Search Results (304)

Quartz is a widely used mineral in dosimetric and geochronological applications due to its stable luminescence properties under ionizing radiation. This study presents an artificial neural network (ANN)-based approach to predict the optically stimulated luminescence (OSL) decay curves of quartz extracted from Mediterranean beach sand samples in Turkey. Experimental OSL signals were obtained from quartz samples irradiated with beta doses ranging from 0.1 Gy to 1034.9 Gy. The dataset was used to train ANN models with three different learning algorithms: Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Forty-seven decay curves were used for training and three for testing. The ANN models were evaluated based on regression accuracy, training–validation–test performance, and their predictive capability for low, medium, and high doses (1 Gy, 72.4 Gy, 465.7 Gy). The results showed that BR achieved the highest overall regression (R = 0.99994) followed by LM (R = 0.99964) and SCG (R = 0.99820), confirming the superior generalization and fits across all dose ranges. LM performs optimally at low-to-moderate doses, and SCG delivers balanced yet slightly noisier predictions. The proposed ANN-based method offers a robust and effective alternative to conventional kinetic modeling approaches for analyzing OSL decay behavior and holds considerable potential for advancing luminescence-based retrospective dosimetry and OSL dating applications. Full article

Meta-analytic deconvolution seeks to recover the distribution of true effects from noisy site-specific estimates. While Efron’s log-spline prior provides an elegant empirical Bayes solution with excellent point estimation properties, its plug-in nature yields severely anti-conservative uncertainty quantification for individual site effects—a critical limitation for what Efron terms “finite-Bayes inference.” We develop a fully Bayesian extension that preserves the computational advantages of the log-spline framework while properly propagating hyperparameter uncertainty into site-level posteriors. Our approach embeds the log-spline prior within a hierarchical model with adaptive regularization, enabling exact finite-sample inference without asymptotic approximations. Through simulation studies calibrated to realistic meta-analytic scenarios, we demonstrate that our method achieves near-nominal coverage (88–91%) for 90% credible intervals while matching empirical Bayes point estimation accuracy. We provide a complete Stan implementation handling heteroscedastic observations—a critical feature absent from existing software. The method enables principled uncertainty quantification for individual effects at modest computational cost, making it particularly valuable for applications requiring accurate site-specific inference, such as multisite trials and institutional performance assessment. Full article

Compared to rigidly-backed absorbers, the selection of appropriate optimization techniques for the optimal design of broadband acoustic mufflers remains under-investigated. This study determines the most effective optimization strategy for maximizing the total dissipation of rainbow-trapping silencers (RTSs), composed of graded side-branch cavities that enable broadband dissipation of sound through visco-thermal effects. Model-based and data-driven optimization strategies are compared, particularly in high-dimensional design spaces with flat cost function landscapes where gradient-based approaches are inadequate. It is found that model-based particle swarm optimization (PSO) outperforms simulated annealing, genetic algorithm, and surrogate method in maximizing RTS total dissipation, especially in high-dimensional designs. PSO uniquely handles flat or valleyed cost landscapes through efficient exploration–exploitation trade-offs. Data-driven approaches using Bayesian regularization neural networks (BRNNs) drastically reduce computational cost in high-dimensional spaces, though they require large datasets to avoid over-smoothing. In low dimensions, direct optimization on BRNN outputs suffices, making global search unnecessary. Both model-based and BRNN methods show robustness to input errors, but data-driven approaches handle output noise better. These findings, validated using transfer matrix models, offer strategic guidance for selecting optimization methods, especially when using computationally expensive visco-thermal finite element simulations. Full article

Efficient bandwidth allocation in 5G networks is essential for optimizing network performance and ensuring high quality of service (QoS), particularly in unmanned aerial vehicle (UAV) communication systems. The dynamic nature of UAV networks presents challenges in managing fluctuating QoS levels, necessitating intelligent bandwidth allocation strategies. This study investigates the effectiveness of two machine learning (ML) models, least square gradient boosting (LSGB) and a Bayesian regularization feedforward neural network (BRFFNN), in predicting bandwidth allocation for UAV ground station (UAV-GS) communication under 5G specifications. Using a simulation-based approach, the study evaluates UAV bandwidth allocation under two movement patterns: circular and random. The QoS metrics considered include the packet delivery ratio (PDR), delay, and throughput. The results demonstrate that the BRFFNN outperforms LSGB, particularly in circular UAV movement, achieving a 100% PDR, a 0.00773 ms delay, and a 3.232 million packets per second (pps) throughput. These findings suggest that ML models, particularly the BRFFNN, can significantly enhance bandwidth allocation strategies in 5G UAV-GS communication systems, improving overall network efficiency and QoS. This study provides valuable insights into ML-driven bandwidth allocation, emphasizing the BRFFNN as a superior approach for enhancing QoS in 5G UAV-GS networks. In the context of 5G UAV-GS bandwidth allocation, this study applies the BRFFNN in a novel way and demonstrates its superiority over tree-based models such as LSGB. In contrast to earlier research that concentrated on static or traditional allocation techniques, our method achieves State-of-the-Art QoS by dynamically predicting bandwidth under actual UAV movement scenarios. Full article

This present study proposes different machine learning-based predictors for the assessment of the residual compressive strength of Self-Compacting Concrete (SCC) subjected to high temperatures. The investigation is based on several literature algorithmic approaches based on Artificial Neural Networks with distinct training algorithms (Bayesian Regularization, Levenberg–Marquardt, Scaled Conjugate Gradient, and Resilient Backpropagation), Support Vector Regression, and Random Forest methods. A training database of 150 experimental data points is derived from a careful literature review, incorporating temperature (20–800 °C), geometric ratio (height/diameter), and corresponding compressive strength values. A statistical analysis revealed complex non-linear relationships between variables, with strong negative correlation between temperature and strength and heteroscedastic data distribution, justifying the selection of advanced machine learning techniques. Feature engineering improved model performance through the incorporation of quadratic terms, interaction variables, and cyclic transformations. The Resilient Backpropagation algorithm demonstrated superior performance with the lowest prediction errors, followed by Bayesian Regularization. Support Vector Regression achieved competitive accuracy despite its simpler architecture. Experimental validation using specimens tested up to 800 °C showed a good reliability of the developed systems, with prediction errors ranging from 0.33% to 23.35% across different temperature ranges. Full article

Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has proven to be an efficient way of solving time series problems. This study employs a multilayer perceptron network with backpropagation training and Bayesian regularisation to enhance generalisation and minimise overfitting errors. The research aggregates real consumption data from 200 households and 348 electric vehicles. The developed method was validated using MAPE, which resulted in errors below 6%. Short-term forecasts were made across the four seasons, predicting the total aggregate demand of households and vehicles for the next 24 h. The methodology produced significant and relevant results for this problem using hybrid training, a few-neuron architecture, deep learning, fast convergence, and low computational cost, with potential for real-world application. The results support the electrical power system by optimising these loads, reducing costs and energy generation, and preparing a new scenario for EV penetration rates. Full article

This paper proposes a novel sparse twin parametric insensitive support vector regression (STPISVR) model, designed to enhance sparsity and improve generalization performance. Similar to twin parametric insensitive support vector regression (TPISVR), STPISVR constructs a pair of nonparallel parametric insensitive bound functions to indirectly determine the regression function. The optimization problems are reformulated as two sparse linear programming problems (LPPs), rather than traditional quadratic programming problems (QPPs). The two LPPs are originally derived from initial L1-norm regularization terms imposed on their respective dual variables, which are simplified to constants via the Karush–Kuhn–Tucker (KKT) conditions and consequently disappear. This simplification reduces model complexity, while the constraints constructed through the KKT conditions— particularly their geometric properties—effectively ensure sparsity. Moreover, a two-stage hybrid tuning strategy—combining grid search for coarse parameter space exploration and Bayesian optimization for fine-grained convergence—is proposed to precisely select the optimal parameters, reducing tuning time and improving accuracy compared to a singlemethod strategy. Experimental results on synthetic and benchmark datasets demonstrate that STPISVR significantly reduces the number of support vectors (SVs), thereby improving prediction speed and achieving a favorable trade-off among prediction accuracy, sparsity, and computational efficiency. Overall, STPISVR enhances generalization ability, promotes sparsity, and improves prediction efficiency, making it a competitive tool for regression tasks, especially in handling complex data structures. Full article

Offshore cranes equipped with active motion compensation (AMC) systems play a vital role in marine engineering tasks such as offshore wind turbine maintenance, subsea operations, and dynamic load positioning under wave-induced disturbances. These systems rely on complex hydraulic actuators whose strongly nonlinear dynamics—often described by differential-algebraic equations (DAEs)—impose significant computational burdens, particularly in real-time applications like hardware-in-the-loop (HIL) simulation, digital twins, and model predictive control. To address this bottleneck, we propose a neural network-based surrogate model that approximates the actuator dynamics with high accuracy and low computational cost. By approximately reducing the original DAE model, we obtain a lower-dimensional ordinary differential equations (ODEs) representation, which serves as the foundation for training. The surrogate model includes three hidden layers, demonstrating strong fitting capabilities for the highly nonlinear characteristics of hydraulic systems. Bayesian regularization is adopted to train the surrogate model, effectively preventing overfitting. Simulation experiments verify that the surrogate model reduces the solving time by 95.33%, and the absolute pressure errors for chambers $p_1$ and $p_2$ are controlled within 0.1001 MPa and 0.0093 MPa, respectively. This efficient and scalable surrogate modeling framework possesses significant potential for integrating high-fidelity hydraulic actuator models into real-time digital and control systems for offshore applications. Full article

Energy resources are abundant in high-altitude regions, and the construction of power transmission and transformation projects has important value. However, harsh natural environments can increase project costs. To address the issue of insufficient accuracy caused by the impact of extreme weather factors on cost predictions for power transmission and transformation projects in high-altitude regions, this paper first constructs a four-dimensional influencing factor system covering climate and environment, engineering scale, material consumption, and technological economy. On this basis, a hybrid deep-learning model combining an improved whale optimization algorithm (IWOA) and a convolutional neural network (CNN) is then proposed. The model improves the training accuracy of CNNs and avoids falling into local optima through the use of an SGDM optimizer, the L2 regularization method, and the Bayesian optimization method. Nonlinear convergence factors and adaptive weights are introduced to enhance the WOA’s ability to optimize the CNN’s learning rate. The case analysis results show that, compared with the comparison model, the proposed IWOA-CNN model exhibits a better convergence performance and fitting effect in the training set and a better prediction effect on the test set. Its mean absolute percentage error is as low as 1.51%, which is 10.1% lower than the optimal comparison model. The root mean square error is reduced to 5.07, and the sum of squared errors is reduced by 72.4%, demonstrating high prediction accuracy. The comparative analysis of scenarios further confirms the crucial role of climate environment; that is, the prediction accuracy of models containing a climate dimension is improved by 51.6% compared to models without such a climate dimension, indicating that the nonlinear impact of low temperatures, frozen soil, and other characteristics of high-altitude regions on costs cannot be ignored. The research results of this paper enrich the method system and application scenarios for the cost prediction for power transmission and transformation projects and provide theoretical reference for engineering predictions in other complex geographical environments. Full article

Grey models have attracted considerable attention as a time series forecasting tool in recent years. Nevertheless, the linear characteristics of the differential equations on which traditional grey models rely frequently result in inadequate predictive accuracy and applicability when addressing intricate nonlinear systems. This study introduces a conformable fractional order unbiased kernel-regularized nonhomogeneous grey model (CFUKRNGM) based on statistical learning theory to address these limitations. The proposed model initially uses a conformable fractional-order accumulation operator to derive distribution information from historical data. A novel regularization problem is then formulated, thereby eliminating the bias term from the kernel-regularized nonhomogeneous grey model (KRNGM). The parameter estimation of the CFUKRNGM model requires solving a linear equation with a lower order than the KRNGM model, and is automatically calibrated through the Bayesian optimization algorithm. Experimental results show that the CFUKRNGM model achieves superior prediction accuracy and greater generalization performance compared to both the KRNGM and traditional grey models. Full article

Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean. Full article

This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor. Full article

The number of hologram points in near-field acoustical holography (NAH) for a vibro-acoustic system plays a vital role in conditioning the transfer function between the source and measuring points. The requirement for many overdetermined hologram points for extended sources to obtain high accuracy poses a problem for the practical applications of NAH. Furthermore, overdetermination does not generally ensure enhanced accuracy, stability, and convergence, owing to the problem of rank deficiency. To achieve satisfactory reconstruction accuracy with underdetermined hologram data, the best practice for choosing hologram points and regularization methods is determined by comparing cross-linked sets of data-sorting and regularization methods. Three typical data selection and treatment methods are compared: iterative discarding of the most dependent data, monitoring singular value changes during the data reduction process, and zero padding in the patch holography technique. To test the regularization method for inverse conditioning, which is used together with the data selection method, the Tikhonov method, Bayesian regularization, and the data compression method are compared. The inverse equivalent source method is chosen as the holography method, and a numerical test is conducted with a point-excited thin plate. The simulation results show that selecting hologram points using the effective independence method, combined with regularization via compressed sensing, significantly reduces the reconstruction error and enhances the modal assurance criterion value. The experimental results also support the proposed best practice for inverting underdetermined hologram data by integrating the NAH data selection and regularization techniques. Full article

Accurate estimation of the uniaxial compressive strength (UCS) of carbonate rocks underpins safe design and stability assessment in karst-influenced geotechnical projects. This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures—radial basis function (RBF), Bayesian regularized (BR), scaled conjugate gradient (SCG), and Levenberg–Marquardt (LM)—to predict UCS from three readily measured variables: water content, interconnected porosity, and real density. Fifty core specimens from the Seybaplaya quarry in Campeche, Mexico, were split into training and testing subsets under uniform preprocessing. Each model’s predictive performance was assessed over 30 independent runs using mean absolute error, root mean squared error, and coefficient of determination, with statistical differences tested via nonparametric hypothesis testing. The RBF network achieved the highest median R² and significantly outperformed the other variants, while the BR model demonstrated robust generalization. SCG and LM converged faster and efficiently but with slightly lower accuracy. Sensitivity analysis identified interconnected porosity as the primary predictor of UCS. These results establish RBF-based ANNs with appropriate regularization and feature importance assessment as a novel, practical, and reliable framework for UCS prediction in heterogeneous carbonate formations. Full article

As a knowledge-intensive activity, the Kansei engineering (KE) process encounters numerous challenges in the design knowledge flow, primarily due to issues related to information reliability, uncertainty, and subjectivity. Bridging this gap, this study introduces an advanced KE framework integrating a cloud model with Z-numbers (CMZ) and Bayesian elastic net regression (BENR). In stage-I of this KE, data mining techniques are employed to process online user reviews, coupled with a similarity analysis of affective word clusters to identify representative emotional descriptors. During stage-II, the CMZ algorithm refines K-means clustering outcomes for market-representative product forms, enabling precise feature characterization and experimental prototype development. Stage-III addresses linguistic uncertainties in affective modeling through CMZ-augmented semantic differential questionnaires, achieving a multi-granular representation of subjective evaluations. Subsequently, stage-IV employs BENR for automated hyperparameter optimization in design knowledge inference, eliminating manual intervention. The framework’s efficacy is empirically validated through a domestic cleaning robot case study, demonstrating superior performance in resolving multiple information processing challenges via comparative experiments. Results confirm that this KE framework significantly improves uncertainty management in design knowledge flow compared to conventional implementations. Furthermore, by leveraging the intrinsic symmetry of the normal cloud model with Z-numbers distributions and the balanced ℓ₁/ℓ₂ regularization of BENR, CMZ–BENR framework embodies the principle of structural harmony. Full article