Search Results (7,606)

The main goal of six-dimensional pose estimation is to accurately ascertain the location and orientation of an object in three-dimensional space, which has a wide range of applications in the field of artificial intelligence. Due to the relative sparseness of the point cloud data captured by the depth camera, the ability of models to fully understand the shape, structure, and other features of the object is hindered. Consequently, the model exhibits weak generalization when faced with objects with significant shape differences in the new scene. The deep integration of feature levels and the mining of local and global information can effectively alleviate the influence of the above factors. To solve these problems, we propose a new Two-Stage Geometric Neighborhood Fusion Network for category-level 6D pose estimation (TGNF-Net) to estimate objects that have not appeared in the training phase, which strengthens the fusion capacity of feature points within a specific range of neighborhoods, enabling the feature points to be more sensitive to both local and global geometric information. Our approach includes a neighborhood information fusion module, which can effectively utilize neighborhood information to enrich the feature set of different modal data and overcome the problem of heterogeneity between image and point cloud data. In addition to this, we design a two-stage geometric information embedding module, which can effectively fuse geometric information of the multi-scale range into keypoint features. This way enhances the robustness of the model and enables the model to exhibit stronger generalization capabilities when faced with unknown or complex scenes. These two strategies enhance the expression of features and make NOCS coordinate predictions more accurate. Many experiments show that our approach is superior to other classical methods on the CAMERA25, REAL275, HouseCat6D, and Omni6DPose datasets. Full article

This study investigates how effectively European football clubs communicate and implement accessibility features for disabled fans, aiming to develop a comprehensive framework for evaluating these practices. Using a multi-phase research design, the study analyzes disability support systems through semi-structured interviews with representatives from eleven European football clubs and a systematic analysis of club documentation. The methodology combined traditional qualitative analysis with large language model (LLM)-assisted content analysis, enabling robust identification of thematic patterns and performance indicators. Our findings reveal significant disparities in disability support practices, with larger clubs demonstrating structured approaches through dedicated Disability Access Officers (DAOs) and comprehensive communication strategies. Analysis identified three distinct performance tiers: Elite Performers, primarily well-resourced clubs with advanced systems; Solid Performers, mid-tier clubs with established frameworks; and Developing Systems, smaller organizations with emerging support structures. We present a validated Fan Communication Model incorporating key weighted criteria, including infrastructure, dedicated personnel, engagement, specific adaptations, ticketing, challenge management, and feedback systems. This model provides a standardized framework for evaluating disability support communication in football organizations. The research demonstrates the importance of integrating technological solutions with human-centered approaches while maintaining universal design principles. Our findings contribute to the sports accessibility literature and provide evidence-based recommendations for football organizations seeking to enhance their communication with disabled fans. Full article

The present study is concerned with the power transfer efficiency enhancement using a combination of the multi-hop node (MHN) and the Intelligent Reflecting Surface (IRS)-based passive beamforming technologies. The primary objective is to ensure a high RF-DC converter power conversion efficiency (PCE) used at the receiving end, which is difficult to achieve due to path loss and multi-path propagation. An electronically tunable reconfigurable reflectarray (RRA) designed to operate at the sub-GHz ISM band (865.5 MHz) is utilized to implement the IRS concept. Both the MHN and RRA were developed and studied in our earlier research. The RRA redirects the reflected power-carrying wave amplified by the MHN toward the intended receiver. It comprises two layers: the RF layer containing tunable phase shifters and the ground plane. Each phase shifter comprises two identical eight-shaped metal patches coupled by a pair of varactor diodes used to achieve the reflection phase tuning. The phase gradient method is used to synthesize the RRA phase profiles, ensuring different desired reflection angles. The RRA prototype, composed of 36 phase shifters, is employed in conjunction with the MHN equipped with two antennas and an amplifier. The RRA parameter optimization is accomplished by randomly varying the varactor diode voltages and measuring the corresponding received power levels until the power reflected in the desired direction is maximized. Two measurement scenarios are examined: power transmission without and with the MHN. In the first scenario, the received power is calculated and measured at several distinct beam steering angles for different distances between the Tx antenna and RRA. The same procedure is applied to different distances between the RRA and MHN in the second scenario. The effect of slight deviations in the operating frequency from the designed one (865.5 MHz) on the RRA performance is also examined. Additionally, the received power levels for both scenarios are estimated via full-wave analysis performed using the full-wave simulation software Ansys HFSS 2023 R1. A Huygens’ surface equivalence principle-based model decomposition method was developed and employed to reduce the CPU time. The calculated results are consistent with the measured ones. However, some discrepancies attributed to the adverse effect of RRA diode biasing lines, manufacturing tolerances, and imperfection of the indoor environment model are observed. Full article

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R²) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R² = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R² = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future. Full article

The optimization of the water-entry strategy for cross-wing underwater vehicles has become a research hotspot in the field of engineering, and its water-entry process is quite different from that of wedges and cylinders. In order to address this problem, a water-entry numerical model for the cross-wing underwater vehicle was first established based on the CFD method. The governing equations and boundary conditions of the dynamic model were defined, along with the basic principles of discretization and turbulent flow of the governing equations. The overset mesh and the VOF multiphase flow model were introduced, and a mesh size independence analysis of the numerical model was conducted. Furthermore, the numerical results were compared with the experimental results to ensure the accuracy of the numerical model. The research focused on the cross-wing underwater vehicle’s impact with calm water and regular waves, respectively. The results show that: (1) the numerical simulations are in good agreement with the experimental results (the maximum predictive error is less than 10%), which verifies the accuracy of the numerical model in this paper; (2) when the cross-wing underwater vehicle impacts calm water, the slamming pressure curve firstly shows a trend of increasing, reaching a peak, and then decreasing sharply, and finally stabilizes. As the water-entry velocity increases, the peak slamming pressure exhibits a gradual increase; (3) during the water entry of the cross-wing underwater vehicle into calm water, the acceleration profile demonstrates a trend of initial increase, followed by a decrease, another increase, and then a subsequent decrease as the entry velocity continues to rise. It should be noted that there are two peaks in the acceleration, with the first peak being significantly smaller than that of the second; (4) when the cross-wing underwater vehicle impacts a regular wave, the slamming pressure is lowest when impacting the crest and highest when impacting the trough. Full article

The reliable operation of power systems is heavily dependent on effective maintenance strategies for critical equipment. Current maintenance methods are typically categorized into corrective, preventive, and predictive approaches. While corrective maintenance often results in significant downtime and preventive maintenance can be inefficient, predictive maintenance emerges as a promising technique for accurately forecasting faults. In this study, we investigated the diagnosis and prediction of fault states, specifically single-phase short circuit (1HCF) and double-phase short circuit (2HCF) faults, using monitoring data from current transformers in 110 kV substations. We proposed a predictive maintenance method for current transformers based on core-type structures, which integrates wavelet transform to extract multi-level frequency domain features, employs feature selection techniques (including the Spearman correlation coefficient and mutual information) to identify key predictive features, and utilizes Random Forest classifiers for fault state prediction. Experimental results demonstrate an overall prediction accuracy of 94%. Full article

Cavitation is a complex multiphase flow phenomenon, and the generation of transient phase transitions between liquid and vapor during cavitation development leads to multi-scale vortex motion. The transient cavitation dynamics and centrifugal pump’s rotor–stator interaction will induce pressure fluctuations in the impeller and the volute fluid of the centrifugal pump, resulting in a complex flow field structure. Based on the Schnerr–Sauer cavitation model and SST k-ω turbulence model, this paper studies the transient characteristics of the cavitation-induced unsteady flow in the centrifugal pump and the excitation response to the pressure pulsation in the volute under different flow conditions, taking the large vertical double-volute centrifugal pump as the research object. The results indicate the following: As the impeller rotates, in the external excitation response, the jet-wake flow structure at the centrifugal pump blade outlet shows an increase in the blade frequency signal. This is evident near the measurement points of the volute tongue and separator. When severe cavitation occurs, the maximum amplitude at the blade frequency in the volute shifts from the pump tongue (30°) to the downstream of the tongue (45°). The value of f_pmax is 3.1 times that when NPSHa = 8.88 m. By applying the Omega vortex identification method, it can be seen that the interaction between the vortices at the blade trailing edge and the stable vortex in the volute tongue undergoes a process of elongation, fusion, separation, and recovery. This represents the downstream influence of the impeller on the volute. When Q = 0.9Q_d, the process of the blade passage vortex tail detaching and dissipating in the impeller flow path can be observed, demonstrating the upstream influence of the volute on the impeller. Full article

Free divers are known to experience a physiological response during extreme breath holding, causing involuntary breathing movements (IBMs). To investigate these movements, a low-cost multi-core ESP32-Pico microcontroller prototype was developed to measure IBMs during a static breath hold. This novel device, called the bioSense, uses a differential measurement between two accelerometers placed on the sternum and the xiphoid process to acquire breathing-related movements. Sensor placement allowed for data acquisition that was posture- and body-shape-agnostic. Sensor placement was also designed to be as non-intrusive as possible and precisely capture breathing movements at configurable sampling rates. Measurements from the device were sent over WiFi to be accessed on a password-protected webserver and backed up to a micro-secure digital (microSD) card. This device was used in a pilot study, where it captured the various phases of breathing experienced by recreational free divers alongside a force plate measurement system for comparison. Full article

Circular airy beam (CAB) is a kind of new structured light with non-diffracting, self-focusing, and self-healing properties. Due to its wide applications, recently, numerous researchers have used various methods to modulate this kind of beam. We theoretically verify and experimentally demonstrate the azimuthal modulation method to shapes the focal spot of the CAB by modulating the CAB with the azimuthally spliced power-exponential phase. The results show that after modulating by an azimuthally spliced power-exponential phase, multi-focal spots can be generated on the self-focusing focal plane of the modulated CAB, and the number of the focal spots can be precisely controlled by controlling the number of segments of the spliced power-exponential phase. The situations of generating three, four, and five focal spots can be achieved via appropriate azimuthally spliced power-exponential phase modulation. We also calculate the intensity distribution, energy flow density, angular momentum density, and optical force of the modulated beam after tight focusing. The results illustrate the theoretical possibility of stable multiparticle trapping by the modulated beam. Our results pave the way for on-demand shaping of the self-focusing focus of the CAB, which will facilitate related applications, such as CAB based multi-particle trapping. Full article

With the emphasis on energy conversion and energy-saving technologies, the single-phase pulse width modulation (PWM) rectifier method is widely used in urban rail transit because of its advantages of bidirectional electric energy conversion and higher power factor. However, due to the complex control and harsh environment, it can easily fail. Faults can cause current and voltage distortion, harmonic increases and other problems, which can threaten the safety of the power system and the train. In order to ensure the stable operation of the rectifier, incidences of faults should be reduced. A fault diagnosis technique based on Euclidean norm fusion multi-frequency bands and multi-scale permutation entropy is proposed. Firstly, by the optimal wavelet function, information on the optimal multi-frequency bands of the fault signal is selected after wavelet packet decomposition. Secondly, the multi-scale permutation entropy of each frequency band is calculated, and multiple fault feature vectors are obtained for each frequency band. To reduce the classifier’s computational cost, the Euclidean norm is used to fuse the multi-scale permutation entropy into an entropy value, so that each frequency band uses an entropy value to characterize the fault information features. Finally, the optimal multi-frequency bands and multi-scale permutation entropy after fusion are used as the fault feature vector. In the simulation system, it is shown that the method’s average accuracy is 78.46%, 97.07%, and 99.45% when the SNR is 5 dB, 10 dB, and 15 dB, respectively. And the fusion of multi-scale permutation entropy can improve the accuracy, recall rate, precision, and F1 score and reduce the False Alarm Rate (FAR) and the Missing Alarm Rate (MAR). The results show that the fault diagnosis method has high diagnosis accuracy, is a simple feature fusion method, and has good robustness to working conditions and noise. Full article

The precise estimation of influential parameters in adsorption is a key point in conducting simulations for the sensitivity analysis and optimal design of cooling systems. This study explores the critical role of a new type of granular activated carbon (GAC-208C) in adsorption refrigeration systems. By fitting experimental and numerical models to the thermophysical properties of GAC/methanol as a working pair, an advanced methodology is established for the thermal analysis of the adsorption bed, addressing the various operating conditions overlooked in prior studies. The physical properties of the studied carbon sample are determined in a laboratory using surface area and pore volume tests, thermal adsorption analysis, and weight loss. To determine the thermal properties of GAC/methanol, the adsorption process is experimentally tested inside an isolated heat exchanger. A three-dimensional (3D) model is created to simulate the procedure and then coupled with the particle swarm optimisation (PSO) algorithm in MATLAB. The optimal thermal parameters for adsorption are determined by minimising the mean square error (MSE) of the adsorption bed temperature between the numerical and experimental data. The laboratory studies yielded accurate results for the physical properties of GAC, including adsorption capacity, porosity, permeability, specific heat capacity, density, activation energy, and the heat of adsorption. The thermal analysis of the adsorption process identified the ideal values for the Dubinin–Astakhov equation constants, diffusion coefficients, heat transfer coefficients, and contact resistance. The numerical model demonstrated strong agreement with the experimental results, and the dynamic behaviour of pressure and uptake distribution showed good agreement with 1.2% relative error. This research study contributes to the improved estimation of adsorption parameters to conduct more accurate numerical simulations and design new adsorption systems with enhanced performance under different operating conditions. Full article

Air Traffic Services play a crucial role in the safety, security, and efficiency of air transportation. The International Civil Aviation Organization (ICAO) performance-based surveillance concept requires monitoring the actual performance of the surveillance systems underpinning these services. This assessment is usually based on the analysis of data gathered during the normal operation of the surveillance systems, also known as opportunity traffic. Processing opportunity traffic requires data association to identify and assign the sensor detections to a flight. Current techniques for association require expert knowledge of the flight dynamics of the target aircraft and have issues with high-manoeuvrability targets like military aircraft and Unmanned Aircraft (UA). This paper addresses the data association problem through the use of the Multi-Sensor Intelligent Data Association (M-SIOTA) algorithm based on Deep Neural Networks (DNNs). This is an innovative perspective on the data association of multi-sensor surveillance through the lens of machine learning. This approach enables data processing without assuming any dynamics model, so it is applicable to any aircraft class or airspace structure. The proposed algorithm is trained and validated using several surveillance datasets corresponding to various phases of flight and surveillance sensor mixes. Results show improvements in association performance in the different scenarios. Full article

The study of the process of laser action on powder materials requires the construction of mathematical models of the interaction of laser radiation with powder particles that take into account the features of energy supply and are applicable in a wide range of beam parameters and properties of the particle material. A model of the interaction of pulsed or pulse-periodic laser radiation with a spherical metal particle is developed. To find the temperature distribution in the particle volume, the non-stationary three-dimensional heat conductivity equation with a source term that takes into account the action of laser radiation is solved. In the plane normal to the direction of propagation of laser radiation, the change in the radiation intensity obeys the Gaussian law. It is possible to take into account changes in the intensity of laser radiation in space due to its absorption by the environment. To accelerate numerical calculations, a computational algorithm is used based on the use of vectorized data structures and parallel implementation of operations on general-purpose graphics accelerators. The features of the software implementation of the method for solving a system of difference equations that arises as a result of finite-volume discretization of the heat conductivity equation with implicit scheme by the iterative method are presented. The model developed describes the heating and melting of a spherical metal particle exposed by multi-pulsed laser radiation. The implementation of the computational algorithm developed is based on the use of vectorized data structures and GPU resources. The model and calculation results are of interest for constructing a two-phase flow model describing the interaction of test particles with laser radiation on the scale of the entire calculation domain. Such a model is implemented using a discrete-trajectory approach to modeling the motion and heat exchange of a dispersed admixture. Full article

Background: Healthcare setting teams were challenged to understand how and what to measure regarding healthcare quality improvement (HQI), who should be involved, and what approach to apply. We aimed to determine if a generic co-design approach involving patients/families, multi-disciplinary care providers, and other staff was feasible to apply for HQI across diverse care settings. Developmental evaluation embedded in the co-design approach would determine its effectiveness, challenges, and other experiences across care settings and teams. Methods: Twenty-two acute and community care settings agreed to participate in applying a phased co-design approach to their HQI initiatives, including developmental evaluation. Each care setting team received co-design orientation and support. Semi-structured interviews and focus groups were conducted with patient/family advisors (PFAs) and care setting staff/care providers to gather their experiences with the co-design approach applied to their phased HQI work. Transcripts were thematically analyzed and triangulated with observation notes of care setting team discussions. Experiences were gathered from 17 PFAs and 68 staff/care providers across the 22 participating healthcare settings. Results: Themes for the orientation and each phase emphasized the importance of participants’ understanding, engagement, and ongoing open communication throughout the HQI co-design process. The orientation was viewed as key to facilitating good outcomes. Participants valued working together, gathering real-time experiences to “make a difference”, and having PFA voices involved in co-designing the HQI initiatives. Challenges were identified, including time commitment. Conclusions: Based on the overall developmental evaluation findings, there was consensus that a generic co-design of HQI initiatives was effective, feasible, and sustainable across care settings. Full article

Unruptured intracranial aneurysms, affecting 2–5% of the population, are characterized by localized wall weakening and irregular morphologies, including features such as blebs, lobulations, or asymmetries, which are significant predictors of rupture risk. Although up to 57% of ruptured intracranial aneurysms exhibit irregular dome geometry, its influence on aneurysm stability remains underexplored. Irregular geometries are associated with adverse hemodynamic forces, such as increased wall shear stress (WSS), amplifying wall stress at specific regions, and promoting flow disturbances, which may increase aneurysm vulnerability. This study investigates the influence of aneurysm dome morphology, particularly in IAs with irregular domes that may include daughter blebs, using Computational Fluid Dynamics (CFD). Unlike prior CFD studies that modeled blood as Newtonian or non-Newtonian, this work employs a three-phase blood flow model, representing plasma and red blood cells (RBCs) as distinct phases. Numerical simulations, conducted via the Finite Volume Method, solve the Navier–Stokes equations to capture complex flow dynamics within cerebral vasculature. Key hemodynamic metrics, such as Wall Shear Stress (WSS), Wall Shear Stress Gradient (WSSG), and Viscous Dissipation Rate, are analyzed to assess the interplay between dome morphology and hemodynamic stressors. Full article