Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin

Todorov, Venelin; Dimov, Ivan

doi:10.3390/atmos14071078

Cited by 12 publications

(7 citation statements)

References 62 publications

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“…It is vital for examining the stability of the model to carry out the sensitivity analysis of hyperparameters in deep learning. Optimal hyperparameters can maximize the performance of the model [37,38]. Different hyperparameters will have different effects on the OA, AA, and Kappa of the MDRDNet.…”

Section: Dropoutmentioning

confidence: 99%

Hybrid Convolutional Network Combining Multiscale 3D Depthwise Separable Convolution and CBAM Residual Dilated Convolution for Hyperspectral Image Classification

Hu,

Tian,

2023

Remote Sensing

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In recent years, convolutional neural networks (CNNs) have been increasingly leveraged for the classification of hyperspectral imagery, displaying notable advancements. To address the issues of insufficient spectral and spatial information extraction and high computational complexity in hyperspectral image classification, we introduce the MDRDNet, an integrated neural network model. This novel architecture is comprised of two main components: a Multiscale 3D Depthwise Separable Convolutional Network and a CBAM-augmented Residual Dilated Convolutional Network. The first component employs depthwise separable convolutions in a 3D setting to efficiently capture spatial–spectral characteristics, thus substantially reducing the computational burden associated with 3D convolutions. Meanwhile, the second component enhances the network by integrating the Convolutional Block Attention Module (CBAM) with dilated convolutions via residual connections, effectively counteracting the issue of model degradation. We have empirically evaluated the MDRDNet’s performance by running comprehensive experiments on three publicly available datasets: Indian Pines, Pavia University, and Salinas. Our findings indicate that the overall accuracy of the MDRDNet on the three datasets reached 98.83%, 99.81%, and 99.99%, respectively, which is higher than the accuracy of existing models. Therefore, the MDRDNet proposed in this study can fully extract spatial–spectral joint information, providing a new idea for solving the problem of large model calculations in 3D convolutions.

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Section: Dropoutmentioning

confidence: 99%

Hybrid Convolutional Network Combining Multiscale 3D Depthwise Separable Convolution and CBAM Residual Dilated Convolution for Hyperspectral Image Classification

Hu,

Tian,

2023

Remote Sensing

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“…Computational modeling methods are widely used to predict the processes of dusty air flow movement [22][23][24][25], which make it possible to determine the parameters of dust transfer processes and the effectiveness of dust collection methods and perform air quality assessment in buildings and structures [26,27]. In general, it is possible to note the high interest of scientists around the world in studies of the dustiness of air in subways [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]28] and methods of reducing dust concentrations.…”

Section: Literature Reviewmentioning

confidence: 99%

Justification of an Energy-Efficient Air Purification System in Subways Based on Air Dust Content Studies

Irgibayev,

Lugin,

Kiyanitsa

2023

Buildings

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It is not uncommon that subways count as densely populated areas, so air quality standards, including fine dust concentration, have been established for them. As passengers and subway staff are exposed to potentially harmful airborne particles, addressing this issue is vital to ensuring a safe and healthy environment on the subway. To reduce the dust concentration in subway systems, the authors propose installing filters to capture dust in ventilation failures between subway tunnels near metro stations. A novel aspect of the proposed method is the fact that airflow will be moved through filters by using the piston action of trains passing through the tunnels. The result of this research provides empirical evidence regarding dust content and mass concentrations of PM2.5 and PM10 in subway environments. While some existing literature discusses air quality in subways, the inclusion of specific measurements and data from the experiment strengthens the understanding of the severity of dust-related air quality issues in such environments. The data for this study were collected in the Almaty subway (Republic of Kazakhstan) at four stations: Raiymbek Batyr, Almaty, Baikonur and Alatau. Measuring points were located on passenger platforms, in the halls and at the entrances to the station. The lab scale tests determined the percentage of particles by their diameters relative to the total volume of dust, the percentage of dust particles smaller than a certain diameter, the percentage of various metal oxides and the average dust density. A preliminary energy assessment has been done on the proposed method of air purification from dust. With a frequency of 24 pairs of trains per hour, the energy savings per ventilation failure will be 240.170 kWh.

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“…Monitoring and controlling the air quality on pig farms has become a pivotal use of technology in pig farming [ 2 , 3 ]. Air quality has a direct impact on the growth processes and production performance of pigs, which in turn affects the physical health of farm workers, the pork quality, and the overall economic benefits of the farm [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Winter Air Quality in Pig-Fattening Houses: A Plasma Deodorization Approach

Zhang,

et al. 2024

Sensors

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This study aimed to evaluate the effect of two circulation modes of a plasma deodorization unit on the air environment of pig-fattening houses in winter. Two pig-fattening houses were selected, one of which was installed with a plasma deodorizing device with two modes of operation, alternating internal and external circulation on a day-by-day basis. The other house did not have any form of treatment and was used as the control house. Upon installing the system, this study revealed that in the internal circulation mode, indoor temperature and humidity were sustained at elevated levels, with the NH3 and H2S concentrations decreasing by 63.87% and 100%, respectively, in comparison to the control house. Conversely, in the external circulation mode, the indoor temperature and humidity remained subdued, accompanied by a 16.43% reduction in CO2 concentration. The adept interchange between these two operational modes facilitates the regulation of indoor air quality within a secure environment. This not only effectively diminishes deleterious gases in the pig-fattening house but also achieves the remote automation of environmental monitoring and hazardous gas management; thereby, it mitigates the likelihood of diseases and minimizes breeding risks.

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Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin

Cited by 12 publications

References 62 publications

Hybrid Convolutional Network Combining Multiscale 3D Depthwise Separable Convolution and CBAM Residual Dilated Convolution for Hyperspectral Image Classification

Hybrid Convolutional Network Combining Multiscale 3D Depthwise Separable Convolution and CBAM Residual Dilated Convolution for Hyperspectral Image Classification

Justification of an Energy-Efficient Air Purification System in Subways Based on Air Dust Content Studies

Optimizing Winter Air Quality in Pig-Fattening Houses: A Plasma Deodorization Approach

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