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1. Reduction of radiation exposure in chest radiography using deep learning-based noise reduction processing: A phantom and retrospective clinical study NSTL国家科技图书文献中心

Mori, K. |  Negishi, T.... -  《Radiography》 - 2025,31(3) - 102958~102958 - 共6页

摘要:), a deep learning-based noise reduction developed by | Introduction: Intelligent noise reduction (INR |  aimed to evaluate the reduction of patient exposure |  can contribute to the reduction of patient radiation |  Canon, is used in planar radiography to improve image
关键词: Deep learning-based noise reduction |  Chest X-ray imaging optimization |  Radiation dose reduction |  Image quality assessment

2. Uncertainty Prediction of Offshore Wind Power based on Outlier Processing, Data Noise Reduction and the Deep Learning Method NSTL国家科技图书文献中心

Yu, Zhen |  Yang, Yinguo... -  《Recent advances in electrical & electronic engineering》 - 2025,18(3) - 359~369 - 共11页

摘要: paper proposes an uncertainty prediction learning |  model based on outlier processing, synchronous wavelet | , the offshore wind power prediction method based on | . After predicting the point prediction results based on |  benchmark model (SWT-LSTM).Conclusion Based on the point
关键词: Offshore wind power prediction |  synchronous extrusion wavelet neural network |  the improved particle swarm optimization |  long short-term neural memory network |  attention mechanism |  deep learning

3. Advances in spatial resolution and radiation dose reduction using super- resolution deep learning-based reconstruction for abdominal computed tomography: A phantom study NSTL国家科技图书文献中心

Funama, Yoshinori |  Nagayama, Yasunori... -  《Academic radiology》 - 2025,32(3) - 1517~1524 - 共8页

摘要: the performance of super-resolution deep learning |  noise reduction strengths. Materials and Methods: A |  three noise reduction strengths: mild, standard, and |  various FOVs, radiation doses, and noise reduction |  superior noise reduction capabilities, with NMR of 0.29
关键词: Super-resolution deep learning based reconstruction |  Spatial resolution |  Noise reduction |  Noise texture |  Computed tomography

4. Reconstructing and analyzing the invariances of low‐dose CT image denoising networks NSTL国家科技图书文献中心

Elias Eulig |  Fabian Jäger... -  《Medical Physics》 - 2025,52(1) - 188~200 - 共13页

摘要:Abstract Background Deep learningbased |  improve the interpretability of deep learningbased low |  applied to four popular deep learningbased low‐dose CT |  analyzing invariances of deep learningbased low‐dose CT |  toward interpreting deep learningbased methods for
关键词: computed tomography |  deep learning |  explainability |  invariances |  low‐dose |  robustness

5. A multi-channel active noise control system using deep learning-based method to estimate secondary path and normalized-clustered control strategy for vehicle interior engine noise NSTL国家科技图书文献中心

Cheng C. |  Liu Z.... -  《Applied acoustics》 - 2025,228(Jan.) - 1.1~1.17 - 共17页

摘要: based on the deep learning prediction model and adopts |  active noise control (ANC) system based on the |  has better noise reduction performance than the |  the proposed deep learning method can accurately |  noise reduction performance of the proposed control
关键词: Active noise control |  Deep learning |  Normalized-clustered control strategy |  Secondary path estimation |  Vehicle interior engine noise

6. State monitoring and fault prediction of power grid equipment are carried out by using deep learning technology NSTL国家科技图书文献中心

Lin Wang |  Chenghao Hou... -  《International Conference on Physics,Photonics,and Optical Engineering (ICPPOE 2024),Part One of Two Parts》 -  International Conference on Physics,Photonics,and Optical Engineering - 2025, - 135521D.1~135521D.8 - 共8页

摘要: deep learning technology and takes power transformer |  loss stack sparse noise reduction autoencoder was |  established by deep learning framework, and the fault |  TPE algorithm was established through deep learning |  find new technical solutions. This study is based on
关键词: Deep learning |  Transformer fault diagnosis and prediction |  Focal loss

7. Satellite-Driven Deep Learning Algorithm for Bathymetry Extraction NSTL国家科技图书文献中心

Xiaohan Zhang |  Xiaolong Chen... -  《Web Information Systems Engineering - WISE 2024,Part IV》 -  International Conference on Web Information Systems Engineering - 2025, - 313~325 - 共13页

摘要: dual-distance noise reduction algorithm to extract |  deep learning model. This approach enables precise | -distance noise reduction algorithm in accurately |  areas, whereas satellite-based methods offer | Accurate bathymetry using remotely sensed data
关键词: Satellite-derived bathymetry |  ICESat-2 |  Sentinel-2 |  UNet

8. Climate Change Impact on Geographical Region and Healthcare Analysis Using Deep Learning Algorithms NSTL国家科技图书文献中心

Ganduri,Srikanth |  Ch V.,Raghavendran... -  《Remote sensing in earth systems sciences》 - 2025,8(1) - 182~190 - 共9页

摘要:) and deep learning (DL) methods have become |  and its healthcare training using deep learning |  processed for noise reduction, normalization, and |  effective and economical policies for reduction and |  one of the many domains where machine learning (ML
关键词: Climate change |  Machine learning (ML) |  Deep learning (DL) |  Geographical region analysis |  Climatic analysis |  Adversarial convolutional Boltzmann neural networks

9. Preliminary phantom study of four-dimensional computed tomographic angiography for renal artery mapping: Low-tube voltage and low-contrast volume imaging with deep learning-based reconstruction NSTL国家科技图书文献中心

Urikura, A. |  Ishii, I.... -  《Radiography》 - 2025,31(3) - 102929~102929 - 共7页

摘要: 320-row detectors and deep learning-based |  deep learning-based reconstruction (DLR) can reduce |  with a noise index of 50 HU. Dose reduction was |  under varying image noise and vessel contrast | . Quantitative analysis included peak contrast-to-noise ratio
关键词: Four-dimensional computed tomography |  (MeSH terms used) |  Cryosurgery (MeSH terms used) |  deep learning-based reconstruction |  Radiation dosage (MeSH terms used) |  Image quality

10. Enhanced defect detection in thermography through temporal denoising and deep feature extraction using a shallow convolution autoencoder NSTL国家科技图书文献中心

Y Naga Prasanthi |  V S Ghali... -  《Insight》 - 2025,67(3) - 162~171 - 共10页

摘要: NDT 4.0 adapt machine learning and deep learning |  capabilities. However, noise and non-uniform thermal | -based techniques to automate and enhance defect |  convolution layers to reduce noise in temporal thermal |  autoencoder (SAE) and stacked deep autoencoder (SDAE), the
关键词: shallow convolution autoencoder |  latent space |  feature learning |  frequency-modulated thermography |  signal-to-noise ratio
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