Results 261 to 270 of about 816,103 (361)

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Training for the Digital Clinical Environment: Preliminary Findings in Implementation of Electronic Health Record Platform in Medical Education. [PDF]

J Med Syst
Tin A, Wong A, Lin KY, Yang J, Buehler A, Chen DE, Dey S, Stefanko N, Nelson C, Vaidyam A.   +9 more
europepmc   +1 more source

Closed-Loop Iterative Self-Calibration of Initial Phase in Phased Arrays. [PDF]

Sensors (Basel)
Huang X, Huang D, Chen B, Wu B.
europepmc   +1 more source

Application of AIIR algorithm for quality improvement and noise reduction in pediatric abdominal contrast-enhanced CT. [PDF]

Front Radiol
Jiazhi X, Dajian D, Shilong T, Xiao F, Luyao Z, Ling H.   +5 more
europepmc   +1 more source

Developing and Integrating Virtual Reality Courses in Medical Education: Tutorial and Implementation Guideline Informed by Best Practices From the National Project "medical tr.AI.ning". [PDF]

JMIR Med Educ
Mergen M, Junga A, Schulze H, Bozdere P, Kockwelp P, Pielage L, Sassen C, Glückselig T, Schmitz M, Ungru K, Graf N, Risse B, Meyerheim M.   +12 more
europepmc   +1 more source

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On initial conditions in iterative learning control

IEEE Transactions on Automatic Control, 2005
Initial conditions, or initial resetting conditions, play a fundamental role in all kinds of iterative learning control methods. In this note, we study five different initial conditions, disclose the inherent relationship between each initial condition and corresponding learning convergence (or boundedness) property.
Jian-Xin Xu
exaly   +5 more sources

Q-learning based fault estimation and fault tolerant iterative learning control for MIMO systems

ISA Transactions, 2023
This paper proposes a Q-learning based fault estimation (FE) and fault tolerant control (FTC) scheme under iterative learning control (ILC) framework. Due to the repetitive demands on control actuators for repetitive tasks, ILC is sensitive to actuator ...
Zhihe Zhuang, Hongfeng Tao, Wojciech Paszke   +2 more
exaly   +2 more sources

The robust stability of iterative learning control. [PDF]

, 2010
This thesis examines the notion of the long term robust stability of iterative learning control (ILC) systems engaged in trajectory tracking, using a robust stability theorem based on a biased version of the nonlinear gap metric. This is achieved through two main results: The first concerns the establishment of a nonlinear robust stability theorem ...
Bradley, Richard Stephen
openaire   +2 more sources

Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths

Transactions of the Institute of Measurement and Control, 2023
In most implementations of iterative learning control (ILC) for trajectory tracking, it is usually required that the trial lengths of different iterations are uniform. However, this requirement may not always be ensured in practical applications. In this
Shanglei Guan, Zhihe Zhuang, Hongfeng Tao, Yiyang Chen, V. Stojanovic, W. Paszke   +5 more
semanticscholar   +1 more source