Modelling of radiative transfer by the Monte Carlo method and solving the inverse problem based on a genetic algorithm according to experimental results of aerosol sensing on short paths using a femtosecond laser source

2015 ◽  
Vol 45 (2) ◽  
pp. 145-152 ◽  
Author(s):  
G G Matvienko ◽  
V K Oshlakov ◽  
A N Stepanov ◽  
A Ya Sukhanov
Keyword(s):  
Genetic Algorithm ◽  
Inverse Problem ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Radiative Transfer ◽  
Femtosecond Laser ◽  
Experimental Results ◽  
Laser Source ◽  
The Monte Carlo Method

Fabrication and Experiment of an Automatic Continuum Robot System Using Image Recognition

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yeong-min Na ◽  
Hyun-seok Lee ◽  
Jong-kyu Park
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Image Recognition ◽  
Performance Test ◽  
Skill Level ◽  
Experimental Results ◽  
Robot System ◽  
The Monte Carlo Method ◽  
Continuum Robot ◽  
Two Stages

Abstract This paper proposes a continuum robot that can be controlled automatically using image recognition. The proposed robot can operate in narrower spaces than the existing robots composed of links and joints. In addition, because it is automatically controlled through image recognition, the robot can be operated irrespective of the human controller's skill level. The manipulator is divided into two stages, with three wires connected to each stage to minimize the energy used to control the manipulator posture. The manipulator's posture is controlled by adjusting the length of the wire, similar to the relaxation and contraction of the muscles. Denavit–Hartenberg transformation and the Monte Carlo method were used to analyze the robot's kinematics and workspace. In a performance test, an experimental plate with nine targets was fabricated and the manipulator speed was adjusted to 5, 10, and 20 mm/s. Experimental results show that the manipulator was automatically controlled and reached all targets, with errors of 2.58, 3.28, and 9.18 mm.

scholarly journals HDUST3 — A chemically realistic, 3-D, NLTE radiative transfer code

2016 ◽  
Vol 12 (S329) ◽  
pp. 390-390
Author(s):  
Alex C. Carciofi ◽  
Jon E. Bjorkman ◽  
Janos Zsargó
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Radiative Transfer ◽  
The Monte Carlo Method

AbstractHDUST is a 3D, NLTE radiative transfer code based on the Monte Carlo method. We report on recent advancements on the code, which is now capable of handling He and other elements in the NLTE regime and in 3D configurations. In this contribution we show initial comparisons with CMFGEN, made with spherical wind models composed of H + He.

The Solution of Transient Radiative Transfer With Collimated Incident Serial Pulse in a Plane-Parallel Medium by the DRESOR Method

2008 ◽  
Vol 130 (10) ◽  
Author(s):  
Qiang Cheng ◽  
Huai-Chun Zhou ◽  
Zhi-Feng Huang ◽  
Yong-Lin Yu ◽  
De-Xiu Huang
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Radiative Transfer ◽  
Incident Radiation ◽  
Anisotropic Scattering ◽  
Time Dependent ◽  
Temporal Response ◽  
One Dimensional ◽  
Unit Energy ◽  
The Monte Carlo Method

A time-dependent distribution of ratios of energy scattered by the medium or reflected by the boundary surfaces (DRESOR) method was proposed to solve the transient radiative transfer in a one-dimensional slab. This slab is filled with an absorbing, scattering, and nonemitting medium and exposed to a collimated, incident serial pulse with different pulse shapes and pulse widths. The time-dependent DRESOR values, representing the temporal response of an instantaneous, incident pulse with unit energy and the same incident direction as that for the serial pulse, were proposed and calculated by the Monte Carlo method. The temporal radiative intensity inside the medium with high directional resolution can be obtained from the time-dependent DRESOR values. The transient incident radiation results obtained by the DRESOR method were compared to those obtained with the Monte Carlo method, and good agreements were achieved. Influences of the pulse shape and width, reflectivity of the boundary, scattering albedo, optical thickness, and anisotropic scattering on the transient radiative transfer, especially the temporal response along different directions, were investigated.

Numerical solution of the multidimensional Gelfand–Levitan equation

2015 ◽  
Vol 23 (5) ◽  
Author(s):  
Sergey I. Kabanikhin ◽  
Karl K. Sabelfeld ◽  
Nikita S. Novikov ◽  
Maxim A. Shishlenin
Keyword(s):  
Inverse Problem ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Two Dimensional ◽  
Nonlinear Inverse Problem ◽  
Coefficient Inverse Problem ◽  
Specific Point ◽  
The Monte Carlo Method ◽  
Linear Integral ◽  
Linear Integral Equations

AbstractThe coefficient inverse problem for the two-dimensional wave equation is solved. We apply the Gelfand–Levitan approach to transform the nonlinear inverse problem to a family of linear integral equations. We consider the Monte Carlo method for solving the Gelfand–Levitan equation. We obtain the estimation of the solution of the Gelfand–Levitan equation in one specific point, due to the properties of the method. That allows the Monte Carlo method to be more effective in terms of span cost, compared with regular methods of solving linear system. Results of numerical simulations are presented.

scholarly journals Sustainable Water Resource Management of Regulated Rivers under Uncertain Inflow Conditions Using a Noisy Genetic Algorithm

2019 ◽  
Vol 16 (5) ◽  
pp. 868 ◽  
Author(s):  
Chunxue Yu ◽  
Xinan Yin ◽  
Zhifeng Yang ◽  
Zhi Dang
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reservoir Operation ◽  
Water Resource Management ◽  
Fitness Function ◽  
Regulated Rivers ◽  
The Monte Carlo Method ◽  
Sustainable Water Resource ◽  
Inflow Conditions

Ecofriendly reservoir operation is an important tool for sustainable water resource management in regulated rivers. Optimization of reservoir operation is potentially affected by the stochastic characteristics of inflows. However, inflow stochastics are not widely incorporated in ecofriendly reservoir operation optimization. The reasons might be that computational cost and unsatisfactory performance are two key issues for reservoir operation under uncertainty inflows, since traditional simulation methods are usually needed to evaluate over many realizations and the results vary between different realizations. To solve this problem, a noisy genetic algorithm (NGA) is adopted in this study. The NGA uses an improved type of fitness function called sampling fitness function to reduce the noise of fitness assessment. Meanwhile, the Monte Carlo method, which is a commonly used approach to handle the stochastic problem, is also adopted here to compare the effectiveness of the NGA. Degree of hydrologic alteration and water supply reliability, are used to indicate satisfaction of environmental flow requirements and human needs. Using the Tanghe Reservoir in China as an example, the results of this study showed that the NGA can be a useful tool for ecofriendly reservoir operation under stochastic inflow conditions. Compared with the Monte Carlo method, the NGA reduces ~90% of the computational time and obtains higher water supply reliability in the optimization.

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