scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

Fault Diagnosis of Rolling Bearings Using Data Mining Techniques and Boosting

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Muhammet Unal ◽  
Yusuf Sahin ◽  
Mustafa Onat ◽  
Mustafa Demetgul ◽  
Haluk Kucuk
Keyword(s):  
Fault Diagnosis ◽  
High Performance ◽  
Acoustic Analysis ◽  
Real Life ◽  
Roc Curves ◽  
Support Vector ◽  
Loading Conditions ◽  
Rolling Bearings ◽  
Novel Approach ◽  
Classification Tasks

Rolling bearings are key components in most mechanical facilities; hence, the diagnosis of their faults is very important in predictive maintenance. Up to date, vibration analysis has been widely used for fault diagnosis in practice. However, acoustic analysis is still a novel approach. In this study, acoustic analysis with classification is used for fault diagnosis of rolling bearings. First, Hilbert transform (HT) and power spectral density (PSD) are used to extract features from the original sound signal. Then, decision tree algorithm C5.0, support vector machines (SVMs) and the ensemble method boosting are used to build models to classify the instances for three different classification tasks. Performances of the classifiers are compared w.r.t. accuracy and receiver operating characteristic (ROC) curves. Although C5.0 and SVM show comparable performances, C5.0 with boosting classifier indicates the highest performance and perfectly discriminates normal instances from the faulty ones in each task. The defect sizes to create faults used in this study are notably small compared to previous studies. Moreover, fault diagnosis is done for rolling bearings operating at different loading conditions and speeds. Furthermore, one of the classification tasks incorporates diagnosis of five states including four different faults. Thus, these models, due to their high performance in classifying multiple defect scenarios having different loading conditions and speeds, can be readily implemented and applied to real-life situations to detect and classify even incipient faults of rolling bearings of any rotating machinery.

scholarly journals Evolving artificial intelligence techniques to model the hydrate-based desalination process of produced water

2019 ◽  
Vol 9 (4) ◽  
pp. 372-384
Author(s):  
Maryam Sadi ◽  
Hajar Fakharian ◽  
Hamid Ganji ◽  
Majid Kakavand
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Treatment Process ◽  
Produced Water ◽  
Model Development ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Inference System ◽  
Average Absolute Relative Error ◽  
Svm Model

Abstract In this study, two artificial intelligence models based on an adaptive neuro-fuzzy inference system (ANFIS) and a support vector machine (SVM) technique have been successfully developed to predict the desalination efficiency of produced water through a hydrate-based desalination treatment process. A genetic algorithm as an evolutionary optimization method has been used to determine the optimal values of SVM model coefficients. To this end, compressed natural gas and CO2 hydrate formation experiments were carried out, and the desalination efficiency of produced water was measured and utilized for model training and validation. After model development, graphical and statistical analysis approaches have been applied to evaluate the performance of suggested models by a comparison of model predictions with measured experimental data. For the ANFIS model, the coefficient of determination (R2) and average absolute relative error (AARE) are 0.9927 and 0.58%, respectively. The values of AARE and R2 for the SVM model are obtained 0.35% and 0.9985, respectively. These statistical criteria confirm excellent accuracy and robustness of intelligent models in predicting the desalination efficiency of produced water through the hydrate-based desalination treatment process. Furthermore, the Leverage statistical technique has been carried out to define the outliers. The obtained results demonstrate that all experimental data are reliable and both ANFIS and SVM models are statistically valid.

scholarly journals Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization

Materials ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 3773
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Tool Wear ◽  
Alloy Steel ◽  
Bayesian Optimization ◽  
Research Trend ◽  
Support Vector ◽  
Machining Parameters ◽  
Svm Model ◽  
Data Points

Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.

scholarly journals Surrogate Models for Performance Prediction of Axial Compressors Using through-Flow Approach

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 169
Author(s):  
Xiaoxiong Wu ◽  
Bo Liu ◽  
Nathan Ricks ◽  
Ghader Ghorbaniasl
Keyword(s):  
Experimental Data ◽  
Total Pressure ◽  
Surrogate Models ◽  
Empirical Models ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Flow Angle ◽  
Flow Calculation ◽  
Transonic Compressor ◽  
Through Flow

Two-dimensional design and analysis issues on the meridional surface, which is important in the preliminary design procedure of compressors, are highly dependent on the accuracy of empirical models, such as the prediction of total pressure loss model and turning flow angle. Most of the widely used models are derived or improved from experimental data of some specific cascades with low-loading and low-speed airfoil types. These models may work for most conventional compressors but are incapable of accurately estimating the performance for some specific cases like transonic compressors. The errors made by these models may mislead the final design results. Therefore, surrogate models are developed in this work to reduce the errors and replace the conventional empirical models in the through-flow calculation procedure. A group of experimental data considering a two-stage transonic compressor is used to generate the airfoils database for training the surrogate models. Sensitivity analysis is applied to select the most influential features. Two supervised learning approaches including support vector regression (SVR) and Gaussian process regression (GPR) are used to train the models with a Bayesian optimization algorithm to obtain the optimal hyper parameters. The trained models are integrated into the through-flow code based on streamline curvature method (SLC) to predict the overall performance and internal flow field of the transonic compressor on five rotational speed lines for validation. The predictions are compared with the experimental data and the results of conventional empirical models. The comparison shows that SVR and GPR respectively reduce the predicted error of empirical models by 62.2% and 55.2% for the total pressure ratio and 48.4% and 50.1% for adiabatic efficiency on average. This suggests that the surrogate models constitute an alternative way to predict the performance of airfoils in through-flow calculation where empirical models are inefficient.

Placental Transfer of Vitamin K1 and Its Implications in Fetal Hemostasis

1988 ◽  
Vol 60 (01) ◽  
pp. 039-043 ◽  
Author(s):  
L Mandelbrot ◽  
M Guillaumont ◽  
M Leclercq ◽  
J J Lefrère ◽  
D Gozin ◽  
...  
Keyword(s):  
Vitamin K ◽  
High Performance ◽  
Ultrasound Guidance ◽  
Placental Transfer ◽  
Vitamin K1 ◽  
Vitamin K Deficiency ◽  
Prothrombin Activity ◽  
Oral Supplementation ◽  
K Deficiency ◽  
The Mean

SummaryVitamin K status was evaluated using coagulation studies and/ or vitamin IQ assays in a total of 53 normal fetuses and 47 neonates. Second trimester fetal blood samples were obtained for prenatal diagnosis under ultrasound guidance. Endogenous vitamin K1 concentrations (determined by high performance liquid chromatography) were substantially lower than maternal levels. The mean maternal-fetal gradient was 14-fold at mid trimester and 18-fold at birth. Despite low vitamin K levels, descarboxy prothrombin, detected by a staphylocoagulase assay, was elevated in only a single fetus and a single neonate.After maternal oral supplementation with vitamin K1, cord vitamin K1 levels were boosted 30-fold at mid trimester and 60 fold at term, demonstrating placental transfer. However, these levels were substantially lower than corresponding supplemented maternal levels. Despite elevated vitamin K1 concentrations, supplemented fetuses and neonates showed no increase in total or coagulant prothrombin activity. These results suggest that the low prothrombin levels found during intrauterine life are not due to vitamin K deficiency.

Theoretical Interpretation of High-Performance Chromatographic Data for a Heterogeneous Series of Compounds

1992 ◽  
Vol 57 (1) ◽  
pp. 33-45
Author(s):  
Vladimír Jakuš
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Theoretical Interpretation ◽  
Retention Data ◽  
Theoretical Evaluation ◽  
New Approach ◽  
Free Energy Of Solvation ◽  
Rp Hplc ◽  
Mobile Phases ◽  
Reversed Phases

A new approach to theoretical evaluation of the Gibbs free energy of solvation was applied for estimation of retention data in high-performance liquid chromatography on reversed phases (RP-HPLC). Simple and improved models of stationary and mobile phases in RP-HPLC were employed. Statistically significant correlations between the calculated and experimental data were obtained for a heterogeneous series of twelve compounds.

One-year real-life results on effect of intravitreal aflibercept in patients with diabetic macular oedema switched from ranibizumab

2020 ◽  
pp. 112067212092727
Author(s):  
Marko Lukic ◽  
Gwyn Williams ◽  
Zaid Shalchi ◽  
Praveen J Patel ◽  
Philip G Hykin ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Diabetic Retinopathy ◽  
Early Treatment ◽  
Macular Oedema ◽  
Real Life ◽  
Diabetic Macular Oedema ◽  
Foveal Thickness ◽  
Macular Volume ◽  
Central Foveal Thickness ◽  
The Mean

Purpose To assess visual and optical coherence tomography–derived anatomical outcomes of treatment with intravitreal aflibercept (Eylea®) for diabetic macular oedema in patients switched from intravitreal ranibizumab (Lucentis®). Design Retrospective, cohort study. Participants Ninety eyes (of 67 patients) receiving intravitreal anti–vascular endothelial growth factor therapy were included. Methods This is a retrospective, real-life, cohort study. Each patient had visual acuity measurements and optical coherence tomography scans performed at baseline and 12 months after the first injection of aflibercept was given. Main Outcome Measures We measured visual acuities in Early Treatment Diabetic Retinopathy Study letters, central foveal thickness and macular volume at baseline and at 12 months after the first aflibercept injection was given. Results Ninety switched eyes were included in this study. The mean (standard deviation) visual acuity was 63 (15.78) Early Treatment Diabetic Retinopathy Study letters. At baseline, the mean (standard deviation) central foveal thickness was 417.7 (158.4) μm and the mean macular volume was 9.96 (2.44) mm3. Mean change in visual acuity was +4 Early Treatment Diabetic Retinopathy Study letters (p = 0.0053). The mean change in macular volume was −1.53 mm 3 in SW group (p = 0.21), while the change in central foveal thickness was −136.8 μm (p = 0.69). Conclusion There was a significant improvement in visual acuity and in anatomical outcomes in the switched group at 12 months after commencing treatment with aflibercept for diabetic macular oedema.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

Sign in / Sign up

Export Citation Format

Share Document