RESUMEN
Usually, the optical transition properties of trivalent rare earth (RE) ions in transparent hosts can be quantitatively investigated in the framework of Judd-Ofelt theory. A standard and commonly accepted calculation procedure based on the absorption spectrum has already been established. However, it is hard to assess the optical transition properties of trivalent RE ions doped in powdered and film materials owing to the difficulty in the absolute absorption spectrum measurements. In this work, we proposed a new route to calculate the Judd-Ofelt parameters of trivalent RE ion-doped materials in any morphological and shaped forms. In this method, the fluorescence decay values bridging the radiative transition rates and the Judd-Ofelt parameters were used. As an application example of the proposed Judd-Ofelt calculation method, the Judd-Ofelt parameters of Er3+ in NaYF4 were calculated via the proposed route, and it was found that the obtained results are in reasonable accordance with those derived from other routes. It was also proved that this proposed Judd-Ofelt calculation method is a practicable and effective route for evaluating optical transition properties of trivalent RE ions in non-transparent hosts as long as the fluorescence decay values can be measured.
RESUMEN
Due to environmental interference and defects in measured objects, measurement signals are frequently affected by unpredictable noise and periodic defects. Moreover, there is a lack of effective methods for accurately distinguishing defect components from measurement signals. In this study, a distribution-based selective optimisation method (SOM) is proposed to mitigate the effects of noise and defect components. The SOM can be seen as a binary- or multiple-class signal classifier based on an error distribution, which can simultaneously eliminate periodic defect components of measurement signals and proceed with signal-fitting regression. The effectiveness, accuracy, and feasibility of the SOM are verified in theoretical and realworld measurement settings. Based on theoretical simulations under various parameter conditions, some criteria for selecting operation variables among a selection of parameter conditions are explained in detail. The proposed method is capable of separating defect components from measurement signals while also achieving a satisfactory fitting curve for the measurement signals. The proposed SOM has broad application prospects in signal processing and defect detection for mechanical measurements, electronic filtering, instrumentation, part maintenance, and other fields.
RESUMEN
PURPOSE: To develop a deep learning-based model for measuring automatic lumbosacral anatomical parameters from lateral lumbar radiographs and compare its performance to that of attending-level radiologists. METHODS: A total of 1791 lateral lumbar radiographs were collected through the PACS system and used to develop the deep learning-based model. Landmarks for the four used parameters, including the lumbosacral lordosis angle (LSLA), lumbosacral angle (LSA), sacral horizontal angle (SHA), and sacral inclination angle (SIA), were identified and automatically labeled by the model. At the same time, the measurement results were obtained through landmarks on the test set compared to manual measurements as the reference standard. Statistical analyses of the Percentage of Correct Key Points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots were performed to evaluate the performance of the model. RESULTS: The mean differences between the reference standard and the model for LSLA, LSA, SHA, and SIA, were 0.39°, 0.09°, 0.13°, and 0.12°, respectively. A strong correlation and consistency between the four parameters were found between the model and reference standard (ICC = 0.92-0.98, r = 0.92-0.97, MAE = 1.35-1.84, RMSE = 1.82-2.51), while with statistically significant difference for LSLA (p = 0.02). CONCLUSIONS: The presented model revealed clinically equivalent measurements in terms of accuracy, while superior measurements were obtained in terms of cost-effectiveness, reliability, and reproducibility. The model may help clinicians improve their understanding and evaluation of lumbar diseases and LBP from a quantitative perspective in practical work. (ChiCTR2100048250).