Deep learning methods for atmospheric PM2.5 prediction: A comparative study of transformer and CNN-LSTM-attention

Cui, Bowen; Liu, Minyi; Li, Shanqiang; Jin, Zhifan; Zhang, Yu; Lin, Xihong

doi:10.1016/j.apr.2023.101833

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…With the rapid development of computer computing power, data-driven methods for predicting and recovering data has become a research hotspot [11,12]. The long short-term memory neural network (LSTM) has significant advantages in time series prediction and finds widespread application in various fields.…”

Section: Introductionmentioning

confidence: 99%

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

Hao,

Liu,

et al. 2024

Meas. Sci. Technol.

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Structure health monitoring (SHM) systems play a crucial role in understanding the condition of structures. However, owing to various uncertain factors, sensor data may be missing, which poses a great challenge to capture the real-time dynamic characteristics of the bridge. To address this problem, this paper proposes a model that integrates time varying filtering based empirical mode decomposition (TVFEMD), convolutional neural network (CNN), and gated recurrent unit (GRU) to forecast and fill in the missing data. The proposed model initially employs TVFEMD to decompose the signal into several intrinsic mode functions (IMFs) of different frequency bands. Subsequently, CNN is utilized to extract data features for each IMF, followed by prediction through GRU. The model linearly combines the prediction results obtained from each IMF to obtain the actual prediction result. Simulation data and measured data from the large railway bridge are utilized in this research to validate the model’s efficacy. The analysis results demonstrate a significant improvement in prediction performance compared to traditional models, showcasing strong generalization ability and robustness. In conclusion, the model proposed in this paper has a high utilization value in health monitoring data recovery.

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Section: Introductionmentioning

confidence: 99%

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

Hao,

Liu,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Due to the rapid development of convolutional neural networks, deep learning based methods have not only achieved remarkable results in the field of computer vision [15,18], but also been rapidly developed and widely applied in the fields of atmospheric monitoring [6,44], wireless transmission [45][46][47] and health assistance [16,19,26,32]. And in the field of industrial safety monitoring, the use of deep learning based object detection methods to realize the automated detection of helmets has become one of the urgent problems to be solved in the current industrial safety management.…”

Section: Introductionmentioning

confidence: 99%

Helmet Detection Based on Context Enhancement Pyramid Under Surveillance Images

Xu,

2024

ITC

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Helmet detection is of great significance for realizing the automated management of industrial safety. To address the problem that existing object detection methods have insufficient ability to detect helmet small objects under surveillance images, this paper proposes a helmet detection based on context enhancement pyramid under surveillance images to realize the automatic detection task of helmet objects. The method helps the network improve position localization for small-scale helmet objects by adding a high-resolution detection layer to YOLOv5. Also, the proposed context enhancement pyramid reduces the semantic differences between different scale features and generates rich contextual features to enhance the network’s discriminative learning ability for helmet small object features. In addition, the proposed multi-scale attention module improves thefeature fusion effect in the pyramid network to further capture multi-scale features and expand the receptive field to enhance the network’s detection precision of helmet objects under surveillance images. The experimental analysis shows that the proposed method has good detection effect compared to existing object detection methods on the Safety Helmet Wearing Dataset (SHWD) as well as the customized dataset.

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“…Models represented by ChatGPT have achieved outstanding computational performance and provide insights into how information travels through the network. The excellent computational efficiency and transparent attention mechanism provide new feasible choices for an extended application to energy, chemistry, engineering, and other fields. − In the chemical industry, Bai and Zhao used a novel transformer-based multivariable multistep prediction method for chemical process fault prognosis. The extensive evaluation of applications in a continuous stirred tank heater system and the TE process demonstrated high prediction accuracy and early fault prognosis compared with representative statistical methods and other advanced deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Forecasting of Large-Scale Green Ammonia Production: An Intelligent Dynamic Modeling Case in the Inner Mongolia Area of China

Zhang,

Li,

Zhang

et al. 2024

Ind. Eng. Chem. Res.

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Renewable energy sources have been viewed as an important approach to solve the energy crisis and are carbon neutral. Compared with other storage mediums, green ammonia has the highest total energy efficiency and is an important raw material in the chemical industry. The fluctuation of renewable energy is a huge challenge for green ammonia production; thus, a high-precision prediction model is required to enable the building of an advanced control system for green ammonia production. In this study, a transformer-based multivariable multistep time-series prediction model that includes the temporal scales feature of renewable energy, called MSPTST, is proposed as the first step to solve the problem of unstable green ammonia production. The model outperformed other state-of-theart models, and its R 2 values were 0.986, 0.9998, and 0.990 under a high load condition, low renewable energy condition, and natural condition, respectively. Stable operation and swift load transitions are facilitated through a model predictive control based on the MSPTST framework. The impact of energy fluctuations on green ammonia synthesis is addressed by further integrating control theory and algorithms.

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Deep learning methods for atmospheric PM2.5 prediction: A comparative study of transformer and CNN-LSTM-attention

Cited by 12 publications

References 26 publications

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

Helmet Detection Based on Context Enhancement Pyramid Under Surveillance Images

Time-Series Forecasting of Large-Scale Green Ammonia Production: An Intelligent Dynamic Modeling Case in the Inner Mongolia Area of China

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