Results 121 to 130 of about 149 (149)
Advanced Theory and Simulations, EarlyView.This study explores machine learning‐driven prediction of fiber length characteristics in sustainable yarn blends made from recycled cotton and Lyocell. By analyzing empirical data through models like Random Forest and Gradient Boosting, and interpreting results with SHAP, key fiber length features from the Staple Diagram and Fibrogram are identified ...
Tuser Tirtha Biswas +2 more
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Advanced Theory and Simulations, EarlyView.This study presents a robust 3D simulation for BBMC, focusing on both fixed and receding receiver scenarios across various conditions. Key parameters such as D, Q, u, and d are tested at different values to assess the performance and reliability of BBMC in applications like drug delivery, addressing critical challenges in nanoscale communication with ...
Mustafa Ozan Duman +3 more
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Advanced Theory and Simulations, EarlyView.This study evaluates the simulation capabilities of lithium‐ion battery (LIB) electrochemical simulation software packages. The benchmark simulation results reveal the impacts of parameter sensitivity to solver performance and stability. The guidelines to troubleshooting common solver failures at high current rates lowers the steep learning curve to ...
Kenneth C. Nwanoro +2 more
wiley +1 more source
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Mathematical theory of optimal control
Journal of Soviet Mathematics, 1981Papers on optimal, control theory, reviewed in Referativnyi Zhurnal “Matematika” during 1971–1975, are surveyed.
R. Gabasov, F. M. Kirillova
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Cancer immunotherapy, mathematical modeling and optimal control
Journal of Theoretical Biology, 2007Clinical immunologists, among other problems, routinely face a question: what is the best time and dose for a certain therapeutic agent to be administered to the patient in order to decrease/eradicate the pathological condition? In cancer immunotherapies the therapeutic agent is something able to elicit an immune response against cancer.
Castiglione F, Piccoli B
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Optimal control via mathematical programming
Journal of Guidance and Control, 1981Introduction O PTIMAL control problems with bounded controls reduce to two-point boundary-value problems which are difficult to handle by conventional methods of calculus of variations. Pontryagin's maximum principle and Bellman's dynamic programming are the other methods for solving such problems.
P. Ramamoorthy, B. V. Sheela
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Mathematical Fundamentals of Optimal Control
1998To describe and study dynamic systems, the notions of system state, control effort and performance measure must be clarified. System state is a set of parameters that characterize the system at each time. The state parameters vary gradually and cannot instantly jump.
Oded Maimon +2 more
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Advances in Mathematical Modeling, Optimization and Optimal Control
2016This book contains extended, in-depth presentations of the plenary talks from the 16th French-German-Polish Conference on Optimization, held in Krakow, Poland in 2013. Each chapter in this book exhibits a comprehensive look at new theoretical and/or application-oriented results in mathematical modeling, optimization, and optimal control.
Hiriart-Urruty, Jean-Baptiste +3 more
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Application of a mathematical model for the control and optimization of a distillation plant
Automatica, 1972The principles of optimizing process control are elucidated by weighing the merits of hill-climbing, feedback, methods against model, feedforward, methods. It is shown that the advantages of the two methods can be employed at both the optimizing and the stabilizing control levels of a chemical plant.
G. Duyfjes, P.M.E.M. van der Grinten
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