The Prediction of Multi-Scale Entropy on Gas Concentration Time Series Based on Variational Mode Decomposition

2019 ◽  
Vol 14 (9) ◽  
pp. 1313-1325
Author(s):  
Wei Dai ◽  
Chang-Peng Ji ◽  
Ying-Jie Wang
Keyword(s):  
Time Series ◽  
Variational Mode Decomposition ◽  
Gas Concentration ◽  
Multi Scale ◽  
Concentration Time ◽  
Mode Decomposition ◽  
Concentration Time Series

scholarly journals Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 161 ◽  
Author(s):  
Tianjun Zhang ◽  
Shuang Song ◽  
Shugang Li ◽  
Li Ma ◽  
Shaobo Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Coal Mine ◽  
Time Series Prediction ◽  
Mean Square ◽  
Gas Concentration ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Concentration Prediction

Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

scholarly journals Research on time series characteristics of the gas drainage evaluation index based on lasso regression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shuang Song ◽  
Shugang Li ◽  
Tianjun Zhang ◽  
Li Ma ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Coal Mine ◽  
Experimental Comparison ◽  
Lasso Regression ◽  
Gas Drainage ◽  
Gas Concentration ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Concentration Prediction

AbstractThe evaluation of the coal mine gas drainage effect is affected by many factors, such as flow rate, wind speed, drainage negative pressure, concentration, and temperature. This paper starts from actual coal mine production monitoring data and based on the lasso regression algorithm, features selection of multiple parameters of the preprocessed gas concentration time series to construct gas concentration feature selection based on the algorithm. The three-time smoothing index method is used to fill in the missing values. Aiming at the problem of different dimensions in the gas concentration time series, the MinMaxScaler method is used to normalize the data. The lasso regression algorithm is used to perform feature selection on the multivariable gas concentration time series, and the gas concentration time series selected by the lasso feature and the gas concentration time series without feature selection are input. The performance of the ANN algorithm for gas concentration prediction is compared and analyzed. The optimal α value and L1 norm are selected based on the grid search method to determine the strong explanatory gas concentration time series feature set of the working face, and an experimental comparison of the gas concentration prediction results before and after the lasso feature selection is performed. We verify the effectiveness of the algorithm.

scholarly journals Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wu Xiang ◽  
Qian Jian-sheng ◽  
Huang Cheng-hua ◽  
Zhang Li
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Prediction Models ◽  
Forecasting Model ◽  
Gas Concentration ◽  
Concentration Time ◽  
Proposed Model ◽  
Concentration Time Series ◽  
Predicted Values ◽  
Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

Analysis and generation of groundwater concentration time series

2018 ◽  
Vol 111 ◽  
pp. 20-30 ◽  
Author(s):  
Maria Crăciun ◽  
Călin Vamoş ◽  
Nicolae Suciu
Keyword(s):  
Time Series ◽  
Concentration Time ◽  
Concentration Time Series

scholarly journals Cluster analysis of WIBS single-particle bioaerosol data

2013 ◽  
Vol 6 (2) ◽  
pp. 337-347 ◽  
Author(s):  
N. H. Robinson ◽  
J. D. Allan ◽  
J. A. Huffman ◽  
P. H. Kaye ◽  
V. E. Foot ◽  
...  
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Single Particle ◽  
Fungal Spores ◽  
Polystyrene Latex ◽  
Data Sets ◽  
Forest Site ◽  
Fluorescence Measurements ◽  
Concentration Time ◽  
Concentration Time Series

Abstract. Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

scholarly journals Spacecraft Anomaly Recognition Based on Morphological Variational Mode Decomposition and JRD

2018 ◽  
Vol 36 (1) ◽  
pp. 20-27
Author(s):  
Haixu Jiang ◽  
Ke Zhang ◽  
Jingyu Wang ◽  
Meibo Lü
Keyword(s):  
Measured Data ◽  
Variational Mode Decomposition ◽  
Telemetry Data ◽  
Recognition Method ◽  
State Recognition ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Sample Data ◽  
Orbital Spacecraft ◽  
Weak Anomaly

Considering the difficulty in identifying the in-orbital spacecraft weak anomaly, a spacecraft anomaly state recognition method based on Morphological variational mode decomposition and JRD distance is proposed. First of all, the telemetry data of the spacecraft is decomposed into multi-scale modal functions with different frequencies via morphological variational modal decomposition. Then the Rényi entropy of each modal function is extracted, which is regarded as the feature of telemetry data. Finally, the recognition of spacecraft anomaly state is realized by comparing the JRD distance between the sample data and the measured data. The proposed method is verified by means of the telemetry data of the weak anomaly speed of a satellite reaction wheel. The simulation results demonstrate that the proposed method can effectively identify the anomaly of the spacecraft and has obvious advantage in recognition speed.

scholarly journals Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 24 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Guowang Zhou ◽  
Dongdong Li ◽  
Haohan Ren
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

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