Results 71 to 80 of about 18,090,481 (292)

Learning-Gain-Adaptive Iterative Learning Control to Linear Discrete-Time-Invariant Systems

IEEE Access, 2019
For a class of repetitive linear discrete-time-invariant systems with the unit relative degree, a learning-gain-adaptive iterative learning control (LGAILC) mechanism is exploited, for which the iteration-wise performance index is to maximize the ...
Xiaohui Li, Xiaoe Ruan, Yan Liu
doaj   +1 more source

Cerebrospinal Fluid Over Plasma Links Analytes to Cognitive Decline in Older Adults at Risk for Alzheimer's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To identify inflammatory analytes in cerebrospinal fluid (CSF) and plasma associated with cognitive decline in cognitively normal (CN) older adults at risk for Alzheimer's disease (AD). Methods In a longitudinal study of 118 CN older adults (65–80 years, 54% APOE ε4, 26% preclinical AD), 1331 CSF and 1501 plasma analytes were ...
Jagan A. Pillai, Audrey Zhu, Chunxuan Ma, Ming Wang, Yifan Yang, Lynn M. Bekris, Maria Khrestian, Frank P. DiFilippo, Mikhail Nasrallah, Aaron Burberry, James B. Leverenz, Yuan Hou, Feixiong Cheng, Stephen M. Rao   +13 more
wiley   +1 more source

Goal-orientated Functional Rehabilitation using Electrical Stimulation and Iterative Learning Control for Motor Recovery in the Upper Extremity Post-Stroke. [PDF]

, 2013
There is increasing evidence that electrical stimulation (ES) combined with task specific training is effective in the recovery of upper extremity dysfunction following stroke.
Kutlu, M., Hughes, A-M., Hallewell, Emma, Burridge, J., Excell, T., Freeman, C., Meadmore, K.   +6 more
core   +5 more sources

Optimized Iterative Learning Control for Linear Discrete-Time-Invariant Systems

IEEE Access, 2019
In this paper, an optimized first-order iterative learning control (OILC) scheme is explored for a class of linear discrete-time-invariant systems with Markov parameters available and the system relative degree being unity.
Yan Liu, Xiaoe Ruan, Xiaohui Li
doaj   +1 more source

Anticoagulation Treatment Response Among Phenotypic ESUS Clusters: Evidence From CASPR

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To evaluate treatment effect heterogeneity across phenotypic clusters of patients with cryptogenic embolic stroke using causal inference methods. Methods From a retrospective, multicenter cohort of patients with cryptogenic embolic stroke, we estimated the treatment effect of anticoagulation versus antiplatelet therapy in patients ...
Elena Badillo Goicoechea, Patrick Ellsworth, James E. Siegler, James R. Brorson, Christina M. Lineback, Frank G. Conyers, Deborah Kerrigan, Kelsey Eklund, Shadi Yaghi, Farid Khasiyev, Emiliya Melkumova, Collin J. Culbertson, Marissa D'Souza, Dinesh V. Jillella, Patrick Glover, Hassan Aboul‐Nour, Fadi Nahab, Mahan Shahrivari, Simona Nedelcu, David S. Liebeskind, Matthew M. Smith, Aaron Rothstein, Anna Bowman, Alicia Zha, Narendra Kala, Ahmad Abu Qdais, Sami Al Kasab, Mudassir Farooqui, Diana Rojas‐Soto, Yasmin Aziz, Franziska Herpich, Jean‐Philippe Daniel, Sean L. Thompson, Vivek Patel, Thanh N. Nguyen, Siddharth Sehgal, Wenzheng Yu, Wayneho Kam, Russel Van Coevering, Oana M. Dumitrascu, Muhammad Alvi, Balaji Krishnaiah, Setareh Salehi Omran, Guillermo Linares, Eesha Singh, Hamid Ali, Mary Penckofer, Jesse M. Thon, Shani Mannion, Dalia Chahien, Lucia Chen, Fawad Talat, Mehdi Abbasi, Richa Sharma   +53 more
wiley   +1 more source

Iterative learning control for performance optimisation [PDF]

2016 American Control Conference (ACC), 2016
Iterative learning control (ILC) is a popular design methodology to achieve high performance trajectory tracking of systems operating in a repetitive manner. This paper further extends the applicability of ILC by showing that ILC can solve a more general problem of optimising some system performance for which trajectory tracking is just a special case.
openaire   +2 more sources

Engagement Patterns With an Artificial Intelligence Health Coach for Systemic Sclerosis Self‐Management: A Mixed Methods Study

Arthritis Care &Research, EarlyView.
Objective To evaluate utility of an artificial intelligence (AI) health coach for systemic sclerosis (SSc) self‐management and identify patterns associated with participant engagement. Methods We conducted a mixed methods study in which an AI health coach, powered by a large language model (LLM), was used to support self‐management for SSc.
Nirali Shah, Melanie Morris, Cristina Daraban, Sophia Johnson, Susan L. Murphy   +4 more
wiley   +1 more source

Experimental evaluation of iterative learning control algorithms for non-minimum phase plants

, 2005
The purpose of this paper is two-fold, firstly it describes the development and modelling of an experimental test facility as a platform on which to assess the performance of Iterative Learning Control (ILC) schemes.
Lewin, P.L., Freeman, C.T., Rogers, E.
core   +1 more source

A novel iterative learning path-tracking control for nonholonomic mobile robots against initial shifts

International Journal of Advanced Robotic Systems, 2017
In this article, we propose a novel discrete-time iterative learning control framework for robust path-tracking problem of nonholonomic mobile robots. The contributions of this iterative learning control framework are threefolds: (1) With the application
Yang Zhao, Fengyu Zhou, Yan Li, Yugang Wang   +3 more
doaj   +1 more source

Clinical Validation of Artificial Intelligence (AI)‐based Cartilage Segmentation Predicting Knee Replacement

Arthritis Care &Research, Accepted Article.
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein, David J. Hunter, C. Kent Kwoh, Wolfgang Wirth   +3 more
wiley   +1 more source