Search Results (233)

In this work, we propose a geolocation framework for distant ground targets integrating laser rangefinder sensors with multimodal visual servo control. By simulating binocular visual servo measurements through monocular visual servo tracking at fixed time intervals, our approach requires only single-session sensor attitude correction calibration to accurately geolocalize multiple targets during a single flight, which significantly enhances operational efficiency in multi-target geolocation scenarios. We design a step-convergent target geolocation optimization algorithm. By adjusting the step size and the scale factor of the cost function, we achieve fast accuracy convergence for different UAV reconnaissance modes, while maintaining the geolocation accuracy without divergence even when the laser ranging sensor is turned off for a short period. The experimental results show that through the UAV’s continuous reconnaissance measurements, the geolocalization error of remote ground targets based on our algorithm is less than 7 m for 3000 m, and less than 3.5 m for 1500 m. We have realized the fast and high-precision geolocalization of remote targets on the ground under the high-altitude reconnaissance of UAVs. Full article

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments. Full article

Path planning for autonomous vehicles is a core component of intelligent transportation systems, playing a key role in ensuring driving safety, improving driving efficiency, and optimizing the user experience. To address the challenges of safety, smoothness, and search efficiency in path planning for autonomous vehicles, this study proposes an improved hybrid A* algorithm based on the lemming optimization algorithm (LOA). Firstly, this study introduces a penalized graph search method, improves the distance heuristic function, and incorporates the Reeds–Shepp algorithm in order to overcome the insufficient safety and smoothness in path planning originating from the hybrid A* algorithm. The penalized graph search method guides the search away from dangerous areas through penalty terms in the cost function. Secondly, the distance heuristic function improves the distance function to reflect the actual distance, which makes the search target clearer and reduces the computational overhead. Finally, the Reeds–Shepp algorithm generates a path that meets the minimum turning radius requirement. By prioritizing paths with fewer reversals during movement, it effectively reduces the number of unnecessary reversals, thereby optimizing the quality of the path. Additionally, the lemming optimization algorithm (LOA) is combined with a two-layer nested optimization framework to dynamically adjust the key parameters of the hybrid A* algorithm (minimum turning radius, step length, and angle change penalty coefficient). Leveraging the LOA’s global search capabilities avoids local optima in the hybrid A* algorithm. By combining the improved hybrid A* algorithm with kinematic constraints within a local range, smooth paths that align with the actual movement capabilities are generated, ultimately enhancing the path search capabilities of the hybrid A* algorithm. Finally, simulation experiments are conducted in two scenarios to validate the algorithm’s feasibility. The simulation results demonstrate that the proposed method can efficiently avoid obstacles, and its performance is better than that of the traditional hybrid A* algorithm in terms of the computational time and average path length. In a simple scenario, the search time is shortened by 33.2% and the path length is reduced by 11.1%; at the same time, in a complex scenario, the search time is shortened by 23.5% and the path length is reduced by 13.6%. Full article

With increasing attention to sustainability and energy efficiency in transportation systems, advanced intelligent algorithms provide promising solutions for optimizing urban rail transit operations. This study addresses the challenge of optimizing train operation plans for urban rail transit systems characterized by spatiotemporal passenger flow imbalance. By exploring a combined short-turning and unpaired train operation mode, a three-objective optimization model was established, aiming to minimize operational costs, reduce passenger waiting times, and enhance load balancing. To effectively solve this complex problem, an Improved GOOSE (IGOOSE) algorithm incorporating elite opposition-based learning, probabilistic exploration based on elite solutions, and golden-sine mutation strategies were developed, significantly enhancing global search capability and solution robustness. A case study based on real operational data adjusted for confidentiality was conducted, and comparative analyses with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) demonstrated the superiority of IGOOSE. Furthermore, an ablation study validated the effectiveness of each enhancement strategy within the IGOOSE algorithm. The optimized operation planning model reduced passenger waiting times by approximately 12.72%, improved load balancing by approximately 39.30%, and decreased the overall optimization objective by approximately 10.25%, highlighting its effectiveness. These findings provide valuable insights for urban rail transit operation management and indicate directions for future research, underscoring the significant potential for energy savings and emission reductions toward sustainable urban development. Full article

Exposure to fine particulate matter (PM2.5) has become an increasing public health concern, particularly in urban areas facing severe air pollution. In response, individuals are increasingly turning to real-time tracking systems and self-monitoring tools. This study aimed to examine the association between PM2.5 risk perception, self-monitoring behaviors, and anxiety levels in the general population of Thailand. A cross-sectional survey was conducted during the dry season using an online questionnaire, which included the 7-item Generalized Anxiety Disorder (GAD-7) scale. A total of 921 participants residing in Bangkok and Chiang Mai were included. Binary logistic regression analysis, adjusted for sex, age, marital status, monthly income, and years of residence, revealed a significant association between anxiety and perceived health risks of PM2.5 exposure (OR = 1.09; 95% CI: 1.06–1.13). Daily self-monitoring of air quality over the past two weeks was also significantly linked to higher anxiety levels compared to non-monitoring individuals: OR = 1.92 (95% CI: 1.11–3.33) for websites, OR = 1.65 (95% CI: 1.01–2.72) for mobile apps, OR = 1.72 (95% CI: 1.12–2.64) for air purifiers, and OR = 3.34 (95% CI: 1.77–6.31) for air quality detectors. Monitoring 4–6 days per week using apps and air detectors was similarly associated with increased anxiety (OR = 1.64 and 2.30, respectively). Heightened perception of PM2.5 health risks and frequent self-monitoring behaviors are associated with increased anxiety among urban residents in Thailand. Public health interventions should consider implementing targeted alert systems during high-pollution periods and prioritize strategies to reduce PM2.5 emissions to alleviate public anxiety. Full article

Returning to work after extended holidays poses significant challenges to employees’ psychological adjustment, yet this phenomenon remains underexplored in organizational research. Drawing on the Conservation of Resources (COR) theory, this study develops and tests a moderated mediation model to examine how pre-holiday work-related irritation influences post-holiday workplace vigor through heightened need for recovery, and how perceived organizational support buffers this process. Data were collected through a four-wave time-lagged design surrounding the Chinese Spring Festival, with a final sample of 349 employees across diverse industries. Results show that pre-holiday emotional strain increases employees’ recovery needs, which in turn undermines their workplace vigor. Moreover, boundary strength at home and perceived organizational support buffer the indirect negative pathway, highlighting the critical roles of both personal and organizational resources in the recovery process. By shifting attention from burnout to positive energy states such as vigor, this study advances theoretical understanding of post-holiday adjustment dynamics and offers practical insights for organizations seeking to foster employee resilience and sustained engagement after structured breaks. Full article

In this study, a novel silicon carbide (SiC) double-trench MOSFET (DT-MOS) combined Schottky barrier diode (SBD) and MOS-channel diode (MCD) is proposed and investigated using TCAD simulations. The integrated MCD helps inactivate the parasitic body diode when the device is utilized as a freewheeling diode, eliminating bipolar degradation. The adjustment of SBD position provides an alternative path for reverse conduction and mitigates the electric field distribution near the bottom source trench region. As a result of the Schottky contact adjustment, the reverse conduction characteristics are less influenced by the source oxide thickness, and the breakdown voltage (BV) is largely improved from 800 V to 1069 V. The gate-to-drain capacitance is much lower due to the removal of the bottom oxide, bringing an improvement to the turn-on switching rise time from 2.58 ns to 0.68 ns. These optimized performances indicate the proposed structure with both SBD and MCD has advantages in switching and breakdown characteristics. Full article

Traditional tools, such as canes, are no longer enough to subsist the mobility and orientation of visually impaired people in complex environments. Therefore, technological solutions based on computer vision tasks are presented as promising alternatives to help detect obstacles. Object detection models are easy to couple to mobile systems, do not require a large consumption of resources on mobile phones, and act in real-time to alert users of the presence of obstacles. However, existing object detectors were mostly trained with images from platforms such as Kaggle, and the number of existing objects is still limited. For this reason, this study proposes to implement a mobile system that integrates an object detection model for the identification of obstacles intended for visually impaired people. Additionally, the mobile application integrates multimodal feedback through auditory and haptic interaction, ensuring that users receive real-time obstacle alerts via voice guidance and vibrations, further enhancing accessibility and responsiveness in different navigation contexts. The chosen scenario to develop the obstacle detection application is the Specialized Educational Unit Dr. Luis Benavides for impaired people, which is the source of images for building the dataset for the model and evaluating it with impaired individuals. To determine the best model, the performance of YOLO is evaluated by means of a precision adjustment through the variation of epochs, using a proprietary data set of 7600 diverse images. The YOLO-300 model turned out to be the best, with a mAP of 0.42. Full article

Urban rail transit (URT) systems frequently face operational challenges arising from temporal and spatial imbalances in passenger demand, resulting in inefficiencies in train scheduling and resource utilization. To address these issues, this study proposes a multi-objective optimization model that jointly plans short-turn and full-length train services. The objectives of the model are to minimize total passenger waiting time and train mileage while improving passenger load distribution across the rail line, subject to practical constraints such as departure frequency limitations, rolling stock availability, and coverage of short-turn services. To efficiently solve this model, an improved Pelican Optimization Algorithm (POA) is developed, incorporating techniques such as Tent chaotic mapping, nonlinear weight adjustment, Cauchy mutation, and the sparrow alert mechanism, significantly enhancing convergence accuracy and computational efficiency. A real-world case study based on Nanjing Metro Line 1 demonstrates that the proposed framework substantially reduces average passenger waiting times and overall train mileage, achieving a more balanced distribution of passenger loads. In addition, the study reveals that flexible-ratio dispatching strategies, representing theoretically optimal solutions, outperform integer-ratio dispatching schemes that reflect real-world operational constraints. This finding underscores that investigating the practical feasibility and optimization potential of flexible-ratio scheduling strategies constitutes a valuable direction for future research. The outcomes of this study provide a scalable and intelligent decision-support framework for train scheduling in URT systems, effectively contributing to the sustainable and intelligent development of rail operations. Full article

The efficiency and smoothness of path planning algorithms are critical factors influencing their practical applications. A traditional A* algorithm suffers from limitations in search efficiency, path smoothness, and obstacle avoidance. To address these challenges, this paper introduces an improved A* algorithm that integrates the Salp Swarm Algorithm (SSA) for heuristic function optimization and proposes a refined B-spline interpolation method for path smoothing. The first major improvement involves enhancing the A* algorithm by optimizing its heuristic function through the SSA. The heuristic function combines Chebyshev distance, Euclidean distance, and obstacle density, with the SSA adjusting the weight parameters to maximize efficiency. The simulation experimental results demonstrate that this modification reduces the number of searched nodes by more than 78.2% and decreases planning time by over 48.1% compared to traditional A* algorithms. The second key contribution is an improved B-spline interpolation method incorporating a two-stage optimization strategy for smoother and safer paths. A corner avoidance strategy first adjusts control points near sharp turns to prevent collisions, followed by a path obstacle avoidance strategy that fine-tunes control point positions to ensure safe distances from obstacles. The simulation experimental results show that the optimized path increases the minimum obstacle distance by 0.2–0.5 units, improves the average distance by over 43.0%, and reduces path curvature by approximately 61.8%. Comparative evaluations across diverse environments confirm the superiority of the proposed method in computational efficiency, path smoothness, and safety. This study presents an effective and robust solution for path planning in complex scenarios. Full article

Phenolic acids are important allelochemicals that contribute to obstacles in continuous cropping systems, significantly impacting soil nutrients, enzyme activities, and the composition of microbial communities. This study explored the effects of treatment time and the concentration of various phenolic acids (salicylic acid and p-hydroxybenzoic acid) on soil nutrients, enzyme activity, and soil microorganisms through cultivation experiments. The results indicated that high-concentration phenolic acid treatment negatively affected the availability of soil nutrients by acidifying the soil, as reflected in the low soil pH, compared to the untreated control. Moreover, the soil extracellular enzymes exhibited varying degrees of improvement when phenolic acids were added. Multi-element analysis revealed that treatment duration, concentration, and the type of phenolic acid significantly affected soil nutrient levels and enzyme activity. Additionally, structural equation modeling indicated a significant correlation between the concentration of phenolic acids and the diversity of microorganisms. Phenolic acids influence the soil ecological environment by altering the relative abundance of functional microorganisms (p_Patescibacteria and p_Mortierellomycota) in the soil. Thus, comprehensive regulation and control of continuous cropping obstacles can be achieved by adjusting the micro-ecological environment of the soil, which, in turn, affects phenolic acid substances present in the soil, thereby alleviating continuous cropping obstacles. Full article

In 5G-and-beyond (B5G) Internet of Things (IoT) networks, the integration of intelligent reflecting surfaces (IRSs) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) techniques can significantly improve signal quality and increase network capacity. However, a single fixed IRS lacks the dynamic adjustment capability to flexibly adapt to complex environmental changes and diverse user demands, while mmWave MIMO is constrained by limited coverage. Motivated by these challenges, we investigate the application of drone-mounted IRS-assisted MIMO communications in B5G IoT networks, where multiple IRS-equipped drones are deployed to provide real-time communication support. To fully exploit the advantages of the proposed MIMO-enabled air-to-ground integrated information transmission framework, we formulate a joint optimization problem involving beamforming, phase shift design, and drone deployment, with the objective of maximizing the sum of achievable weighted data rates (AWDRs). Given the NP-hard nature of the problem, we develop an iterative optimization algorithm to solve it, where the optimization variables are tackled in turn. By employing the quadratic transformation technique and the Lagrangian multiplier method, we derive closed-form solutions for the optimal beamforming and phase shift design strategies. Additionally, we optimize drone deployment by using a distributed discrete-time convex optimization approach. Finally, the simulation results show that the proposed scheme can improve the sum of AWDRs in comparison with the state-of-the-art schemes. Full article

In the era of Industry 4.0 and the transition toward Industry 5.0, advanced manufacturing is increasingly driven by data analytics, artificial intelligence, and cyber-physical systems. The integration of intelligent monitoring systems and self-learning algorithms is reshaping machining processes, enabling higher efficiency, precision, and sustainability. Recent advancements in smart factories emphasize the use of AI-powered process control, enabling machines to self-optimize, self-correct, and even self-retrain to maintain optimal performance. This paper proposes a hybrid control and monitoring framework designed to enhance turning operations by integrating artificial neural networks (ANNs) for predictive modeling and adaptive recalibration. The system leverages machine learning (ML) to improve machining efficiency, tool longevity, and energy consumption optimization. By implementing forward and inverse ANN models, the framework enables real-time estimation of cutting forces and energy consumption, facilitating data-driven decision-making in machining processes. Furthermore, an adaptive recalibration mechanism ensures continuous model updates, allowing the system to dynamically adjust based on evolving machining conditions such as tool wear, material properties, and environmental variations. This research contributes to these advancements by proposing an ANN-based hybrid approach, predictive modeling, and adaptive recalibration. The proposed framework ensures continuous monitoring, automated adjustments, and intelligent decision-making, making it a scalable and adaptable solution for modern machining operations. Full article

Aiming at the problem that the pseudo-velocity measurement noise of non-holonomic constraints (NHCs) in the integrated navigation of vehicle-mounted a global navigation satellite system/inertial navigation system (GNSS/INS) is time-varying and thick-tailed in complex road conditions (turning, sideslip, etc.) and cannot be accurately predicted, an adaptive estimation method for the initial value of NHC lateral velocity noise based on multiple linear regression is proposed. On the basis of this method, a Gaussian Student’s T distribution variational Bayesian filtering algorithm (Ga-St VBAKF) based on NHC pseudo-velocity measurement noise modeling is proposed through modeling and analysis of pseudo-velocity measurement noise. Firstly, in order to adaptively adjust the initial value of NHC lateral velocity noise, a vehicle turning detection algorithm is used to detect whether the vehicle is turning. Secondly, based on the vehicle motion state, the variational Bayesian method is used to adaptively estimate the statistical characteristics of the measurement noise in real time based on modeling of the lateral velocity noise as Gaussian white noise or Student’s T distribution thick-tail noise. The test results show that compared to the traditional Kalman filtering algorithm with fixed noise, the Ga-St VBAKF algorithm with noise adaptation reduces the maximum horizontal position error by 65.9% in the GNSS/NHC/OD/INS (where OD stands for odometer and INS stands for inertial measurement unit) system when the vehicle is in a turning state, and by 42.3% in the NHC/OD/INS system. This indicates that the algorithm can effectively suppress the divergence of positioning errors during turning and improve the performance of integrated navigation. Full article

Radiometric vegetation indices are considered good indicators of vegetation health and can contribute to explaining its current and future evolutions. This study is carried out in the arid mountain rangeland of Toujane (southeast of Tunisia). The aim is to predict how climate change will affect the Soil-Adjusted Vegetation Index (SAVI) values under dryland conditions. Current and future SAVI indices are analyzed using the maximum entropy algorithm (MaxEnt). The Canadian Earth System Model version 5 (CanESM5) represents the data source of two future climatic scenarios. These last, called Shared Socioeconomic Pathways (SSP245, SSP585), concern four time periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100). Three topographic, twelve soil, and nineteen climatic variables are undertaken during each period. The main results of the jackknife test show that temperature, precipitation, and some soil variables are the main factors influencing SAVI indices. Specifically, they affect plant growth and vegetation cover, which in turn modify the SAVI index. Based on the area under the receiving curve, the model shows high predictive accuracy for a high SAVI (AUC = 0.88 − 0.92). These findings show that land management strategies may be incumbent upon to reduce the vulnerability linked to climate change in Toujane rangelands. Full article