A Comparative Evaluation of Unconstrained Optimization Methods Applied to the Thermal Tomography Problem

1985 ◽  
Vol 107 (3) ◽  
pp. 228-233 ◽  
Author(s):  
S. T. Clegg ◽  
R. B. Roemer
Keyword(s):  
Unconstrained Optimization ◽  
Blood Perfusion ◽  
Optimization Methods ◽  
Sensitivity Study ◽  
Initial Guess ◽  
Optimization Techniques ◽  
Temperature Fields ◽  
Tissue Temperature ◽  
Initial Attempt ◽  
Error Space

In cancer hyperthermia treatments, it is important to be able to predict complete tissue temperature fields from sampled temperatures taken at the limited number of locations allowed by clinical constraints. An initial attempt to do this automatically using unconstrained optimization techniques to minimize the differences between experimental temperatures and temperatures predicted from treatment simulations has been previously reported [1]. This paper reports on a comparative study which applies a range of different optimization techniques (relaxation, steepest descent, conjugate gradient, Gauss, Box-Kanemasu, and Modified Box-Kanemasu) to this problem. The results show that the Gauss method converges more rapidly than the others, and that it converges to the correct solution regardless of the initial guess for the unknown blood perfusion vector. A sensitivity study of the error space is also performed, and the relationships between the error space characteristics and the comparative speeds of the optimization techniques are discussed.

scholarly journals Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

2021 ◽  
Vol 65 (1) ◽  
pp. 42-52
Author(s):  
Hamed Keshmiri Neghab ◽  
Hamid Keshmiri Neghab
Keyword(s):  
Nonlinear Optimization ◽  
Model Calibration ◽  
Optimization Methods ◽  
Dc Motor ◽  
Optimization Techniques ◽  
Unknown Parameters ◽  
Labview Software ◽  
Dc Motors ◽  
Industrial Software ◽  
Nonlinear Optimization Methods

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

Lumped Parameter Thermal Model for the Study of Vascular Reactivity in the Fingertip

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
O. Ley ◽  
C. Deshpande ◽  
B. Prapamcham ◽  
M. Naghavi
Keyword(s):  
Heat Flux ◽  
Blood Flow ◽  
Thermal Model ◽  
Vascular Reactivity ◽  
Arterial Occlusion ◽  
Blood Perfusion ◽  
Cost Effective ◽  
Venous Occlusion ◽  
Cardiovascular Complications ◽  
Tissue Temperature

Vascular reactivity (VR) denotes changes in volumetric blood flow in response to arterial occlusion. Current techniques to study VR rely on monitoring blood flow parameters and serve to predict the risk of future cardiovascular complications. Because tissue temperature is directly impacted by blood flow, a simplified thermal model was developed to study the alterations in fingertip temperature during arterial occlusion and subsequent reperfusion (hyperemia). This work shows that fingertip temperature variation during VR test can be used as a cost-effective alternative to blood perfusion monitoring. The model developed introduces a function to approximate the temporal alterations in blood volume during VR tests. Parametric studies are performed to analyze the effects of blood perfusion alterations, as well as any environmental contribution to fingertip temperature. Experiments were performed on eight healthy volunteers to study the thermal effect of 3min of arterial occlusion and subsequent reperfusion (hyperemia). Fingertip temperature and heat flux were measured at the occluded and control fingers, and the finger blood perfusion was determined using venous occlusion plethysmography (VOP). The model was able to phenomenologically reproduce the experimental measurements. Significant variability was observed in the starting fingertip temperature and heat flux measurements among subjects. Difficulty in achieving thermal equilibration was observed, which indicates the important effect of initial temperature and thermal trend (i.e., vasoconstriction, vasodilatation, and oscillations).

Comparison of PSO, AGA, SA and Memetic Algorithms for Surface Grinding Optimization

2016 ◽  
Vol 852 ◽  
pp. 241-247 ◽  
Author(s):  
K. Babu
Keyword(s):  
Memetic Algorithm ◽  
Optimization Methods ◽  
Memetic Algorithms ◽  
Global Market ◽  
Optimization Techniques ◽  
Surface Grinding ◽  
Adaptive Genetic Algorithm ◽  
Machining Processes ◽  
Grinding Operations ◽  
Machining Optimization

In striving to remain competitive in the global market, the concept of optimization of manufacturing processes has been extensively employed to meet the diverse production requirements. Optimization analysis of machining processes is usually based on either minimizing or maximizing certain objective functions. Recently, various non-traditional optimization techniques have evolved to optimize the process parameters of machining processes. The objective of this work is to study the effectiveness of the most commonly used non-traditional optimization methods as applied to a particular machining optimization problem. In this work, surface grinding processes are optimized using i) Particle Swarm Optimization (PSO) ii) Adaptive Genetic Algorithm (AGA) iii) Simulated Annealing (SA) and iv) Memetic algorithm (MA). Memetic algorithm used here has two variations as MA-1 and MA-2, each having the combination of PSO and SA and AGA and SA respectively. The mathematical model of surface grinding operations was adopted from a literature. A computer program was written in Visual C++ for the optimization computations. The computation results of various optimization methods are compared and it is observed that the results of PSO method have outperformed the results of other methods in terms of the combined objective function (COF).

Modeling and Optimization of Gas Metal Arc Welding (GMAW) Process

2012 ◽  
pp. 339-367
Author(s):  
R. Venkata Rao
Keyword(s):  
Process Parameters ◽  
Arc Welding ◽  
Gas Metal Arc Welding ◽  
Welding Process ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Modeling And Optimization ◽  
Metal Arc Welding ◽  
Process Output ◽  
Set Up

Weld quality is greatly affected by the operating process parameters in the gas metal arc welding (GMAW) process. The quality of the welded material can be evaluated by many characteristics, such as bead geometric parameters, deposition efficiency, weld strength, weld distortion, et cetera. These characteristics are controlled by a number of welding process parameters, and it is important to set up proper process parameters to attain good quality. Various optimization methods can be applied to define the desired process output parameters through developing mathematical models to specify the relationship between the input parameters and output parameters. The method capable of accurate prediction of welding process output parameters would be valuable for rapid development of welding procedures and for developing control algorithms in automated welding applications. This chapter presents the details of various techniques used for modeling and optimization of GMAW process parameters. The optimization methods covered in this chapter are appropriate for modeling and optimizing the GMAW process. It is found that there is high level of interest in the adaptation of RSM and ANN techniques to predict responses and to optimize the GMAW process. Combining two optimization techniques, such as GA and RSM, would reveal good results for finding out the optimal welding conditions. Furthermore, efforts are required to apply advanced optimization techniques to find out the optimal parameters for GMAW process at which the process could be considered safe and more economical.

Optimization Methods in Continuous Improvement Models

2019 ◽  
Vol 6 (1) ◽  
pp. 46-59
Author(s):  
Brian J. Galli
Keyword(s):  
Continuous Improvement ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Problem Analysis ◽  
Measure Problem ◽  
Continuous Improvement Model ◽  
Current Utilization ◽  
And Control ◽  
Theory Of Constraint

There are numerous processes used to implement quality, such as TQM, 6 Sigma, and Lean. For these quality processes to remain effective, a continuous improvement model is required and implemented from time to time. Some of these models include Define, Measure, Analyse, Improve and Control (DMAIC); Plan, Do, Check, and Act (PDCA); Identify, Measure, Problem Analysis, Remedy, Operationalize, Validate, and Evaluate (IMPROVE); and Theory of Constraint (TOC). Furthermore, continuous improvement tools need to remain effective through the use of optimization techniques to produce the best possible outcomes. This article discusses some of the current utilization of these tools and proposes different optimizing techniques and variations to make robust quality implementation tools.

Torsion Angle Refinement and Dynamics as a Tool to Aid Crystallographic Structure Determination

2009 ◽  
Author(s):  
Ralf W. Grosse-Kunstleve ◽  
Nigel W. Moriarty ◽  
Paul D. Adams
Keyword(s):  
Torsion Angle ◽  
Data Bank ◽  
Optimization Methods ◽  
Rigid Body Dynamics ◽  
Optimization Techniques ◽  
Conformational Space ◽  
Crystallographic Structure ◽  
Local Minima ◽  
Body Dynamics ◽  
Angle Space

Crystallographic methods using experimental diffraction data have produced about 85% of the macromolecular structures in the Protein Data Bank. Before deposition, nearly all crystal structures are refined with gradient-driven optimization techniques. Refinement is typically performed with iterative local optimization methods. A common problem is convergence to local minima. Reparameterization of the model in torsion angle space reduces the number of parameters. This in itself can help to escape from local minima. Combination with rigid-body dynamics algorithms results in an important tool for sampling conformational space. This paper presents the torsion angle refinement and dynamics algorithms implemented for the phenix.refine program and the results of various tests.

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