Results 61 to 70 of about 7,831,201 (331)
Contamination Detection From Highly Cluttered Waste Scenes Using Computer Vision
IEEE AccessAs the global production of waste continues to rise, there is a growing demand for more effective waste management strategies to handle this expanding problem.
Dishant Mewada +8 more
doaj +1 more source
An Extended Kalman Filter for Data-enabled Predictive Control
, 2020 The literature dealing with data-driven analysis and control problems has significantly grown in the recent years. Most of the recent literature deals with linear time-invariant systems in which the uncertainty (if any) is assumed to be deterministic and
Alpago, Daniele +2 more
core +1 more source
Application of Vision Transformers to Contamination Detection in Densely Cluttered Waste Scenes
IEEE Open Journal of the Computer SocietyWith the increasing global waste production, there is a rising need for improved waste management solutions to address this growing issue. In the United States, less than 35% of recyclable materials are actually recycled, leading to higher levels ...
Dishant Mewada +8 more
doaj +1 more source
Data-Driven Power Control for State Estimation: A Bayesian Inference Approach
, 2015 We consider sensor transmission power control for state estimation, using a Bayesian inference approach. A sensor node sends its local state estimate to a remote estimator over an unreliable wireless communication channel with random data packet drops ...
Lau, Vincent +4 more
core +2 more sources
COVID‐19 in cancer patients: The impact of vaccination on outcomes early in the pandemic
Cancer Medicine, 2023 Background With the rapid evolution of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pandemic, the development of effective and safe vaccines was of utmost importance to protect vulnerable individuals, including cancer patients ...
Fareed Khawaja +10 more
doaj +1 more source
Noniterative Data-Driven Gain-Scheduled Controller Design Based on Fictitious Reference Signal
IEEE Access, 2023 This paper proposes a noniterative direct data-driven gain-scheduled control. Gain-scheduled proportional–integral–derivative (PID) control is one of the most popular approaches for nonlinear systems.
Shuichi Yahagi, Itsuro Kajiwara
doaj +1 more source
Journal of Intelligent ManufacturingNowadays, artificial intelligence (AI) has become a crucial Key Enabling Technology with extensive application in diverse industrial sectors. Recently, considerable focus has been directed towards utilizing AI for the development of optimal control in ...
G. Mattera, A. Caggiano, L. Nele
semanticscholar +1 more source
A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization [PDF]
, 2017 The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty.
Kammer, Christoph, Karimi, Alireza
core +2 more sources
Data-driven simulation and control [PDF]
International Journal of Control, 2008 Classical linear time-invariant system simulation methods are based on a transfer function, impulse response, or input/state/output representation. We present a method for computing the response of a system to a given input and initial conditions directly from a trajectory of the system, without explicitly identifying the system from the data ...
Markovsky, Ivan, Rapisarda, Paolo
openaire +3 more sources
Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model
IEEE Access, 2022 In this paper, we present a data-driven tuning method for model-free control based on an ultra-local model (MFC-ULM), which is also called intelligent proportional-integral-derivative control.
Shuichi Yahagi, Itsuro Kajiwara
doaj +1 more source