Search Results (11)

This study introduces an advanced automated guided vehicle (AGV) specifically designed for application in recirculating aquaculture systems (RASs). The proposed AGV seamlessly integrates automated feeding, real-time monitoring, and an intelligent path-planning system to enhance operational efficiency. To achieve optimal and adaptive navigation, a hybrid algorithm is developed, incorporating Newton–Raphson-based optimisation (NRBO) alongside ant colony optimisation (ACO). Additionally, dueling deep Q-networks (dueling DQNs) dynamically optimise critical parameters, thereby improving the algorithm’s adaptability to the complexities of RAS environments. Both simulation-based and real-world experiments substantiate the system’s effectiveness, demonstrating superior convergence speed, path quality, and overall operational efficiency compared to traditional methods. The findings of this study highlight the potential of AGV to enhance precision and sustainability in recirculating aquaculture management. Full article

In the realm of renewable energy, harnessing wind power efficiently is crucial for establishing a low-carbon power system. However, the intermittent and uncertain nature of wind speed poses significant challenges for accurate prediction, which is essential for effective grid integration and dispatch management. To address this challenge, this paper introduces a novel hybrid model, NRBO-TXAD, which integrates a Newton–Raphson-based optimizer (NRBO) with a Transformer and XGBoost, further enhanced by adaptive denoising techniques. The interquartile range–adaptive moving average filter (IQR-AMAF) method is employed to preprocess the data by removing outliers and smoothing the data, thereby improving the quality of the input. The NRBO efficiently optimizes the hyperparameters of the Transformer, thereby enhancing its learning performance. Meanwhile, XGBoost is utilized to compensate for any residual prediction errors. The effectiveness of the proposed model was validated using two real-world wind speed datasets. Among eight models, including LSTM, Informer, and hybrid baselines, NRBO-TXAD demonstrated superior performance. Specifically, for Case 1, NRBO-TXAD achieved a mean absolute percentage error (MAPE) of 11.24% and a root mean square error (RMSE) of 0.2551. For Case 2, the MAPE was 4.90%, and the RMSE was 0.2976. Under single-step forecasting, the MAPE for Case 2 was as low as 2.32%. Moreover, the model exhibited remarkable robustness across multiple time steps. These results confirm the model’s effectiveness in capturing wind speed fluctuations and long-range dependencies, making it a reliable solution for short-term wind forecasting. This research not only contributes to the field of signal analysis and machine learning but also highlights the potential of hybrid models in addressing complex prediction tasks within the context of artificial intelligence. Full article

Airborne synthetic aperture radar (SAR) serves as critical battlefield reconnaissance equipment, yet it remains vulnerable to electromagnetic interference (EMI) in combat environments, leading to image-quality degradation. To address this challenge, this study proposes an EMI-effect prediction framework for airborne SAR electromagnetic environments, based on the Newton–Raphson-based optimization (NRBO) and XGBoost algorithms. The methodology enables interference-level prediction through electromagnetic signal parameters obtained from reconnaissance operations, providing operational foundations with which SAR systems can mitigate the impacts of EMI. A laboratory-based airborne SAR EMI test system was developed to establish mapping relationships between EMI signal parameters and SAR imaging performance degradation. This experimental platform facilitated EMI-effect investigations across diverse interference scenarios. An evaluation methodology for SAR image degradation caused by EMI was formulated, revealing the characteristic influence patterns of different interference signals in the context of SAR imagery. The NRBO–XGBoost framework was established through algorithmic integration of Newton–Raphson search principles with trap avoidance mechanisms from the Newton–Raphson optimization algorithm, optimizing the XGBoost hyperparameters. Utilizing the developed test system, comprehensive EMI datasets were constructed under varied interference conditions. Comparative experiments demonstrated the NRBO–XGBoost model’s superior accuracy and generalization performance relative to conventional prediction approaches. Full article

In this paper, a new type of combined column, square steel tube hybrid steel–basalt fiber reinforced concrete column (BSFCFST), is proposed for the first time, and a new hybrid machine learning model, NRBO-XGBoost, is proposed to predict the axial compressive load capacity of BSFCFST. Eleven specimens were designed and fabricated to investigate the axial mechanical properties of BSFCFST. The variables considered include basalt fiber volume content, steel fiber volume content, steel tube wall thickness and specimen length to slenderness ratio. The characteristics of damage modes, load-displacement curves and load-strain curves of the new combined columns were mainly investigated. The results showed that the hybrid fibers improved the ultimate load carrying capacity of the specimen, and the improvement of the ductility was obvious. On the basis of the experiments, a parametric expansion analysis of several structural parameters of the specimen was carried out by using ABAQUS finite element software, and a combined model NRBO-XGBoost, based on the Newton-Raphson optimization algorithm (NRBO), and the advanced machine learning model XGBoost was proposed for the prediction of the BSFCFST’s ultimate carrying capacity. The combined model NRBO-XGBoost was evaluated by comparing it with several prediction methods. The results show that the prediction accuracy of the NRBO-XGBoost model is significantly higher than that of other prediction methods, with R² = 0.988, which is a good alternative to existing empirical models. Full article

To accurately capture the dynamic displacement of super-tall buildings under complex conditions, this study proposes a data fusion algorithm that integrates NRBO-FMD optimization with Adaptive Robust Kalman Filtering (ARKF). The NRBO-FMD method preprocesses GNSS and accelerometer data to mitigate GNSS multipath effects, unmodeled errors, and high-frequency noise in accelerometer signals. Subsequently, ARKF fuses the preprocessed data to achieve high-precision displacement reconstruction. Numerical simulations under varying noise conditions validated the algorithm’s accuracy. Field experiments conducted on the Hairong Square Building in Changchun further demonstrated its effectiveness in estimating three-dimensional dynamic displacement. Key findings are as follows: (1) The NRBO-FMD algorithm significantly reduced noise while preserving essential signal characteristics. For GNSS data, the root mean square error (RMSE) was reduced to 0.7 mm for the 100 s dataset and 1.0 mm for the 200 s dataset, with corresponding signal-to-noise ratio (SNR) improvements of 3.0 dB and 6.0 dB. For accelerometer data, the RMSE was reduced to 3.0 mm (100 s) and 6.2 mm (200 s), with a 4.1 dB SNR gain. (2) The NRBO-FMD–ARKF fusion algorithm achieved high accuracy, with RMSE values of 0.7 mm (100 s) and 1.9 mm (200 s). Consistent PESD and POSD values demonstrated the algorithm’s long-term stability and effective suppression of irregular errors. (3) The algorithm successfully fused 1 Hz GNSS data with 100 Hz accelerometer data, overcoming the limitations of single-sensor approaches. The fusion yielded an RMSE of 3.6 mm, PESD of 2.6 mm, and POSD of 4.8 mm, demonstrating both precision and robustness. Spectral analysis revealed key dynamic response frequencies ranging from 0.003 to 0.314 Hz, facilitating natural frequency identification, structural stiffness tracking, and early-stage performance assessment. This method shows potential for improving the integration of GNSS and accelerometer data in structural health monitoring. Future work will focus on real-time and predictive displacement estimation to enhance monitoring responsiveness and early-warning capabilities. Full article

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has demonstrated significant potential for soil moisture content (SMC) monitoring due to its high spatiotemporal resolution. However, GNSS-IR inversion experiments are notably influenced by vegetation and meteorological factors. To address these challenges, this study proposes a multi-factor SMC inversion method. Six GNSS stations from the Plate Boundary Observatory (PBO) were selected as study sites. A low-order polynomial was applied to separate the reflected signals, extracting parameters such as phase, frequency, amplitude, and effective reflector height. Auxiliary variables, including the Normalized Microwave Reflection Index (NMRI), cumulative rainfall, and daily average evaporation, were used to further improve inversion accuracy. A multi-factor SMC inversion dataset was constructed, and three machine learning models were selected to develop the SMC prediction model: Support Vector Regression (SVR), suitable for small and medium-sized regression tasks; Convolutional Neural Networks (CNN), with robust feature extraction capabilities; and NRBO-XGBoost, which supports automatic optimization. The multi-factor SMC inversion method achieved remarkable results. For instance, at the P038 station, the model attained an R² of 0.98, with an RMSE of 0.0074 and an MAE of 0.0038. Experimental results indicate that the multi-factor inversion model significantly outperformed the traditional univariate model, whose R² (RMSE, MAE) was only 0.88 (0.0179, 0.0136). Further analysis revealed that NRBO-XGBoost surpassed the other models, with its average R² outperforming SVR by 0.11 and CNN by 0.03. Additionally, the analysis of different surface types showed that the method achieved higher accuracy in grassland and open shrubland areas, with all models reaching R² values above 0.9. Therefore, the accuracy of the multi-factor SMC inversion model was validated, supporting the practical application of GNSS-IR technology in SMC inversion. Full article

The precise acquisition of water depth data in nearshore shallow waters bears considerable strategic significance for marine environmental monitoring, resource stewardship, navigational infrastructure development, and military security. Conventional bathymetric survey methodologies are constrained by their spatial and temporal limitations, thus failing to satisfy the requirements of large-scale, real-time surveillance. While satellite remote sensing technologies present a novel approach to water depth inversion in shallow waters, attaining high-precision inversion in nearshore areas characterized by elevated levels of suspended sediments and diminished transparency remains a formidable challenge. To tackle this issue, this study introduces an enhanced XGBoost model grounded in the Newton–Raphson optimizer (NRBO–XGBoost) and successfully applies it to water depth inversion investigations in the nearshore shallow waters of the Beibu Gulf. The research amalgamates Sentinel-2B multispectral imagery, nautical chart data, and in situ water depth measurements. By ingeniously integrating the Newton–Raphson optimizer with the XGBoost framework, the study realizes the automatic configuration of model training parameters, markedly elevating inversion accuracy. The findings reveal that the NRBO–XGBoost model attains a coefficient of determination (R²) of 0.85 when compared to nautical chart water depth data, alongside a scatter index (SI) of 21%, substantially surpassing conventional models. Additional validation analyses indicate that the model achieves a coefficient of determination (R²) of 0.86 with field-measured data, a mean absolute error (MAE) of 1.60 m, a root mean square error (RMSE) of 2.13 m, and a scatter index (SI) of 13%. Moreover, the model exhibits exceptional performance in extended applications within the waters of Zhanjiang Port (R² = 0.90), unequivocally affirming its dependability and practicality in intricate nearshore water environments. This study not only provides a fresh solution for remotely sensing water depth in complex nearshore water settings but also imparts valuable technical insights into the associated underwater surveys and marine resource exploitation. Full article

In order to solve the difficulties that the uncertain characteristics of PV output, such as volatility and intermittency, will bring to the development of microgrid scheduling plans, this paper proposes a Transformer–Bidirectional Long Short-Term Memory (BiLSTM) neural network PV power generation forecasting fusion model based on the Newton–Raphson optimization algorithm (NRBO) and Variational Modal Decomposition (VMD). Firstly, the principle of the VMD technique and the Gray Wolf Optimization (GWO) algorithm’s key parameter optimization method for VMD are introduced. Then, the Transformer decoder partially fuses the BiLSTM network and retains the encoder to obtain the body of the prediction model, followed by explaining the principle of the NRBO algorithm. And finally, the VMD-NRBO-Transformer-BiLSTM prediction model and hyperparameter selection are evaluated by the NRBO algorithm. The algorithm sets up a multi-model comparison experiment, and the results show that the prediction model proposed in this paper has the best prediction accuracy and the optimal evaluation index. Full article

To address the shortcomings of traditional bionic algorithms in path planning, such as inefficient search processes, extended planning distances and times, and suboptimal dynamic obstacle avoidance, this paper introduces a fusion algorithm called NRBO-DWA. This algorithm is specifically applied to plan the path for a tube-changing robot in a knitting workshop. The process begins with spatial modeling based on the actual parameters of the workshop, followed by the development of a comprehensive, objective function for the robot in line with the relevant constraints. The NRBO algorithm is then integrated with the DWA algorithm to boost its dynamic obstacle avoidance capabilities, while a path correction mechanism is introduced to minimize unnecessary detours. Finally, a comparative experiment is designed to evaluate the algorithm against the GA, PSO, and SSA algorithms. Simulation results demonstrate that in a dynamically complex 3D environment, the NRBO-DWA algorithm outperforms in terms of higher 3D search efficiency, shorter total path length, and faster planning times. Full article

Ash content is an important production indicator of flotation performance, reflecting the current operating conditions of the flotation system and the recovery rate of clean coal. It also holds significant importance for the intelligent control of flotation. In recent years, the development of machine vision and deep learning has made it possible to detect ash content in flotation-recovered clean coal. Therefore, a prediction method for ash content in flotation-recovered clean coal based on image processing of the surface characteristics of flotation froth is studied. A convolutional neural network –long short-term memory (CNN-LSTM) model optimized by Newton–Raphson is proposed for predicting the ash content of flotation froth. Initially, the collected flotation froth video is preprocessed to extract the feature dataset of flotation froth images. Subsequently, a hybrid CNN-LSTM network architecture is constructed. Convolutional neural networks are employed to extract image features, while long short-term memory networks capture time series information, enabling the prediction of ash content. Experimental results indicate that the prediction accuracy on the training set achieves an R value of 0.9958, mean squared error (MSE) of 0.0012, root mean square error (RMSE) of 0.0346, and mean absolute error (MAE) of 0.0251. On the test set, the prediction accuracy attains an R value of 0.9726, MSE of 0.0028, RMSE of 0.0530, and MAE of 0.0415. The proposed model effectively extracts flotation froth features and accurately predicts ash content. This study provides a new approach for the intelligent control of the flotation process and holds broad application prospects. Full article

In the context of COVID-19, energy conservation is becoming increasingly crucial to the overwhelmed tourism industry, and the heating, ventilation, and air conditioning system (HVAC) is the most energy-consuming factor in the indoor area of scenic spots. As tourist flows are not constant, the intelligent control of an HVAC system is the key to tourist satisfaction and energy consumption management. This paper proposes a noise-reduced and Bayesian-optimized (NRBO) light-gradient-boosting machine (LightGBM) to predict the probability of tourists entering the next scenic spot, hence adopting the feedforward dynamic adaptive adjustment of the ventilation and air conditioning system. The customized model is more robust and effective, and the experimental results in Luoyang City Hall indicate that the proposed system outperforms the baseline LightGBM model and a random-search based method concerning prediction loss by 5.39% and 4.42%, respectively, and saves energy by 23.51%. The study illustrates a promising step in the advancement of tourism energy consumption management and sustainable tourism in the experimental area by improving tourist experiences and conserving energy efficiently, and the software-based system can also be smoothly applied to other indoor scenic spots. Full article