Assessment of acoustic iterative inverse method for bubble sizing to experimental data

2006 ◽  
Vol 41 (4) ◽  
pp. 195-199 ◽  
Author(s):  
Bok Kyoung Choi ◽  
Bong -Chae Kim ◽  
Byoung -Nam Kim ◽  
Suk Wang Yoon
Keyword(s):  
Experimental Data ◽  
Inverse Method ◽  
Bubble Sizing

Influence of the Transducer Acoustic Power on Focal Location of the Beam During HIFU Ablation Procedure

2012 ◽  
Author(s):  
Seyed Ahmad Reza Dibaji ◽  
Rupak K. Banerjee
Keyword(s):  
Experimental Data ◽  
Temperature Rise ◽  
Inverse Method ◽  
Focused Ultrasound ◽  
Clinical Importance ◽  
Acoustic Power ◽  
High Intensity Focused Ultrasound ◽  
Focal Distance ◽  
Initial Location ◽  
Tissue Mimicking Material

The location of High Intensity Focused Ultrasound (HIFU) beam in a tissue medium is an important parameter in assessment of the thermal field as it influences the temperature rise in the tissue. Our hypothesis is that the location of the beam can be affected by the power of the transducer. HIFU procedure with 30s of sonication time was performed at different powers of transducer (5 to 60 W) as well as different initial locations of beam in a tissue mimicking material. Eight thermocouples were embedded at 4 different layers in a phantom to measure the temperature rise during HIFU procedure. An inverse method based on experimental data and optimization algorithm was used to find the actual location of beam based on the experimental data. Our experimental data showed that for a higher power (60 W) as compared to a lower power (5 W), the focal distance that the actual position of beam moves away from its initial location increased with the raise in power. Thus, beam location can change at different powers of transducer. Using inverse method we showed that there is a direct linear correlation (R2 = 0.95) between the transducer power and the distance that beam moves away from its initial location. Therefore, it is of great clinical importance to study the effects of transducer power on the location of HIFU beam in an attempt to minimize the damage to healthy cells.

Research on Elastic Modulus of Wooden Crates for Simulation

2012 ◽  
Vol 200 ◽  
pp. 528-531
Author(s):  
Zu Ju Shu ◽  
Sheng Quan Liu ◽  
Yan Yuan
Keyword(s):  
Experimental Data ◽  
Finite Element Method ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Elastic Modulus ◽  
Structural Design ◽  
Inverse Method ◽  
Element Analysis ◽  
Structural Design Optimization ◽  
Solid Edge

The manufacturing process sheet can be output by the design of wooden crates based on Solid Edge. According to the process sheet, the sample crate was produced to obtain and process experimental data of stacking test. By applying inverse method combined with finite element method, the CAE model can be reconstructed quickly. Deflections of the wooden crate were computed by finite element analysis. By comparing deflections to experimental data, elastic modulus of the wooden crate can be adjusted to meet the requirements. The research on elastic modulus will lay the foundation for determining CAE model and structural design optimization of wooden crates.

Microdiffraction Patterns of Small Metallic Particles II; Theoretical Analysis

1982 ◽  
Vol 40 ◽  
pp. 668-669
Author(s):  
A. Gómez ◽  
P. Schabes-Retchkiman ◽  
M. José-Yacamán ◽  
T. Ocaña
Keyword(s):  
Experimental Data ◽  
Electron Diffraction ◽  
Theoretical Analysis ◽  
Size Range ◽  
Dynamical Theory ◽  
Metallic Particles ◽  
Splitting Effect

The splitting effect that is observed in microdiffraction pat-terns of small metallic particles in the size range 50-500 Å can be understood using the dynamical theory of electron diffraction for the case of a crystal containing a finite wedge. For the experimental data we refer to part I of this work in these proceedings.

Evidence for ordered Fe3Ni

1987 ◽  
Vol 45 ◽  
pp. 216-217
Author(s):  
K.B. Reuter ◽  
D.B. Williams ◽  
J.I. Goldstein
Keyword(s):  
Experimental Data ◽  
Martensitic Transformation ◽  
Electron Diffraction ◽  
Room Temperature ◽  
Direct Evidence ◽  
Transformation Product ◽  
Iron Meteorites ◽  
X Ray ◽  
Ni Content ◽  
Temperature Transformation

In the Fe-Ni system, although ordered FeNi and ordered Ni3Fe are experimentally well established, direct evidence for ordered Fe3Ni is unconvincing. Little experimental data for Fe3Ni exists because diffusion is sluggish at temperatures below 400°C and because alloys containing less than 29 wt% Ni undergo a martensitic transformation at room temperature. Fe-Ni phases in iron meteorites were examined in this study because iron meteorites have cooled at slow rates of about 10°C/106 years, allowing phase transformations below 400°C to occur. One low temperature transformation product, called clear taenite 2 (CT2), was of particular interest because it contains less than 30 wtZ Ni and is not martensitic. Because CT2 is only a few microns in size, the structure and Ni content were determined through electron diffraction and x-ray microanalysis. A Philips EM400T operated at 120 kV, equipped with a Tracor Northern 2000 multichannel analyzer, was used.

EELS white line intensities calculated for the 3d and 4d metals

1989 ◽  
Vol 47 ◽  
pp. 388-389
Author(s):  
C. C. Ahn ◽  
D. H. Pearson ◽  
P. Rez ◽  
B. Fultz
Keyword(s):  
Experimental Data ◽  
Line Intensity ◽  
Transition Probabilities ◽  
Initial Increase ◽  
White Line ◽  
Hartree Fock ◽  
Line Intensities ◽  
Integrated Intensity ◽  
X Ray Absorption ◽  
The Continuum

Previous experimental measurements of the total white line intensities from L2,3 energy loss spectra of 3d transition metals reported a linear dependence of the white line intensity on 3d occupancy. These results are inconsistent, however, with behavior inferred from relativistic one electron Dirac-Fock calculations, which show an initial increase followed by a decrease of total white line intensity across the 3d series. This inconsistency with experimental data is especially puzzling in light of work by Thole, et al., which successfully calculates x-ray absorption spectra of the lanthanide M4,5 white lines by employing a less rigorous Hartree-Fock calculation with relativistic corrections based on the work of Cowan. When restricted to transitions allowed by dipole selection rules, the calculated spectra of the lanthanide M4,5 white lines show a decreasing intensity as a function of Z that was consistent with the available experimental data.Here we report the results of Dirac-Fock calculations of the L2,3 white lines of the 3d and 4d elements, and compare the results to the experimental work of Pearson et al. In a previous study, similar calculations helped to account for the non-statistical behavior of L3/L2 ratios of the 3d metals. We assumed that all metals had a single 4s electron. Because these calculations provide absolute transition probabilities, to compare the calculated white line intensities to the experimental data, we normalized the calculated intensities to the intensity of the continuum above the L3 edges. The continuum intensity was obtained by Hartree-Slater calculations, and the normalization factor for the white line intensities was the integrated intensity in an energy window of fixed width and position above the L3 edge of each element.

A general inverse method for modelling extensional sedimentary basins

2000 ◽  
Vol 12 (3-4) ◽  
pp. 219-226 ◽  
Author(s):  
P. Bellingham ◽  
N. White
Keyword(s):  
Inverse Method ◽  
Sedimentary Basins

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Use of Posterior Probabilities to Evaluate Methods of Discriminant Analysis

1981 ◽  
Vol 20 (04) ◽  
pp. 207-212 ◽  
Author(s):  
J. Hermans ◽  
B. van Zomeren ◽  
J. W. Raatgever ◽  
P. J. Sterk ◽  
J. D. F. Habbema
Keyword(s):  
Experimental Data ◽  
Discriminant Analysis ◽  
Posterior Probabilities

By means of a case study the choice between several methods of discriminant analysis is presented. Experimental data of a two-groups problem with one or two variables is analysed. The different methods are compared according to posterior probabilities which can be computed for each subject and which are the basis of discriminant analysis. These posterior probabilities are analysed graphically as well as numerically.

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

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