Fuzzy Controller for Indoor Air Quality Control: A Sport Complex Case Study

Omarov, Bakhytzhan; Altayeva, Aigerim; Demeuov, Akhan; Tastanov, Adilbay; Kassymbekov, Zhakipbek; Koishybayev, Arman

doi:10.1007/978-981-16-3660-8_5

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…These complexities are compounded by the spectrum of lung conditions, each introducing unique segmentation hurdles, often leading to boundary ambiguities and inaccuracies in volumetric quantification. Consequently, these factors demand advanced segmentation strategies capable of discerning subtle lung pathologies and anatomical variances with heightened precision, thereby necessitating continual advancements in computational methodologies to support reliable diagnostic imaging [17], [18].…”

Section: B Challenges In Lung Ct Segmentationmentioning

confidence: 99%

Enhanced U-Net Architecture for Lung Segmentation on Computed Tomography and X-Ray Images

Saimassay,

Begenov,

Sadyk

et al. 2024

ijacsa

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In the expanding field of medical imaging, precise segmentation of anatomical structures is critical for accurate diagnosis and therapeutic interventions. This research paper introduces an innovative approach, building upon the established U-Net architecture, to enhance lung segmentation techniques applied to Computed Tomography (CT) images. Traditional methods of lung segmentation in CT scans often confront challenges such as heterogeneous tissue densities, variability in human anatomy, and pathological alterations, necessitating an approach that embodies greater robustness and precision. Our study presents a modified U-Net model, characterized by an integration of advanced convolutional layers and innovative skip connections, improving the reception field and facilitating the retention of high-frequency details essential for capturing the lung's intricate structures. The enhanced U-Net architecture demonstrates substantial improvements in dealing with the subtleties of lung parenchyma, effectively distinguishing between precarious nuances of tissues, and pathologies. Rigorous quantitative evaluations showcase a significant increase in the Dice coefficient and a decrease in the Hausdorff distance, indicating a more refined segmentation output compared to predecessor models. Additionally, the proposed model manifests exceptional versatility and computational efficiency, making it conducive for real-time clinical applications. This research underlines the transformative potential of employing advanced deep learning architectures for biomedical imaging, paving the way for early intervention, accurate diagnosis, and personalized treatment paradigms in pulmonary disorders. The findings have profound implications, propelling forward the nexus of artificial intelligence and healthcare towards unprecedented horizons.

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Section: B Challenges In Lung Ct Segmentationmentioning

confidence: 99%

Enhanced U-Net Architecture for Lung Segmentation on Computed Tomography and X-Ray Images

Saimassay,

Begenov,

Sadyk

et al. 2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…The results showed high accuracy for the root mean square error (RMSE) between the predicted and observed data: PMV 0.2243, CO 2 0.8816, 0.4645 and 0.6646, respectively, for PM 10 and PM 2.5 , indicating good applicability for buildings' integrated controls. Omarov et al [111] applied a fuzzy algorithm to intelligently optimize the control system for electric drive ventilation and an air-conditioning system for indoor CO 2 reduction. They applied the developed model to a sports complex at a university, and they obtained promising results, showing the developed fuzzy model was more effective than the traditional automatic control system.…”

Section: For Hvac Controlsmentioning

confidence: 99%

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Ogundiran,

Asadi,

Gameiro da Silva

2024

Sustainability

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Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

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“…The information about studies using various algorithms in occupancy state/number/activities prediction. indoor CO 2 concentration [139]. Another research found Multilayer Perceptron (MLP) follows the pattern of CO 2 changes more quickly and with higher accuracy compared to other algorithms (Support Vector Machine (SVM), AdaBoost (AdB), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR)).…”

Section: Tablementioning

confidence: 99%

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

Zhang

Calautit

2022

Renewable and Sustainable Energy Reviews

104

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Fuzzy Controller for Indoor Air Quality Control: A Sport Complex Case Study

Cited by 4 publications

References 23 publications

Enhanced U-Net Architecture for Lung Segmentation on Computed Tomography and X-Ray Images

Enhanced U-Net Architecture for Lung Segmentation on Computed Tomography and X-Ray Images

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment

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