Machine learning algorithms to forecast air quality: a survey

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(9), P. 10031 - 10066

Published: Feb. 16, 2023

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

Language: Английский

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

Sensors, Journal Year: 2023, Volume and Issue: 23(9), P. 4512 - 4512

Published: May 5, 2023

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

Language: Английский

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Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

Nano-Micro Letters, Journal Year: 2024, Volume and Issue: 16(1)

Published: Aug. 14, 2024

Abstract As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing cross-response to ambient gases has always been a difficult important point in the sensing area. Pattern recognition based on sensor array is most conspicuous way overcome cross-sensitivity of sensors. It crucial choose an appropriate pattern method enhancing data analysis, errors improving system reliability, obtaining better classification or concentration prediction results. In this review, we analyze mechanism We further examine types, working principles, characteristics, applicable detection range algorithms utilized gas-sensing arrays. Additionally, report, summarize, evaluate outstanding novel advancements methods identification. At same time, work showcases recent utilizing these identification, particularly within three domains: ensuring food safety, monitoring environment, aiding medical diagnosis. conclusion, study anticipates future research prospects considering existing landscape challenges. hoped that will make positive contribution towards mitigating gas-sensitive devices offer valuable insights algorithm selection applications.

Language: Английский

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A separate modeling approach to noisy displacement prediction of concrete dams via improved deep learning with frequency division

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 60, P. 102367 - 102367

Published: Jan. 25, 2024

Language: Английский

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Machine vision and novel attention mechanism TCN for enhanced prediction of future deposition height in directed energy deposition

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 216, P. 111492 - 111492

Published: May 3, 2024

Language: Английский

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A novel electronic nose classification prediction method based on TETCN

Sensors and Actuators B Chemical, Journal Year: 2024, Volume and Issue: 405, P. 135272 - 135272

Published: Jan. 6, 2024

Language: Английский

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A model with high-precision on proton exchange membrane fuel cell performance degradation prediction based on temporal convolutional network-long short-term memory

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 74, P. 414 - 422

Published: June 14, 2024

Language: Английский

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Linear Model for Concentration Measurement of Mixed Gases

ACS Sensors, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Electronic noses have been widely used in industrial production, food preservation, agricultural product storage, environmental monitoring, and other fields. However, due to the cross-sensitivity of gas-sensing responses, accurately measuring concentration mixed gases remains challenging. To address this issue, study attempts determine number state variables that produce cross-influence based on experimental data, establish space model from equivalent circuit model, obtain parameters through parameter correlation iterative algorithms a Kalman filter. The sensor response measurement are established accordingly. simulation results show these two models high accuracy predicting concentrations under influence sensors.

Language: Английский

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Prediction of gas drainage changes from nitrogen replacement: A study of a TCN deep learning model with integrated attention mechanism

Fuel, Journal Year: 2023, Volume and Issue: 357, P. 129797 - 129797

Published: Sept. 14, 2023

Language: Английский

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An electronic nose for harmful gas early detection based on a hybrid deep learning method H-CRNN

Microchemical Journal, Journal Year: 2023, Volume and Issue: 195, P. 109464 - 109464

Published: Oct. 6, 2023

Language: Английский

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