Inverse thermal modeling and experimental validation for breast tumor detection by using highly personalized surface thermal patterns and geometry of the breast

2020 ◽  
pp. 095440622097059
Author(s):  
O Mukhmetov ◽  
A Mashekova ◽  
Y Zhao ◽  
EYK Ng ◽  
A Midlenko ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
Response Surface ◽  
Inverse Modeling ◽  
Breast Tumor ◽  
Optimization Method ◽  
Efficient Design ◽  
Ir Thermography ◽  
Direct Optimization ◽  
Response Surface Optimization ◽  
Thermal Pattern

Infrared (IR) Thermography is currently a supplementary technique for breast cancer diagnosis. There have been studies using IR thermography and numerical modeling in an attempt to detect tumor inside the breast. Most of these studies focused on either the “forward modeling” problem or only used idealized or population-averaged patients’ data, whereas identification of the tumor inside the breast based on the thermal pattern is an “inverse modeling” problem dependent on personalized information of the patient. Inverse modeling is based on the idea that the surface thermal pattern of the breast can be used to determine the tumor features based on physical and physiological principles. The current study aims to develop a well-validated inverse thermal modeling framework that could be used to determine the depth and size of tumor inside a breast based on personalized patients’ breast data, such as thermogram and 3D geometry using efficient design optimization techniques and Finite Element Modeling (FEM) to support the process. The numerical modeling was validated by the experiments, conducted using artificial breasts. Results show that although DIRECT Optimization method can be employed to find the depth and size of the tumor with good accuracy, the technique can be very time consuming. On the other hand, Response Surface Optimization method is also able to find the depth and size of the tumor with less accuracy but faster when compared with DIRECT Optimization. The last method tested, Nelder-Mead method, failed to detect the tumor. The study concludes that Response Surface Optimization method should be used first, and after the range of parameters are found, the DIRECT optimization method can be applied for more accurate results. However the GA method was found to be the only viable and efficient design optimization method for reverse modeling when blood perfusion was adopted in the breast model and many parameters were searched for with patient specific data input for breast tumor diagnosis.

Performance analysis of wave rotor based on response surface optimization method

2021 ◽  
pp. 1-18
Author(s):  
Peiqi Liu ◽  
Mingyu Feng ◽  
Xinyu Liu ◽  
Haitao Wang ◽  
Dapeng Hu
Keyword(s):  
Entropy Production ◽  
Response Surface ◽  
Relative Velocity ◽  
Pressure Ratio ◽  
Pressure Fluctuations ◽  
Optimization Method ◽  
Wave Rotor ◽  
Compression Waves ◽  
Isentropic Expansion ◽  
Response Surface Optimization

Abstract An optimized wave rotor refrigerator (WRR) that can convert part of the expansion work into shaft work to improve the refrigeration performance is obtained by optimization method. Bézier curve is used to establish a two-dimensional simplified model, and response surface method and NLPQL optimization algorithm are used to search for the optimal wave rotor structure. The results show that the optimized wave rotor shape is rear back bending. Compared with original rotor, the isentropic expansion efficiency of the optimized rotor is higher under different pressure ratios and relative velocity, and changes more gently under different pressure ratios. Moreover, the expansion power of the optimized rotor is mainly converted into shaft powder, while the pressure energy and thermal energy increase at the hot end is relatively small. The pressure fluctuations on the inlet and outlet sides of the optimized rotor are smoother, and the compression waves that are constantly reflected during the low-temperature exhaust stage have a smaller intensity, which helps to improve the performance of WRR. The optimized rotor can significantly reduce the entropy production in the refrigeration process, especially the entropy production by velocity gradients. When the pressure ratio is 2.0 and relative velocity is 23 m/s, the isentropic expansion efficiency increases from 56.8% of the original rotor to 62.08% of the optimized rotor.

scholarly journals Research on optimization of perforation parameters for formation fractures based on response surface optimization method

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255793
Author(s):  
Wei Liu ◽  
Suling Wang ◽  
Kangxing Dong ◽  
Tiancai Cheng
Keyword(s):  
Hydraulic Fracturing ◽  
Response Surface ◽  
Optimization Method ◽  
Regression Equation ◽  
Global Response ◽  
Fracture Length ◽  
Response Surface Optimization ◽  
Length Difference ◽  
Deviation Rate ◽  
Surface Optimization

For staged multi-cluster fracturing, methods for controlling perforation friction to adjust the flow distribution of each cluster can effectively promote the uniform extension of multiple fractures but lacks a fast and quantitative optimization method for different perforation parameters of each cluster. By establishing a numerical model of single-stage three-cluster flow-limited fracturing under stress-seepage coupling, and based on the response surface optimization method, fully considering the impact of perforation parameters interaction among three perforation clusters, according to the regression equation fitted under the global response, the rapid optimization of perforation parameters of segmented multi-cluster fracturing model is realized. The results show that: in determining the three factors of the study, it is found that there is an obvious interaction between the number of intermediate cluster perforations and the number of cluster perforations on both sides, the number of cluster perforations on both sides and the diameter of intermediate cluster perforations, the response surface optimization method gives the optimal perforation parameter combination of three clusters of fractures under global response; When the perforation parameters were combined before optimization, the fracture length difference was 32.550m, and the intermediate perforation cluster evolved into invalid perforation cluster, when the perforation parameters were combined after optimization, the fracture length difference was 0.528m, the three perforation clusters spread uniformly, and there are no invalid clusters. At the same time, the regression equation under the response is optimized before and after the comparison between the predicted value of the equation and the actual simulation value. It is found that the estimated deviation rate of the equation before optimization is 1.2%, and the estimated deviation rate after optimization is 0.4%. The estimated deviation rates are all less, and the response regression equation based on the response surface optimization method can quickly optimize the perforation parameters. The response surface optimization method is suitable for the multi parameter optimization research of formation fracturing which is often affected by many geological and engineering factors. Combining with the engineering practice and integrating more factors to optimize the hydraulic fracturing parameters, it is of great significance to improve the success rate of hydraulic fracturing application.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

scholarly journals Combining Response Surface Optimization and Stochastic Analysis for Crashworthiness Design – an Introductory Study

2001 ◽  
Vol 8 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Lars A. Fredriksson ◽  
Uwe Schramm
Keyword(s):  
Response Surface ◽  
Stochastic Analysis ◽  
Cost Effective ◽  
Initial Study ◽  
Dynamic Responses ◽  
Stochastic Methods ◽  
Explicit Finite Element ◽  
Transient Dynamic ◽  
Response Surface Optimization ◽  
Safety Margins

Objective of the design process are cost effective designs that meet certain expectations with respect to functionality and appearance. Designs are created in an iterative process where analyses of the structural behavior lead to changes in the design. The use of optimization technology makes design changes to be driven directly by analysis results. The application of optimization allows an efficient search for the right combination of design variables for a certain design. Additional use of stochastic methods in order to analyze the design from a statistical standpoint adds robustness to the design and prevents unpleasant surprises in later physical testing.This paper discusses methodology to optimize structures that undergo impact loading. Objective and constraints are transient dynamic responses. The optimization problem is solved using a sequential response surface method. An explicit finite element code is used to solve the transient dynamic problem. The optimization is not performed on results from single simulations but on statistical results from a stochastic analysis. The stochastic analysis is driven using a Monte Carlo method. Commercial software is used for the implementation of the methodology.The results from the study indicate that a combination of optimization and stochastic analysis can add safety margins to a design with respect to robustness against physical errors in the design itself and against changes in load levels and load cases. However, this initial study must be followed up by more in-depth investigations to fully understand the benefits of combined optimization-stochastic analysis.

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