NSTL国家科技图书文献中心

1. A review of rolling bearing fault diagnosis: data preprocessing and model optimization NSTL国家科技图书文献中心

Fu, Wenlong | Li, Shuai ... - 《Measurement Science & Technology》 - 2025,36(6) - 062002.1～062002.29 - 共29页

摘要： strategy optimization* in enhancing model performance* | lack a systematic summary of optimization* strategies* | role of various optimization* strategies. This* | optimization strategies for rolling bearing fault diagnosis | perspectives: data preprocessing and model* algorithm*

关键词： rolling bearing | fault diagnosis | measurement optimization | data preprocessing | model optimization | deep learning | review

2. Enhancing Hippocampus Segmentation: SwinUNETR Model Optimization with CPS NSTL国家科技图书文献中心

Wangang Cheng | Guanghua He ... - 《Pattern Recognition and Computer Vision,Part XIV》 - Chinese Conference on Pattern Recognition and Computer Vision - 2025, - 76～89 - 共14页

摘要： presents a novel optimization* method called Combining* | of the pre-trained model* while updating only a* | -of-the-art SwinUNETR model* for medical image* | , this enhanced model* is subsequently applied to three* | Deep learning techniques have made remarkable

关键词： Deep learning | Hippocampus segmentation | Parameter-efficient transfer learning | SwinUNETR model

3. Chinese Grammatical Error Correction via Large Language Model Guided Optimization Training NSTL国家科技图书文献中心

Xiao Liu | Ying Li ... - 《Chinese Computational Linguistics》 - China National Conference on Computational Linguistics - 2025, - 522～539 - 共18页

摘要： extremely useful for traditional CGEC model optimization | Pre-trained language model-based methods for | (LLMs), we propose a large language model-guided | optimization training method to exploit LLMs to extract error | knowledge to optimize the traditional CGEC model* training*

关键词： Chinese grammatical error correction | Large language model | Optimization training

4. Digital Twin-Aided Vehicular Edge Network: A Large-Scale Model Optimization by Quantum-DRL NSTL国家科技图书文献中心

Anal Paul | Keshav Singh ... - 《IEEE Transactions on Vehicular Technology》 - 2025,74(2) - 2156～2173 - 共18页 - 被引量：1

摘要：This paper presents an innovative large model | comprehensive model* that integrates quantum computing with a* | deep reinforcement learning (DRL) model, supported by | , and quality-of-service management. Our model | synchronization and adaptive strategy optimization. We employ a

关键词： Task analysis | Computational modeling | Quantum computing | Optimization | Ultra reliable low latency communication | Edge computing | Data models

5. Fault diagnosis of wind turbine gearbox based on VMD-XGBoost model optimization NSTL国家科技图书文献中心

Yunhao Wang | Wenlei Sun ... - 《Fourth International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2024)》 - International Conference on Advanced Manufacturing Technology and Electronic Information - 2025, - 1351514.1～1351514.5 - 共5页

摘要： by particle swarm optimization* algorithm, a data* | model is used for intelligent diagnosis and | Based on variational mode and XGBoost solved | -driven early fault diagnosis method for wind turbine | gearbox was proposed. Through the VMD decomposition of

关键词： Gearbox | Fault warning | XGBoost | Variational mode decomposition

6. A hybrid CNN-transformer surrogate model for the multi-objective robust optimization of geological carbon sequestration NSTL国家科技图书文献中心

Zhao Feng | Bicheng Yan ... - 《Advances in water resources》 - 2025,196(Feb.) - 104897.1～104897.21 - 共21页

摘要：The optimization* of well controls over time* | optimization problems, due to the extensive number of | a hybrid CNN-Transformer surrogate model* to* | accelerate the well control optimization* in GCS* | applications. The surrogate model* encompasses a Convolution*

关键词： Geological carbon sequestration | Surrogate model | Optimization | Deep learning | Transformers

7. Injection-mining scheme optimization of underground gas storage based on agent model NSTL国家科技图书文献中心

Yang, Huohai | He, Qinghui ... - 《Geoenergy Science and Engineering》 - 2025,244 - 共23页

摘要：-objective optimization* model was established after the* | optimization algorithm was preferred based on the agent model | comparing the optimization results to the numerical model | optimization model that can assist and guide UNGS injection | . Optimization* of UNGS injection-production operations is*

关键词： Underground gas storage | Injection-mining scheme | Machine learning | Agent model | Optimization algorithm

8. An efficient three-dimensional mesh quality optimization method based on gradient-enhanced probabilistic model NSTL国家科技图书文献中心

Deng, Longwei | Yin, Junhui ... - 《Computer physics communications》 - 2025,312 - 共16页

摘要： model required for simulations, and the quality of the | quality optimization* method based on gradient-enhanced* | probabilistic model. The proposed method includes a | the rapid optimization* of early node movement and* | of nodes. By establishing a probabilistic model* for*

关键词： Tetrahedral elements | Mesh quality | Probabilistic model | Optimization-based smoothing | Stochastic process

9. Robust Optimization Using the Mean Model with Bias Correction NSTL国家科技图书文献中心

Oliver, Dean S. - 《Mathematical geosciences》 - 2025,57(1) - 23～47 - 共25页

摘要：Optimization of the expected outcome for | subsurface model* are uncertain can be costly, especially* | "mean model," that is, the model* whose properties are* | the means of properties of an ensemble of model | realizations. Straightforward use of the mean model* without*

关键词： Uncertainty | Robust optimization | Bias correction | Reservoir management | Mean model

10. Edge computing-oriented model optimization for synchronous acquisition of key physical parameters governing building collapses in fire NSTL国家科技图书文献中心

Xiaofeng Zheng | Guo-Qiang Li ... - 《Advanced engineering informatics》 - 2025,65(May Pt.C) - 103303.1～103303.19 - 共19页

摘要： the challenge of minimizing model* volume and* | approach focuses on refining the model* through a* | comprehensive optimization* strategy. This includes an advanced* | efforts results in a significantly more compact model | . For predicting displacements at vertices, the model

摘要： strategy optimization* in enhancing model performance* | lack a systematic summary of optimization* strategies* | role of various optimization* strategies. This* | optimization strategies for rolling bearing fault diagnosis | perspectives: data preprocessing and model* algorithm*

关键词： rolling bearing | fault diagnosis | measurement optimization | data preprocessing | model optimization | deep learning | review

摘要： presents a novel optimization* method called Combining* | of the pre-trained model* while updating only a* | -of-the-art SwinUNETR model* for medical image* | , this enhanced model* is subsequently applied to three* | Deep learning techniques have made remarkable

关键词： Deep learning | Hippocampus segmentation | Parameter-efficient transfer learning | SwinUNETR model

摘要： extremely useful for traditional CGEC model optimization | Pre-trained language model-based methods for | (LLMs), we propose a large language model-guided | optimization training method to exploit LLMs to extract error | knowledge to optimize the traditional CGEC model* training*

关键词： Chinese grammatical error correction | Large language model | Optimization training

摘要：This paper presents an innovative large model | comprehensive model* that integrates quantum computing with a* | deep reinforcement learning (DRL) model, supported by | , and quality-of-service management. Our model | synchronization and adaptive strategy optimization. We employ a

关键词： Task analysis | Computational modeling | Quantum computing | Optimization | Ultra reliable low latency communication | Edge computing | Data models

摘要： by particle swarm optimization* algorithm, a data* | model is used for intelligent diagnosis and | Based on variational mode and XGBoost solved | -driven early fault diagnosis method for wind turbine | gearbox was proposed. Through the VMD decomposition of

关键词： Gearbox | Fault warning | XGBoost | Variational mode decomposition

摘要：The optimization* of well controls over time* | optimization problems, due to the extensive number of | a hybrid CNN-Transformer surrogate model* to* | accelerate the well control optimization* in GCS* | applications. The surrogate model* encompasses a Convolution*

关键词： Geological carbon sequestration | Surrogate model | Optimization | Deep learning | Transformers

关键词： Underground gas storage | Injection-mining scheme | Machine learning | Agent model | Optimization algorithm

摘要： model required for simulations, and the quality of the | quality optimization* method based on gradient-enhanced* | probabilistic model. The proposed method includes a | the rapid optimization* of early node movement and* | of nodes. By establishing a probabilistic model* for*

关键词： Tetrahedral elements | Mesh quality | Probabilistic model | Optimization-based smoothing | Stochastic process

摘要：Optimization of the expected outcome for | subsurface model* are uncertain can be costly, especially* | "mean model," that is, the model* whose properties are* | the means of properties of an ensemble of model | realizations. Straightforward use of the mean model* without*

关键词： Uncertainty | Robust optimization | Bias correction | Reservoir management | Mean model

摘要： the challenge of minimizing model* volume and* | approach focuses on refining the model* through a* | comprehensive optimization* strategy. This includes an advanced* | efforts results in a significantly more compact model | . For predicting displacements at vertices, the model

关键词： Edge computing | Lightweight neural network | Explainable artificial intelligence | Genetic algorithm-based long short-term memory

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： strategy optimization in enhancing model performance | lack a systematic summary of optimization strategies | role of various optimization strategies. This | optimization strategies for rolling bearing fault diagnosis | perspectives: data preprocessing and model algorithm

关键词： rolling bearing | fault diagnosis | measurement optimization | data preprocessing | model optimization | deep learning | review

摘要： presents a novel optimization method called Combining | of the pre-trained model while updating only a | -of-the-art SwinUNETR model for medical image | , this enhanced model is subsequently applied to three | Deep learning techniques have made remarkable

关键词： Deep learning | Hippocampus segmentation | Parameter-efficient transfer learning | SwinUNETR model

摘要： extremely useful for traditional CGEC model optimization | Pre-trained language model-based methods for | (LLMs), we propose a large language model-guided | optimization training method to exploit LLMs to extract error | knowledge to optimize the traditional CGEC model training

关键词： Chinese grammatical error correction | Large language model | Optimization training

摘要：This paper presents an innovative large model | comprehensive model that integrates quantum computing with a | deep reinforcement learning (DRL) model, supported by | , and quality-of-service management. Our model | synchronization and adaptive strategy optimization. We employ a

关键词： Task analysis | Computational modeling | Quantum computing | Optimization | Ultra reliable low latency communication | Edge computing | Data models

摘要： by particle swarm optimization algorithm, a data | model is used for intelligent diagnosis and | Based on variational mode and XGBoost solved | -driven early fault diagnosis method for wind turbine | gearbox was proposed. Through the VMD decomposition of

关键词： Gearbox | Fault warning | XGBoost | Variational mode decomposition

摘要：The optimization of well controls over time | optimization problems, due to the extensive number of | a hybrid CNN-Transformer surrogate model to | accelerate the well control optimization in GCS | applications. The surrogate model encompasses a Convolution

关键词： Geological carbon sequestration | Surrogate model | Optimization | Deep learning | Transformers

关键词： Underground gas storage | Injection-mining scheme | Machine learning | Agent model | Optimization algorithm

摘要： model required for simulations, and the quality of the | quality optimization method based on gradient-enhanced | probabilistic model. The proposed method includes a | the rapid optimization of early node movement and | of nodes. By establishing a probabilistic model for

关键词： Tetrahedral elements | Mesh quality | Probabilistic model | Optimization-based smoothing | Stochastic process

摘要：Optimization of the expected outcome for | subsurface model are uncertain can be costly, especially | "mean model," that is, the model whose properties are | the means of properties of an ensemble of model | realizations. Straightforward use of the mean model without

关键词： Uncertainty | Robust optimization | Bias correction | Reservoir management | Mean model

摘要： the challenge of minimizing model volume and | approach focuses on refining the model through a | comprehensive optimization strategy. This includes an advanced | efforts results in a significantly more compact model | . For predicting displacements at vertices, the model

关键词： Edge computing | Lightweight neural network | Explainable artificial intelligence | Genetic algorithm-based long short-term memory

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： strategy optimization* in enhancing model performance* | lack a systematic summary of optimization* strategies* | role of various optimization* strategies. This* | optimization strategies for rolling bearing fault diagnosis | perspectives: data preprocessing and model* algorithm*

摘要： presents a novel optimization* method called Combining* | of the pre-trained model* while updating only a* | -of-the-art SwinUNETR model* for medical image* | , this enhanced model* is subsequently applied to three* | Deep learning techniques have made remarkable

摘要： extremely useful for traditional CGEC model optimization | Pre-trained language model-based methods for | (LLMs), we propose a large language model-guided | optimization training method to exploit LLMs to extract error | knowledge to optimize the traditional CGEC model* training*

摘要：This paper presents an innovative large model | comprehensive model* that integrates quantum computing with a* | deep reinforcement learning (DRL) model, supported by | , and quality-of-service management. Our model | synchronization and adaptive strategy optimization. We employ a

摘要： by particle swarm optimization* algorithm, a data* | model is used for intelligent diagnosis and | Based on variational mode and XGBoost solved | -driven early fault diagnosis method for wind turbine | gearbox was proposed. Through the VMD decomposition of

摘要：The optimization* of well controls over time* | optimization problems, due to the extensive number of | a hybrid CNN-Transformer surrogate model* to* | accelerate the well control optimization* in GCS* | applications. The surrogate model* encompasses a Convolution*

摘要： model required for simulations, and the quality of the | quality optimization* method based on gradient-enhanced* | probabilistic model. The proposed method includes a | the rapid optimization* of early node movement and* | of nodes. By establishing a probabilistic model* for*

摘要：Optimization of the expected outcome for | subsurface model* are uncertain can be costly, especially* | "mean model," that is, the model* whose properties are* | the means of properties of an ensemble of model | realizations. Straightforward use of the mean model* without*

摘要： the challenge of minimizing model* volume and* | approach focuses on refining the model* through a* | comprehensive optimization* strategy. This includes an advanced* | efforts results in a significantly more compact model | . For predicting displacements at vertices, the model