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AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition
2024
Journal of Cloud Computing: Advances, Systems and Applications
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Recurrent neural networks (RNNs) and long short-term memory (LSTM) neural networks have shown promise in predicting air quality trends in time series data. ...
This innovative approach involves decomposing the input signal using EEMD (Ensemble Empirical Mode Decomposition) and CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to extract ...
The EEMD-CEEMDAN-GCN hybrid prediction model combines the Graph Convolutional Network (GCN), a deep learning method, with the Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical ...
doi:10.1186/s13677-024-00598-9
fatcat:ywiynzuhs5cfdb4cvpmupiufti
A Novel Deep Learning Approach to Predict the Instantaneous NOx Emissions from Diesel Engine
2021
IEEE Access
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This paper presents a method for estimating transient NO x emissions by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a long-and short-term memory neural network (LSTM ...
Accurate and stable prediction of NO x emissions from diesel vehicles plays a crucial role in the establishment of virtual NO x sensors and the development and design of diesel engines. ...
EEMD
ensemble empirical mode Decomposition
NRMSE
normalized root-mean square Error
ANFIS
adaptive neuro-fuzzy inference system
CEEMDAN complete ensemble empirical mode
decomposition with adaptive ...
doi:10.1109/access.2021.3050165
fatcat:o3kb2uebvna33ngrn7oibfsyui
Interval Short-Term Traffic Flow Prediction Method Based on CEEMDAN-SE Nosie Reduction and LSTM Optimized by GWO
2022
Wireless Communications and Mobile Computing
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The Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the traffic flow data, and sample entropy (SE) is used to reconstruct the subsequence, which ...
Three models are used to compare with the ensemble model proposed in this paper, including back propagation neural network (BPNN), LSTM, and long-short-term memory optimized by Grey Wolf Optimizer (GWO-LSTM ...
Acknowledgments Thanks to all authors for their contributions in this work and the related work [33] of the lead author contributed to this paper. ...
doi:10.1155/2022/5257353
fatcat:soirmbrp3rbk5pydjo2wxvzpdm
A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks
2019
Energies
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in combination with the signal processing algorithm empirical mode decomposition (EMD). ...
For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. ...
Empirical Mode Decomposition Solar radiance sequences are non-stationary time series with certain periodicity and randomness. ...
doi:10.3390/en12244762
fatcat:gxqfpgaocfbfhk3fo3kf2vs644
Spatial-Temporal Analysis of Environmental Data of North Beijing District Using Hilbert-Huang Transform
2016
PLoS ONE
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The analysis used the empirical mode decomposition (EMD/EEMD) to break a time sequence of data down to a finite set of intrinsic mode functions (IMFs). ...
Temperature, solar radiation and water are major important variables in ecosystem models which are measurable via wireless sensor networks (WSN). ...
First, we describe the wireless environment sensor network which we deployed for data collection in Yanqing, Beijing, China. ...
doi:10.1371/journal.pone.0167662
pmid:27936056
pmcid:PMC5147931
fatcat:d4iqineg2fg25b6yvto4giyogm
Multi-Sensor Data Fusion for Remaining Useful Life Prediction of Machining Tools by IABC-BPNN in Dry Milling Operations
2020
Sensors
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The system integrates multi-sensor signal collection, signal preprocess by a complementary ensemble empirical mode decomposition, feature extraction in time domain, frequency domain and time-frequency ...
In this paper, a multi-sensor data fusion system for online RUL prediction of machining tools is proposed. ...
Then, the captured signals from force and vibration sensors are de-noised by a complementary ensemble empirical mode decomposition (CEEMD). ...
doi:10.3390/s20174657
pmid:32824889
fatcat:shx7eauuhvd6ja3irtyl357yhq
Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
2022
Symmetry
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The wireless sensor networks monitor the urban river levels and other natural environmental conditions for predicting the floods before they occur so that the people at risk evacuate in time. ...
In [57] , the authors developed the NARX neural network model for empirically predicting chaotic laser, variable bit rate, and video traffic time series of real-world datasets. ...
doi:10.3390/sym14050955
dblp:journals/symmetry/RamadeviB22
fatcat:3oa3go7rdzdurjl4yxcivjsbf4
A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles
2022
Processes
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The application scenarios, system diagnosis accuracies, and model structures of various studies in the literature are also compared and analyzed. ...
This paper reviews the current research status of rolling bearing fault diagnosis technology for railway vehicles. ...
Normal data decomposition methods in the fault diagnosis of train rolling bearings are wavelet transform (WT), wavelet packet decomposition (WPD), empirical mode decomposition (EMD), ensemble empirical ...
doi:10.3390/pr10040724
fatcat:7tchb35xr5hu5m3qmmk2vqf5wi
An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition
2019
Sensors
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As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis ...
Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. ...
Acknowledgments: We would like to thank Cambio Software Engineering for establishing the Cambio Wearable Computing Lab in the department where we obtained our ECG signal capturing device. ...
doi:10.3390/s19204495
pmid:31623279
pmcid:PMC6832168
fatcat:u57be6kqfzdwzntguqeubxhev4
Short‐term passenger flow forecasting using CEEMDAN meshed CNN‐LSTM‐attention model under wireless sensor network
2022
IET Communications
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In this study, building on convolutional neural network (CNN) and long short-term memory network (LSTM), a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attentionbased ...
In recent years, by using the wireless sensor network to sense the passenger data in advance, the technique of machine learning and neural networks has been utilized to assist the short-term passenger ...
CEEMDAN algorithm The CEEMDAN algorithm is a signal decomposition algorithm proposed by Torres at [26] based on the empirical mode decomposition algorithm (EMD) and the ensemble empirical mode decomposition ...
doi:10.1049/cmu2.12350
fatcat:cgjkprszzbetdolz5eiydcm7ny
Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring
2020
Sensors
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This approach was implemented in a prototype of wearable sensor network for human activity monitoring. ...
The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond ...
Another method for electrocardiogram data compression was based on empirical mode decomposition and feature dictionary construction [19] . ...
doi:10.3390/s21010085
pmid:33375625
fatcat:ymrwnknzhfhfrfj6vtajoibbly
Keyword Index
2020
2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
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mode decomposition
536
Empirical mode decomposition
498, 1418
Empirical Mode Decomposition
492, 509, 542, 826, 1250, 1267, 1302
Empirical mode decomposition.
1262
Energy
1424
energy
209
Energy ...
234
Network communication
98
network security
673
Network Wireless
1010
networking
1307, 1313
Neural Network
1228
neural network
169
Neuro-fuzzy controller
379
Neuronal Population
620 ...
doi:10.1109/icecce49384.2020.9179372
fatcat:vqx7xk7hcrabxhg75tifg76d2e
2018 Index IEEE Journal of Biomedical and Health Informatics Vol. 22
2018
IEEE journal of biomedical and health informatics
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
-that appeared in this periodical during 2018, and items from previous years that were commented upon or corrected in 2018. ...
., +, JBHI July 2018 1036-1045 Ensemble Empirical Mode Decomposition With Principal Component Analysis: A Novel Approach for Extracting Respiratory Rate and Heart Rate From Photoplethysmographic Signal ...
doi:10.1109/jbhi.2018.2880294
fatcat:3cy3e7no55emlgbxfe3mwef3vu
A review of artificial intelligence in marine science
2023
Frontiers in Earth Science
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In addition, we analyze the applications of artificial intelligence models in the prediction of ocean components, including physics-driven numerical models, model-driven statistical models, traditional ...
This article discusses the evolution of methodologies for the building of ocean observations, the application of artificial intelligence to remote sensing satellites, smart sensors, and intelligent underwater ...
Ensemble empirical mode decomposition (EEMD) and CEEMD are two improved algorithms of EMD. ...
doi:10.3389/feart.2023.1090185
fatcat:crhemksdtjgutkjibwpqasufdy
Index
[chapter]
2020
Smart Manufacturing
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See Ensemble Empirical mode
Decomposition (EEMD)
EIOA. ...
(TSN), 64 Time series models, for outlier detection, 326 Time synchronous averaging (TSA), 231-232 Total least squares, 184 TSN. ...
doi:10.1016/b978-0-12-820027-8.09992-5
fatcat:gnsto6lhvfbnrmheyk55himgqy
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