scholarly journals Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications

2021 ◽  
Vol 11 (24) ◽  
pp. 11814
Author(s):  
Guilherme C. Duran ◽  
André K. Sato ◽  
Edson K. Ueda ◽  
Rogério Y. Takimoto ◽  
Hossein G. Bahabadi ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Objective Function ◽  
Electrical Impedance ◽  
Sparse Linear Systems ◽  
Objective Functions ◽  
The Finite Element Method ◽  
Impedance Tomography ◽  
Different Types ◽  
Feedback Strategies ◽  
Annealing Algorithms

This paper represents how typical advanced engineering design can be structured using a set of parameters and objective functions corresponding to the nature of the problem. The set of parameters can be in different types, including integer, real, cyclic, combinatorial, interval, etc. Similarly, the objective function can be presented in various types including integer (discrete), float, and interval. The simulated annealing with crystallization heuristic can deal with all these combinations of parameters and objective functions when the crystallization heuristic presents a sensibility for real parameters. Herein, simulated annealing with the crystallization heuristic is enhanced by combining Bates and Gaussian distributions and by incorporating feedback strategies to emphasize exploration or refinement, or a combination of the two. The problems that are studied include solving an electrical impedance tomography problem with float parameters and a partially evaluated objective function represented by an interval requiring the solution of 32 sparse linear systems defined by the finite element method, as well as an airplane design problem with several parameters and constraints used to reduce the explored domain. The combination of the proposed feedback strategies and simulated annealing with the crystallization heuristic is compared with existing simulated annealing algorithms and their benchmark results are shown. The enhanced simulated annealing approach proposed herein showed better results for the majority of the studied cases.

scholarly journals Interval Simulated Annealing applied to Electrical Impedance Tomography image reconstruction with fast objective function evaluation

2016 ◽  
Vol 72 (5) ◽  
pp. 1230-1243 ◽  
Author(s):  
Thiago de Castro Martins ◽  
Marcos de Sales Guerra Tsuzuki ◽  
Erick Dario León Bueno de Camargo ◽  
Raul Gonzalez Lima ◽  
Fernando Silva de Moura ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Image Reconstruction ◽  
Objective Function ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Function Evaluation ◽  
Impedance Tomography ◽  
Tomography Image ◽  
Electrical Impedance Tomography Image

scholarly journals Electrical Impedance Tomography and Prone Position During Ventilation in COVID-19 Pneumonia: Case Reports and a Brief Literature Review

2020 ◽  
Vol 24 (4) ◽  
pp. 287-292
Author(s):  
Serena Tomasino ◽  
Rosa Sassanelli ◽  
Corrado Marescalco ◽  
Francesco Meroi ◽  
Luigi Vetrugno ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Literature Review ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Case Reports ◽  
Prone Positioning ◽  
Impedance Tomography ◽  
Pneumonia Case ◽  
Different Types ◽  
Novel Coronavirus

At the end of 2019, a novel coronavirus (COVID-19) was identified as the cause of a cluster of pneumonia cases, with high needs of mechanical ventilation in critically ill patients. It is still unclear whether different types of COVID-19 pneumonia require different ventilator strategies. With electrical impedance tomography (EIT) we evaluated, in real time and bedside, the distribution of ventilation in the different pulmonary regions before, during, and after pronation in COVID-19 respiratory failure. We present a brief literature review of EIT in non-COVID-19 patients and a report of 2 COVID-19 patients: one that did not respond well and another one that improved during and after pronation. EIT might be a useful tool to decide whether prone positioning should or should not be used in COVID-19 pneumonia.

A Discussion of Objective Functions in Structure-Control Simultaneous Optimum Design

1993 ◽  
Author(s):  
Takuzo Iwatsubo ◽  
Shozo Kawamura ◽  
Kazuhiko Adachi
Keyword(s):  
Quadratic Form ◽  
Objective Function ◽  
Linear Combination ◽  
Cantilever Beam ◽  
Optimum Design ◽  
Feedback Gain ◽  
Objective Functions ◽  
Structure Control ◽  
Different Types ◽  
Control Objective

Abstract The effect of the objective functions on the results of the structure-control simultaneous optimum design is discussed, comparing the optimum designs obtained by minimizing several different types of objective functions. These objective functions are defined as a linear combination of the structural and the control objective functions. One of the objective functions is a structural weight, another is a feedback gain norm and the others are transient characteristics values which are expressed as the quadratic form. A flexible cantilever beam is designed in the numerical example, and the results indicate that the usefulness of the structure-control simultaneous optimum design depends on the objective function for the example.

scholarly journals Boundary element method and simulated annealing algorithm applied to electrical impedance tomography image reconstruction

2013 ◽  
Vol 35 (2) ◽  
Author(s):  
Olavo H. Menin ◽  
Vanessa Rolnik ◽  
Alexandre S. Martinez
Keyword(s):  
Simulated Annealing ◽  
Boundary Element Method ◽  
Image Reconstruction ◽  
Boundary Element ◽  
Electrical Impedance Tomography ◽  
Electrical Impedance ◽  
Simulated Annealing Algorithm ◽  
Impedance Tomography ◽  
Annealing Algorithm ◽  
Element Method

Physics has played a fundamental role in medicine sciences, specially in imaging diagnostic. Currently, image reconstruction techniques are already taught in Physics courses and there is a growing interest in new potential applications. The aim of this paper is to introduce to students the electrical impedance tomography, a promising technique in medical imaging. We consider a numerical example which consists in finding the position and size of a non-conductive region inside a conductive wire. We review the electrical impedance tomography inverse problem modeled by the minimization of an error functional. To solve the boundary value problem that arises in the direct problem, we use the boundary element method. The simulated annealing algorithm is chosen as the optimization method. Numerical tests show the technique is accurate to retrieve the non-conductive inclusion.

Sign in / Sign up

Export Citation Format

Share Document