NSTL国家科技图书文献中心

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 learning‐based | improve the interpretability of deep* learning‐based low* | applied to four popular deep* learning‐based low‐dose CT* | analyzing invariances of deep* learning‐based low‐dose CT* | toward interpreting deep* learning‐based 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. A diffusion model-based deep learning approach for denoising acoustic emission signals in concrete NSTL国家科技图书文献中心

Fu, Wei | Zhou, Ruohua ... - 《Measurement》 - 2025,251 - 117143.1～117143.16 - 共16页

摘要： learning techniques in noise reduction and signal | learning approach based on diffusion models, referred to | anisotropy and various noise* sources like external* | detection. Effective noise* filtering while preserving* | health monitoring (SHM). This study introduces a deep

关键词： Acoustic emission | Diffusion model | Signal denoising | Concrete damage detection

8. 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

9. 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

10. A time series CGAN denoising model targeting vessel's radiated noise for underwater acoustic communications☆ NSTL国家科技图书文献中心

Tao, Weiliang | Liu, Yan ... - 《Ocean engineering》 - 2025,318(Feb.15) - 1.1～1.11 - 共11页

摘要： presents a deep* learning model based on low-frequency* | generative adversarial nets (CGAN) noise-reduction* model* | to a ship, the vessel's underwater radiated noise | LOFAR feature extraction structure based* on a* | based on a frequency-domain sliding window for

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

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

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

摘要：Abstract Background Deep learning‐based | improve the interpretability of deep* learning‐based low* | applied to four popular deep* learning‐based low‐dose CT* | analyzing invariances of deep* learning‐based low‐dose CT* | toward interpreting deep* learning‐based methods for*

关键词： computed tomography | deep learning | explainability | invariances | low‐dose | robustness

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

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

摘要： learning techniques in noise reduction and signal | learning approach based on diffusion models, referred to | anisotropy and various noise* sources like external* | detection. Effective noise* filtering while preserving* | health monitoring (SHM). This study introduces a deep

关键词： Acoustic emission | Diffusion model | Signal denoising | Concrete damage detection

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

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

摘要： presents a deep* learning model based on low-frequency* | generative adversarial nets (CGAN) noise-reduction* model* | to a ship, the vessel's underwater radiated noise | LOFAR feature extraction structure based* on a* | based on a frequency-domain sliding window for

关键词： Underwater acoustic communication | Vessel's underwater radiated noise | Noise-reduction | Deep neural network

4008-161-200 800-990-8900

国家科技图书文献中心

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

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

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

摘要：Abstract Background Deep learning‐based | improve the interpretability of deep learning‐based low | applied to four popular deep learning‐based low‐dose CT | analyzing invariances of deep learning‐based low‐dose CT | toward interpreting deep learning‐based methods for

关键词： computed tomography | deep learning | explainability | invariances | low‐dose | robustness

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

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

摘要： learning techniques in noise reduction and signal | learning approach based on diffusion models, referred to | anisotropy and various noise sources like external | detection. Effective noise filtering while preserving | health monitoring (SHM). This study introduces a deep

关键词： Acoustic emission | Diffusion model | Signal denoising | Concrete damage detection

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

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

摘要： presents a deep learning model based on low-frequency | generative adversarial nets (CGAN) noise-reduction model | to a ship, the vessel's underwater radiated noise | LOFAR feature extraction structure based on a | based on a frequency-domain sliding window for

关键词： Underwater acoustic communication | Vessel's underwater radiated noise | Noise-reduction | Deep neural network

4008-161-200 800-990-8900

国家科技图书文献中心

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

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*

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*

摘要：Abstract Background Deep learning‐based | improve the interpretability of deep* learning‐based low* | applied to four popular deep* learning‐based low‐dose CT* | analyzing invariances of deep* learning‐based low‐dose CT* | toward interpreting deep* learning‐based methods for*

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

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*

摘要： learning techniques in noise reduction and signal | learning approach based on diffusion models, referred to | anisotropy and various noise* sources like external* | detection. Effective noise* filtering while preserving* | health monitoring (SHM). This study introduces a deep

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

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*

摘要： presents a deep* learning model based on low-frequency* | generative adversarial nets (CGAN) noise-reduction* model* | to a ship, the vessel's underwater radiated noise | LOFAR feature extraction structure based* on a* | based on a frequency-domain sliding window for