Artículos de revistas sobre el tema "Supervised Motor Learning"
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Xian, Xiaoyu, Haichuan Tang, Yin Tian, Qi Liu y Yuming Fan. "Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors". Journal of Physics: Conference Series 2095, n.º 1 (1 de noviembre de 2021): 012058. http://dx.doi.org/10.1088/1742-6596/2095/1/012058.
Texto completoEt. al., Rameshwar D. Chintamani,. "Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm". INFORMATION TECHNOLOGY IN INDUSTRY 9, n.º 2 (26 de marzo de 2021): 541–53. http://dx.doi.org/10.17762/itii.v9i2.381.
Texto completoSingh, Puneet, Sumitash Jana, Ashitava Ghosal y Aditya Murthy. "Exploration of joint redundancy but not task space variability facilitates supervised motor learning". Proceedings of the National Academy of Sciences 113, n.º 50 (29 de noviembre de 2016): 14414–19. http://dx.doi.org/10.1073/pnas.1613383113.
Texto completoShe, Zhou, Gan, Ma y Luo. "Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning". Electronics 8, n.º 11 (1 de noviembre de 2019): 1273. http://dx.doi.org/10.3390/electronics8111273.
Texto completoRaymond, Jennifer L. y Javier F. Medina. "Computational Principles of Supervised Learning in the Cerebellum". Annual Review of Neuroscience 41, n.º 1 (8 de julio de 2018): 233–53. http://dx.doi.org/10.1146/annurev-neuro-080317-061948.
Texto completoJigyasu, R., V. Shrivastava y S. Singh. "Prognostics and health management of induction motor by supervised learning classifiers". IOP Conference Series: Materials Science and Engineering 1168, n.º 1 (1 de julio de 2021): 012006. http://dx.doi.org/10.1088/1757-899x/1168/1/012006.
Texto completoZhang, Weiwei, Deji Chen y Yang Kong. "Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images". Sensors 21, n.º 14 (13 de julio de 2021): 4774. http://dx.doi.org/10.3390/s21144774.
Texto completoTang, Xian-Lun, Wei-Chang Ma, De-Song Kong y Wei Li. "Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition". Neural Computation 31, n.º 5 (mayo de 2019): 919–42. http://dx.doi.org/10.1162/neco_a_01183.
Texto completoPyle, Ryan y Robert Rosenbaum. "A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity". Neural Computation 31, n.º 7 (julio de 2019): 1430–61. http://dx.doi.org/10.1162/neco_a_01198.
Texto completoCingireddy, Anirudh Reddy, Robin Ghosh, Venkata Kiran Melapu, Sravanthi Joginipelli y Tor A. Kwembe. "Classification of Parkinson's Disease Using Motor and Non-Motor Biomarkers Through Machine Learning Techniques". International Journal of Quantitative Structure-Property Relationships 7, n.º 2 (abril de 2022): 1–21. http://dx.doi.org/10.4018/ijqspr.290011.
Texto completoWang, Chiao-Sheng, I.-Hsi Kao y Jau-Woei Perng. "Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning". Sensors 21, n.º 11 (22 de mayo de 2021): 3608. http://dx.doi.org/10.3390/s21113608.
Texto completoOu, Yanghan, Siqin Sun, Haitao Gan, Ran Zhou y Zhi Yang. "An improved self-supervised learning for EEG classification". Mathematical Biosciences and Engineering 19, n.º 7 (2022): 6907–22. http://dx.doi.org/10.3934/mbe.2022325.
Texto completoRovini, Erika, Carlo Maremmani, Alessandra Moschetti, Dario Esposito y Filippo Cavallo. "Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches". Annals of Biomedical Engineering 46, n.º 12 (20 de julio de 2018): 2057–68. http://dx.doi.org/10.1007/s10439-018-2104-9.
Texto completoSharrar, Labib. "Anomaly Detection System for Stepper Motors". International Journal of Engineering Research in Electronics and Communication Engineering 9, n.º 6 (30 de junio de 2022): 26–35. http://dx.doi.org/10.36647/ijerece/09.06.a005.
Texto completoDevlaminck, Dieter, Bart Wyns, Moritz Grosse-Wentrup, Georges Otte y Patrick Santens. "Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI". Computational Intelligence and Neuroscience 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/217987.
Texto completoDas, Arun, Jeffrey Mock, Yufei Huang, Edward Golob y Peyman Najafirad. "Interpretable Self-Supervised Facial Micro-Expression Learning to Predict Cognitive State and Neurological Disorders". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 1 (18 de mayo de 2021): 818–26. http://dx.doi.org/10.1609/aaai.v35i1.16164.
Texto completoShifat, Tanvir Alam y Jang-Wook Hur. "EEMD assisted supervised learning for the fault diagnosis of BLDC motor using vibration signal". Journal of Mechanical Science and Technology 34, n.º 10 (24 de julio de 2020): 3981–90. http://dx.doi.org/10.1007/s12206-020-2208-7.
Texto completoZaki Zadeh, Mohammad, Ashwin Ramesh Babu, Ashish Jaiswal y Fillia Makedon. "Self-Supervised Human Activity Representation for Embodied Cognition Assessment". Technologies 10, n.º 1 (17 de febrero de 2022): 33. http://dx.doi.org/10.3390/technologies10010033.
Texto completoQi, Yugang, Sijie Tan, Mingyang Sui y Jianxiong Wang. "SUPERVISED PHYSICAL TRAINING IMPROVES FINE MOTOR SKILLS OF 5-YEAR-OLD CHILDREN". Revista Brasileira de Medicina do Esporte 24, n.º 1 (enero de 2018): 9–12. http://dx.doi.org/10.1590/1517-869220182401177117.
Texto completoXu, Yilu, Hua Yin, Wenlong Yi, Xin Huang, Wenjuan Jian, Canhua Wang y Ronghua Hu. "Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI". Computational Intelligence and Neuroscience 2022 (17 de octubre de 2022): 1–19. http://dx.doi.org/10.1155/2022/1603104.
Texto completoFujita, Masahiko. "New Supervised Learning Theory Applied to Cerebellar Modeling for Suppression of Variability of Saccade End Points". Neural Computation 25, n.º 6 (junio de 2013): 1440–71. http://dx.doi.org/10.1162/neco_a_00448.
Texto completoAbu Al-Haija, Qasem y Moez Krichen. "A Lightweight In-Vehicle Alcohol Detection Using Smart Sensing and Supervised Learning". Computers 11, n.º 8 (3 de agosto de 2022): 121. http://dx.doi.org/10.3390/computers11080121.
Texto completoRodrigues, Luis Guilherme Silva, Diego Roberto Colombo Dias, Marcelo De Paiva Guimarães, Alexandre Fonseca Brandão, Leonardo C. Rocha, Rogério Luiz Iope y José Remo Ferreira Brega. "Supervised Classification of Motor-Rehabilitation Body Movements with RGB Cameras and Pose Tracking Data". Journal on Interactive Systems 13, n.º 1 (6 de septiembre de 2022): 221–31. http://dx.doi.org/10.5753/jis.2022.2409.
Texto completoLi, Hailong, Zhiyuan Li, Kevin Du, Yu Zhu, Nehal A. Parikh y Lili He. "A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants". Diagnostics 13, n.º 8 (21 de abril de 2023): 1508. http://dx.doi.org/10.3390/diagnostics13081508.
Texto completoSadouk, Lamyaa, Taoufiq Gadi y El Hassan Essoufi. "A Novel Deep Learning Approach for Recognizing Stereotypical Motor Movements within and across Subjects on the Autism Spectrum Disorder". Computational Intelligence and Neuroscience 2018 (10 de julio de 2018): 1–16. http://dx.doi.org/10.1155/2018/7186762.
Texto completoSchwarz, Andreas, Julia Brandstetter, Joana Pereira y Gernot R. Müller-Putz. "Direct comparison of supervised and semi-supervised retraining approaches for co-adaptive BCIs". Medical & Biological Engineering & Computing 57, n.º 11 (14 de septiembre de 2019): 2347–57. http://dx.doi.org/10.1007/s11517-019-02047-1.
Texto completoLakshmi Praveena, T. y N. V. Muthu Lakshmi. "Prediction of Autism Spectrum Disorder Using Supervised Machine Learning Algorithms". Asian Journal of Computer Science and Technology 8, n.º 3 (15 de noviembre de 2019): 15–18. http://dx.doi.org/10.51983/ajcst-2019.8.3.2734.
Texto completoChen, Junjian, Zhuliang Yu y Zhenghui Gu. "Semi-supervised Deep Learning in Motor Imagery-Based Brain-Computer Interfaces with Stacked Variational Autoencoder". Journal of Physics: Conference Series 1631 (septiembre de 2020): 012007. http://dx.doi.org/10.1088/1742-6596/1631/1/012007.
Texto completoShe, Qingshan, Jie Zou, Zhizeng Luo, Thinh Nguyen, Rihui Li y Yingchun Zhang. "Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine". Medical & Biological Engineering & Computing 58, n.º 9 (16 de julio de 2020): 2119–30. http://dx.doi.org/10.1007/s11517-020-02227-4.
Texto completoLing, Xufeng, Yapeng Wu, Rahman Ali y Huaizhong Zhu. "Magnetic Tile Surface Defect Detection Methodology Based on Self-Attention and Self-Supervised Learning". Computational Intelligence and Neuroscience 2022 (3 de agosto de 2022): 1–10. http://dx.doi.org/10.1155/2022/3003810.
Texto completoSaxena, Abhinav, Rajat Kumar, Arun Kumar Rawat, Mohd Majid, Jay Singh, S. Devakirubakaran y Gyanendra Kumar Singh. "Abnormal Health Monitoring and Assessment of a Three-Phase Induction Motor Using a Supervised CNN-RNN-Based Machine Learning Algorithm". Mathematical Problems in Engineering 2023 (30 de enero de 2023): 1–8. http://dx.doi.org/10.1155/2023/1264345.
Texto completoZhao, Xianghong, Jieyu Zhao, Weiming Cai y Shuangqing Wu. "Transferring Common Spatial Filters With Semi-Supervised Learning for Zero-Training Motor Imagery Brain-Computer Interface". IEEE Access 7 (2019): 58120–30. http://dx.doi.org/10.1109/access.2019.2913154.
Texto completoWang, Fang, Kai Xu, Qiao Sheng Zhang, Yi Wen Wang y Xiao Xiang Zheng. "A Multi-Step Neural Control for Motor Brain-Machine Interface by Reinforcement Learning". Applied Mechanics and Materials 461 (noviembre de 2013): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amm.461.565.
Texto completoGhorbani, Saeed, Amir Dana y Zynalabedin Fallah. "The effects of external and internal focus of attention on motor learning and promoting learner’s focus". Biomedical Human Kinetics 11, n.º 1 (1 de enero de 2019): 175–80. http://dx.doi.org/10.2478/bhk-2019-0024.
Texto completoLee, Seyoung, Jiye Lee y Jehee Lee. "Learning Virtual Chimeras by Dynamic Motion Reassembly". ACM Transactions on Graphics 41, n.º 6 (30 de noviembre de 2022): 1–13. http://dx.doi.org/10.1145/3550454.3555489.
Texto completoLiu, Minjie, Mingming Zhou, Tao Zhang y Naixue Xiong. "Semi-supervised learning quantization algorithm with deep features for motor imagery EEG Recognition in smart healthcare application". Applied Soft Computing 89 (abril de 2020): 106071. http://dx.doi.org/10.1016/j.asoc.2020.106071.
Texto completoAltaf, Saud, Muhammad Waseem Soomro y Mirza Sajid Mehmood. "Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique". Modelling and Simulation in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/1292190.
Texto completoRedkar, Sangram. "Using Deep Learning for Human Computer Interface via Electroencephalography". IAES International Journal of Robotics and Automation (IJRA) 4, n.º 4 (1 de diciembre de 2015): 292. http://dx.doi.org/10.11591/ijra.v4i4.pp292-310.
Texto completoCardenas, Javier A., Uriel E. Carrero, Edgar C. Camacho y Juan M. Calderon. "Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning". Machines 11, n.º 6 (2 de junio de 2023): 606. http://dx.doi.org/10.3390/machines11060606.
Texto completoAich, Satyabrata, Jinyoung Youn, Sabyasachi Chakraborty, Pyari Mohan Pradhan, Jin-han Park, Seongho Park y Jinse Park. "A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals". Diagnostics 10, n.º 6 (20 de junio de 2020): 421. http://dx.doi.org/10.3390/diagnostics10060421.
Texto completoE., Sica, Savarese G., Criscitiello G. y Grano R. "The “School in the Green”: An Experience of Developing Scholastic Intelligence Through the Enhancement of the Naturalistic and Visual-Spatial Ones". British Journal of Education, Learning and Development Psychology 6, n.º 3 (14 de agosto de 2023): 1–6. http://dx.doi.org/10.52589/bjeldp-sqmy0hxc.
Texto completoBrons, Annette, Antoine de Schipper, Svetlana Mironcika, Huub Toussaint, Ben Schouten, Sander Bakkes y Ben Kröse. "Assessing Children’s Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach". Journal of Medical Internet Research 23, n.º 4 (22 de abril de 2021): e24237. http://dx.doi.org/10.2196/24237.
Texto completoAnitha Kumari, K., Avinash Sharma, S. Nivethitha, V. Dharini, V. Sanjith, R. Vaishnavi, G. Jothika y K. Shophiya. "Automated Outlier Detection for Electrical Motors and Transformers". Journal of Computational and Theoretical Nanoscience 17, n.º 9 (1 de julio de 2020): 4703–8. http://dx.doi.org/10.1166/jctn.2020.9304.
Texto completoSammut, Stephen, Ryan G. L. Koh y José Zariffa. "Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study". Sensors 21, n.º 2 (12 de enero de 2021): 506. http://dx.doi.org/10.3390/s21020506.
Texto completoWENG, JUYANG, TIANYU LUWANG, HONG LU y XIANGYANG XUE. "A MULTILAYER IN-PLACE LEARNING NETWORK FOR DEVELOPMENT OF GENERAL INVARIANCES". International Journal of Humanoid Robotics 04, n.º 02 (junio de 2007): 281–320. http://dx.doi.org/10.1142/s0219843607001072.
Texto completoBaker, Sunderland, Anand Tekriwal, Gidon Felsen, Elijah Christensen, Lisa Hirt, Steven G. Ojemann, Daniel R. Kramer, Drew S. Kern y John A. Thompson. "Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson’s disease: A proof of concept study". PLOS ONE 17, n.º 10 (20 de octubre de 2022): e0275490. http://dx.doi.org/10.1371/journal.pone.0275490.
Texto completoDai, Mengxi, Dezhi Zheng, Shucong Liu y Pengju Zhang. "Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification". Computational and Mathematical Methods in Medicine 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/9871603.
Texto completoAnastasiev, Alexey, Hideki Kadone, Aiki Marushima, Hiroki Watanabe, Alexander Zaboronok, Shinya Watanabe, Akira Matsumura, Kenji Suzuki, Yuji Matsumaru y Eiichi Ishikawa. "Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides". Sensors 22, n.º 22 (11 de noviembre de 2022): 8733. http://dx.doi.org/10.3390/s22228733.
Texto completoTorabi, Faraz. "Imitation Learning from Observation". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 9900–9901. http://dx.doi.org/10.1609/aaai.v33i01.33019900.
Texto completoMohammed, Mohammed Guhdar, Belnd Saadi Salih y Vaman Muhammed Haji. "Employing EMG sensors in Bionic limbs based on a New Binary Trick Method". Science Journal of University of Zakho 11, n.º 1 (29 de enero de 2023): 54–58. http://dx.doi.org/10.25271/sjuoz.2023.11.1.1027.
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