Multi-level CEP rules automatic extraction approach for air quality detection and energy conservation decision based on AI technologies

Liu, Yuan; Yu, Wangyang; Zhai, Xiaojun; Zhang, Beiming; McDonald-Maier, Klaus D.; Fasli, Maria

doi:10.1016/j.apenergy.2024.123724

Applied Energy

2024

DOI: 10.1016/j.apenergy.2024.123724

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Multi-level CEP rules automatic extraction approach for air quality detection and energy conservation decision based on AI technologies

Yuan Liu,

Wangyang Yu,

Xiaojun Zhai

et al.

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2024

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Cited by 2 publications

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Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications

Varun,

Rajesh,

Lokeshwari

et al. 2024

Advances in IT Standards and Standardization Research

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This chapter points out machine learning-Ml-that is set to alter air and water quality monitoring technologies. Traditional monitoring systems usually work satisfactorily; however, there always exists deficiencies regarding data processing and accuracy of response in real time. For this kind of system, ML algorithms would become advantageous for such systems to carry out large-scale environmental data analysis, thereby enhancing predictive capabilities to realize early detection of pollutants. The chapter goes on to explain ML techniques under supervised and unsupervised learning, along with their applications to sensor networks, remote sensing, and data fusion. Case studies on the successful implementation of the ML-driven solution to air and water quality monitoring show the improvement in accuracy and decision-making. Further, the chapter addresses the challenge associated with ML integration in data privacy, algorithmic bias, and the need for robust training datasets.

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Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications

Varun,

Rajesh,

Lokeshwari

et al. 2024

Advances in IT Standards and Standardization Research

View full text Add to dashboard Cite

show abstract

Advanced Image Enhancement Techniques for Small Object Detection in Synthetic Aperture Radar (SAR) Imagery

Khan,

Sheikh,

Rahman

2024

Advances in IT Standards and Standardization Research

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The product-based image enhancement model enhances the detection of small objects in unstable environments, with the seminal line 'Hermitian Product' delivering promising results. The proposed work utilizes Sub-Aperture Cross-Correlation Magnitude (SCM) to identify small ship targets in synthetic aperture radar (SAR) images of sea water. The “SCM” method employs various techniques such as Azimuth bandwidth reduction, sub-band extraction, up-sampling, Hermitian Inner product, low pass filter, down-sampling, and Local Averaging. I-SCM is analyzed using Coefficient of Variations (CV) and Spatial Resolution (SV), assessing target and background heterogeneity and sub aperture bandwidth influence. Experiments show that constant mean value and decreasing standard deviation improve target spotting in terms of 'CV', while partially over-lapped sub apertures provide best contrast in 'SV' relationships.

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Multi-level CEP rules automatic extraction approach for air quality detection and energy conservation decision based on AI technologies

Cited by 2 publications

References 25 publications

Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications

Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications

Advanced Image Enhancement Techniques for Small Object Detection in Synthetic Aperture Radar (SAR) Imagery

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