Search Results (433)

In a variety of UAV applications, visual–inertial navigation systems (VINSs) play a crucial role in providing accurate positioning and navigation solutions. However, traditional VINS struggle to adapt flexibly to varying environmental conditions due to fixed covariance matrix settings. This limitation becomes especially acute during high-speed drone operations, where motion blur and fluctuating image clarity can significantly compromise navigation accuracy and system robustness. To address these issues, we propose an innovative adaptive covariance matrix estimation method for UAV-based VINS using Gaussian formulas. Our approach enhances the accuracy and robustness of the navigation system by dynamically adjusting the covariance matrix according to the quality of the images. Leveraging the advanced Laplacian operator, detailed assessments of image blur are performed, thereby achieving precise perception of image quality. Based on these assessments, a novel mechanism is introduced for dynamically adjusting the visual covariance matrix using a Gaussian model according to the clarity of images in the current environment. Extensive simulation experiments across the EuRoC and TUM VI datasets, as well as the field tests, have validated our method, demonstrating significant improvements in navigation accuracy of drones in scenarios with motion blur. Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors. Full article

The aim of this study was to improve the efficiency of in situ uranium leaching by developing a specialized methodology for selecting rational parameters for the chemical treatment of production wells. This approach was designed to enhance the filtration properties of ores and extend the uninterrupted operation period of wells, considering the clay content of the productive horizon, the geological characteristics of the ore-bearing layer, and the composition of precipitation-forming materials. The mineralogical characteristics of ore and precipitate samples formed during the in situ leaching of uranium under various mining and geological conditions at a uranium deposit in the Syrdarya depression were identified using an X-ray diffraction analysis. It was established that ores of the Santonian stage are relatively homogeneous and consist mainly of quartz. During well operation, the precipitates formed are predominantly gypsum, which has little impact on the filtration properties of the ore. Ores of the Maastrichtian stage are less homogeneous and mainly composed of quartz and smectite, with minor amounts of potassium feldspar and kaolinite. The leaching of these ores results in the formation of gypsum with quartz impurities, which gradually reduces the filtration properties of the ore. Ores of the Campanian stage are heterogeneous, consisting mainly of quartz with varying proportions of clay minerals and gypsum. The leaching of these ores generates a variety of precipitates that significantly reduce the filtration properties of the productive horizon. Effective compositions and concentrations of decolmatant (clog removal) solutions were selected under laboratory conditions using a specially developed methodology and a TESCAN MIRA scanning electron microscope. Based on a scanning electron microscope analysis of the samples, the effectiveness of a decolmatizing solution based on hydrochloric and hydrofluoric acids (taking into account the concentration of the acids in the solution) was established for the destruction of precipitate formation during the in situ leaching of uranium. Geological blocks were ranked by their clay content to select rational parameters of decolmatant solutions for the efficient enhancement of ore filtration properties and the prevention of precipitation formation. Pilot-scale testing of the selected decolmatant parameters under various mining and geological conditions allowed the optimal chemical treatment parameters to be determined based on the clay content and the composition of precipitates in the productive horizon. An analysis of pilot well trials using the new approach showed an increase in the uninterrupted operational period of wells by 30%–40% under average mineral acid concentrations and by 25%–45% under maximum concentrations with surfactant additives in complex geological settings. As a result, an effective methodology for ranking geological blocks based on their ore clay content and precipitate composition was developed to determine the rational parameters of decolmatant solutions, enabling a maximized filtration performance and an extended well service life. This makes it possible to reduce the operating costs of extraction, control the geotechnological parameters of uranium well mining, and improve the efficiency of the in situ leaching of uranium under complex mining and geological conditions. Additionally, the approach increases the environmental and operational safety during uranium ore leaching intensification. Full article

This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human voices. Initial validation was performed through ground testing. Acoustic data acquisition is optimized during cruise flight, when wing-mounted motors are shut down and the rear motor operates at 40–60% capacity, significantly reducing noise interference. To address residual motor noise, a preprocessing module was developed using reference recordings obtained in an anechoic chamber. Two configurations were tested to capture the motor’s acoustic profile by changing the UAV’s orientation relative to the fixed microphone. The embedded system processes incoming audio in real time, enabling low-latency classification without data transmission. Field experiments confirmed the model’s high precision and robustness under varying flight and environmental conditions. Results validate the feasibility of real-time, onboard acoustic event detection using spectrogram-based deep learning on UAV platforms, and support its applicability for scalable aerial monitoring tasks. Full article

Coal gangue, a major by-product of coal mining, poses significant environmental challenges due to its large-scale accumulation, land occupation, and potential for air and water pollution. This manuscript presents a comprehensive overview of continuous chamber gangue storage technology as a sustainable mining solution for coal mines. The principles of this approach emphasize minimizing disturbance to overlying strata, enabling uninterrupted mining operations, and reducing both production costs and environmental risks. By storing the surface or underground gangue in continuous chambers, the proposed method ensures the roof stability, maximizes the waste storage, and prevents the interaction between mining and waste management processes. Detailed storage sequences and excavation methods are discussed, including continuous and jump-back excavation strategies tailored to varying roof conditions. The process flows for both underground and ground-based chamber storage are described, highlighting the integration of gangue crushing, paste preparation, and pipeline transport for efficient underground storage. In a case study with annual storage of 500,000 t gangue, the annual economic benefit reached CNY 1,111,425,000. This technology not only addresses the urgent need for sustainable coal gangue management, but also aligns with the goals of resource conservation, ecological protection, and the advancement of green mining practices in the coal industry. Full article

Accurate prediction of drifting trajectories is essential for improving the operational efficiency of maritime search and rescue (SAR), particularly within the complex geomorphological settings of island and reef regions, such as those in the South China Sea. This study investigates the drift characteristics of life rafts under varying loading conditions across both open-sea and island–reef regions. Comprehensive field experiments were conducted over 15 days in the waters around the Wanshan Archipelago, using advanced instruments to collect wind, current, and drift trajectory data. Based on these observations, two models—the AP98 leeway model and a BP neural network model—were developed and validated. The results show that the AP98 model performs better in open-sea conditions, whereas the BP neural network provides more accurate predictions in island and reef areas with complex environmental factors. A Monte Carlo simulation was also integrated to enhance the robustness of drift area predictions. These findings offer valuable insights into life raft drift behavior in complex marine environments and provide technical support for improving SAR operations in island–reef regions. Full article

Investigating the coupled hydrodynamic and thermal wakes induced by underwater vehicles is vital for non-acoustic detection and environmental monitoring. Here, the standard SUBOFF model is simulated under eight operating conditions—speeds of 10, 15, and 20 kn; depths of 10, 20, and 30 m; and both with and without thermal discharge—using Delayed Detached Eddy Simulation (DDES) coupled with the Volume of Fluid (VOF) method. Results indicate that, under heat emission conditions, higher speeds accelerate wake temperature decay, making the thermal wake difficult to detect downstream; without heat emission, turbulent mixing dominates the temperature field, and speed effects are minor. With increased speed, wake vorticity at a fixed location grows by about 30%, free-surface wave height rises from 0.05 to 0.15 m, and wavelength remains around 1.8 m, all positively correlated with speed. Dive depth is negatively correlated with wave height, decreasing from 0.15 to 0.04 m as depth increases from 5 to 20 m, while wavelength remains largely unchanged. At a 10 m submergence depth, the thermal wake is clearly detectable on the surface but becomes hard to detect beyond 20 m, indicating a pronounced depth effect on its visibility. These results not only confirm the positive correlation between vessel speed and wake vorticity reported in earlier studies but also extend those findings by providing the first quantitative evaluation of how submergence depth critically limits thermal wake visibility beyond 20 m. This research provides quantitative evaluations of wake characteristics under varying speeds, depths, and heat emissions, offering valuable insights for stealth navigation and detection technologies. Full article

Steel wire ropes used in transport and aerospace applications are critical components whose failure can lead to significant safety, operational, and environmental consequences. Current diagnostic practices based on magnetic rope testing (MRT) often suffer from signal misalignment and subjective interpretation, particularly under varying operational conditions or in polymer-impregnated ropes with delayed damage indicators. This study explores the application of the Dynamic Time Warping (DTW) algorithm to enhance the reliability of MRT diagnostics. The research involved analyzing long-term MRT recordings of wire ropes used in mining operations, including different scanning resolutions and signal acquisition methods. A mathematical formulation of DTW is provided along with its implementation code in R and Python. The DTW algorithm was applied to synchronize diagnostic signals with their baseline recordings, as recommended by ISO 4309:2017 and EN 12927:2019 standards. Results show that DTW enables robust alignment of time series with slowly varying spectra, thereby improving the comparability and interpretation of MRT data. This approach reduces the risk of unnecessary rope discard and increases the effectiveness of degradation monitoring. The findings suggest that integrating DTW into existing diagnostic protocols can contribute to safer operation, lower maintenance costs, and reduced environmental impact. Full article

Decontamination robots are becoming more common in environments where reducing human exposure to hazardous substances is essential, including healthcare settings, laboratories, and industrial cleanrooms. Designing terrain-capable decontamination robots quickly is challenging due to varying operational surfaces and mobility limitations. To tackle this issue, a structured recommendation framework is proposed to automate selecting optimal locomotion types and track configurations, significantly cutting down design time. The proposed system features a two-stage evaluation process: first, it creates an annotated compatibility score matrix by validating locomotion types against a robust dataset based on factors like friction coefficient, roughness, payload capacity, and slope gradient; second, it employs a weighted scoring model to rank wheel/track types based on their appropriateness for the identified environmental conditions. User needs are processed dynamically using a large language model, enabling flexible and scalable management of various deployment scenarios. A prototype decontamination robot was developed following the proposed algorithm’s guidance. This framework speeds up the configuration process and establishes a foundation for more intelligent, terrain-aware robot design workflows that can be applied to industrial, healthcare, and service robotics sectors. Full article

In the coal industry, miner safety is increasingly challenged by growing mining depths and complex environments. The failure to wear Personal Protective Equipment (PPE) is a frequent issue in accidents, threatening lives and reducing operational efficiency. Additionally, existing PPE datasets are inadequate for model training due to their small size, lack of diversity, and poor labeling. Current methods often struggle with the complexity of multi-scenario and multi-type PPE detection, especially under varying environmental conditions and with limited training data. In this paper, we propose a novel minersPPE dataset and an improved algorithm based on YOLOv8, enhanced with Dilated-CBAM (Dilated Convolutional Block Attention Module) and DBB (Diverse Branch Block) Detection Block (YOLOv8-DCDB), to address these challenges. The minersPPE dataset constructed in this paper includes 14 categories of protective equipment needed for various body parts of miners. To improve detection performance under complex lighting conditions and with varying PPE features, the algorithm incorporates the Dilated-CBAM module. Additionally, a multi-branch structured detection head is employed to effectively capture multi-scale features, especially enhancing the detection of small targets. To mitigate the class imbalance issue caused by the long-tail distribution in the dataset, we adopt a K-fold cross-validation strategy, optimizing the detection results. Compared to standard YOLOv8-based models, experiments on the minersPPE dataset demonstrate an 18.9% improvement in detection precision, verifying the effectiveness of the proposed YOLOv8-DCDB model in multi-scenario, multi-type PPE detection tasks. Full article

Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. A 128-channel LiDAR sensor captures signal intensity images, which are processed by a ResNet-18 convolutional neural network to classify floor types as wood, smooth, or rough. Simultaneously, pitch angles from an onboard IMU detect terrain inclination. These inputs are transformed into fuzzy sets and evaluated using a Mamdani-type fuzzy inference system. The controller adjusts brush height, brush speed, and robot velocity through 81 rules derived from 48 structured cleaning experiments across varying terrain and slopes. Validation was conducted in low-light (night-time) conditions, leveraging LiDAR’s lighting-invariant capabilities. Field trials confirm that the robot responds effectively to environmental conditions, such as reducing speed on slopes or increasing brush pressure on rough surfaces. The integration of deep learning and fuzzy control enables safe, energy-efficient, and adaptive cleaning in complex outdoor environments. This work demonstrates the feasibility and real-world applicability for combining perception and inference-based control in terrain-adaptive robotic systems. Full article

The dynamic control of vibrations in skyscrapers is a critical consideration in sustainable building design, particularly in response to environmental excitations such as wind impact or seismic activity. Effective vibration neutralisation plays a crucial role in providing the safety of high-rise buildings. This research introduces an innovative concept for an active vibration damper that operates based on fluid dynamic transport to adaptively alter a skyscraper’s natural frequency, thereby counteracting resonant vibrations. A distinctive feature of this system is an adjustable-length cable mechanism, allowing for the dynamic modification of the pendulum’s effective length in real time. The structure, based on cable length adjustment, enables the PTMD to precisely tune its natural frequency to variable excitation conditions, thereby improving damping during transient or resonance phenomena of the building’s dynamic behaviour. A comprehensive mathematical model based on Lagrangian mechanics outlines the governing equations for this system, capturing the interactions between pendulum motion, fluid flow, and the damping forces necessary to maintain stability. Simulation analyses examine the role of initial excitation frequency and variable damping coefficients, revealing critical insights into optimal damper performance under varied structural conditions. The findings indicate that the proposed pendulum damper effectively mitigates resonance risks, paving the way for sustainable skyscraper design through enhanced structural adaptability and resilience. This adaptive PTMD, featuring an adjustable-length cable, provides a solution for creating safe and energy-efficient skyscraper designs, aligning with sustainable architectural practices and advancing future trends in vibration management technology. The study presented in this article supports the development of modern skyscraper design, with a focus on dynamic vibration control for sustainability and structural safety. It combines advanced numerical modelling, data-driven control algorithms, and experimental validation. From a sustainability perspective, the proposed PTMD system reduces the need for oversized structural components by providing adaptive, efficient damping, thereby lowering material consumption and embedded carbon. Through dynamically retuning structural stiffness and mass, the proposed PTMD enhances resilience and energy efficiency in skyscrapers, lowers lifetime energy use associated with passive damping devices, and enhances occupant comfort. This aligns with global sustainability objectives and new-generation building standards. Full article

This study investigates the behavior of spark discharges under various environmental conditions to simulate aspects of early-stage lightning dynamics, with a focus on their spectral characteristics, propagation, and ionization behavior. In a laboratory setting, spark discharges generated by a Tesla coil operating with high-frequency alternating current (AC) were analyzed under varying air humidity and water surface conductivity. Spectral analysis revealed that the discharges are dominated by the second positive system of molecular nitrogen N₂ (2P) and also exhibit the first negative system of molecular nitrogen ions N₂⁺ (1N). Notably, the N₂ (2P) emissions show strong peaks in the 350–450 nm range, closely matching spectral features typically associated with corona and streamer discharges in natural lightning. Environmental factors significantly influenced discharge morphology: in dry air, sparks exhibited longer and more branched paths, while in moist air, the discharges were shorter and more confined. Over water surfaces, the sparks spread radially, forming star-shaped patterns. Deionized (DI) water, with low conductivity, supported wider lateral propagation, whereas higher conductivity in tap water and saltwater suppressed discharge spread. The gap between the electrode tip and the surface also affected discharge extent and brightness. These findings demonstrate that Tesla coil discharges reproduce key features of early lightning processes and offer insights into how environmental factors influence discharge development. Full article

This study investigates supersonic flow within a nozzle under low-pressure conditions at the continuum mechanics boundary. This phenomenon is commonly encountered in applications such as the differentially pumped chamber of an Environmental Scanning Electron Microscope (ESEM), which employs an aperture to separate two regions with a great pressure gradient. The nozzle geometry and flow control in this region can significantly influence the scattering and loss of the primary electron beam traversing the differentially pumped chamber and aperture. To this end, an experimental chamber was designed to explore aspects of this low-pressure regime, characterized by a varying ratio of inertial to viscous forces. The initial experimental results obtained using pressure sensors from the fabricated experimental chamber were utilized to refine the Ansys Fluent simulation setup, and in this combined approach, initial analyses of supersonic flow and shock waves in low-pressure environments were conducted. The refined Ansys Fluent system demonstrated a very good correspondence with the experimental findings. Subsequently, an analysis of the influence of surface roughness on the resulting flow behavior in low-pressure conditions was performed on this refined model using the refined CFD model. Based on the obtained results, a comparison of the influence of nozzle roughness on the resulting electron beam scattering was conducted for selected low-pressure variants relevant to the operational conditions of the Environmental Scanning Electron Microscope (ESEM). The influence of roughness at elevated working pressures within the ESEM operating regime on reduced electron beam scattering has been demonstrated. At lower pressure values within the ESEM operating regime, this influence is significantly diminished. Full article

This work presents the implementation of a dual-extended Kalman filter (DEKF) in a double pipe counter-current heat exchanger. The DEKF aims to estimate online the heat transfer coefficient (HTC) to monitor the process. Some investigations estimate parameters in heat exchangers to detect fouling. However, there is limited research on online estimation using DEKF. The tests were performed at two operating conditions: in the first condition, the inlet temperatures were without perturbation; meanwhile, in the second operating condition, the cold-water inlet temperature was perturbed by the environmental heat. The experimental tests were carried out at different cold mass flow rates, which impact the temperatures and vary the heat transfer coefficient of the heat exchanger. The results showed adequate agreement between the estimated values of the heat transfer coefficients and those calculated with algebraic equations. This adequate agreement indicates that the DEKF method is conducive to detecting some problems in heat exchanger applications, such as poor heat transfer performance caused by fouling. Full article

This paper presents an enhanced hub location model tailored to port–hinterland logistics planning, grounded in the Capacitated Multiple-Allocation Hub Location Problem (CMAHLP). The formulation incorporates nonlinear cost structures, hub-specific operating costs, adaptive capacity constraints, and a feasibility condition based on the Social Net Present Value (NPVsocial) to support the design of intermodal freight networks under asymmetric spatial and socio-environmental conditions. The empirical case focuses on Spain, leveraging its strategic position between Asia, North Africa, and Europe. The model includes four major ports—Barcelona, Valencia, Málaga, and Algeciras—as intermodal gateways connected to the 47 provinces of peninsular Spain through calibrated cost matrices based on real distances and mode-specific road and rail costs. A Genetic Algorithm is applied to evaluate 120 scenarios, varying the number of active hubs (4, 6, 8, 10, 12), transshipment discounts (α = 0.2 and 1.0), and internal parameters. The most efficient configuration involved 300 generations, 150 individuals, a crossover rate of 0.85, and a mutation rate of 0.40. The algorithm integrates guided mutation, elitist reinsertion, and local search on the top 15% of individuals. Results confirm the central role of Madrid, Valencia, and Barcelona, frequently accompanied by high-performance inland hubs such as Málaga, Córdoba, Jaén, Palencia, León, and Zaragoza. Cities with active ports such as Cartagena, Seville, and Alicante appear in several of the most efficient network configurations. Their recurring presence underscores the strategic role of inland hubs located near seaports in supporting logistical cohesion and operational resilience across the system. The COVID-19 crisis, the Suez Canal incident, and the persistent tensions in the Red Sea have made clear the fragility of traditional freight corridors linking Asia and Europe. These shocks have brought renewed strategic attention to southern Spain—particularly the Mediterranean and Andalusian axes—as viable alternatives that offer both geographic and intermodal advantages. In this evolving context, the contribution of southern hubs gains further support through strong system-wide performance indicators such as entropy, cluster diversity, and Pareto efficiency, which allow for the assessment of spatial balance, structural robustness, and optimal trade-offs in intermodal freight planning. Southern hubs, particularly in coordination with North African partners, are poised to gain prominence in an emerging Euro–Maghreb logistics interface that demands a territorial balance and resilient port–hinterland integration. Full article