Search Results (13,438)

The research delves into the connection between green finance, the digital economy, and energy consumption in ten (10) MENA region countries over a 40-year period, from 1983 to 2023. The research used panel-corrected standard errors (PCSE) estimators to get rid of cross-sectional dependence and heteroscedasticity. According to the findings, the digital economy mainly contributes to cutting energy consumption, whereas green finance has a positive association which is likely due to the rebound effects and the embedded energy costs of green infrastructures. The control variables such as trade openness, technological innovation, and industrial structure are linked in a positive way to higher energy consumption. The findings imply a policy approach that has these two sectors as its main focus, in addition to emphases on digitalization for more efficient operation and green finance for the fostering of the transition of energy sources in the region. Full article

We propose a stepwise in-flight method for automatically determining flight-envelope-related parameters for the longitudinal control of small fixed-wing unmanned aerial vehicles (UAVs), including pitch-angle limits, maximum climb and sink rate limits, and the cruise (trim) throttle. The method performs steady-state evaluation using onboard state estimates and sequentially updates the parameter set of ArduPilot’s energy-based longitudinal controller (Total Energy Control System, TECS). The algorithm was implemented in ArduPilot Plane v4.6.1 via Lua scripting, enabling real-time parameter determination and immediate application during flight. The proposed procedure was assessed in software-in-the-loop (SITL) simulations and further validated through flight experiments. The results demonstrated that the target parameters could be automatically identified during flight and implemented in real time. The proposed method is expected to reduce reliance on expert trial-and-error and contribute to improving portability across airframes and configuration changes. Full article

Autonomous navigation in mobile robots operating in dynamic and partially known environments demands the coordinated integration of perception, decision-making, and control while ensuring stability, safety, and energy efficiency. This paper presents an integrated navigation framework for differential-drive mobile robots that combines deep learning-based visual perception, reinforcement learning (RL) for high-level decision-making, and a Lyapunov-based trajectory reference generator for low-level motion execution. A convolutional neural network processes RGB-D images to classify obstacle configurations in real time, enabling navigation without prior map information. Based on this perception layer, an RL policy generates adaptive navigation subgoals in response to environmental changes. To ensure stable motion execution, a Lyapunov-based control strategy is formulated at the kinematic level to generate smooth velocity references, which are subsequently tracked by embedded PID controllers, explicitly decoupling learning-based decision-making from stability-critical control tasks. The local stability of the trajectory-tracking error is analyzed using a quadratic Lyapunov candidate function, ensuring asymptotic convergence under ideal kinematic assumptions. Experimental results demonstrate that while higher control gains provide faster convergence in simulation, an intermediate gain value (K = 0.5I) achieves a favorable trade-off between responsiveness and robustness in real-world conditions, mitigating oscillations caused by actuator dynamics, delays, and sensor noise. Validation across multiple navigation scenarios shows average tracking errors below 1.2 cm, obstacle detection accuracies above 95% for human obstacles, and a significant reduction in energy consumption compared to classical A* planners, highlighting the effectiveness of integrating learning-based navigation with analytically grounded control. Full article

As a key indicator of air quality, effective forecasting of ${\mathrm{PM}}_{2.5}$ concentration can provide key technical support for the scientific and precise implementation of air pollution prevention and control. However, predicting ${\mathrm{PM}}_{2.5}$ concentrations faces challenges such as multiple influencing factors, long-term temporal dependencies, and inherent nonlinearity. Furthermore, traditional Long Short-Term Memory (LSTM) networks not only fail to effectively grasp the dependency relationships in long-time-span data, but also encounter difficulties in fully integrating and exploiting the information of numerous influencing factors. In order to solve these problems, a novel prediction model (OVMD–PeepholeLSTM–attention) for ${\mathrm{PM}}_{2.5}$ concentration was presented in this study, which includes Peephole Long Short-Term Memory (PeepholeLSTM), optimal variational mode decomposition (OVMD) and an attention mechanism (AM). In this study, K modal components result from the initial decomposition of ${\mathrm{PM}}_{2.5}$ monitoring data using OVMD. The obtained components are then individually predicted by the PeepholeLSTM–attention model, and the final prediction is reconstructed. The proposed model was comprehensively evaluated on ${\mathrm{PM}}_{2.5}$ concentration monitoring data sets from Guangzhou and Shenzhen in China from 2020 to 2022, through a series of comparative experiments. The model proposed in this study is shown by experimental results to reduce mean absolute error (MAE) by approximately 39%, root mean square error (RMSE) by 45%, and increases the fitting coefficient ($R^2$) by 0.0457 in Guangzhou compared to the single PeepholeLSTM model. The corresponding improvements in Shenzhen are 45% for MAE, 51% for RMSE, and 0.0765 for $R^2$. This indicates that the model proposed in this paper exhibits higher accuracy in terms of predicting ${\mathrm{PM}}_{2.5}$ concentrations, and the research results can provide a basis for quantitative assessment and scientific decision-making for the sustainable development of urban ecological environments. Full article

The sport of orienteering requires athletes to reach specific points marked on a map (called “punching stations”) in the shortest possible time. Currently, the recording of athletes’ passages through the stations is performed offline. In addition to delays in generating intermediate and final rankings, this approach often leads to detection errors and potential cheating related to the lack of authentication of an athlete’s actual passage at a given station. This paper aims to define and design a system enabling three main functionalities: 1. real-time monitoring of athletes’ trajectories through a sensor network connected to control stations; 2. multi-modal authentication of athletes at each station; and 3. immutable certification of each athlete’s passage through blockchain-based recording. System performance is evaluated in terms of wireless network coverage and data collection efficiency across three representative environments: urban, rural, and forested areas. Results are obtained through a measurement campaign for two dedicated wireless technologies: ZigBee for local mesh network and LoRa for long-range links to connect local mesh networks to the cloud over the Internet, which is then accessed by the race organizers. Furthermore, two supporting subsystems are described, addressing athlete authentication and data integrity assurance, as well as a blockchain recording for the overall event management framework. Results are in terms of coverage distances for both technologies, proving highly effective across varied terrains. Field tests demonstrated significant communication capabilities, achieving distances of up to 1800 m in open spaces. Even in challenging, dense wooded environments, the system maintained reliable coverage, reaching transmission distances of up to 600 m. Local ZigBee links between punching stations achieved ranges between 70 and 150 m in forested areas. These findings validate the use of a wireless multi-hop network designed to minimize packet loss and ensure reliable data delivery in competitive scenarios. The feasibility is also investigated in terms of WSN performance, delay analysis and power consumption evaluation. Full article

Topology optimization using density-based approaches often requires high-resolution meshes to achieve reliable compliance evaluation and robustness against mesh dependency. However, increasing the problem sizes—especially in 3D—results in prohibitively expensive computation times. Coarse-mesh approaches significantly accelerate runtimes; however, they also introduce discretization errors that can guide the optimizer towards incorrect topology families if left unregulated. To address this issue, a multifidelity framework with acceptance control was developed that enables runtime verification and explicitly manages the optimizer state. The main idea is to use coarse discretizations to generate new design proposals and transfer candidate designs to fine discretizations at periodic intervals for verification. Proposals are then accepted or rejected using a best-referenced criterion; if verification fails, the optimizer reverts to the best verified state. The proposed framework balances fine-discretization accountability with coarse-discretization efficiency through configurable verification schedules and a cleanup phase. The framework is evaluated on standard 2D and 3D structural benchmark problems with deterministic load perturbations, and performance is assessed in terms of final verified compliance, wall-clock runtime, acceptance rate, and gray fraction. Full article

Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing. Full article

In the complex operational environment of coal mine shafts, trajectory tracking control of crawler hydraulic drill-anchor robots is susceptible to track slippage and internal–external uncertain disturbances, leading to low tracking accuracy. This issue hinders the implementation of efficient and precise coal mine roadway support operations. To address these challenges, enhance the automation level of coal mine roadway support, and improve operational safety and reliability, research on high-precision trajectory tracking control for crawler hydraulic drill-anchor robots is imperative. Therefore, this paper takes crawler hydraulic drill-anchor robots as the research object and focuses on the trajectory tracking control of such robots. First, a kinematic model incorporating track slippage was established for the crawler hydraulic drill-anchor robot. Second, a cascade predictive control strategy is proposed. The upper-layer trajectory tracking control adopts an adaptive model predictive controller, which adjusts controller weights according to tracking error variations and provides reference rotational speeds for the lower-layer predictive controller. Simulation results of linear and sinusoidal trajectory tracking show that the proposed strategy can effectively compensate for the effects of track slippage and improve trajectory tracking accuracy. Finally, a friction-compensated predictive control method was designed to regulate the rotational speeds of the left and right track drive wheels, and the proposed method achieves optimal control performance with a minimum MEAE of 0.12292 rpm, SDAE of 0.44366 rpm, ITAE of 4.9168, MEACI of 3.0607 mA, SDACI of 1.2497 mA, and ITACI of 122.4283. This performance is significantly superior to that of the conventional PID, ADRC, and MPC methods, thereby realizing high-precision track speed control. Full article

Photovoltaic (PV) system monitoring, optimization, and control have completely changed as a result of the convergence of internet of things (IoT) and machine learning (ML) technologies. While IoT makes it possible to gather, transmit, and store electrical and environmental data, ML offers intelligent data analysis for prediction and adaptive decision-making. This review provides a comprehensive analysis of recent advances in the application of IoT as well as ML for improving PV performance and efficiency. It examines the IoT hardware and communication architectures and highlights their roles in achieving high-resolution and real-time monitoring. In addition, this paper explores the application of ML in PV systems, including power forecasting, maximum power point tracking (MPPT), fault detection, and energy management. Moreover, it analyzes the benefits and performance improvements as well as challenges and limitations of the combined IoT–ML framework with PV systems. It outlines the future directions, such as federated learning, edge intelligence, and digital-twin integration. This combination enhances the system performance by improving tracking efficiency, reducing forecasting error, and decreasing operational cost, which makes these technologies key parts of the next generation of PV systems. Full article

This paper addresses the transient voltage stability problem in power systems with high penetration of renewable energy by proposing a hierarchical risk-warning method that integrates Koopman-theory-based transient voltage trajectory prediction and stability margin quantification. First, an online Koopman-theory-based transient voltage trajectory prediction model is constructed through the adaptive optimization of basis functions, a dynamic operator update mechanism, and multistage error correction, significantly enhancing prediction accuracy and generalization capability. Second, a piecewise-weighted quantitative index for transient voltage stability margins is proposed, achieving refined stability assessments ranging from individual nodes to the entire system. Finally, a risk-mapping function based on utility theory is established to convert continuous margin indices to discrete risk levels, forming a complete hierarchical warning system for the transient voltage risk. Simulation results demonstrate that the proposed method achieves precise voltage trajectory prediction and stable-state judgment across various scenarios, effectively identifies critical system weaknesses, and provides reliable technical support for the safety prevention and control of the power system’s transient voltage. Full article

Doubly Fed Induction Generators (DFIGs) are widely employed in variable-speed wind turbine systems due to their high efficiency, enhanced controllability, and economic viability. This paper presents an intelligent neural-network-based control strategy aimed at maximizing wind energy extraction while ensuring accurate speed regulation of a DFIG by continuously tracking the maximum power point under fluctuating wind conditions. Two independent control schemes are developed for the decoupled regulation of active and reactive power in a grid-connected DFIG wind turbine. The first scheme is based on conventional field-oriented control using proportional integral regulators (FOC–PI), while the second employs an Artificial Neural Network Controller (ANNC). The effectiveness of both controllers is evaluated through MATLAB/Simulink 2020 Version simulations of a 1.5 MW DFIG-based wind energy conversion system and experimentally validated using a real wind profile implemented on an eZdsp TMS320F28335 digital signal processor. The proposed control approach achieves low output ripple, a steady-state error below 0.16%, total harmonic distortion of 0.38%, and a limited overshoot of 5%. The obtained results confirm the robustness and reliability of the implemented control strategies in enhancing power capture and improving overall system stability under variable wind conditions. Full article

In order to reduce the influence of external radial disturbances on the control accuracy and stability of the vehicle magnetic suspension flywheel battery system during driving, and to further enhance the system’s disturbance rejection ability, this paper designs a control method based on the Accelerated Engineering Fastest Controller (AEFC) and the improved differential optimization algorithm. A mathematical model of the flywheel battery system is established, and the AEFC scheme with engineering disturbance rejection is adopted in the control loop. The improved differential optimization algorithm is used to obtain the optimal control parameters of AEFC, and a multi-criteria optimization function combining tracking error and smoothness is established. The overall control scheme effectively integrates the characteristics of rapid tracking, interference suppression, and rapid parameter adjustment. The experimental results show that compared with the Engineering Fastest Controller (EFC), in the vehicle turning process, the AEFC controller can reduce the offset by 28% during vehicle driving, and compared with the traditional PID control, it can reduce the offset by 41.94%. In the process of vehicle uphill and speed change, the control effect of AEFC also has a significant improvement. Full article

Monitoring multi-temporal forest vertical structure in anthropogenically disturbed and topographically complex landscapes remains a major challenge, particularly when low-cost remote sensing technologies are used. This study aims to quantify forest vertical structure change and to determine whether these changes are systematically regulated by geomorphometric controls rather than occurring randomly. A multi-temporal unmanned aerial vehicle (UAV) photogrammetry workflow based on Structure from Motion (SfM) was applied to generate annual Canopy Height Models (CHMs) for 2023, 2024, and 2025. To ensure temporal robustness, the 95th percentile of canopy height (P₉₅) was adopted as the primary structural metric, and vertical change was quantified using a difference-based indicator (ΔP₉₅). Random Forest (RF) regression was used to model the relationship between canopy height change and terrain-derived predictors, including slope, aspect, and Topographic Wetness Index (TWI). The results reveal a consistent vertical growth signal across the study area, with a mean ΔP₉₅ increase of 0.65 m over the monitoring period, clearly exceeding the photogrammetric vertical error (RMSE = 0.082 m). Positive canopy height changes are concentrated on moisture-favored, moderately sloping and north-facing terrain, whereas negative changes (down to −1.20 m) are mainly associated with mining-disturbed and steep surfaces. The RF model achieved high explanatory performance (training R² = 0.919) and identified aspect (20%), slope (18%), and TWI (18%) as the dominant controls on forest vertical dynamics. These findings demonstrate that forest vertical structure evolution in disturbed landscapes is not stochastic but is systematically governed by terrain-driven hydro-morphological and microclimatic conditions. The main contribution of this study is the development of an interpretable, change-focused UAV–machine learning framework that moves beyond single-epoch canopy height estimation and enables process-oriented analysis of terrain–vegetation interactions. The proposed approach provides a cost-effective and transferable tool for forest monitoring and post-mining restoration planning in complex terrain settings. Full article

To address dynamic positioning (DP) challenges encountered by ships navigating amid unknown model parameters, environmental disturbances, and input saturation, this study proposes a nonlinear gains recursive sliding mode (RSM) DP control law. Within this control framework, an RSM strategy is devised, leveraging variable-gain technology to enhance DP system control performance. A variable-gain adaptive radial basis function (RBF) neural network is employed for real-time online training to approximate the unknown ship model. Simultaneously, an auxiliary dynamic system is incorporated to deal with input saturation. Furthermore, a robust control item is implemented to counteract the influence of RBF neural network approximation errors and external disturbances on the DP system. By constructing an appropriate Lyapunov function, it is proven that all signals in the DP closed-loop control system are uniformly ultimately bounded. Finally, simulation results demonstrate the ship DP system’s rapid response and high accuracy under the proposed control law, along with an enhanced ability to reject environmental disturbances. Full article

We considerthe single-machine group scheduling problem with controllable processing times (i.e., resource allocation) under a common due-window ($condw$) assignment. The objective is to minimize a total cost composed of earliness, tardiness, due-window-related penalties, and resource consumption. Motivated by realistic production settings such as aerospace component machining and electronics batch assembly, the study addresses the joint optimization of group sequence, job sequence, due-window placement, and resource allocation. For linear and convex resource models, we propose a branch-and-bound ($\widehat{BaB}$) algorithm and efficient heuristics. Numerical experiments show that the $\widehat{BaB}$ algorithm can solve instances with up to 250 jobs and 16 groups. The heuristics ($\widehat{UB}$), including a simulated annealing ($\widehat{SA}$) algorithm, obtain near-optimal solutions with an average error below 0.05% much faster, demonstrating their practical usefulness for real-time scheduling. Full article