A Bidirectional Self-Rectifying Network With Bayesian Modeling for Vision-Based Crack Detection

Zhu, Qiuchen; Ha, Q.P.

doi:10.1109/tii.2022.3172995

Cited by 4 publications

(1 citation statement)

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“…This condition is impractical for deep learning with air pollution data in the presence of spatial-temporal correlations and influences of external changes in emissions, weather patterns and the multifaceted factors of environmental volatility [20]. Indeed, as mentioned in a recent survey [21], uncertainties associated with those conditions and incoming data imperfectness, such as missing and out-of-distribution values, pose a challenge in deep learning, for which the integration of probabilistic methods such as Bayesian reasoning into deep neural networks [22] is worth exploring to deal with uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast

Nguyen

et al. 2023

IEEE Access

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This paper presents a data fusion framework to enhance the accuracy of air-pollutant forecast in the state of New South Wales (NSW), Australia using deep learning (DL) as a core model. Here, we propose a long short-term memory Bayesian neural network (LSTM-BNN) to improve performance of the predictive profiles via quantifying uncertainties and adjusting model parameters. For this, we develop a new inferring technique for kernel density estimation with subdivision tuning to ensure both forecast accuracy and computational efficiency with a limited number of samples from the prediction distributions. Moreover, a novel algorithm called spatially-adjusted multivariate imputation by chained equation is also developed to take into account spatial correlations between nearby air-quality stations for correctly imputing the incoming data, and hence, to enable forecasting at a local scale. The LSTM-BNN framework is evaluated with observed datasets collected from stations and modeling outputs generated by the Conformal Cubic Atmospheric Model -Chemical Transport Model (CCAM-CTM) currently used in NSW. The airborne pollutants under investigation are P M 2.5 and ozone, which frequently exceed the standards. The results obtained from data fusion with our framework demonstrated high performance of the proposed LSTM-BNN model in air-pollutant prediction with reductions of over 30% in root mean square error compared to CCAM-CTM and over 50 % in inferring time compared to a DL model with Gaussian-based inference. Accuracy and reliability of the proposed model were also achieved with air pollution forecast in various seasons and suburbs.

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Section: Introductionmentioning

confidence: 99%