NSTL国家科技图书文献中心

1. Mathematical analysis for interacting multi functional extreme learning machines NSTL国家科技图书文献中心

Cho, Ilwoo | Jorgensen, Palle E. ... - 《Journal of Applied Mathematics and Computing》 - 2025,71(1) - 203～246 - 共44页

摘要： functional extreme learning* machines by applying graph* | In this paper, we analyze interacting multi | theory, groupoid theory, representation theory | , operator theory, operator algebra theory and free | probability.

关键词： Functional extreme learning machines (FELMs) | Directed graphs | Graph groupoids | Operators

2. Adaptive multilayer extreme learning machines NSTL国家科技图书文献中心

Christos K. Filelis-... | John P. Morrison ... - 《Mathematics and computers in simulation》 - 2025,231(May) - 71～98 - 共28页

摘要：Extreme learning* machines is a neural network* | type that has been utilized in tasks such as | regression and classification, due to their efficient | training process, which is based on pseudoinverse | matrices and randomized weights, avoiding the

关键词： Recursive pseudoinverse matrix | Modeling | Multilayer Extreme Learning Machine | Graphics Processing Unit

3. Laplacian Support Vector Machines as Data Classifier in Machine Learning Approaches of Structural Health Monitoring NSTL国家科技图书文献中心

Fazeli, Hassan | Hassani, Nemat ... - 《Journal of earthquake and tsunami》 - 2025,19(1) - 共28页

摘要： toward using improved classifiers in machine learning | research, Laplacian Support Vector Machines* (LapSVMs) are* | used as a semi-supervised learning* algorithm to* | -supervised learning* algorithm using LapSVMs to classify* | unlabeled and labeled data. Also, Support Vector Machines

关键词： Statistical pattern recognition | support vector machines method | semi-supervised learning algorithms | system identification

4. A roadmap to fault diagnosis of industrial machines via machine learning: A brief review NSTL国家科技图书文献中心

Vashishtha, Govind | Chauhan, Sumika ... - 《Measurement》 - 2025,242PD - 116216～ - 共28页

摘要：In fault diagnosis, machine learning* theories* | identifies the health conditions of the machines | systematically review the progress of machine learning* theories* | learning theories has reformed the fault diagnosis | deep learning* theories have also filled the gap*

关键词： Condition monitoring | Machine learning | Roadmap | Deep learning

5. Complex-Valued Restricted Boltzmann Machines for Learning Image Data Distributions NSTL国家科技图书文献中心

Maciej Jaworski | Agnieszka Niemczynow...... - 《Artificial Intelligence and Soft Computing,Part I》 - International Conference on Artificial Intelligence and Soft Computing - 2025, - 134～143 - 共10页

摘要：The quest to learn image data distributions | efficiently, particularly in environments constrained by | computational and data resources, is a significant challenge | in the field of computer vision. This paper | introduces a modified version of the traditional Restricted

关键词： Restricted boltzmann machine | Image data | Sobel filter | Complex valued RBM

6. Open-Set Fault Diagnosis for Industrial Rotating Machines Based on Trustworthy Deep Learning NSTL国家科技图书文献中心

Dongdong Wei | Mingjian Zuo ... - 《IEEE Transactions on Industrial Cyber-Physical Systems》 - 2025,3 - 181～189 - 共9页

摘要： machines is crucial for ensuring the safety and | Classifier based on trustworthy deep learning. It can | Detecting and diagnosing faults in rotating | reliability of modern industrial cyber-physical systems | . Traditional data-driven fault diagnosis methods have

关键词： Training | Fault diagnosis | Uncertainty | Training data | Testing | Perturbation methods | Data models | Employee welfare | Deep learning | Feature extraction

7. Deep learning enhanced framework for multi-objective optimization of cement-slag concrete for the balancing performance, economics, and sustainability NSTL国家科技图书文献中心

Amol Shivaji,Mali | Atul,Kolhe ... - 《Asian Journal of Civil Engineering》 - 2025,26(4) - 1669～1681 - 共13页

摘要： boosting machines (GBM), and extreme learning machines | Abstract This research presents an innovative | computational approach that merges artificial intelligence | with multi-objective optimization techniques to | enhance cement slag concrete design. The proposed

关键词： Cement-slag concrete | Deep neural networks (DNN) | Gradient boosting machines (GBM) | Extreme learning machines (ELM) | Particle swarm optimization (PSO)

8. Robust ELM-PID tracing control on autonomous mobile robot via transformer-based pavement crack segmentation NSTL国家科技图书文献中心

Zhang, Jianqi | Yang, Xu ... - 《Measurement》 - 2025,242PC - 116045～ - 共16页

摘要：, combining Extreme Learning* Machines (ELM) and Proportional* | Pavement crack tracing is paramount to | missions encompassing automated crack sealing for road | maintenance. However, existing methods still face several | challenges, including the incapability to precisely extract

关键词： Pavement crack tracing | ELM-PID control | Extreme learning machines | Transformer-based crack segmentation | Embedded deep learning | Autonomous mobile robot

9. A Bayesian machine learning-driven control algorithm for one-phase winding permanent-magnet brushless DC machines NSTL国家科技图书文献中心

Sayed, Biju Theruvil | Rodrigues, Paul ... - 《International journal of dynamics and control》 - 2025,13(1) - 1～13 - 共13页

摘要： on machine learning* to enhance the BLDC motor's* | machine learning-based control algorithm demonstrates | The one-phase brushless DC motor (BLDC) has | become indispensable in-home appliances due to its high | -power density, flexible control, and straightforward

关键词： Numerical simulation | Dynamic performance | BLDC motor | Machine learning | Robust controller

10. Detection of Inter-Turn Short Circuit in Stator Windings of Electric Machines Using Magnetic Symmetry Index and Machine Learning Methods NSTL国家科技图书文献中心

Vahid Rafiei | Mahshid Khoshlessan ... - 《IEEE Transactions on Energy Conversion》 - 2025,40(1) - 642～652 - 共11页

摘要：Electric machines* have been in the spotlight* | agreeable that nearly all the competing electric machines | as the transportation industry, along with many | other high-impact industries, undergo the | electrification process. While researchers debate whether one

摘要： functional extreme learning* machines by applying graph* | In this paper, we analyze interacting multi | theory, groupoid theory, representation theory | , operator theory, operator algebra theory and free | probability.

关键词： Functional extreme learning machines (FELMs) | Directed graphs | Graph groupoids | Operators

摘要：Extreme learning* machines is a neural network* | type that has been utilized in tasks such as | regression and classification, due to their efficient | training process, which is based on pseudoinverse | matrices and randomized weights, avoiding the

关键词： Recursive pseudoinverse matrix | Modeling | Multilayer Extreme Learning Machine | Graphics Processing Unit

摘要： toward using improved classifiers in machine learning | research, Laplacian Support Vector Machines* (LapSVMs) are* | used as a semi-supervised learning* algorithm to* | -supervised learning* algorithm using LapSVMs to classify* | unlabeled and labeled data. Also, Support Vector Machines

关键词： Statistical pattern recognition | support vector machines method | semi-supervised learning algorithms | system identification

摘要：In fault diagnosis, machine learning* theories* | identifies the health conditions of the machines | systematically review the progress of machine learning* theories* | learning theories has reformed the fault diagnosis | deep learning* theories have also filled the gap*

关键词： Condition monitoring | Machine learning | Roadmap | Deep learning

摘要：The quest to learn image data distributions | efficiently, particularly in environments constrained by | computational and data resources, is a significant challenge | in the field of computer vision. This paper | introduces a modified version of the traditional Restricted

关键词： Restricted boltzmann machine | Image data | Sobel filter | Complex valued RBM

摘要： machines is crucial for ensuring the safety and | Classifier based on trustworthy deep learning. It can | Detecting and diagnosing faults in rotating | reliability of modern industrial cyber-physical systems | . Traditional data-driven fault diagnosis methods have

关键词： Training | Fault diagnosis | Uncertainty | Training data | Testing | Perturbation methods | Data models | Employee welfare | Deep learning | Feature extraction

摘要： boosting machines (GBM), and extreme learning machines | Abstract This research presents an innovative | computational approach that merges artificial intelligence | with multi-objective optimization techniques to | enhance cement slag concrete design. The proposed

关键词： Cement-slag concrete | Deep neural networks (DNN) | Gradient boosting machines (GBM) | Extreme learning machines (ELM) | Particle swarm optimization (PSO)

摘要：, combining Extreme Learning* Machines (ELM) and Proportional* | Pavement crack tracing is paramount to | missions encompassing automated crack sealing for road | maintenance. However, existing methods still face several | challenges, including the incapability to precisely extract

关键词： Pavement crack tracing | ELM-PID control | Extreme learning machines | Transformer-based crack segmentation | Embedded deep learning | Autonomous mobile robot

摘要： on machine learning* to enhance the BLDC motor's* | machine learning-based control algorithm demonstrates | The one-phase brushless DC motor (BLDC) has | become indispensable in-home appliances due to its high | -power density, flexible control, and straightforward

关键词： Numerical simulation | Dynamic performance | BLDC motor | Machine learning | Robust controller

摘要：Electric machines* have been in the spotlight* | agreeable that nearly all the competing electric machines | as the transportation industry, along with many | other high-impact industries, undergo the | electrification process. While researchers debate whether one

关键词： Circuit faults | Indexes | Magnetic flux | Support vector machines | Motors | Stator windings | Windings

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： functional extreme learning machines by applying graph | In this paper, we analyze interacting multi | theory, groupoid theory, representation theory | , operator theory, operator algebra theory and free | probability.

关键词： Functional extreme learning machines (FELMs) | Directed graphs | Graph groupoids | Operators

摘要：Extreme learning machines is a neural network | type that has been utilized in tasks such as | regression and classification, due to their efficient | training process, which is based on pseudoinverse | matrices and randomized weights, avoiding the

关键词： Recursive pseudoinverse matrix | Modeling | Multilayer Extreme Learning Machine | Graphics Processing Unit

摘要： toward using improved classifiers in machine learning | research, Laplacian Support Vector Machines (LapSVMs) are | used as a semi-supervised learning algorithm to | -supervised learning algorithm using LapSVMs to classify | unlabeled and labeled data. Also, Support Vector Machines

关键词： Statistical pattern recognition | support vector machines method | semi-supervised learning algorithms | system identification

摘要：In fault diagnosis, machine learning theories | identifies the health conditions of the machines | systematically review the progress of machine learning theories | learning theories has reformed the fault diagnosis | deep learning theories have also filled the gap

关键词： Condition monitoring | Machine learning | Roadmap | Deep learning

摘要：The quest to learn image data distributions | efficiently, particularly in environments constrained by | computational and data resources, is a significant challenge | in the field of computer vision. This paper | introduces a modified version of the traditional Restricted

关键词： Restricted boltzmann machine | Image data | Sobel filter | Complex valued RBM

摘要： machines is crucial for ensuring the safety and | Classifier based on trustworthy deep learning. It can | Detecting and diagnosing faults in rotating | reliability of modern industrial cyber-physical systems | . Traditional data-driven fault diagnosis methods have

关键词： Training | Fault diagnosis | Uncertainty | Training data | Testing | Perturbation methods | Data models | Employee welfare | Deep learning | Feature extraction

摘要： boosting machines (GBM), and extreme learning machines | Abstract This research presents an innovative | computational approach that merges artificial intelligence | with multi-objective optimization techniques to | enhance cement slag concrete design. The proposed

关键词： Cement-slag concrete | Deep neural networks (DNN) | Gradient boosting machines (GBM) | Extreme learning machines (ELM) | Particle swarm optimization (PSO)

摘要：, combining Extreme Learning Machines (ELM) and Proportional | Pavement crack tracing is paramount to | missions encompassing automated crack sealing for road | maintenance. However, existing methods still face several | challenges, including the incapability to precisely extract

关键词： Pavement crack tracing | ELM-PID control | Extreme learning machines | Transformer-based crack segmentation | Embedded deep learning | Autonomous mobile robot

摘要： on machine learning to enhance the BLDC motor's | machine learning-based control algorithm demonstrates | The one-phase brushless DC motor (BLDC) has | become indispensable in-home appliances due to its high | -power density, flexible control, and straightforward

关键词： Numerical simulation | Dynamic performance | BLDC motor | Machine learning | Robust controller

摘要：Electric machines have been in the spotlight | agreeable that nearly all the competing electric machines | as the transportation industry, along with many | other high-impact industries, undergo the | electrification process. While researchers debate whether one

关键词： Circuit faults | Indexes | Magnetic flux | Support vector machines | Motors | Stator windings | Windings

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： functional extreme learning* machines by applying graph* | In this paper, we analyze interacting multi | theory, groupoid theory, representation theory | , operator theory, operator algebra theory and free | probability.

摘要：Extreme learning* machines is a neural network* | type that has been utilized in tasks such as | regression and classification, due to their efficient | training process, which is based on pseudoinverse | matrices and randomized weights, avoiding the

摘要： toward using improved classifiers in machine learning | research, Laplacian Support Vector Machines* (LapSVMs) are* | used as a semi-supervised learning* algorithm to* | -supervised learning* algorithm using LapSVMs to classify* | unlabeled and labeled data. Also, Support Vector Machines

摘要：In fault diagnosis, machine learning* theories* | identifies the health conditions of the machines | systematically review the progress of machine learning* theories* | learning theories has reformed the fault diagnosis | deep learning* theories have also filled the gap*

摘要：, combining Extreme Learning* Machines (ELM) and Proportional* | Pavement crack tracing is paramount to | missions encompassing automated crack sealing for road | maintenance. However, existing methods still face several | challenges, including the incapability to precisely extract

摘要： on machine learning* to enhance the BLDC motor's* | machine learning-based control algorithm demonstrates | The one-phase brushless DC motor (BLDC) has | become indispensable in-home appliances due to its high | -power density, flexible control, and straightforward

摘要：Electric machines* have been in the spotlight* | agreeable that nearly all the competing electric machines | as the transportation industry, along with many | other high-impact industries, undergo the | electrification process. While researchers debate whether one