Search Results (58)

Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts. Full article

This study advances crime analysis methodologies in Maryland by leveraging sophisticated machine learning (ML) techniques designed to cater to the state’s varied urban, suburban, and rural contexts. Our research utilized an enhanced combination of machine learning models, including random forest, gradient boosting, XGBoost, extra trees, and advanced ensemble methods like stacking regressors. These models have been meticulously optimized to address the unique dynamics and demographic variations across Maryland, enhancing our capability to capture localized crime trends with high precision. Through the integration of a comprehensive dataset comprising five years of detailed police reports and multiple crime databases, we executed a rigorous spatial and temporal analysis to identify crime hotspots. The novelty of our methodology lies in its technical sophistication and contextual sensitivity, ensuring that the models are not only accurate but also highly adaptable to local variations. Our models’ performance was extensively validated across various train–test split ratios, utilizing R-squared and RMSE metrics to confirm their efficacy and reliability for practical applications. The findings from this study contribute significantly to the field by offering new insights into localized crime patterns and demonstrating how tailored, data-driven strategies can effectively enhance public safety. This research importantly bridges the gap between general analytical techniques and the bespoke solutions required for detailed crime pattern analysis, providing a crucial resource for policymakers and law enforcement agencies dedicated to developing precise, adaptive public safety strategies. Full article

In recent years, electric vehicles have become increasingly widespread, both in the logistics sector and in personal use. This increase, together with factors such as environmental concerns and government incentives, has brought energy consumption and range estimation issues to the forefront. In this study, the energy consumption of an electric cargo vehicle under different speed and load conditions is examined with an experimental and data-driven approach, and then used for range estimation. The raw data collected from the vehicle on the selected ~2 km route in Eskisehir Osmangazi University campus are combined into per-second samples with time synchronization and data cleaning. The route is divided into average of 150 m segments, and variables such as slope, energy consumption, and acceleration are calculated for each segment. Then, the data are used to train various machine learning models, such as Extra Trees, CatBoost, LightGBM, Voting Regressor, and XGBoost, and their performances regarding energy consumption-based range estimation are compared. The findings show that driving dynamics such as high speed and sudden acceleration, as well as road slope and load conditions, significantly shape the energy consumption and thus the remaining range. In particular, Extra Trees outperforms other machine learning models in terms of metrics such as R², RMSE and, MAE, with a reasonable computational time. The results provide applicable guidance in areas such as route optimization, smart battery management, and charging infrastructure to reduce range anxiety and increase the operational efficiency of electric vehicles. Full article

This study focuses on optimizing C2 yields in the oxidative coupling of methane (OCM), a pivotal process for sustainable chemical production. By harnessing advanced machine learning (ML) techniques, this research aimed to predict C2 yields and identify the factors that drive catalytic performance. The Extra Trees Regressor emerged as the most effective model after a comprehensive evaluation across multiple datasets and methodologies. Key to the method was the use of an innovative Aggregated Catalyst Physicochemical Descriptor (ACPD) and stratified cross-validation, which effectively addressed feature complexity and target skewness. Hyperparameter optimization using Modified Sequential Model-Based Optimization (SMBO) further enhanced the model’s performance, achieving optimized R² values of 61.7%, 75.9%, and 92.0% for datasets A, B, and C, respectively, with corresponding reductions in the Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Additionally, SHAP (SHapley Additive exPlanations) analysis provided a detailed understanding of the model’s decision-making process, revealing the relative importance of individual features and their contributions to the predictive outcomes. This research not only achieved state-of-the-art predictive accuracy, but also deepened our understanding of the underlying chemical dynamics, offering practical guidance for catalyst design and operational optimization. These findings mark a significant advancement in catalysis, paving the way for future innovations in sustainable chemical manufacturing. Full article

Wind turbine blades are subjected to cyclic loading conditions throughout their operational lifetime, making fatigue a critical factor in their design. Accurate prediction of the fatigue performance of wind turbine blades is important for optimizing their design and extending the operational lifespan of wind energy systems. This study aims to develop predictive models of laminated composite fatigue life based on experimental results published by Montana State University, Bozeman, Composite Material Technologies Research Group. The models have been trained on a dataset consisting of 855 data points. Each data point consists of the stacking sequence, fiber volume fraction, stress amplitude, loading frequency, laminate thickness, and the number of cycles of a fatigue test carried out on a laminated composite specimen. The output feature of the dataset is the number of cycles, which indicates the fatigue life of a specimen. Random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and extra trees regressor models have been trained to predict the fatigue life of the specimens. For optimum performance, the hyperparameters of these models were optimized using GridSearchCV optimization. The total number of cycles to failure could be predicted with a coefficient of determination greater than 0.9. A feature importance analysis was carried out using the SHapley Additive exPlanations (SHAP) approach. LightGBM showed the highest performance among the models (R² = 0.9054, RMSE = 1.3668, and MSE = 1.8682). Full article

Natural fiber composites have gained significant attention in recent years due to their environmental benefits and unique mechanical properties. These materials combine natural fibers with polymer matrices to create sustainable alternatives to traditional synthetic composites. In addition to natural fiber reinforcement, the usage of recycled aggregates in concrete has been proposed as a remedy to combat the rapidly increasing amount of construction and demolition waste in recent years. However, the accurate prediction of the structural performance metrics, such as tensile strength, remains a challenge for concrete composites reinforced with natural fibers and containing recycled aggregates. This study aims to develop predictive models of natural-fiber-reinforced recycled aggregate concrete based on experimental results collected from the literature. The models have been trained on a dataset consisting of 482 data points. Each data point consists of the amounts of cement, fine and coarse aggregate, water-to-binder ratio, percentages of recycled coarse aggregate and natural fiber, and the fiber length. The output feature of the dataset is the splitting tensile strength of the concrete. Extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and extra trees regressor models were trained to predict the tensile strength of the specimens. For optimum performance, the hyperparameters of these models were optimized using the blended search strategy (BlendSearch) and cost-related frugal optimization (CFO). The tensile strength could be predicted with a coefficient of determination greater than 0.95 by the XGBoost model. To make the predictive models accessible, an online graphical user interface was also made available on the Streamlit platform. A feature importance analysis was carried out using the Shapley additive explanations (SHAP) approach. Full article

Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), and Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy and optimize feature selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) of 13.17 mg/dL, a root mean square error (RMSE) of 15.36 mg/dL, and 94.74% of predictions falling within the clinically acceptable Clarke error grid (CEG) zone A, with none in dangerous zones. This research underscores the efficiency of utilizing HRV and PPG for non-invasive glucose monitoring, demonstrating the effectiveness of integrating metaheuristic and ML approaches for enhanced diabetes monitoring. Full article

This paper presents a machine learning-based approach to grade engine health and generate a respective score ranging from 0 to 100 for tuned high-performance vehicles. It integrates the technical intricacies of automotive engineering with machine learning practices in a clear and sequential process. Data are collected from sensors monitoring revolutions per minute, boost, rail pressure, timing, and temperature. The data are processed for supervised learning and analyzed using visualizations such as a heatmap and t-SNE plots. Models are trained, innovatively tuned through hyperparameter optimization, and tested for their ability to grade new data logs. The results highlight K-Neighbors, Extra Trees, and Extreme Gradient Boosting as exceptional regressors for this task. The automated grading of engine health and performance enhances objectivity and efficiency in the tuning process and potentially serves as a basis for a digital twin. The developed methodology is discussed in the context of health evaluation for any sensor-based system, with practical applications extending across various domains and industries. Full article

Tracking density in universities is essential for planning services like food, transportation, and social activities on campus. However, food waste remains a critical challenge in campus dining operations, leading to significant environmental and economic consequences. Addressing this issue is crucial not only for minimizing environmental impact but also for achieving sustainable operational efficiency. Campus food services significantly influence students’ university choices; thus, forecasting meal consumption and preferences enables effective planning. This study tackles food waste by analyzing daily campus data with machine learning, revealing strategic insights related to food variety and sustainability. The algorithms Linear Regression, Extra Tree Regressor, Lasso, Decision Tree Regressor, XGBoost Regressor, and Gradient Boosting Regressor were used to predict food preferences and daily meal counts. Among these, the Lasso algorithm demonstrated the highest accuracy with an R² metric value of 0.999, while the XGBRegressor also performed well with an R² metric value of 0.882. The results underline that factors such as meal variety, counts, revenue, campus mobility, and temperature effectively influence food preferences. By balancing production with demand, this model significantly reduced food waste to 28%. This achievement highlights the potential for machine learning models to enhance sustainable dining services and operational efficiency on university campuses. Full article

The cost of a wind turbine support structure is high and this support structure is difficult to repair, especially for offshore wind turbines. As such, the loads and stresses that occur during the actual operation of wind turbines must be monitored from the perspective of maintenance planning and lifetime prediction. Strain measurement methods are generally used to monitor the load on a structure and are highly accurate, but their widespread implementation across all wind turbines is impractical due to cost and labor constraints. In this study, a method for predicting the tower load was developed, using simple measurements applied during power generation, for onshore wind turbines. The method consists of a machine learning model, using the nacelle displacement and nacelle angle as inputs, which are highly correlated with loads at the bottom of the tower. Nacelle displacements can be derived from accelerations, which are already monitored in regard to most wind turbines; the nacelle angle can be calculated from the nacelle angle velocity, measured with a gyroscope. The low-frequency components that cannot be captured with these parameters were predicted using the operational condition data used for wind turbine control. Additionally, the prediction accuracy was increased by creating and integrating separate machine learning models for each typical vibration component. The method was evaluated through the aeroelastic simulation of a 2 MW wind turbine. The results showed that the fatigue and extreme loads of the fore–aft and side–side bending moments at the bottom of the tower can be predicted using operational conditions and nacelle accelerations, and the prediction accuracy in regard to the high-frequency components can be increased by adding the nacelle angle velocity into the model. Furthermore, the fatigue loads of the torsional torque can be evaluated using the nacelle angle velocity. The proposed method has the ability to predict the loads at the bottom of the tower without any, or with only a few, additional sensors. Full article

Corn (Zea mays L.) silage yield monitor data offer crucial insights into spatial and temporal yield variability. However, equipment’s sensor malfunctioning can result in data loss, and yield sensor systems are expensive to purchase and maintain. In this study, we analyzed corn silage yield data from two fields and three years each for two dairy farms (Farm A and B). We aimed to explore the potential of integrating high-resolution satellite data, topography, and climate data with machine learning models to estimate missing yield data for a field or a year. Our objectives were to identify key yield-explaining features and assess the accuracy of different machine learning models in estimating silage yield. Results showed that the features differed among farms with a Two-Band Enhanced Vegetation Index, EVI2 (Farm A), and elevation (Farm B) emerging as the most prominent predictors. Ensemble-based models like XGBoost, Random Forest, and Extra Tree regressors exhibited superior predictive performance. However, XGBoost performed poorly when applied to unseen fields or years, whereas Extra Tree regressor, followed closely by Random Forest, emerged as a more reliable model for predicting missing data. Despite achieving reasonable accuracy, the best performance for estimating data for a missing field (6.46 Mg/ha) and year (5.51 Mg/ha) fell short of the acceptable error threshold of 4.9 Mg/ha currently used in state policy to evaluate if a management change resulted in a yield increase. These findings emphasize the need for higher-resolution data and extended years of yield records to better capture the trends in farm-scale yield applications. Full article

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones’ features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an $R^2$ of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal. Full article

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics. Full article

In wind turbines, to investigate the cause of failures and evaluate the remaining lifetime, it may be necessary to measure their loads. However, it is often difficult to do so with only strain gauges in terms of cost and time, so a method to evaluate loads by utilizing only simple measurements is quite useful. In this study, we investigated a method with machine learning to estimate hub center loads, which is important in terms of preventing damage to equipment inside the nacelle. Traditionally, measuring hub center loads requires performing complex strain measurements on rotating parts, such as the blades or the main shaft. On the other hand, the tower is a stationary body, so the strain measurement difficulty is relatively low. We tackled the problem as follows: First, machine learning models that predict the time history of hub center loads from the tower top loads and operating condition data were developed by using aeroelastic analysis. Next, the accuracy of the model was verified by using measurement data from an actual wind turbine. Finally, individual pitch control, which is one of the applications of the time history of hub center loads, was performed using aeroelastic analysis, and the load reduction effect with the model prediction values was equivalent to that of the conventional method. Full article

The performance of ultra-high-performance concrete (UHPC) allows for the design and creation of thinner elements with superior overall durability. The compressive strength of UHPC is a value that can be reached after a certain period of time through a series of tests and cures. However, this value can be estimated by machine-learning methods. In this study, multilayer perceptron (MLP) and Stacking Regressor, an ensemble machine-learning models, is used to predict the compressive strength of high-performance concrete. Then, the ML model’s performance is explained with a feature importance analysis and Shapley additive explanations (SHAPs), and the developed models are interpreted. The effect of using different random splits for the training and test sets has been investigated. It was observed that the stacking regressor, which combined the outputs of Extreme Gradient Boosting (XGBoost), Category Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extra Trees regressors using random forest as the final estimator, performed significantly better than the MLP regressor. It was shown that the compressive strength was predicted by the stacking regressor with an average R² score of 0.971 on the test set. On the other hand, the average R² score of the MLP model was 0.909. The results of the SHAP analysis showed that the age of concrete and the amounts of silica fume, fiber, superplasticizer, cement, aggregate, and water have the greatest impact on the model predictions. Full article