Results 261 to 270 of about 585,872 (348)
Design of discrete PI‐PR2 controllers for time‐delayed systems using dominant pole placement method
Abstract This paper presents a new controller structure known as the proportional‐integral proportional‐double‐retarded (PI‐PR2) designed for discrete‐time systems with time delay. The dominant pole placement technique, which is frequently encountered in control systems, is used as the primary design method. The design method starts that dominant poles
Ayşe Duman Mammadov +2 more
wiley +1 more source
A MacMahon analysis view of cylindric partitions. [PDF]
Li R, Uncu AK.
europepmc +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
Solutions behavior of mechanical oscillator equations with impulsive effects under Power Caputo fractional operator and its symmetric cases. [PDF]
Saber H +5 more
europepmc +1 more source
Interval‐valued Caputo–Fabrizio fractional derivative in continuous programming
Abstract This study investigates a novel class of variational programming problems characterized by fractional interval values, formulated under the Caputo–Fabrizio fractional derivative with an exponential kernel. Invex and generalized invex functions are used to discuss the Mond–Weir‐type dual problem for the considered variational problem.
Krishna Kummari +2 more
wiley +1 more source
Universality of the Microcanonical Entropy at Large Spin. [PDF]
Pal S, Qiao J, van Rees BC.
europepmc +1 more source
Dislocated quasi-b-metric spaces and fixed point theorems for cyclic weakly contractions
Cholatis Suanoom +2 more
openalex +2 more sources
Generative Deep Learning for Advanced Battery Materials
This review explores the role of generative deep learning (DL) in battery materials analysis and highlights the fundamental principles of generative DL and its applications in designing battery materials. The importance of using multimodal data is underscored to effectively address the challenges faced during the development of battery materials across
Deepalaxmi Rajagopal +3 more
wiley +1 more source

