Prediction of CO concentration in different conditions based on Gaussian-TCN

Ni, Sen; Jia, Pengfei; Xu, Yuebin; Zeng, Liwen; Li, Xiaoyü; Xu, Min

doi:10.1016/j.snb.2022.133010

Cited by 31 publications

(3 citation statements)

References 29 publications

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“…( 2021 ), and a Gaussian-TCN model was recently used to predict CO concentration Ni et al. ( 2023 ). Finally, it is worth to mention the recent application of Complex Event Processing (CEP) Corral-Plaza et al.…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms to forecast air quality: a survey

2023

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Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011–2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.

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Section: Discussionmentioning

confidence: 99%

Machine learning algorithms to forecast air quality: a survey

2023

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“…The GELU function applies the identity function to positive inputs and smoothly maps negative inputs to zero, using the CDF of the standard normal distribution to introduce non-linearity. The resulting function is continuous and differentiable everywhere [71].…”

Section: Proposed Architecturementioning

confidence: 99%

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Borrè

Seman

Camponogara

et al. 2023

Sensors

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The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The time series data are from a sensor attached to an electrical machine (motor) measuring vibration variations in three axes: X (axial), Y (radial), and Z (radial X). The dataset is used to train a hybrid convolutional neural network with long short-term memory (CNN-LSTM) architecture. By employing quantile regression at the network output, the proposed approach aims to manage the uncertainties present in the data. The application of the hybrid CNN-LSTM attention-based model, combined with the use of quantile regression to capture uncertainties, yielded superior results compared to traditional reference models. These results can benefit companies by optimizing their maintenance schedules and improving the overall performance of their electric machines.

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“…For complex multivariable systems, it is difficult to establish an accurate dynamic model for simple conventional networks due to the absence of feature extraction or memory function. − The LSTM network and TCN network have unique advantages in modeling time series data with long-term dependence because of their long-term memory and feature extraction capabilities . The LSTM network is a variant of the recurrent neural network (RNNS) to solve the problems of gradient disappearance and gradient explosion when processing long sequences .…”

Section: Introductionmentioning

confidence: 99%

Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection

Shao,

Tang,

Liu

et al. 2023

ACS Omega

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Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.

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Prediction of CO concentration in different conditions based on Gaussian-TCN

Cited by 31 publications

References 29 publications

Machine learning algorithms to forecast air quality: a survey

Machine learning algorithms to forecast air quality: a survey

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection

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