Tool condition monitoring for cavity milling based on bispectrum analysis and Bayesian optimized SVM

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: 133(7-8), P. 3873 - 3889

Published: June 18, 2024

Language: Английский

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Intelligent optimization strategy for electrochemical removal of ammonia nitrogen by neural network embedded in a non-dominated sorting genetic algorithm

Journal of Water Process Engineering, Journal Year: 2023, Volume and Issue: 56, P. 104502 - 104502

Published: Nov. 1, 2023

Language: Английский

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Evaluating the impact of knowledge management and database management on decision-making process: A case study of subsea project services

Journal of Open Innovation Technology Market and Complexity, Journal Year: 2024, Volume and Issue: 10(3), P. 100340 - 100340

Published: July 14, 2024

The oil and gas industry is known for its rapid technological advancements the complexity of operations, increasingly relying on data-intensive management. As subsea projects—from exploration to decommissioning—become more complex data-driven, integrating knowledge management (KM) database systems (DBMS) has become essential. research specifically explores how KM DBMS contribute decision-making, utilizing a quantitative methodology through questionnaire survey. Findings reveal that processes significantly improve effectiveness non-spatial data management, highlighting KM's crucial role in facilitating technology adoption operational efficiency. However, influence spatial overall decision-making found be limited, indicating necessity adaptive integrated strategies serve unique requirements systems. This study underscores critical but nuanced industry, advocating tailored optimize It suggests future into interplay with emerging technologies like AI machine learning enhance investigation stresses importance holistic practices effectively managing intricate landscapes project services, thereby contributing broader discourse KM, DBMS, settings.

Language: Английский

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Assessing the nonlinear impact of green space exposure on psychological stress perception using machine learning and street view images

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: Sept. 18, 2024

Introduction Urban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS are yet to be fully explored. The development machine learning street view images offers method large-scale measurement precise empirical analysis. Methods This study focuses on central area Shanghai, examining complex psychological perception. By constructing multidimensional perception scale integrating algorithms with extensive data, we successfully developed framework measuring Using scores from provided by volunteers labeled predicted in Shanghai's through Support Vector Machine (SVM) algorithm. Additionally, this employed interpretable model eXtreme Gradient Boosting (XGBoost) algorithm reveal nonlinear relationship between residents' stress. Results indicate that Shanghai generally low, significant spatial heterogeneity. has positive impact reducing effect threshold; when exceeds 0.35, its gradually diminishes. Discussion We recommend combining threshold identify spaces, thereby guiding strategies enhancing GS. research not only demonstrates mitigating but also emphasizes importance considering “dose-effect” it planning construction. Based open-source methods have potential applied different environments, thus providing more comprehensive support future planning.

Language: Английский

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Exploring the Potential of the Machine Learning Techniques in the Water Quality Assessment: A Review of Applications and Performance

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 626 - 639

Published: Jan. 1, 2024

Language: Английский

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