Search Results (888)

Free Space Optical (FSO) communication is extensively utilized in the telecommunication industry for both ground and space wireless links, as well as last-mile applications, as a result of its lesser Bit Error Rate (BER), free spectrum, and easy relocation. However, atmospheric turbulence, also known as Wavefront Aberration (WA), is considered a serious issue because it causes higher BER and affects coupling efficiency. In order to address this issue, a Sensor-Less Adaptive Optics (SLAO) system is developed for FSO to enhance performance. In this research, the compensation of WA in SLAO is obtained by proposing the Brownian motion and Directional mutation scheme-based Coati Optimization Algorithm, BDCOA. Here, the BDCOA is developed to search for an optimum control signal value of actuators in Deformable Mirror (DM). The incorporated Brownian motion and directional mutation are used to avoid the local optimum issue and enhance search space efficiency while searching for the control signal. Therefore, the dynamic control signal optimization for DM using BDCOA helps to enhance the coupling efficiency. Thus, the WAs are compensated for and optical signal concentration is enhanced in FSO. The metrics used for analyzing the BDCOA are Root Mean Square (RMS), BER, coupling efficiency, and Strehl Ratio (SR). The existing methods, such as Simulated Annealing (SA) and Stochastic Parallel Gradient Descent (SPGD), Advanced Multi-Feedback SPGD (AMFSPGD), and Oppositional-Breeding Artificial Fish Swarm (OBAFS), are used for evaluating the performance of BDCOA. The RMS of BDCOA for iterations 500 is 0.12, which is less than that of the SA-SPGD and OBAFS. Full article

In the field of oil and gas well perforation, it is imperative to accurately forecast the casing entry hole diameter under full downhole conditions. Precise prediction of the casing entry hole diameter enhances the design of both conventional and limited entry hydraulic fracturing, mitigates the risk of proppant screenout, reduces skin factors attributable to perforation, guarantees the presence of sufficient flow areas for the effective pumping of cement during a squeeze operation, and reduces issues related to sand production. Implementing machine learning and deep learning models yields immediate and precise estimations of entry hole diameter, thereby facilitating the attainment of these objectives. The principal aim of this research is to develop sophisticated machine learning-based models proficient in predicting entry hole diameter under full downhole conditions. Ten machine learning and deep learning models have been developed utilizing readily available parameters routinely gathered during perforation operations, including perforation depth, rock density, shot phasing, shot density, fracture gradient, reservoir unconfined compressive strength, casing elastic limit, casing nominal weight, casing outer diameter, and gun diameter as input variables. These models are trained by utilizing actual casing entry hole diameter data acquired from deployed downhole cameras, which serve as the output for the X’ models. A comprehensive dataset from 53 wells has been utilized to meticulously develop and fine-tune various machine learning algorithms. These include Gradient Boosting, Linear Regression, Stochastic Gradient Descent, AdaBoost, Decision Trees, Random Forest, K-Nearest Neighbor, neural network, and Support Vector Machines. The results of the most effective machine learning models, specifically Gradient Boosting, Random Forest, AdaBoost, neural network (L-BFGS), and neural network (Adam), reveal exceptionally low values of mean absolute percent error (MAPE), root mean square error (RMSE), and mean squared error (MSE) in comparison to actual measurements of entry hole diameter. The recorded MAPE values are 4.6%, 4.4%, 4.7%, 4.9%, and 6.3%, with corresponding RMSE values of 0.057, 0.057, 0.058, 0.065, and 0.089, and MSE values of 0.003, 0.003, 0.003, 0.004, and 0.008, respectively. These low MAPE, RMSE, and MSE values verify the remarkably high accuracy of the generated models. This paper offers novel insights by demonstrating the improvements achieved in ongoing perforation operations through the application of a machine learning model for predicting entry hole diameter. The utilization of machine learning models presents a more accurate, expedient, real-time, and economically viable alternative to empirical models and deployed downhole cameras. Additionally, these machine learning models excel in accommodating a broad spectrum of guns, well completions, and reservoir parameters, a challenge that a singular empirical model struggled to address. Full article

This paper measures the impact of the number of Halton draws in excess of $\lceil \sqrt{n}\rceil$ on technical efficiency in the generalized true random effects (four-component) stochastic frontier model estimated by simulated maximum likelihood. A substantial set of Monte Carlo simulations demonstrates that increasing the number of Halton draws to $\lceil n^{3/4}\rceil$ ($\lceil n^{2/3}\rceil$) decreases the mean squared error of the total technical efficiency estimates by $6.1$ ($4.9$) percent. Furthermore, increasing the number of Halton draws either improves or has no detrimental impact on correlation, mean squared error, relative bias, and upward bias for persistent, transient, and total technical efficiency. An energy sector application is included, to demonstrate how these issues can arise in practice, and how increasing Halton draws can improve parameter and efficiency estimates in empirical work. Full article

In situ dynamic testing is conducted to study the dynamic characteristics of the wooden structure of the North House main hall. The velocity response signals on the measurement points are obtained and analyzed using the self-interaction spectral method and stochastic subspace method, yielding natural frequencies, mode shapes, and damping ratios. This study reveals that the natural frequencies and damping ratios are highly consistent between the two methods. Therefore, to eliminate errors, the average of the results from both modal identification methods is taken as the final measured modal parameters of the structure. The natural frequencies of the first and second order in the X direction were 2.097 Hz and 3.845 Hz and in the Y direction were 3.955 Hz and 5.701 Hz. The modal frequency in the Y direction of the structure exceeds that in the X direction. Concurrently, a three-dimensional finite element model was established using ANSYS 2021R1, considering the semi-rigid properties of mortise–tenon connections, and validated based on in situ dynamic testing. The sensitivity analysis indicates adjustments to parameters such as beam–column elastic modulus, tenon–mortise joint stiffness, and roof mass for finite element model refinement. Modal parameter calculations from the corrected finite element model closely approximate the measured modal results, with maximum errors of 9.41% for the first two frequencies, both within 10% of the measured resonant frequencies. The adjusted finite element model closely matches the experimental results, serving as a benchmark model for the wooden structure of North House main hall. The validation confirms the rationality of the benchmark finite element model, providing valuable insights into ancient timber structures along transportation routes. Full article

To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, and uses Kmeans++ for clustering processing. On this basis, a Transformer model based on an adversarial adaptive mechanism is designed, which aligns the data distribution of the source domain and target domain through a domain discriminator and feature extractor, thereby reducing the impact of domain offset on prediction accuracy. The mean square error of the Fourier transform clustering method used in this study was 0.154, which was lower than other methods and had a better data denoising effect. In load forecasting, the mean square errors of the model in predicting long-term load, short-term load, and real-time load were 0.026, 0.107, and 0.107, respectively, all lower than the values of other comparative models. Therefore, the load forecasting model designed for research has accuracy and stability, and it can provide a foundation for the precise control of urban power systems. The contributions of this study include improving the accuracy and stability of the load forecasting model, which provides the basis for the precise control of urban power systems. The model tracks periodicity, short-term load stochasticity, and high-frequency fluctuations in long-term loads well, and possesses high accuracy in short-term, long-term, and real-time load forecasting. Full article

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states. Full article

Recent advancements in Dynamic Wireless Power Transfer (DWPT) have highlighted the need for further research, particularly in the area of modelling and simulation techniques. As the power transferred between charging pads depends on vehicle position, the load profile of the DWPT is therefore a function of the vehicle’s movement which is dependent on user behaviour and is inherently stochastic. For DWPT, these events involve high instantaneous power and are short in duration. To better understand the impact of DWPT, accurate models are required to test control systems and potential solutions. Additionally, these systems require high-frequency simulation for DWPT, which results in long simulation times during development. This paper presents a simplified model for circuit components that eliminates high-frequency switching elements, enabling the use of larger simulation time steps and significantly reducing simulation time. By applying circuit analysis and calculating equivalent impedances, the model provides average circuit values that effectively represent waveform amplitudes without the need to simulate instantaneous, high-frequency variations. To ensure the efficiency of grid-connected simulations and achieve a level of accuracy that reflects the internal dynamics of wireless charging, subsystem simulations demonstrated significant time improvements at the cost of minimal accuracy loss. For DC/DC converters operating at 2 kHz, simulation time was reduced by 3× with only a 1% error. The DWPT subsystem, operating at 85 kHz, achieved an 18× reduction in simulation time with a 2.5% deviation. When combined, the full system resulted in a 30-fold reduction in simulation time with only a 6% deviation from the base model. Full article

The pitch angle regulation in Remotely Operated Towed Vehicles (ROTVs) is essential to ensure the robustness of emitted signals within the maritime surveillance domain. Characterized by inherent nonlinear dynamics and stochastic uncertainties, the pitch angle model poses significant challenges to conventional tracking controls relying on linearization. This study introduces an adaptive pitch-control algorithm designed for ROTVs, which adeptly manages nonlinear dynamics as well as unmeasurable states through a synergistic integration of dynamic and static gains. A key feature of our approach is the incorporation of a high-order observer that adeptly estimates the system’s unmeasurable states, thereby enhancing control precision. Our proposed algorithm greatly exceeds traditional PID and fuzzy PID methods in both settling time and steady-state error, particularly in high-order nonlinear and unmeasurable state scenarios. Compared to sliding mode control, the proposed control strategy improved the settling time by 74% and the steady-state error was enhanced from ${10}^{-6}$ to ${10}^{-8}$, as confirmed by numerical simulations. The efficacy of the algorithm in achieving the desired tracking trajectories highlights its potential for deep-water operations and fine-tuned attitude adjustments for ROTVs. Full article

The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes an integrated deflection interval prediction method based on a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a probability density estimation layer, and bridge monitoring data. A time-series training dataset consisting of environmental temperature, vehicle load, and deflection monitoring data was built based on bridge health monitoring data. The CNN-LSTM combined layer is used to capture both local features and long-term dependencies in the time series. A Gaussian distribution (GD) is adopted as the probability density function, and its parameters are estimated using the maximum likelihood method, which outputs the optimal deflection prediction and probability intervals. Furthermore, this paper proposes a method for identifying abnormal deflections of the main girder in existing suspension bridges and establishes warning thresholds. This study indicates that, for short time scales, the CNN-LSTM-GD model achieves a 47.22% improvement in Root Mean Squared Error (RMSE) and a 12.37% increase in the coefficient of determination (${\mathrm{R}}^2$) compared to the LSTM model. When compared to the CNN-LSTM model, it shows an improvement of 28.30% in RMSE and 6.55% in ${\mathrm{R}}^2$. For long time scales, the CNN-LSTM-GD model shows a 54.40% improvement in RMSE and a 10.22% increase in ${\mathrm{R}}^2$ compared to the LSTM model. Compared to the CNN-LSTM model, it improves RMSE by 38.43% and $R^2$ by 5.31%. This model is instrumental in more accurately identifying abnormal deflections and determining deflection thresholds, making it applicable to bridge deflection early-warning systems. Full article

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil Core^TM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical–chemical–biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil–crop relationships, making this method well suited for industrial-scale precision agriculture applications. Full article

This study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an always overbought condition in the Day-Ahead as well as in the Futures Market, the excess energy returns without revenue to the market, and the results are compared with a standard contract in Greece, which stands as the lowest as far as the billing price is concerned. The analysis achieved a mean absolute percentage error (MAPE) of 12.89% as the best fitted model and without using any kind of pre-processing methods. Full article

Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG. Full article

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (1984–2017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts. Full article

To satisfy the demands of high image quality and resolutions, telescope alignment is indispensable. In this paper, a wavefront sensorless method based on a modified stochastic parallel gradient descent algorithm (SPGD) called the adaptive moment estimation SPGD (Adam SPGD) algorithm is proposed. Simulations are carried out using a four-mirror telescope, whose aperture is 6 m and fields of view are Φ2°. Three misalignments are shown as examples. Positions of the secondary mirror and third mirror are employed to compensate aberrations. The results show that merit functions and energy distributions of corrected images match with the designed ones. The mean RMS of residual wavefront errors is smaller than λ/14 (λ = 0.5 μm), indicating that the misalignments are well compensated. The results verify the effectiveness of our method. Full article

With the development of technology, the working processes of rolling equipment have become more and more complex, and the traditional rolling model encounters difficulties in meeting current demands for accuracy. To reduce the thickness error of the rolling system, we propose a high-precision rolling force prediction method based on SSA–Bilstm–Attention, which reduces the thickness error of the rolling system by predicting the high-precision rolling force. Firstly, a mechanical model is established, and the parameters involved are analyzed to extract suitable parameters as inputs to the network to reduce the feature loss of the network inputs. Secondly, an improved sparrow search algorithm is used to search for the hyperparameters of the network to obtain better training results. Finally, the attention mechanism is introduced to increase the network’s training accuracy. A stochastic small-batch gradient descent method is used to improve the training speed of the network. In addition, this paper establishes a web-based host computer, which provides an effective data source for the experimental analysis. The experimental results show that the optimized model has a mean square error of 1.22%, which is more accurate than other models, and has good generalization ability. The experiments confirm the method’s effectiveness in improving the thickness accuracy of the rolling system and provide a new optimization scheme for the industry. Full article