According to the spectral line broadening mechanism of laser induced breakdown spectroscopy, the emission lines from the standard spectral database of NIST were simulated, and they were compared with the spectral data of experiment. In the process of comparison similarity measure was used to measure the similarity between the simulative spectra and the experimental spectra. The automatic recognition method of elemental spectra was studied, and the spectral data of soil between 340 and 345 nm was recognized by computing the proportional coefficients of the spectral lines. Using principle of nonlinear least squares, the recognition process was completed. The feasibility and the advantage of the method were proved by the results of experiment.
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Sci Data
January 2025
Faculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham, B4 7XG, UK.
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland.
View Article and Find Full Text PDFFront Neurorobot
January 2025
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, Shanxi, China.
Accurate building segmentation has become critical in various fields such as urban management, urban planning, mapping, and navigation. With the increasing diversity in the number, size, and shape of buildings, convolutional neural networks have been used to segment and extract buildings from such images, resulting in increased efficiency and utilization of image features. We propose a building semantic segmentation method to improve the traditional Unet convolutional neural network by integrating attention mechanism and boundary detection.
View Article and Find Full Text PDFInt Dent J
January 2025
Department of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. Electronic address:
Introduction And Aim: The assessment of gingival inflammation surface features mainly depends on subjective judgment and lacks quantifiable and reproducible indicators. Therefore, it is a need to acquire objective identification information for accurate monitoring and diagnosis of gingival inflammation. This study aims to develop an automated method combining intraoral scanning (IOS) and deep learning algorithms to identify the surface features of gingival inflammation and evaluate its accuracy and correlation with clinical indicators.
View Article and Find Full Text PDFNat Commun
January 2025
Los Alamos National Laboratory, EES-17 National Security Earth Science, Los Alamos, NM, 87545, USA.
Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
Background: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems.
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