Simulation method of magneto-acousto-electrical tomography for improving computational efficiency

2021 ◽  
Vol 130 (14) ◽  
pp. 145105
Author(s):  
Cailian Li ◽  
Sanxi Wu ◽  
Shuaiyu Bu ◽  
Yuanyuan Li ◽  
Guoqiang Liu
Keyword(s):  
Computational Efficiency ◽  
Simulation Method ◽  
Electrical Tomography

Novel Intelligent Pigging Tool For Deposit Inspection Using Electrical Tomography with High Computational Efficiency Enabled Through Neural Networks

2021 ◽  
Author(s):  
Antti Nissinen ◽  
Ossi Lehtikangas ◽  
Arto Voutilainen ◽  
Pasi Laakkonen ◽  
Anssi Lehikoinen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Electrical Properties ◽  
Computational Efficiency ◽  
Deep Neural Network ◽  
Computation Time ◽  
Electrical Tomography ◽  
Deposit Thickness ◽  
The Neural Network ◽  
Pipe Line

Objectives/Scope Deposition inspection sensor based on electrical tomography has been proposed recently. In this work, a next generation electrical tomography sensor is introduced and a novel mathematical approach for the estimation of the deposit thickness is described. It is essential for the pipeline operators to keep the lines open for smooth flow and high flow efficiency. Deposit thickness, deposit type and location of deposit is required for optimal pipeline cleaning. The usage of chemicals as well as number of cleaning pig runs can be optimized based on the information that intelligent pig is giving. Methods, Procedures, Process In electrical tomography, electrodes are attached on the surface of the sensor and excitations are applied to some electrodes and responses are measured from other electrodes. The electrical properties of the medium are estimated based on these measurements. In pigging applications, the distribution of electrical properties between the PIG surface and metal pipe is estimated. The thickness and type of deposit (wax/scale) can be identified from the estimated electrical properties. In the proposed approach the estimation of the parameters is done by using a novel deep neural network based approach. In practice, number of measurements that are analyzed after each PIG run can be hundreds of thousands. The neural network based approach was chosen in order to achieve reasonable computational efficiency (computation time) in real applications with large amounts of data. Results, Observations, Conclusions The introduced sensor is for 12-inch lines and designed to be used when the oil line is filled with water. This sensor was tested in a laboratory test line with artificial deposit samples. After these tests and calibration, the sensor is deployed to be used in real pipe line inspections. The major challenges in pipe line runs include the movement of the sensor during measurements, electrical noise and changing excitations. In the neural network model, the position of the PIG is estimated simultaneously with the electrical properties and the effect of all these aforementioned uncertainties are also modelled. Based on the results conclusions can be drawn on the efficiency and performance using neural networks and the high suitability of electrical tomography for deposit mapping. Novel/Additive Information In this study, it is shown that intelligent pig based on the electrical tomography can be used reliable for deposit inspection. Furthermore, the computation approach based on the deep neural network is computationally efficient and it is tolerable for measurement noise and other uncertainties in real measurements.

Application of Gauss principle of least constraint in multibody systems with redundant constraints

2020 ◽  
pp. 146441932097530
Author(s):  
Liusong Yang ◽  
Shifeng Xue ◽  
Wenli Yao
Keyword(s):  
Computational Efficiency ◽  
Multibody Systems ◽  
Mechanical Energy ◽  
Dynamic Modelling ◽  
Simulation Method ◽  
Optimization Method ◽  
Constrained Mechanical Systems ◽  
Redundant Constraints ◽  
Time Simulation ◽  
Gauss Principle

Redundancy in the constrained mechanical systems often occurs in complex multibody mechanic systems in the existence of excessive constraints and singular positions due to system motion. In this work, Gauss principle of least constraint (GPLC) is applied to solve the dynamic motion of system with redundant constraints without changing the physics of system. Furthermore, the particle swarm optimization method is used to handle the minimization optimization problem. Eventually, the effectiveness of GPLC is validated through the dynamic modelling and simulation of two numerical examples (a planar four-bar mechanism and a spatial parallelogram mechanism). The simulation results are analyzed and compared with those obtained from Udwaia-Phohomsiri formulation and augmented Lagrangian formulation, in terms of constraint violation, computational efficiency and variation of the mechanical energy. From the viewpoint of computational efficiency and accuracy, GPLC can be regarded as a practical real-time simulation method for multibody systems with redundant constraints.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

New Simplified Low Cost Simulation Method for SME Manufacturing System

2015 ◽  
Vol 9 (2) ◽  
pp. 206
Author(s):  
Tawfik Benabdallah ◽  
Nor Nait Sadi ◽  
Mustapha Kamel Abdi
Keyword(s):  
Manufacturing System ◽  
Low Cost ◽  
Simulation Method ◽  
Cost Simulation

Color processing and management in Ghostscript

2019 ◽  
Vol 2019 (1) ◽  
pp. 62-68
Author(s):  
Michael J. Vrhel ◽  
Artifex Software
Keyword(s):  
Open Source ◽  
Computational Efficiency ◽  
Color Processing ◽  
Description Languages

Ghostscript has a long history in the open source community and was developed at the same time that page description languages were evolving to the complex specification of PDF today. Color is a key component in this specification and its description and proper implementation is as complex as any other part of the specification. In this document, the color processing and management that takes place in Ghostscript is reviewed with a focus on how its design achieves computational efficiency while providing flexibility for the developer and user.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

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