Mode decomposition based deep learning model for multi-section traffic prediction

Pholsena, Khouanetheva; Pan, Li; Zheng, Zhenpeng

doi:10.1007/s11280-020-00791-1

Cited by 21 publications

(7 citation statements)

References 25 publications

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“…Typically, when a signal is decomposed using the adaptive methods, it produces several IMFs. In the previous studies, the authors considered all the individual IMFs as individual samples and applied multichannel CNN for classification [38][39][40]. However, that approach requires n-channel CNN for n-IMFs, enhancing the preprocessing and classification duration n times.…”

Section: Features For Deep Learningmentioning

confidence: 99%

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Faysal

Ngui

Lim

et al. 2021

Sensors

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Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis.

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Section: Features For Deep Learningmentioning

confidence: 99%

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Faysal

Ngui

Lim

et al. 2021

Sensors

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“…EMD method is usually applied for original signal decomposition into its intrinsic multi-scale characteristics [20]. Generally, prediction methods that are based on signal's multi-scale characteristics are widely applied in different fields like short-term rainfall forecasting [21], short-term traffic flow prediction [22][23][24] and short-term wind power forecasting [25][26]. In the fields of water quality forecasting in aquaculture environment, Li et al [18] applied the ensemble empirical mode decomposition method to propose an efficient hybrid model for DO concentration forecasting in aquaculture based on original signal multi-scale features in order to increase the forecasting accuracy of DO content [27] in aquaculture environment.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Eze¹,

Ajmal²

2020

Preprint

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Dissolved Oxygen (DO) concentration is a vital parameter that indicates water quality. DO short term forecasting using time series analysis on data collected from an aquaculture pond is presented here. This can provide data support for an early warning system for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD) based LSTM (Long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that are strongly correlated with the original sensor data and integrate both into inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. Performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, three statistical performance indices were adopted: MAE, MSE, and RMSE. Results presented for short term (12-hour period) and long term (1-month period) give a strong indication of suitability of this method for forecasting DO values.

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“…Expressway section flow prediction refers to the calculation of the number of vehicles passing through the study section in a future period through historical traffic data by analyzing the historical change pattern of section flow. [3]. By predicting the section flow in a certain time period in the future, it can provide a reference for the traffic management department and help to improve the efficiency of highway operation.…”

Section: Introductionmentioning

confidence: 99%

Expressway cross-sectional flow prediction algorithm based on spatio-temporal data fusion

Li¹,

et al. 2022

International Conference on Smart Transportation and City Engineering (STCE 2022)

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The short-term traffic flow prediction is of practical significance for the operation and management of the expressway. Combining the traffic flow data collected from the expressway network in Shandong Province, the short-term traffic flow prediction of expressway sections is studied to provide support for the establishment of vehicles travel path selection model. Firstly, the spatio-temporal correlation of traffic flow is analyzed and the correlation coefficients are calculated. Secondly, the summation autoregression and average long-term and short-term memory models based on time series, and regression prediction model based on spatial correlation are selected to predict the traffic flow at the section. Finally, the weighted least squares method is used for the spatio-temporal data integration prediction. The prediction results show that the prediction accuracy of the spatio-temporal traffic flow data fusion algorithm is higher than that of the single prediction model. The average absolute percentage error of the data fusion algorithm is reduced to 8.127%, and the average absolute error and root mean square error are lower than those of the single prediction model. The combined prediction model improves the prediction accuracy.

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Mode decomposition based deep learning model for multi-section traffic prediction

Cited by 21 publications

References 25 publications

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Expressway cross-sectional flow prediction algorithm based on spatio-temporal data fusion

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