Search Results (275)

Regarding the distortion correction problem of large field of view wide-angle cameras commonly used in railway visual inspection systems, this paper proposes a novel online calibration method for non-specially made cooperative calibration objects. Based on the radial distortion divisor model, first, the spatial coordinates of natural spatial landmark points are constructed according to the known track gauge value between two parallel rails and the spacing value between sleepers. By using the image coordinate relationships corresponding to these spatial coordinates, the coordinates of the distortion center point are solved according to the radial distortion fundamental matrix. Then, a constraint equation is constructed based on the collinear constraint of vanishing points in railway images, and the Levenberg–Marquardt algorithm is used to found the radial distortion coefficients. Moreover, the distortion coefficients and the coordinates of the distortion center are re-optimized according to the least squares method (LSM) between points and the fitted straight line. Finally, based on the above, the distortion correction is carried out for the distorted railway images captured by the camera. The experimental results show that the above method can efficiently and accurately perform online distortion correction for large field of view wide-angle cameras used in railway inspection without the participation of specially made cooperative calibration objects. The whole method is simple and easy to implement, with high correction accuracy, and is suitable for the rapid distortion correction of camera images in railway online visual inspection. Full article

This study aimed to train and validate two artificial neural network (ANN) models, one with four hidden layers and the other with five hidden layers, to predict the daily photovoltaic power output of a 20 Kw photovoltaic power plant with bifacial photovoltaic modules with tilt angle variation from 0° to 90° in two Moroccan cities, Ouarzazate and Oujda. To validate the two proposed models, photovoltaic power data calculated using the System Advisor Model (SAM) software version 2023.12.17 were employed to predict the average daily power of the photovoltaic plant for December, utilizing MATLAB software Version R2020a 9.8, and for the tilt angles corresponding to the latitudes of the two cities studied. The results differ from one model to another according to their mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²) values. The artificial neural network model with five hidden layers obtained better results with a R² value of 0.99354 for Ouarzazate and 0.99836 for Oujda. These two proposed models are trained using the Levenberg Marquardt (LM) optimizer, which is proven to be the best training procedure. Full article

This paper presents a decentralized energy management system (EMS) based on Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and a Modified Cheetah Optimizer (MCO) to account for uncertainty in renewable generation and load demand. The proposed framework applies an MLP-ANN with Levenberg–Marquardt (LM) training for high-precision forecasts of photovoltaic/wind generation, ambient temperature, and load demand, greatly outperforming traditional statistical methods (e.g., time-series analysis) and resilient backpropagation (RP) in precision. The new MCO algorithm eliminates local trapping and premature convergence issues in classical optimization methods like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). Simulations on a test microgrid verily demonstrate the advantages of the framework, achieving a 26.8% cost-of-operation reduction against rule-based EMSs and classical PSO/GA, and a 15% improvement in forecast accuracy using an LM-trained MLP-ANN. Moreover, demand response programs embodied in the system reduce peak loads by 7.5% further enhancing grid stability. The MLP-ANN forecasting–MCO optimization duet is an effective and cost-competitive decentralized microgrid management solution under uncertainty. Full article

In this paper, we investigate the possibility of recognizing a superposition of wave aberrations from a focal pattern based on a matrix of partial sums. Due to the peculiarities of the focal pattern, some types of the considered superpositions are recognized ambiguously from the intensity pattern in the focal plane by standard error-reduction algorithms. It is numerically shown that when recognizing superpositions of Zernike functions from the intensity pattern in the focal plane, the use of step-by-step optimization in combination with the Levenberg–Marquardt algorithm yields good results only with an initial approximation close to the solution. In some cases, the root mean square reaches 0.3, which is unacceptable for precise detection in optical systems that require prompt correction of aberrations in real time. Therefore, to overcome this drawback, an algorithm was developed that considers partial sums, which made it possible to increase the convergence range and achieve unambiguous recognition results for aberrations (root mean square does not exceed 10⁻⁸) described by superpositions of Zernike functions up to n = 5. Full article

Three-dimensional (3D) reconstruction measurement technology utilizing line-structured light offers non-contact operation, making it widely applicable in industrial production. An effective scanning-direction calibration method in a line-structured light-based 3D measurement system can not only enhance the system accuracy but also mitigate the production inefficiencies caused by measurement errors. Consequently, developing a high-efficiency and high-precision scanning-direction calibration technique is a pivotal challenge for advancing structured light-based 3D measurement systems. In this study, we propose an improved method to calibrate the sensor’s scanning direction that iteratively optimizes control points via plane transformation while leveraging the rotational invariance of the rotation matrix during translation. By minimizing the reprojection error, an optimized rotation matrix is identified, and the Levenberg–Marquardt (LM) algorithm is subsequently employed to iteratively refine the displacement vector, enabling precise estimation of the scanning direction. Usually, in line-structured light-based 3D reconstruction measurement, a 5 mm standard gauge block is first reconstructed, and then, the reconstruction error of the standard gauge block is used to compare the accuracy of the scanning-direction calibration (other quantities remain unchanged). Hence, we conducted a comparison experiment using the constructed line-structured light-based 3D reconstruction measurement system, and the experimental results demonstrated that the proposed method reduces the reconstruction errors by 29% compared to the classical independent estimation method and by 5% compared to the current joint estimation method. Furthermore, our method eliminates strict distance constraints, thereby enhancing its adaptability in practical applications. Full article

Magnetic anomaly data rapidly decay with distance and are susceptible to environmental magnetic noise, which leads to reduced accuracy and robustness in estimating magnetic source parameters. This shows significant differences between estimated and true values. Therefore, this study proposes a method for estimating magnetic source parameters based on the LM-OBF algorithm. This method transforms magnetic anomaly data into a two-dimensional orthogonal basis function space using the Gram–Schmidt orthogonalization process, establishing a new forward modeling relationship. It then constructs an objective function within a least squares framework and optimizes it using the Levenberg–Marquardt (LM) algorithm to achieve a stable estimation of magnetic source parameters. The experimental section tests this method using synthetic and field data, comparing it to traditional detection methods. The results demonstrated that the method maintains stable and accurate estimation of magnetic source parameters even at a signal-to-noise ratio (SNR) of −10 dB, outperforming traditional methods in terms of performance under strong noise interference conditions. Full article

The accurate and timely determination of crop leaf area indices (LAIs) assists in making agricultural decisions. The objective of this study was to estimate crop LAIs using C-band RADARSAT-2 synthetic aperture radar (SAR) datasets and a modified water cloud model (MWCM). The WCM was improved through two steps: (1) constructing a vegetation coverage ratio ($f_v$) using normalized difference vegetation indices calculated from Landsat-8 images and introducing it into the traditional WCM, and (2) incorporating field-collected crop height into the vegetation canopy described in the scattering model. The proposed MWCM parameters were calibrated using an iterative optimization algorithm named the Levenberg–Marquardt (LM) algorithm. The model’s performance before and after improvement was systematically calibrated and validated using field data collected from Yigen Farm (Hulunbuir City, Inner Mongolia Autonomous Region, China). The results show that the MWCM performed better than the original WCM in four polarization channels—HH, VV, HV, and VH—for both wheat and rape oilseed LAI inversion. HH polarization showed the best performance using both the MWCM and WCM for wheat, with R² values of 0.4626 and 0.3327, respectively; meanwhile, for oilseed rape, the R² values were 0.4912 and 0.3128, respectively. The RMSEs of the wheat inversion results were reduced from 1.5227 m²m⁻² to 1.4898 m²m⁻², and those for oilseed rape were reduced from 1.0411 m²m⁻² to 0.7968 m²m⁻². This study proved the feasibility and superiority of the MWCM, which provides new technical support for accurate crop growth monitoring. Full article

Accurate kinematic calibration of subreflector actuators is essential for pointing precision of large radio telescopes, particularly at high frequencies. Conventional least-squares methods are vulnerable to noise and outliers, and their accuracy may degrade when limited pose diversity leads to poor parameter excitation. To address these challenges, this paper proposes a novel robust self-calibration framework that integrates Huber loss and $L_2$ regularization into the Levenberg–Marquardt (LM) algorithm—yielding a hybrid optimization approach that combines residual robustness, numerical stability, and convergence reliability. A comprehensive simulation study was conducted under varying workspace sizes and sensor noise levels. The proposed method maintained stable performance even under reduced excitation and high-noise conditions, where traditional LM methods typically degrade, confirming its robustness and applicability to realistic calibration scenarios. The framework was further validated using a structured-light 6-DOF pose measurement system, the proposed method achieved over 90% improvement in both position and orientation accuracy compared to the traditional LM approach. These findings confirm the method’s effectiveness for high-precision 6-DOF calibration in parallel mechanisms, and its suitability for real-world applications in radio telescope subreflector alignment. Full article

The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. Twenty experimental trials have been performed utilizing a face-centered central composite design through a response surface approach. Artificial Neural Network (ANN) models were built to forecast outcomes, utilizing four distinct learning algorithms: the Batch Back Propagation Algorithm (BBP), Quick Propagation Algorithm (QP), Incremental Back Propagation Algorithm (IBP), and Levenberg–Marquardt Back Propagation Algorithm (LMBP). The efficacy of these models was evaluated using RMSE, revealing that the LMBP model yielded the lowest RMSE for surface roughness (Ra), nose radius wear, and resultant cutting force, hence demonstrating superior predictive capability within the trained dataset. Additionally, a Genetic Algorithm (GA) was employed to ascertain the optimal machining settings, revealing that the ideal parameters include a cutting speed of 85 m/min, a feed rate of 0.07 mm/tooth, and a rounding radius of 7 mm. Moreover, the detachment of the coating layer resulted in alterations to the tooltip cutting edge on the machined surface as the circular loop distance increased. The initial arc radius fluctuated by 33.82% owing to tooltip defects that alter the edge micro-geometry of machining. The measured and expected values of the surface roughness, resultant cutting force, and nose radius wear exhibited discrepancies of 6.49%, 4.26%, and 4.1%, respectively. The morphologies of the machined surfaces exhibited scratches along with laces, and side flow markings. The back surface of the chip structure appears rough and jagged due to the shearing action. Full article

Accurate parameter estimation is essential for effective monitoring and treatment of high-salinity industrial wastewater. Traditional methods such as spectroscopy, ion chromatography, and electrochemical analysis offer high sensitivity but are often complex, costly, and unsuitable for real-time monitoring. This research integrates Deep Neural Networks (DNNs) with the Levenberg–Marquardt (LM) algorithm to develop an advanced RFID-based sensing system for real-time monitoring of sodium chloride solutions in wastewater. The DNN extracts essential features from raw data, while the LM algorithm optimizes parameter estimation for enhanced precision and stability. Experimental results show that the dielectric constant sample variance at various flow rates under wireless frequency is 0.08509, while the sample total variance is 0.06807, both below 0.1. Additionally, the sample standard deviation and total standard deviation are both below 0.3, at 0.26090 and 0.29169, respectively. These findings confirm that the proposed system is robust against flow rate variations, ensuring accurate, real-time monitoring and supporting sustainable industrial practices. Full article

Ensuring efficient long-term quality control of the raw mix remains a priority for the cement industry, supporting initiatives to lower the CO₂ footprint by incorporating significant amounts of alternative fuels and raw materials in clinker production. This study presents an effective method for creating a robust auto-tuner for proportional–integral–differential (PID) controller control of the lime saturation factor (LSF) of the raw mix using artificial neural networks (ANNs). This auto-tuner, combined with a previously studied robust PID controller, forms an integrated system that adapts to process changes and maintains low long-term variance in LSF. The ANN links each of the three PID gains to the process dynamic parameters, with the three ANNs also interconnected. We employed the Levenberg–Marquardt method to optimize the ANNs’ synaptic weights and applied the weight decay method to prevent overfitting. The industrial implementation of our control system, using the auto-tuner for 16,800 h of raw mill operation, shows an average LSF standard deviation of 2.5, with fewer than 10% of the datasets exceeding a standard deviation of 3.5. Considering that the measurement reproducibility is 1.44 and assuming a low mixing ratio of the raw meal in the silo equal to 2, the LSF standard deviation in the kiln feed approaches the analysis reproducibility, indicating that disturbances in the raw meal largely diminish in the kiln feed. In conclusion, integrating traditional, well-established tools like PID controllers with newer advanced techniques, such as ANNs, can yield innovative solutions. Full article

In response to the need for cable outer sound source localization, this paper proposes a collaborative localization method based on an improved generalized cross-correlation phase transform (GCC-PHAT) and multi-sensor fusion. By constructing a secondary cross-shaped sensor array model, employing a phase transform weighting function to suppress environmental noise, and incorporating an adaptive environmental compensation algorithm to eliminate multipath effects, a set of spatial localization equations is established. Innovatively, a dynamic weighting factor linked to the startup threshold is introduced; the Levenberg–Marquardt optimization algorithm is then used to iteratively solve the nonlinear equations to achieve preliminary localization in a single-pile coordinate system. Finally, a dynamic weighted fusion model is constructed through DBSCAN spatial clustering to determine the final sound source position. Experimental results demonstrate that this method reduces the mean square error of time delay estimation by 94.7% in a 90 dB industrial noise environment, decreases the localization error by 65.4% in multi-obstacle scenarios, and ultimately maintains localization accuracy within 3 m over a range of 100 m. This performance is significantly superior to that of traditional TDOA and SRP-PHAT methods, offering a high-precision localization solution for underground cable protection. Full article

Underground engineering rock masses are significantly affected by stress redistribution induced by mining or adjacent engineering disturbances, leading to initial damage accumulation in coal-rock masses. Under sustained geostress, these masses exhibit pronounced time-dependent creep behavior, posing serious threats to long-term engineering stability. Dynamic loading effects triggered by adjacent mining activities (manifested as medium strain-rate loading) further exacerbate damage evolution and significantly influence creep characteristics. In this study, coal samples with identical initial damage were prepared, and graded loading creep tests were conducted at rates of 0.005 mm·s⁻¹ (50 microstrains·s⁻¹), 0.01 mm·s⁻¹ (100 microstrains·s⁻¹), 0.05 mm·s⁻¹ (500 microstrains·s⁻¹), and 0.1 mm·s⁻¹ (1000 microstrains·s⁻¹) to systematically analyze the coupled effects of loading rate on creep behavior. Experimental results demonstrate that increased loading rates markedly shorten creep duration, with damage rates during the acceleration phase showing nonlinear surges (e.g., abrupt instability at 0.1 mm·s⁻¹ (1000 microstrains·s⁻¹)). Based on experimental data, an integer-order viscoelastic-plastic creep model incorporating stress-dependent viscosity coefficients and damage correlation functions was developed, fully characterizing four behaviors stages: instantaneous deformation, deceleration, steady-state, and accelerated creep. Optimized via the Levenberg–Marquardt algorithm, the model achieved correlation coefficients exceeding 0.96, validating its accuracy. This model clarifies the impact mechanisms of loading rates on the long-term mechanical behavior of initially damaged coal samples, providing theoretical support for stability assessment and hazard prevention in underground engineering. Full article

Currently, most crude oil viscosity correlations rely only on API gravity and temperature, resulting in significant limitations in accurately predicting crude oil viscosity. To address this issue, this paper systematically explores the effect of API gravity, and crude oil components (saturates, aromatics, resins, and asphaltenes content), on viscosity based on 251 crude oil samples through sensitivity analysis. To overcome the shortcoming of traditional models, this paper proposes an innovative generalized viscosity model that combines the Levenberg–Marquardt (LM) and universal global optimization (UGO) methods to fully consider the effects of API gravity and various crude oil components. To verify the effectiveness of the model, this paper divides the 251 crude oil samples into a training set (202 samples) and a test set (49 samples) and compares the prediction results of the new model with the traditional. The results show that the prediction accuracy of the new model on the training set and test set is significantly better than that of the traditional model, with the minimum average absolute relative deviation reaching 10.13% and 12.4%, respectively. This study not only improves the accuracy of crude oil viscosity prediction but also provides early warning of increased pipeline friction caused by abnormal viscosity, avoids suspension accidents, and ensures the safe operation of long-distance pipelines. Full article

In the context of Al-Kharj city, which is steadily advancing as an industrial and manufacturing hub within Saudi Arabia, this study has significant relevance. The city’s focus on metal forming, fabrication, and materials engineering makes it crucial to optimize processes such as hot deformation of metallic alloys for various sectors, including aerospace, automotive, oil and gas, and structural applications. By assessing and comparing phenomenological and physical material models for nickel, aluminum, titanium, and iron-based alloys, this study aids Al-Kharj industries in advancing their process simulation and predictive performance. Thus, this study aims to evaluate the proposed phenomenological and physically based constitutive models for Ni-, Al-, Ti-, and Fe-based alloys to enhance the accuracy of high-temperature deformation simulations. Phenomenological models investigated include the Johnson–Cook (JC), Fields and Backofen (FB), and Khan–Huang–Liang (KHL) formulations, while the Zerilli–Armstrong (ZA) model represents the physical category. Additionally, various modifications to these models are explored. Model parameters are calibrated using the Levenberg–Marquardt algorithm to minimize mean square error. Performance is assessed through key statistical metrics, including the correlation coefficient (R), average absolute relative error (AARE), and root mean square error (RMSE). Of the 32 models analyzed, a modified version of the JC model delivers the highest accuracy across all alloys. Furthermore, four other modifications, one each for the JC and ZA models and two for the FB model, exhibit superior predictive capability for specific alloys. This makes this study valuable not just academically, but also as a practical resource to boost Al-Kharj’s industrial competitiveness and innovation capacity. Full article