NSTL国家科技图书文献中心

1. 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

2. 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

3. 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

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

4. 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

5. 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

6. 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

7. 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

8. 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

9. Optimization Design of Dual-Parallel Rotor Permanent Magnet Motor Based on Dynamic Kriging Surrogate Model NSTL国家科技图书文献中心

Yang Chen | Dajun Tao ... - 《IEEJ Transactions on Electrical and Electronic Engineering》 - 2025,20(1) - 127～137 - 共11页

摘要： optimization efficiency and poor model accuracy of the | model. The optimization parameters are determined, and | surrogate model* is proposed to optimize its structural* | Kriging surrogate model, the concept of key sampling | traditional static surrogate model* based on 'one-time*

关键词： Dual-parallel rotor | Permanent magnet motor | Dynamic kriging surrogate model | Optimization design | Criteria for adding sample points

10. Mathematical Model-Based Optimization of Continuous Flow Photobioreactor Operating at Steady State Using MATLAB Optimization Function NSTL国家科技图书文献中心

Ibrahim M. Abu-Reesh - 《Arabian journal for science and engineering》 - 2025,50(6) - 3779～3792 - 共14页

摘要： methods. The model-based optimization* predicts the best* | state using a mathematical model* describes growth of* | optimization function (fmincon) is used to find the best | ), respectively. The fmincon optimization* results agree well* | with the literature that used different optimization

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： optimization efficiency and poor model accuracy of the | model. The optimization parameters are determined, and | surrogate model* is proposed to optimize its structural* | Kriging surrogate model, the concept of key sampling | traditional static surrogate model* based on 'one-time*

关键词： Dual-parallel rotor | Permanent magnet motor | Dynamic kriging surrogate model | Optimization design | Criteria for adding sample points

关键词： Photobioreactor | Continuous stirred tank reactor | Optimization | Microalgae | Mathematical model | MATLAB

4008-161-200 800-990-8900

国家科技图书文献中心

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： optimization efficiency and poor model accuracy of the | model. The optimization parameters are determined, and | surrogate model is proposed to optimize its structural | Kriging surrogate model, the concept of key sampling | traditional static surrogate model based on 'one-time

关键词： Dual-parallel rotor | Permanent magnet motor | Dynamic kriging surrogate model | Optimization design | Criteria for adding sample points

摘要： methods. The model-based optimization predicts the best | state using a mathematical model describes growth of | optimization function (fmincon) is used to find the best | ), respectively. The fmincon optimization results agree well | with the literature that used different optimization

关键词： Photobioreactor | Continuous stirred tank reactor | Optimization | Microalgae | Mathematical model | MATLAB

4008-161-200 800-990-8900

国家科技图书文献中心

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： optimization efficiency and poor model accuracy of the | model. The optimization parameters are determined, and | surrogate model* is proposed to optimize its structural* | Kriging surrogate model, the concept of key sampling | traditional static surrogate model* based on 'one-time*