...: ethanjlau@gmail.com Email: vaibhav@egr.msu.edu Email: shaunak@egr.msu.edu Dynamics and control Human-machine interfaces Machine learning robotics ASME Letters in Dynamic Systems and Control. Received July 19, 2024; Accepted manuscript posted September 19, 2024. doi:10.1115/1.4066629...

... network approach which enables near optimal real-time adaptation of primitive motion profiles. 27 07 2024 07 09 2024 10 09 2024 24 09 2024 Email: slightamj@gmail.com Email: asteyer@sandia.gov Email: lbeaver@odu.edu Email: ccyoung@sandia.gov Machine learning...

... No. IMECE2023-114223. 01 11 2023 04 01 2024 12 01 2024 07 03 2024 Graphical Abstract Figure kinematic trajectory tracking PID mobile robot non-holonomic Kane canonical momenta sliding mode LQR pole placement machine learning agents based systems autonomous...

...Hyunjin Ahn; Heran Shen; Xingyu Zhou; Yung-Chi Kung; Junmin Wang Accurate estimation of the state of charge (SOC) is crucial for ensuring the safe and efficient operation of lithium-ion batteries. Machine learning (ML) models may achieve high SOC estimation accuracy, but typically require large...

... machine learning neural networks eco-driving connected and autonomous vehicles Advanced Research Projects Agency - Energy 10.13039/100006133 DE-AR0000794 The introduction of connected and automated vehicles (CAVs) has had a significant impact due to its potential to improve...

... , C. K. , and Rasmussen , C. E. , 2006 , Gaussian Processes for Machine Learning , MIT Press , Cambridge, MA . 1 Corresponding author. Email: hady@ku.edu Email: mmhc@ku.edu Email: keshmiri@ku.edu 29 03 2023 11 09 2023 17 09 2023 25 10 2023...

.... Machine learning (ML) is a powerful technique allowing for data analysis and pattern recognition. The goal is to input harmonics of the servo-valve control signal to a diagnosis tool and generate output for the fault cases. Additionally, the ability to detect mixed cases is valuable as it allows multiple...

...Md Ferdous Wahid; Reza Tafreshi; Zurwa Khan; Albertus Retnanto The simultaneous liquid–liquid flow usually manifests various flow configurations due to a diverse range of fluid properties, flow-controlling processes, and equipment. This study investigates the performance of machine learning (ML...

...@villanova.edu Email: garrett.clayton@villanova.edu Email: c.nataraj@villanova.edu 18 04 2021 01 02 2022 13 02 2022 22 03 2022 machine learning neural networks This article presents a classification tool to assist an explosive ordnance disposal (EOD) technician...

.../biomet/83.1.81 [14] Andrieu , C. , de Freitas , N. , Doucet , A. , and Jordan , M. I. , 2003 , “ An Introduction to MCMC for Machine Learning ,” Mach. Learn. , 50 ( 1 ), pp. 5 – 43 . 10.1023/A:1020281327116 [15] Piga , D. , Bemporad , A. , and Benavoli...

... Humanoid Robot ,” 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146) , Leuven, Belgium , May 20 . [4] Halilaj , E. , Rajagopal , A. , Fiterau , M. , Hicks , J. L. , Hastie , T. J. , and Delp , S. L. , 2018 , “ Machine Learning in Human...

... 09 2020 18 02 2021 22 02 2021 02 04 2021 autonomous systems control applications machine learning model predictive control optimal control predictive control for nonlinear systems vehicle dynamics and control Mobility, safety, and fuel efficiency are some...

... intelligence machine learning human computation crowdsourcing data management Physical intelligence is of fundamental importance for robots to interact with the real world. Compared to cognitive intelligence for tasks such as language processing and image recognition, robot physical intelligence...

... 08 2020 chaos classification hidden Markov modeling Hénon map Duffing Rössler systems Lorenz attractor chaotic system machine learning nonlinear systems Edward Lorenz introduced the concept of chaos in the form of the Butterfly Effect [ 1 ], where he suggested...

... to severe vehicle damage, significant financial loss, and even operator injury or death. This paper investigates the impact of understory vegetation density on obstacle detection in off-road traversability analysis. By leveraging a physics-based autonomous driving simulator, a machine learning–based...

...% improvement over conventional methods such as K-nearest neighbor (KNN) and support vector machine (SVM), pushing the accuracy above 90%. In addition to performance improvements, the proposed method requires a similar amount of training data to traditional machine learning methods, making the method easily...

.... Email: hailin@vt.edu Email: jingyq1@vt.edu Email: bentzvi@vt.edu 23 10 2019 26 02 2020 15 03 2020 27 03 2020 dynamics and control machine learning neural networks nonlinear systems robotics Flat systems have gained popularity for analyzing and designing...