Search Results (33,396)

Recent advancements in satellite Earth observation (EO) technology have significantly improved the accuracy and density of data available for monitoring rivers and streams, as well as for diagnosing errors in hydraulic models. Laser and radar altimetry missions, such as ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) and SWOT (Surface Water and Ocean Topography), offer high-resolution measurements of land and water surface elevation (WSE), covering entire river reaches and providing high-resolution WSE profiles along the river chainage, which can be directly compared to hydraulic model results. In this study, we implemented a workflow to assess the accuracy of simulated WSE and evaluate the performance of hydraulic models in the Chao Phraya (CPY) River, using WSE data from ICESat-2 and SWOT. The evaluation of ICESat-2, SWOT, and simulated WSE from the model, compared to in situ data, resulted in root mean square error (RMSE) values of 0.34 m, 0.35 m, and 0.37 m, respectively. Despite this, both ICESat-2 and SWOT data proved effective for error detection and performance evaluation along the CPY river in point, profile, and spatial map comparisons, with overall RMSE values of 0.36 m and 0.33 m, respectively, when compared with simulated WSE. This paper demonstrates that ICESat-2 and SWOT are valuable tools for diagnosing errors and improving hydraulic model performance, providing critical insights for river monitoring and model validation. Full article

One of the unexpected consequences of interregional integration is the risk of environmental degradation. The lack of barriers for goods, services, and economic resources within the area of integration association results in monetary expansion and facilitates economic growth. Indeed, the further consequence is environmental degradation in accordance with the Kuznets environmental curve hypothesis. Therefore, the dynamics of trade turnover and the GDP of the Eurasian Economic Union (EAEU) countries for 2023, the lack of the environmental empirical studies in the EAEU, and the impact of integration processes on environmental quality within the integration association are extremely relevant. The aim of this study is to identify the impact of integration spillover effects on the ecological footprint of five EAEU countries between 1992 and 2023. In order to achieve this research objective, an analysis sequence was carried out through the following steps: analyse the stationarity of the variables; check for cross-sectional dependence; evaluate the consistency of an estimator; calculate the Moran’s I index; estimate research results using the Spatial Error Model (SEM), Spatial Autoregressive Model (SAR), and Spatial Dubin Model (SDM), or eliminate the spatial models; analyse and diagnose the model; correct multicollinearity. By applying the Common Correlated Effects Mean Group (CCEMG) model (the model obtained showed a high coefficient of determination R-squared ~69%), the results are summarised: (1) Economic growth and integration processes have a positive and statistically significant impact on ecological footprints. (2) Financial development does not have a long-term statistically significant impact on environmental quality in the EAEU countries. These findings underscore the urgent need for a sustainability-oriented approach to economic integration within the EAEU. This study proposes a comprehensive roadmap for policymakers, emphasising the integration of green finance mechanisms, the adoption of sustainable trade practices, and the establishment of a regional environmental governance framework. Full article

Array Geiger-mode avalanche photodiode (GM-APD) Light Detection and Ranging (LiDAR) has the advantages of high sensitivity and long imaging range. However, due to its operating principle, GM-APD LiDAR requires processing based on multiple-laser-pulse data to complete the target reconstruction. Therefore, the influence of the device’s movement or scanning motion during GM-APD LiDAR imaging cannot be ignored. To solve this problem, we designed a reconstruction method based on coordinate system transformation and the Position and Orientation System (POS). The position, attitude, and scanning angles provided by POS and angular encoders are used to reduce or eliminate the dynamic effects in multiple-laser-pulse detection. Then, an optimization equation is constructed based on the negative-binomial distribution detection model of GM-APD. The spatial distribution of photons in the scene is ultimately computed. This method avoids the need for field-of-view registration, improves data utilization, and reduces the complexity of the algorithm while eliminating the effect of LiDAR motion. Moreover, with sufficient data acquisition, this method can achieve super-resolution reconstruction. Finally, numerical simulations and imaging experiments verify the effectiveness of the proposed method. For a 1.95 km building scene with SBR ~0.137, the 2 × 2-fold super-resolution reconstruction results obtained by this method reduce the distance error by an order of magnitude compared to traditional methods. Full article

The accurate prediction of cryptocurrency prices is crucial due to the volatility and complexity of digital asset markets, which pose significant challenges to traders, investors, and researchers. This research addresses these challenges by leveraging machine learning and deep learning techniques to forecast closing prices for cryptocurrencies, focusing on Bitcoin, Ethereum, Binance Coin, and Litecoin cryptocurrency datasets. A Random Forest ensemble learning algorithm, a Gradient Boosting model, and a feedforward neural network were implemented to handle the complexities in cryptocurrency data. A Z-Score-based anomaly detection framework was integrated to classify closing prices as normal or abnormal, aiding in identifying significant market events. Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. The deep learning model indicates strong generalization capabilities, suggesting potential advantages on more complex datasets. These findings highlight the importance of combining advanced machine learning techniques and cryptocurrencies to develop a robust framework for cryptocurrency forecasting and anomaly detection. Full article

To enhance the safety of hybrid inspection robots (HIRs) landing on power transmission lines (PTLs) with inclination and flexibility, this research derives a coupled dynamic model for a developed flying–walking power transmission line inspection robot (FPTLIR) to analyze the dynamic behavior of the FPTLIR during the landing process. The model uses the absolute nodal coordinate formulation (ANCF) for the dynamics of the PTL and the Hunt–Crossley theory for the contact model, integrating these components with the Euler–Lagrange method. A modular simulation was conducted to evaluate the effects of different landing positions and robot masses. An experimental platform was designed to evaluate the landing performance and validate the model, which confirms the method’s accuracy, with a mean relative Z-displacement error of 0.004. Simulation results indicate that Z-displacement decreases with increased landing distance, with the farthest point showing only 34.4% of the Z-displacement observed at the closest point. Conversely, roll increases, with the closest point exhibiting 3.7% of the roll at the farthest point. Both Z-displacement and roll are directly correlated with the robot’s mass; the lightest robot’s Z-displacement and roll are 9.2% and 12.8% of those of the heaviest robot, highlighting the safety advantage of lighter robots. This research enables precise analysis and prediction of the system’s responses during the landing process, providing significant insights for safe landing and design. Full article

Ensuring the accurate tracking of hand and fingers movements is an ongoing challenge for upper limb rehabilitation assessment, as the high number of degrees of freedom and segments in the limited volume of the hand makes this a difficult task. The objective of this study is to evaluate the performance of two markerless approaches (the Leap Motion Controller and the Google MediaPipe API) in comparison to a marker-based one, and to improve the precision of the markerless methods by introducing additional data processing algorithms fusing multiple recording devices. Fifteen healthy participants were instructed to perform five distinct hand movements while being recorded by the three motion capture methods simultaneously. The captured movement data from each device was analyzed using a skeletal model of the hand through the inverse kinematics method of the OpenSim software. Finally, the root mean square errors of the angles formed by each finger segment were calculated for the markerless and marker-based motion capture methods to compare their accuracy. Our results indicate that the MediaPipe-based setup is more accurate than the Leap Motion Controller-based one (average root mean square error of 10.9° versus 14.7°), showing promising results for the use of markerless-based methods in clinical applications. Full article

In 2020, BDS-3 began broadcasting high-precision positioning correction products through B2b signals, effectively addressing the limitations of ground-based augmentation. However, challenges such as the “south wall effect” from geostationary orbit (GEO) satellites, issues of data (IOD) mismatch, and signal priority conflicts often result in interruptions and anomalies during real-time positioning with the B2b service. This paper proposes a continuous B2b-PPP (B2b signal-based Precise Point Positioning) model that incorporates signal-in-space range error (SISRE) residuals and predictions for B2b orbits and clock corrections to achieve seamless, high-precision continuous positioning. In our experiments, we first analyze the characteristics of B2b SISRE for both BDS-3 and GPS. We then evaluate the positioning accuracy of several models, B2b-PPP, EB2b-PPP, PB2b-PPP, EB2bS-PPP, and PB2bS-PPP, through simulated and real dynamic experiments. Here, ‘E’ indicates the direct utilization of the previous observation corrections from B2b before the signal interruption, ‘P’ represents B2b prediction products, and ‘S’ signifies the incorporation of the SISRE residuals. The results show that EB2b-PPP exhibits significant deviations as early as 10 min into a B2b signal interruption. Both PB2b-PPP and EB2bS-PPP demonstrate comparable performances, with PB2bS-PPP emerging as the most effective method. Notably, in real dynamic experiments, PB2bS-PPP maintains positioning accuracy in the E/N directions like B2b-PPP, even after 40 min of signal interruption, ensuring continuous and stable positioning upon signal restoration. This achievement significantly enhances the capability for high-precision continuous positioning based on B2b signals. Full article

Accurate oil and gas production forecasting is essential for optimizing field development and operational efficiency. Steady-state capacity prediction models based on machine learning techniques, such as Linear Regression, Support Vector Machines, Random Forest, and Extreme Gradient Boosting, effectively address complex nonlinear relationships through feature selection, hyperparameter tuning, and hybrid integration, achieving high accuracy and reliability. These models maintain relative errors within acceptable limits, offering robust support for reservoir management. Recent advancements in spatiotemporal modeling, Physics-Informed Neural Networks (PINNs), and agent-based modeling have further enhanced transient production forecasting. Spatiotemporal models capture temporal dependencies and spatial correlations, while PINN integrates physical laws into neural networks, improving interpretability and robustness, particularly for sparse or noisy data. Agent-based modeling complements these techniques by combining measured data with numerical simulations to deliver real-time, high-precision predictions of complex reservoir dynamics. Despite challenges in computational scalability, data sensitivity, and generalization across diverse reservoirs, future developments, including multi-source data integration, lightweight architectures, and real-time predictive capabilities, can further improve production forecasting, addressing the complexities of oil and gas production while supporting sustainable resource management and global energy security. Full article

This paper presents a passivity-based twisting sliding mode control (PBSMC) approach for series elastic actuators (SEAs). To address the time-varying position trajectory tracking control problem in SEAs, a fourth-order dynamic model is developed to accurately characterize the system. The control framework comprises an internal loop and an external loop controller, each designed to ensure precise trajectory tracking. The internal loop controller manages the second derivative of the joint trajectory position error, while the external loop focuses on the error itself. Both controllers are based on the PBSMC methodology to reduce complex nonlinear disturbances and minimize tracking errors. The finite-time convergence of the proposed method is rigorously analyzed. The performance and advantages of the method are evaluated and compared through various simulations. Full article

The continuous assessment of cover crop growth throughout the season is a crucial baseline observation for making informed crop management decisions and sustainable farming operation. Precision agriculture techniques involving applications of sensors and unmanned aerial vehicles provide precise and prompt spectral and structural data, which allows for effective evaluation of cover crop biomass. Vegetation indices are widely used to quantify crop growth and biomass metrics. The objective of this study was to evaluate the accuracy of biomass estimation using a machine learning approach leveraging spectral and canopy height data acquired from unmanned aerial vehicles (UAVs), comparing different neural network architectures, optimizers, and activation functions. Field trials were carried out at two sites in Louisiana involving winter cover crops. The canopy height was estimated by subtracting the digital surface model taken at the time of peak growth of the cover crop from the data captured during a bare ground condition. When evaluated against the validation dataset, the neural network model facilitated with a Keras TensorFlow library with Adam optimizers and a sigmoid activation function performed the best, predicting cover crop biomass with an average of 96 g m⁻² root mean squared error (RMSE). Other statistical metrics including the Pearson correlation and R² also showed satisfactory conditions with this combination of hyperparameters. The observed cover crop biomass ranged from 290 to 1217 g m⁻². The present study findings highlight the merit of comprehensive analysis of cover crop traits using UAV remote sensing and machine learning involving realistic underpinning biophysical mechanisms, as our approach captured both horizontal (vegetation indices) and vertical (canopy height) aspects of plant growth. Full article

Location is critical for the safe and effective completion of Unmanned Aerial Vehicle (UAV) missions. Since positioning errors tend to accumulate over time, uncorrected measurements from Inertial Navigation Systems (INSs) are unreliable. Aiming for UAV self-positioning under the challenges of a Global Navigation Satellite System (GNSS), this article integrates Digital Terrestrial Multimedia Broadcast (DTMB) signals and assisted INS components as external radiation sources for system design. The trigonometric geometry algorithm is proposed to estimate the pseudo-measurement, and the impact factors of the positioning error are analyzed. After filtering the pseudo-measurement by multi-point initiation, we designed a model for cross-regional positioning scenarios using the nearest-neighbor navigation association and scalar weighted distributed fusion. The simulation results demonstrate that the model can effectively track the target. Finally, the effectiveness of the positioning at a constant altitude is evaluated through different vehicle-mounted scenarios with a speed of 60 km/h. The experimental results show that the minimum positioning error can reach 18.95 m over a 525 m trajectory, thus meeting actual UAV requirements and having practical value. Full article

Precise prediction of mooring tension is essential for the safety and operational efficiency of semi-submersible aquaculture platforms. Traditional numerical methods struggle with real-time performance due to the nonlinear and dynamic characteristics of environmental loads. This study proposes a novel neural network approach to enhance real-time forecasting of mooring line responses, combining Ensemble Empirical Mode Decomposition (EEMD), Temporal Convolutional Networks (TCNs), and a Self-Attention (SA) mechanism. The training dataset encompasses time-domain analysis results, including mooring tensions, motion responses, and total structural forces. Firstly, Pearson Correlation Analysis (PCA) is utilized to assess the linear relationships among the hydrodynamic variables. Subsequently, EEMD is applied to decompose the mooring tension data, which is then combined with the highly correlated variables to form the input dataset. Finally, the TCN model is trained to predict the time series, while an SA mechanism is integrated to weigh the significance of different moments within the sequence, thereby further enhancing prediction accuracy. The results demonstrate that the evaluation metrics of the EEMD-TCN-SA model outperform those of other neural network models, effectively predicting mooring tension for semi-submersible platforms and significantly reducing prediction errors. Full article

Accurate source location is a critical component of microseismic monitoring and early warning systems. To improve the accuracy of microseismic source location, this manuscript proposes a GA-PSO algorithm that combines the Genetic Algorithm (GA) with Particle Swarm Optimization (PSO). The GA-PSO algorithm enhances the PSO algorithm by dynamically adjusting the balance between global exploration and local exploitation through a sinusoidal function for the nonlinear adjustment of both learning factors, and an adaptive inertia weight that decreases quadratically with iterations. Additionally, the precision of the solutions is further improved through the crossover and mutation operations of the GA. In the simulated location model, the GA-PSO algorithm demonstrated the smallest error value, outperforming both the GA and PSO algorithm in terms of accuracy. Furthermore, the GA-PSO algorithm exhibited minimal sensitivity to wave speed fluctuations of ±1%, ±3%, and ±5%, maintaining the error within 0.5 m. The validation through the blasting experiment at the Shizhuyuan mine further confirmed the enhanced accuracy of the GA-PSO algorithm, with a location error of 20.08 m, representing an improvement of 59% over the GA and 43% over the PSO algorithm. Full article

The health monitoring system has been the main technological approach to extending the life of gas turbine engines and reducing maintenance costs resulting from performance degradation caused by asymmetric factors like carbon deposition, damage, or deformation. One of the most critical techniques within the health monitoring system is performance degradation prediction. At present, most research on degradation prediction is carried out using NASA’s open dataset, C-MAPSS, without considering that monitoring measurements are not always available, as in the ideal dataset. This limitation makes fault diagnosis algorithms and remaining useful life prediction methods difficult to apply to real gas turbine engines. Therefore, to solve the problem of performance degradation prediction in light-duty gas turbine engines, a prediction diagram is proposed based on Long Short-Term Memory (LSTM). Various types of onboard signals are taken into consideration among the experimental data. Only accumulated usage time, total temperature and total pressure before the inlet, low-pressure rotor speed, high-pressure rotor speed, fuel flow rate, exhaust temperature, and thrust are used in the training process, which is indispensable for an aero-engine. A genetic algorithm (GA) is introduced into the training process to optimize the hyperparameters of LSTM. The performance degradation prediction modeled with the GA-LSTM method is validated using experimental data. The maximum prediction error of thrust is 70 daN, and the mean absolute percentage error (MAPE) is less than 0.04. This study provides a practical approach to implementing performance degradation prediction in health monitoring systems to improve gas turbine engine reliability, economy, and environmental performance. Full article

Solar diffuse radiation (DIFRA) is an important component of solar radiation, but current research into the estimation of DIFRA is relatively limited. This study, based on remote sensing data, topographic data, meteorological reanalysis materials, and measured data from radiation observation stations in Chongqing, combined key factors such as the solar elevation angle, water vapor, aerosols, and cloud cover. A high-precision DIFRA estimation model was developed using the random forest algorithm, and a distributed simulation of DIFRA in Chongqing was achieved. The model was validated using 8179 measured data points, demonstrating good predictive capability with a correlation coefficient (R²) of 0.72, a mean absolute error (MAE) of 35.99 W/m², and a root mean square error (RMSE) of 50.46 W/m². Further validation was conducted based on 14 radiation observation stations, with the model demonstrating high stability and applicability across different stations and weather conditions. In particular, the fit was optimal for the model under overcast conditions, with R² = 0.70, MAE = 32.20 W/m², and RMSE = 47.51 W/m². The results indicate that the model can be effectively adapted to all weather calculations, providing a scientific basis for assessing and exploiting solar energy resources in complex terrains. Full article