Support Vector Machine Regression Model Research Articles

Predicting Factor of Safety of Slope Using an Improved Support Vector Machine Regression Model

Predicting Factor of Safety of Slope Using an Improved Support Vector Machine Regression Model

Predicting response to immunotherapy in oral squamous cell carcinoma via a CT-based radiomics model

BackgroundTo investigate whether radiomics models derived from pretreatment CT could help to predict response to immunotherapy in oral squamous cell carcinoma (OSCC).MethodsRetrospectively, a total of 40 patients with measurable OSCC were included. The patients were divided into responder group and non-responder group according to the comparison of pre-treatment and post-treatment CT findings. Radiomics features were extracted from pre-treatment CT images, and optimal features were selected by univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis. Neural network, support vector machine, random forest and logistic regression models were used to predict response to immunotherapy in OSCC, and leave-one-out cross validation was employed to assess the performance of the classifiers. The area under the curve (AUC), accuracy, sensitivity and specificity were calculated to quantify the predictive efficacy.ResultsA total of 7 features were selected to build models upon machine learning methods. By comparing different machine learning based models, the neural network model achieved the best predictive ability, with an AUC of 0.864, an accuracy of 82.5%, a sensitivity of 82.5%, and a specificity of 82.5%.ConclusionsThe pretreatment CT-based radiomics model showed good performance in predicting response to immunotherapy in OSCC. Pretreatment CT-based radiomics model might provide an alternative approach for the selection of patients who benefit from immunotherapy.

Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models.

This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. Fifteen (male/female, 10/5; age, 41.60±10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient ( ). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient ( ). The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.

Suppression of cross-interference in the absorption spectra of gas mixtures

In the quantitative analysis of mixed gases by tunable diode laser absorption spectroscopy, the overlapping of absorption spectra and mutual interference of multi-component gases can lead to problems of large measurement errors and low analysis accuracy. In this paper, an improved firefly algorithm is proposed and applied to the support vector machine regression model to solve this problem. The specific method includes introducing an adaptive step size to balance the local and global searches and using the gradient descent method to accelerate the parameter optimization process so as to improve the model’s generalization ability and prediction accuracy. The experimental results show that the maximum errors of the improved algorithm in the prediction of CH4 and CO gas concentrations are no more than 0.0443 % and 2 ppm, with coefficients of determination, R2, of 0.9994 and 0.99815. The promising results obtained by the system provide theoretical support for the realization of high-precision detection of multicomponent gases with a single source of light, and also demonstrate the high efficiency and feasibility of the method in practical detection.

Leveraging MRI radiomics signature for predicting the diagnosis of CXCL9 in breast cancer

ObjectiveA non-invasive predictive model was developed using radiomic features to forecast CXCL9 expression level in breast cancer patients. MethodsCXCL9 expression data and MRI images of breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases, respectively. Local tissue samples from 20 breast cancer patients were collected to measure CXCL9 expression levels. Radiomic features were extracted from MRI images using 3DSlicer, and the minimum Redundancy Maximum Relevance and Recursive Feature Elimination (mRMR_RFE) method was employed to select the most pertinent radiomic features associated with CXCL9 expression levels. Support vector machine (SVM) and Logistic Regression (LR) models were utilized to construct the predictive model, and the area under the receiver operating characteristic curve (AUC) was calculated for performance evaluation. ResultsCXCL9 was found to be upregulated in breast cancer patients and linked to breast cancer prognosis. Nine radiomic features were ultimately selected using the mRMR_RFE method, and SVM and LR models were trained and validated. The SVM model achieved AUC values of 0.748 and 0.711 in the training and validation sets, respectively. The LR model obtained AUC values of 0.771 and 0.724 in the training and validation sets, respectively. ConclusionThe utilization of MRI radiomic features for predicting CXCL9 expression level provides a novel non-invasive approach for breast cancer Prognostic research.

Production monitoring and quality characterization of black garlic using Vis-NIR hyperspectral imaging integrated with chemometrics strategies

As a new deep-processing garlic product with notable health benefits, the accurate discrimination of processing stages and prediction of key physicochemical constituents in black garlic are vital for maintaining product quality. This study proposed a novel method utilizing hyperspectral imaging technology to both rapidly monitor the processing stages and quantitatively predict changes in the key physicochemical constituents during black garlic processing. Multiple methods of noise reduction and feature screening were used to process the acquired hyperspectral information. To differentiate processing stages, pattern recognition methods including linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine classification (SVC) analysis were utilized, achieving a discriminant accuracy of up to 98.46 %. Furthermore, partial least squares regression (PLSR) and support vector machine regression (SVR) analysis were performed to achieve quantitative prediction of the key physicochemical constituents including moisture and 5-HMF. PLSR models outperformed SVR models, with correlation coefficient of prediction of 0.9762 and 0.9744 for moisture and 5-HMF content, respectively. The current study can not only offer an effective approach for quality detection and assessment during black garlic processing, but also have a positive significance for the advancement of black garlic related industries.

Susceptibility assessment of multi-hazards using random forest—back propagation neural network coupling model: a Hangzhou city case study

As the demand for regional geological disaster risk assessments in large cities continues to rise, our study selected Hangzhou, one of China’s megacities, as a model to evaluate the susceptibility to two major geological hazards in the region: ground collapse and ground subsidence. Given that susceptibility assessments for such disasters mainly rely on knowledge-driven models, and data-driven models have significant potential for application, we proposed a high-accuracy Random Forest—Back Propagation Neural Network Coupling Model. By using nine evaluation factors selected based on field surveys and expert recommendations, along with disaster data, the model's predictive results indicate a 3–40% improvement in model performance metrics such as AUC, accuracy, precision, recall, and F1-score, compared to single models and traditional SVM and logistic regression models. Ultimately, using the predictive results of this model, we created susceptibility maps for individual disasters and developed a muti-hazards susceptibility map by employing the expert weight discrimination method and the overlay evaluation method. Furthermore, we discussed the feature importance in the prediction process. Our study validated the feasibility of using advanced machine learning models for urban geological disaster assessment, providing a replicable template for other cities.

Construction and evaluation of a liver cancer risk prediction model based on machine learning.

Liver cancer is one of the most prevalent malignant tumors worldwide, and its early detection and treatment are crucial for enhancing patient survival rates and quality of life. However, the early symptoms of liver cancer are often not obvious, resulting in a late-stage diagnosis in many patients, which significantly reduces the effectiveness of treatment. Developing a highly targeted, widely applicable, and practical risk prediction model for liver cancer is crucial for enhancing the early diagnosis and long-term survival rates among affected individuals. To develop a liver cancer risk prediction model by employing machine learning techniques, and subsequently assess its performance. In this study, a total of 550 patients were enrolled, with 190 hepatocellular carcinoma (HCC) and 195 cirrhosis patients serving as the training cohort, and 83 HCC and 82 cirrhosis patients forming the validation cohort. Logistic regression (LR), support vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) regression models were developed in the training cohort. Model performance was assessed in the validation cohort. Additionally, this study conducted a comparative evaluation of the diagnostic efficacy between the ASAP model and the model developed in this study using receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) to determine the optimal predictive model for assessing liver cancer risk. Six variables including age, white blood cell, red blood cell, platelet counts, alpha-fetoprotein and protein induced by vitamin K absence or antagonist II levels were used to develop LR, SVM, RF, and LASSO regression models. The RF model exhibited superior discrimination, and the area under curve of the training and validation sets was 0.969 and 0.858, respectively. These values significantly surpassed those of the LR (0.850 and 0.827), SVM (0.860 and 0.803), LASSO regression (0.845 and 0.831), and ASAP (0.866 and 0.813) models. Furthermore, calibration and DCA indicated that the RF model exhibited robust calibration and clinical validity. The RF model demonstrated excellent prediction capabilities for HCC and can facilitate early diagnosis of HCC in clinical practice.

Regression prediction model for shear strength of cold joint in concrete

The shear resistance of cold joint in concrete is influenced by various design parameters. Traditional mechanical models typically consider only a limited number of parameters to predict the shear strength of cold joints. This research aims to explore the complex nonlinear relationship between design parameters and cold joint shear strength using statistical method and machine learning technology. The goal is to develop a more accurate, reliable, and engineering applicable regression model for predicting shear strength. A dataset of 546 Z-shaped shear specimens characterizing cold joint in concrete was constructed, involving a total of 16 variables that may affect shear performance. Correlation analysis and recursive elimination were adopted to eliminate correlated and insignificant variables based on their importance. Multiple linear regression (MLR), random forest regression (RFR), and support vector machine regression (SVR) prediction models for cold joint shear strength were established based on rigorously screened variables and comprehensively evaluated using multiple methods. It was found that the most significant factors influencing the shear strength of cold joints are concrete strength, interface shear key, product of interface reinforcement strength and its reinforcement ratio, normal stress, fiber length of new concrete, casting method of the new concrete, and product of the fiber length and its tensile strength of old concrete. The MLR, SVR, and RFR models all exhibited superior performance relative to traditional mechanics-based models with regard to shear strength prediction of cold joints. The RFR model is recommended for predicting the shear strength of cold joints due to its superior evaluation indexes in comparison to the MLR and SVR models, and variable sensitivity analysis shows that it does not yield common-sense errors.

Spatial and temporal dynamics of livestock grazing intensity in the Selinco region: Towards sustainable grassland management

Grazing pressure in the Selinco region exhibits significant complexity and variability. To optimize sustainable grassland management in the region, understanding the spatial and temporal dynamics of grazing intensity is crucial. Previous studies have shown the substantial potential of machine learning algorithms in accurately modeling grazing intensity. Consequently, this study predicted grazing intensity for the period 2001–2020 on a 500 × 500 m grid, using variables such as climate, topography, vegetation, anthropogenic disturbances, water sources, and roads as inputs to the machine learning model, with livestock density at the township level in suitable grazing areas as the output variable. Subsequently, the spatial and temporal dynamics of grazing intensity from 2001 to 2020 were synthesized and analyzed. Of these, the Support Vector Machine Regression (SVMR) model exhibited the best fit, with a mean absolute error (MAE) of just 0.068 SU/km2 and a coefficient of determination (R2) of 0.98. The results indicate that grazing intensity shows a spatial pattern of being high in the southeast and low in the northwest, with significant regional differences. The average annual trend declined by −0.006/10a, indicating a continuous downward trend; however, a substantial increase is anticipated in the future, as suggested by a mean Hurst index value of 0.39. Additionally, the grazing pattern in the northern region, including the Qiangtang Nature Reserve, remains relatively stable. However, it is worth noting that the southeastern region exhibited a significant exacerbation of grazing patterns during 2015–2020, necessitating focused attention. These findings are crucial for making informed decisions regarding grassland-livestock balance management and can aid in optimizing resource allocation and ecological conservation strategies. Future research will utilize higher resolution geographic data to enhance the accuracy and efficiency of grazing intensity models.

Machine learning-assisted colorimetric sensor array for rapid identification of adulterated Panax notoginseng powder

Panax notoginseng (PN) is a popular functional food worldwide, yet adulterated PN powders are prevalent in the commercial market. Herein, a method combining a low-cost colorimetric sensor array and machine learning is proposed to rapidly identify and quantify adulterated PN powder. We constructed a sensitive indicator displacement assay (IDA) sensor array by innovatively using Job's plot to optimize sensor units. The array was used to identify adulterated PN powders after its discriminatory ability was evaluated by amino acid analysis. Among the four machine learning models, the support vector machine (SVM) models achieved the highest accuracy, with above 98.3% for authenticity and 99.0% for adulteration types. The blending percentages of four PN adulterants were further analyzed quantitatively using support vector machine regression (SVR) models with good prediction ability (R > 0.93). Finally, our sensor array method was applied to identify commercially available PN powders with satisfactory results. This study offers a low-cost new method for the rapid identification of PN powder, contributing to quality control of powdered food.

A measurement model of pedestrian tolerance time under signal-controlled conditions

The tolerance of pedestrians to red light signals is a crucial factor in urban residents’ travel experiences and road traffic safety. To address the current lack of research on methods for measuring pedestrian red light tolerance time at crossings, this paper proposes a stacking model for predicting the red light tolerance time of individual pedestrians. The model employs the XGBoost (XGB), random forest (RF), support vector machine regression (SVR), and multilayer perceptron (MLP) models as primary models, with a multiple linear regression model as the secondary-layer meta-model, as well as Bayesian hyperparameter tuning. Using random survival forest (RSF) and K-means clustering methods, the dataset was divided into three categories: the low, medium, and high tolerance groups. The feature variables influencing the red light tolerance time were grouped accordingly. Stacking models were established for each tolerance and feature group. The experimental results demonstrated that the feature group voted on by multiple machine learning models performed the best in all three tolerance groups. In the low tolerance group, the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 6.58, 1.91, and 19.78%, respectively. For the medium tolerant group, the MSE, MAE, and MAPE were 4.82, 1.53, and 7.63%, respectively. For the high tolerance group, the MSE, MAE, and MAPE were 33.32, 3.89, and 10.14%, respectively. Individual XGB, RF, SVR, MLP, and ungrouped stacking models were established for comparative analysis of the test set. The results indicated that the proposed grouped stacking model outperformed the other models overall. The method provides a means of obtaining the probability distribution function of the length of the waiting crowd’s tolerance time for each red light range and thus determining the duration of the red light at the crossing signal for different personnel compositions.

Prediction of Clinical Outcomes in Psychotic Disorders Using Artificial Intelligence Methods: A Scoping Review.

Psychotic disorders are major psychiatric disorders that can impact multiple domains including physical, social, and psychological functioning within individuals with these conditions. Being able to better predict the outcomes of psychotic disorders will allow clinicians to identify illness subgroups and optimize treatment strategies in a timely manner. In this scoping review, we aimed to examine the accuracy of the use of artificial intelligence (AI) methods in predicting the clinical outcomes of patients with psychotic disorders as well as determine the relevant predictors of these outcomes. This review was guided by the PRISMA Guidelines for Scoping Reviews. Seven electronic databases were searched for relevant published articles in English until 1 February 2024. Thirty articles were included in this review. These studies were mainly conducted in the West (63%) and Asia (37%) and published within the last 5 years (83.3%). The clinical outcomes included symptomatic improvements, illness course, and social functioning. The machine learning models utilized data from various sources including clinical, cognitive, and biological variables such as genetic, neuroimaging measures. In terms of main machine learning models used, the most common approaches were support vector machine, random forest, logistic regression, and linear regression models. No specific machine learning approach outperformed the other approaches consistently across the studies, and an overall range of predictive accuracy was observed with an AUC from 0.58 to 0.95. Specific predictors of clinical outcomes included demographic characteristics (gender, socioeconomic status, accommodation, education, and employment); social factors (activity level and interpersonal relationships); illness features (number of relapses, duration of relapses, hospitalization rates, cognitive impairments, and negative and disorganization symptoms); treatment (prescription of first-generation antipsychotics, high antipsychotic doses, clozapine, use of electroconvulsive therapy, and presence of metabolic syndrome); and structural and functional neuroimaging abnormalities, especially involving the temporal and frontal brain regions. The current review highlights the potential and need to further refine AI and machine learning models in parsing out the complex interplay of specific variables that contribute to the clinical outcome prediction of psychotic disorders.

Quantitative analysis of zearalenone in wheat leveraging support vector machine and olfactory visualization technology

Zearalenone (ZEN) is a contaminant found naturally in grains such as wheat that poses a potential threat to human and animal health. This study describes a colorimetric sensor array detection system based on chemically reactive dyes and chemometric algorithms for the determination of ZEN in wheat. Firstly, six chemically responsive dyes were screened to construct a colorimetric sensor array to obtain the response fingerprints of the volatile components of wheat. The RGB differences of the chemically reactive dyes before and after the response were then calculated to obtain the color component data of the image. The maximum information coefficient (MIC) and ReliefF methods were then used to select the best color features. Finally, the Support Vector Machine Regression (SVR) prediction model was developed and validated based on the optimized features on 132 sample sets. The results showed that the SVR model developed on the initial 132 wheat samples had good predictive ability. Its root mean square error (RMSE) on the prediction set was 38.1625 μg∙kg−1 with a correlation coefficient (R) 0.9516. In particular, the SVR model combining 13 color components reduced the RMSE on the prediction set to 30.4484 μg∙kg−1, with an improved R of 0.9695. Olfactory visualization techniques were shown to be effective for the determination of ZEN in wheat. This study is expected to provide a scientifically novel approach for quality stabilization during the storage of wheat or other cereals.

High-precision detection of dibutyl hydroxytoluene in edible oil via convolutional autoencoder compressed Fourier-transform near-infrared spectroscopy

The quality of edible oils is closely related to their chemical compositions. Antioxidants have widespread application in edible oil production. In this study, a pioneering detection approach involving the use of a one-dimensional convolutional autoencoder (1D-CAE) was introduced to compress spectral data for assessing antioxidant levels in edible oils. Fourier-transform near-infrared (FT-NIR) characterisation of edible oil samples with varying antioxidant concentrations was also conducted. An 1D-CAE model was developed to compress different pre-processed spectra into a condensed representation. These compressed features were then integrated with a support vector machine and partial least squares regression models to establish correlations for each target. The study examined the influence of pre-processing steps and feature engineering methods on near-infrared spectral analysis through independent or combined model analysis. The findings revealed that features derived from the 1D-CAE model demonstrated remarkable repeatability and can be utilised to construct robust detection models. The experimental results showed that the optimal detection model derived based on the 1D-CAE compression features has an average R2, RPD and RMSE of 0.9953, 15.1664 and 1.2035, respectively, on the prediction set. FT-NIR spectroscopy can be used to accurately detect butylated hydroxytoluene in edible oils. Therefore, autoencoders are an effective tool in spectroscopic analysis, offering promising avenues for future research and application.

Application of m6A regulators to predict transformation from myelodysplastic syndrome to acute myeloid leukemia via machine learning.

Myelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make the treatment plan. Levels of expression of N6-methyladenosine (m6A) regulators is difference in patients with AML, MDS, and MDS transformed into AML. Seven machine learning algorithms were established based on all of 26 m6A or main differentially expressed m6A regulator genes, and attempted to establish a risk assessment method to distinguish AML from MDS and predict the transformation of MDS into AML. In collective of m6A regulators sets, support vector machine (SVM) and neural network (NNK) model best distinguished AML or MDS from control, with area under the ROC curve (AUROC) 0.966 and 0.785 respectively. The SVM model best distinguished MDS from AML, with AUROC 0.943, sensitivity 0.862, specificity 0.864, and accuracy 0.864. In differentially expressed gene sets, SVM and logistic regression (LR) model best distinguished AML or MDS from control, with AUROC 0.945 and 0.801 respectively. The random forest (RF) model best distinguished between MDS and AML, with AUROC 0.928, sensitivity 0.725, specificity 0.898, and accuracy 0.818. For predictive capacity of MDS transformed into AML, SVM model showed the best predicted in collective m6A regulators sets, with AUROC 0.781 and accuracy 0.740. The LR model showed the best predicted in differential expression m6A regulators sets, with AUROC 0.820 and accuracy 0.760. All results suggested that machine learning model established by m6A regulators can be used to distinguished AML or MDS from control, distinguished AML from MDS and predicted the transformation of MDS into AML.

A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru.

Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure-property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru. A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM-RF-GBM model. An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39-0.40 and R2 = 0.73-0.74. Notably, the SVM-RF-GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness. Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.

Analysis and Prediction of the Leaching Process of Ionic Rare Earth: A Data Mining Study with Scarce Data

To unveil the impact of each condition variable on the leaching efficiency index during the heap leaching process of rare earth ore and establish a prediction model for leaching conditions and efficiency, common parameters in the heap leaching process of rare earth ore were selected. In addition, the pilot-scale test data were collected over 50 days. Based on the collected data, the Ordinary Least Squares (OLS) linear regression method was used for fitting analysis to determine each variable’s influence on the change in leaching efficiency. The results indicated a linear relationship between the flow rate of the leaching solution and leaching efficiency. In contrast, no obvious linear relationship was observed between other condition variables and leaching efficiency. Spearman’s rank correlation coefficient was calculated to analyze the nonlinear correlation between the abovementioned variables and the leaching efficiency index. The correlation coefficients were found to be −0.78, 0.88, −0.93, −0.53, 0.71, and −0.93 for ammonium content in the leaching agent, pH of the leaching agent, rare earth content, ammonium content in the leaching solution, pH of the leaching solution, and the flow rate of the leaching solution, respectively. This suggests that the flow rate of the leaching solution, rare earth content, and pH of the leaching agent significantly influence leaching efficiency, thus affecting the rare earth leaching efficiency index. Based on the correlation analysis results of leaching conditions and efficiency, a dataset with limited data trained by the common Ordinary Least Squares model, linear regression model, random forest model, and support vector machine regression model was selected to develop a prediction model for the leaching process data. The results indicated that the random forest model had the lowest mean square error of 7.47 among the four models and the coefficient of determination closest to 1 (0.99). This model can effectively analyze and predict condition variables’ data and leaching efficiency index in the heap leaching process of rare earth ore, with a prediction accuracy exceeding 90%, thus providing intelligent guidance for the heap leaching process of rare earth ores.

FRT capability improvement of a wind power system using support vector regression and deep neural network models

ABSTRACT The increasing energy demand and environmental conditions have led many countries to favor renewable energy,such as wind and solar,over conventional power plants. As per the Central Electricity Authority (CEA), India’s wind power capacity has grown substantially, reaching 45,154 MW in 2023, from 22,465 MW in 2013. However, integrating wind generators to an existing power system network can reduce transient stability during faults. To prevent wind turbine disconnection during faults, Indian grid code authorities mandate Fault Ride Through (FRT) or Low Voltage Ride Through (LVRT) capability for wind turbines. This paper proposes Dynamic Voltage Restorer (DVR) for enhancing the FRT capability of a constant speed wind turbine. Analysis and simulation are done on a fixed speed wind turbine employing Squirrel Cage Induction Generator (SCIG) under balanced and unbalanced voltage sags. Performance of DVR with two Artificial Intelligence-based controllers, Support Vector Machines(SVM) Regression Model Based Supervised Machine Learning algorithm, and Deep Neural Network (DNN) controller, is analysed and compared. The SVM Regression algorithm, achieves 100% voltage sag compensation under unbalanced voltage sags, reduces torque pulsations to 25% and maintains rotor speed at rated values, enabling the turbine to remain connected to the grid. Additionally it reduces Total Harmonic Distortion to 2%.

Estimation of mode I quasi-static fracture of notched aluminum–lithium AW2099-T83 alloy using local approaches and machine learning

Aluminum–lithium (Al–Li) alloys offer superior performance under different conditions that involve mechanical loading, high temperature and corrosive environment. Therefore, Al–Li alloys are being used in the defense, aerospace, and aircraft industries, specifically in structural parts such as fuselage, empennage, and wings. By modifying the chemical composition, a third generation of Al–Li alloys has been introduced to overcome the mechanical and thermal shortcomings of previous Al–Li generations. As structural parts usually contain notches, it is of paramount importance to study the strength of the newly introduced alloys in the presence of such geometrical discontinuities to select the suitable alloy for a particular application. The aim of this work is to analyze the strength of U and V-notched specimens machined from extruded AW2099-T83 Al–Li alloys under quasi-static loading. The fracture stress of specimens with various notch radii and angles were estimated using strain energy density (SED) and the theory of critical distances (TCD) methods. A support vector machine (SVM) regression model was also implemented to assess the applicability of estimating fracture stress using machine learning approaches. The results show an average absolute discrepancy of 6.6 %, 7.8 % and 2.8 % for SED, TCD and SVM methods, respectively.