scholarly journals Prediction of Residual Stress of Carburized Steel Based on Machine Learning

2020 ◽  
Vol 10 (21) ◽  
pp. 7759
Author(s):  
Zhenlong Zhu ◽  
Yilong Liang
Keyword(s):  
Machine Learning ◽  
Residual Stress ◽  
Carbon Content ◽  
Lath Martensite ◽  
Semantic Segmentation ◽  
Support Vector ◽  
New Approach ◽  
Carburized Steel ◽  
Machine Learning Applications

In recent years, the number of machine learning applications (especially those involving deep learning) applied to predicting and discovering material properties has been increasing. This paper is based on using microstructure and carbon content to train machine learning models to predict the residual stress of carburized steel. First, a semantic segmentation model of the material organization structure (SegModel-MOS) was constructed based on the AlexNet network and initially trained on the PASCAL VOC2012 dataset. Then, the trained model was fine-tuned on an enhanced homemade dataset consisting of optical microstructures. The experimental results show that SegModel-MOS can distinguish acicular martensite, retained austenite, and lath martensite in microstructures. Finally, we used both support vector machine (SVM) and decision tree (DT) algorithms to establish a mapping relationship between the microstructure, carbon content, and residual stress to predict the residual stress of steel from its microstructure and carbon content. The experiments verified that the prediction model constructed in this study exhibits high accuracy and can directly predict residual stress without requiring any long-term measurements. Thus, the developed model provides a new approach to the study of residual stress in steel.

scholarly journals Long-term streamflow forecasting for the Cascade Reservoir System of Han River using SWAT with CFS output

2018 ◽  
Vol 50 (2) ◽  
pp. 655-671
Author(s):  
Tian Liu ◽  
Yuanfang Chen ◽  
Binquan Li ◽  
Yiming Hu ◽  
Hui Qiu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Assessment Tool ◽  
Flow Simulation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Distribution Models ◽  
Han River ◽  
Reservoir System

Abstract Due to the large uncertainties of long-term precipitation prediction and reservoir operation, it is difficult to forecast long-term streamflow for large basins with cascade reservoirs. In this paper, a framework coupling the original Climate Forecasting System (CFS) precipitation with the Soil and Water Assessment Tool (SWAT) was proposed to forecast the nine-month streamflow for the Cascade Reservoir System of Han River (CRSHR) including Shiquan, Ankang and Danjiangkou reservoirs. First, CFS precipitation was tested against the observation and post-processed through two machine learning algorithms, random forest and support vector regression. Results showed the correlation coefficients between the monthly areal CFS precipitation (post-processed) and observation were 0.91–0.96, confirming that CFS precipitation post-processing using machine learning was not affected by the extended forecast period. Additionally, two precipitation spatio-temporal distribution models, original CFS and similar historical observation, were adopted to disaggregate the processed monthly areal CFS precipitation to daily subbasin-scale precipitation. Based on the reservoir restoring flow, the regional SWAT was calibrated for CRSHR. The Nash–Sutcliffe efficiencies for three reservoirs flow simulation were 0.86, 0.88 and 0.84, respectively, meeting the accuracy requirement. The experimental forecast showed that for three reservoirs, long-term streamflow forecast with similar historical observed distribution was more accurate than that with original CFS.

scholarly journals Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Bidhan Lamichhane ◽  
Andy G. S. Daniel ◽  
John J. Lee ◽  
Daniel S. Marcus ◽  
Joshua S. Shimony ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glioblastoma Multiforme ◽  
Functional Connectivity ◽  
Resting State ◽  
Learning Analytics ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Term Survival ◽  
Long Term Survival

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

scholarly journals Support Vector Regression Method for Regional Economic Mid- and Long-Term Predictions Based on Wireless Network Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Lingyu Dong
Keyword(s):  
Machine Learning ◽  
Communication Network ◽  
Support Vector Regression ◽  
Wireless Network ◽  
Regression Method ◽  
Network Communication ◽  
Support Vector ◽  
Network Coverage ◽  
Data Packets

In recent years, wireless sensor network technology has continued to develop, and it has become one of the research hotspots in the information field. People have higher and higher requirements for the communication rate and network coverage of the communication network, which also makes the problems of limited wireless mobile communication network coverage and insufficient wireless resource utilization efficiency become increasingly prominent. This article is aimed at studying a support vector regression method for long-term prediction in the context of wireless network communication and applying the method to regional economy. This article uses the contrast experiment method and the space occupancy rate algorithm, combined with the vector regression algorithm of machine learning. Research on the laws of machine learning under the premise of less sample data solves the problem of the lack of a unified framework that can be referred to in machine learning with limited samples. The experimental results show that the distance between AP1 and AP2 is 0.4 m, and the distance between AP2 and Client2 is 0.6 m. When BPSK is used for OFDM modulation, 2500 MHz is used as the USRP center frequency, and 0.5 MHz is used as the USRP bandwidth; AP1 can send data packets. The length is 100 bytes, the number of sent data packets is 100, the gain of Client2 is 0-38, the receiving gain of AP2 is 0, and the receiving gain of AP1 is 19. The support vector regression method based on wireless network communication for regional economic mid- and long-term predictions was completed well.

Machine Learning Approach for Estimating Residual Stresses in Girth Welds of Topside Piping

2020 ◽  
Author(s):  
Sachin Bhardwaj ◽  
R. M. Chandima Ratnayake ◽  
Arvind Keprate ◽  
Xavier Ficquet
Keyword(s):  
Machine Learning ◽  
Residual Stress ◽  
Residual Stresses ◽  
Gradient Boosting ◽  
Support Vector ◽  
Welding Parameters ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Boosting Algorithm ◽  
Girth Welds

Abstract Residual stresses are internal self-equilibrating stresses that remain in the component even after the removal of external load. The aforementioned stress when superimposed by the operating stresses on the offshore piping, enhance the chances of fracture failure of the components. Thus, it is vital to accurately estimate the residual stresses in topside piping while performing their fitness for service (FFS) evaluation. In the present work, residual stress profiles of girth welded topside sections of P91 pipes piping are estimate using a machine learning approach. The training and testing data for machine learning is acquired from experimental measurements database by Veqter, UK. Twelve different machine learning algorithms, namely, multi-linear regression (MLR), Random Forest (RF), Gaussian process regression (GPR), support vector regression (SVR), Gradient boosting (GB) etc. have been trained and tested. In order to compare the accuracy of the algorithms, four metrics, namely, Root Mean Square Error (RMSE), Estimated Variance Score (EVS), Maximum Absolute Error (AAE), and Coefficient of Determination (R^2) are used. Gradient boosting algorithm gives the best prediction of the residual stress, which is then used to estimate the residual stress for the simulated input parameter space. In the future work authors shall utilize the residual stress predictions from Gradient boosting algorithm to train the Bayesian Network, which can then be used for estimating less conservative through-thickness residual stresses distribution over a wide range of pipe geometries (radius to thickness ratio) and welding parameters (based on heat input). Furthermore, besides topside piping, the proposed approach finds its potential applications in structural integrity assessment of offshore structures, and pressure equipment’s girth welds.

scholarly journals Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5947
Author(s):  
William Mounter ◽  
Chris Ogwumike ◽  
Huda Dawood ◽  
Nashwan Dawood
Keyword(s):  
Machine Learning ◽  
Building Energy ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Energy Usage ◽  
Research Attention ◽  
Learning Techniques ◽  
Term Energy

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building’s long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively.

scholarly journals A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

2020 ◽  
Vol 12 (9) ◽  
pp. 3612 ◽  
Author(s):  
Davut Solyali
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Historical Data ◽  
Sustainable Growth ◽  
Solar Irradiation ◽  
Support Vector ◽  
Short Term ◽  
Inference System ◽  
Electricity Load

Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.

scholarly journals Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 247
Author(s):  
Juan Camilo Henao-Rojas ◽  
María Gladis Rosero-Alpala ◽  
Carolina Ortiz-Muñoz ◽  
Carlos Enrique Velásquez-Arroyo ◽  
William Alfonso Leon-Rueda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
P Value ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Discriminating Power ◽  
Hierarchical Agglomerative Clustering ◽  
Machine Learning Applications ◽  
Germplasm Banks ◽  
Selection Of

Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization.

scholarly journals Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Minerals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 148
Author(s):  
Dahee Jung ◽  
Yosoon Choi
Keyword(s):  
Machine Learning ◽  
Mineral Exploration ◽  
Mining Industry ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Research Papers ◽  
Recent Developments ◽  
Machine Learning Applications ◽  
Exploration Exploitation ◽  
Mining Exploration

Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.

scholarly journals Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer

Polymers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1768
Author(s):  
Chunhao Yang ◽  
Wuning Ma ◽  
Jianlin Zhong ◽  
Zhendong Zhang
Keyword(s):  
Machine Learning ◽  
Support Vector ◽  
Polyurethane Elastomer ◽  
Learning Approaches ◽  
Creep Stress ◽  
Machine Learning Approach ◽  
Creep Time ◽  
Multilayer Perceptron Network ◽  
Testing Set

The long-term mechanical properties of viscoelastic polymers are among their most important aspects. In the present research, a machine learning approach was proposed for creep properties’ prediction of polyurethane elastomer considering the effect of creep time, creep temperature, creep stress and the hardness of the material. The approaches are based on multilayer perceptron network, random forest and support vector machine regression, respectively. While the genetic algorithm and k-fold cross-validation were used to tune the hyper-parameters. The results showed that the three models all proposed excellent fitting ability for the training set. Moreover, the three models had different prediction capabilities for the testing set by focusing on various changing factors. The correlation coefficient values between the predicted and experimental strains were larger than 0.913 (mostly larger than 0.998) on the testing set when choosing the reasonable model.

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