal thickness and compared with the prediction results of the traditional BP neural network. The coal thickness prediction method proposed in this paper has high accuracy and basically coincides with the coal seam information exposed by existing boreholes. The minimum absolute error of the predicted coal thickness is only 0.08 m, and the maximum absolute error is 0.48 m. This indicates that VMD-LSTM has high accuracy in predicting coal thickness. The proposed coal thickness prediction method can be used as a new method for coal thickness prediction.

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Gearshift Sensorless Control for Direct-Drive-Type AMT Based on Improved GA-BP Neural Network Algorithm

Mathematical Problems in Engineering ◽

10.1155/2020/6456410 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Bo Li ◽

Wenqing Ge ◽

Qiang Li ◽

Yujiao Li ◽

Cao Tan

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Sensorless Control ◽

Prediction Method ◽

Absolute Error ◽

Linear Displacement ◽

Displacement Sensor ◽

Mechanical Transmission ◽

Direct Drive ◽

Displacement Prediction

The automated mechanical transmission (AMT) based on the electromagnetic linear driving device (EMLDD) has good potential for shift performance. However, the direct-drive shifting mechanism based on the displacement sensor is difficult to meet the compactness of the structure and control robustness in complex environment. Through analyzing the working principle of the electromagnetic linear driving device and features of sensorless control strategy, a new displacement prediction method based on the improved GA-BP neural network is proposed to replace the displacement sensor. With current, voltage, and input shaft speed of the electromagnetic linear driving device as input, displacement prediction is obtained by the GA-BP neural network with improved selection factor. Finally, the experiment validated the effectiveness of displacement prediction based on the improved GA-BP neural network of shift control. The results showed that prediction accuracy of the improved GA-BP neural network was greater than 96% under all shift working conditions. The average RMSE was reduced by 21.8%, absolute error of displacement prediction was controlled within ±0.5 mm, and average shift time was less than 0.18 s. In this paper, the BP neural network is applied to complex linear displacement prediction, which has important application and popularization value.

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Prediction of cold rolling gas based on EEMD-LSTM deep learning technology

Assembly Automation ◽

10.1108/aa-02-2021-0018 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hui Zhai ◽

Wei Xiong ◽

Fujin Li ◽

Jie Yang ◽

Dongyan Su ◽

...

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Back Propagation ◽

Prediction Method ◽

Absolute Error ◽

Learning Technology ◽

Intrinsic Mode Functions ◽

Content Type ◽

Gas Consumption

Purpose The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises. Design/methodology/approach In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result. Findings The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963. Originality/value A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.

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Air Pollutant Concentration Prediction Based on a CEEMDAN-FE-BiLSTM Model

Atmosphere ◽

10.3390/atmos12111452 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1452

Author(s):

Xuchu Jiang ◽

Peiyao Wei ◽

Yiwen Luo ◽

Ying Li

Keyword(s):

Short Term Memory ◽

Prediction Method ◽

Input Sequence ◽

Absolute Error ◽

Original Data ◽

Ensemble Empirical Mode Decomposition ◽

Air Pollutant ◽

Percentage Error ◽

Mode Decomposition ◽

Concentration Prediction

The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The “CEEMDAN-FE” decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model’s performance in predicting the real-time concentrations of PM2.5.

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Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽

10.3390/en13051094 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1094 ◽

Cited By ~ 3

Author(s):

Lanjun Wan ◽

Hongyang Li ◽

Yiwei Chen ◽

Changyun Li

Keyword(s):

Neural Network ◽

Working Conditions ◽

Bp Neural Network ◽

Evidence Theory ◽

Prediction Method ◽

Rolling Bearing ◽

Fault Prediction ◽

Fault Classification ◽

Bearing Fault ◽

Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

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Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1014.501 ◽

2014 ◽

Vol 1014 ◽

pp. 501-504 ◽

Cited By ~ 2

Author(s):

Shu Guo ◽

You Cai Xu ◽

Xin Shi Li ◽

Ran Tao ◽

Kun Li ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Roller Bearing ◽

Vibration Signal ◽

Classification Performance ◽

Outer Race ◽

Mode Decomposition ◽

Diagnosis Method ◽

Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

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Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

Frontiers in Research Metrics and Analytics ◽

10.3389/frma.2021.673928 ◽

2021 ◽

Vol 6 ◽

Author(s):

Tahani Aljohani ◽

Alexandra I. Cristea

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Methodological Approach ◽

High Accuracy ◽

Directional Model ◽

Textual Representations ◽

The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

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Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.933.384 ◽

2014 ◽

Vol 933 ◽

pp. 384-389

Author(s):

Xin Zhao ◽

Shuang Xin Wang

Keyword(s):

Neural Network ◽

Wind Speed ◽

Wind Power ◽

Bp Neural Network ◽

Wind Direction ◽

Wind Farm ◽

Prediction Method ◽

Small World ◽

Power Method ◽

Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

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