Search Results (401)

Background/Objectives: To investigate the utility of transperineal ultrasound in detecting stress urinary incontinence (SUI) and identify optimal anatomical and functional parameters. Methods: Thirty-four women presenting with SUI with or without pelvic organ prolapse between 2012 and 2016 were studied. The control group included patients without SUI who underwent surgery for mild gynecologic disorders or pelvic organ prolapse. The relationship between selected ultrasound parameters and SUI was determined. Results: Among the 20 variables measured in ultrasonography using 4 angles and the bladder–symphysis distance (BSD) values, we found that the difference in the BSD obtained at rest and during the Valsalva maneuver (odds ratio [OR]: 1.15, 95% confidence interval [CI]: 1.05–1.27, p = 0.004), the mean urethral diameter (UD; OR: 4.29, 95% CI: 2.07–8.83, p = 0.0001), and the occurrence of the funneling sign during the Valsalva maneuver (OR: 21; 95% CI: 6.1–71.9, p < 0.0001) were associated with urinary incontinence in the logistic regression analysis. The optimal cut-off point for BSD was >8 mm (area under the curve (AUC), 0.71; sensitivity, 91.2%; specificity, 56.8%; p = 0.001) and that for UD was >6 mm (AUC, 0.84; sensitivity, 82.1%; specificity, 73%; p < 0.001). Conclusions: Transperineal ultrasonography is a useful tool for detecting SUI. Our findings highlighted the utility of several sonographic parameters, mainly the urethral diameter, in the diagnosis of urinary incontinence. Full article

A refractive sail is a special type of solar sail concept, whose membrane exposed to the Sun’s rays is covered with an advanced engineered film made of micro-prisms. Unlike the well-known reflective solar sail, an ideally flat refractive sail is able to generate a nonzero thrust component along the sail’s nominal plane even when the Sun’s rays strike that plane perpendicularly, that is, when the solar sail attitude is Sun-facing. This particular property of the refractive sail allows heliocentric orbital transfers between orbits with different values of the semilatus rectum while maintaining a Sun-facing attitude throughout the duration of the flight. In this case, the sail control is achieved by rotating the structure around the Sun–spacecraft line, thus reducing the size of the control vector to a single (scalar) parameter. A gradient-index solar sail (GIS) is a special type of refractive sail, in which the membrane film design is optimized though a transformation optics-based method. In this case, the membrane film is designed to achieve a desired refractive index distribution with the aid of a waveguide array to increase the sail efficiency. This paper analyzes the optimal transfer performance of a GIS with a Sun-facing attitude (SFGIS) in a series of typical heliocentric mission scenarios. In addition, this paper studies the attitude control of the Sun-facing GIS using a simplified mathematical model, in order to investigate the effective ability of the solar sail to follow the (optimal) variation law of the rotation angle around the radial direction. Full article

The application of deep learning in autonomous vehicles has surged over the years with advancements in technology. This research explores the integration of deep learning algorithms into autonomous vehicles (AVs), focusing on their role in perception, decision-making, localization, mapping, and navigation. It shows how deep learning, as a part of machine learning, mimics the human brain’s neural networks, enabling advancements in perception, decision-making, localization, mapping, and overall navigation. Techniques like convolutional neural networks are used for image detection and steering control, while deep learning is crucial for path planning, automated parking, and traffic maneuvering. Localization and mapping are essential for AVs’ navigation, with deep learning-based object detection mechanisms like Faster R-CNN and YOLO proving effective in real-time obstacle detection. Apart from the roles, this study also revealed that the integration of deep learning in AVs faces challenges such as dataset uncertainty, sensor challenges, and model training intricacies. However, these issues can be addressed through the increased standardization of sensors and real-life testing for model training, and advancements in model compression technologies can optimize the performance of deep learning in AVs. This study concludes that deep learning plays a crucial role in enhancing the safety and reliability of AV navigation. This study contributes to the ongoing discourse on the optimal integration of deep learning in AVs, aiming to foster their safety, reliability, and societal acceptance. Full article

The purpose of this study was to examine the impact of a sensorimotor training program on maximum ankle dorsiflexion (ankle DF), coordination, dynamic balance and postural control, and lower-limb muscle power, in competitive junior surfers, and its relation to parameters of sensorimotor control required to perform aerial maneuvers. Twelve junior competitive surfers followed a 7-week sensorimotor training program, being assessed pre- and post-program with the knee-to-wall test (KW), Y-Balance test—lower quarter (YBT-LQ), and the countermovement jump test (CMJ). Post-training assessment revealed positive effects on the KW (ankle DF) distance, which increased approximately 2 cm (p < 0.001) for both ankles, and all scores for the YTB-LQ (coordination, dynamic balance, and postural control) variables increased, being significant (p < 0.005) for some reach distances (YBT-LQ—Anterior Left, YBT-LQ—Postero-medial Left, and YTB-LQ Anterior Right). YBT-LQ Anterior Reach Asymmetry also improved by decreasing 1.62 cm (p < 0.001) and the CMJ height (lower limb muscle power) increased 2.89 cm (p < 0.001). The training program proved to effectively enhance parameters of physical performance for this sample, including ankle DF, coordination, dynamic balance, postural control, and lower limb muscle power. This tailored-made task approach can help to optimize surfing performance capabilities and contribute to reducing the risk of injuries while performing aerials. Full article

This paper introduces a novel approach for modeling and optimizing the trajectory and behavior of small solid rocket missiles. The proposed framework integrates a six-degree-of-freedom (6DoF) simulation environment experimentally tuned for accuracy, with a combination of genetic algorithms (GAs) and machine learning (ML) to enhance the performance of the missile path. In the initial phase, a GA is employed to optimize the missile’s trajectory for efficient target acquisition, defining key launch parameters such as the ramp angle and lateral maneuver force to minimize positional errors and to ensure effective target engagement. Following trajectory optimization, the derived data are used to train an ML model that predicts setup parameters, significantly reducing computational costs and time. This close integration enables real-time adjustments for acquiring moving targets, thereby improving accuracy and minimizing maneuvering costs. This study also explores the application of fluidic thrust vectoring for small rockets, providing an innovative solution to enhance maneuverability and control, especially at low speeds. The proposed framework was validated using experimental launch data from the Icarus Team. The methodology offers a robust and cost-effective solution for precision targeting and improved maneuverability in aerospace and defense contexts. Full article

A novel tailless X-type flapping-wing micro air vehicle with two pairs of independent drive wings is designed and fabricated in this paper. Due to the complexity and unsteady of the flapping wing mechanism, the geometric and kinematic parameters of flapping wings significantly influence the aerodynamic characteristics of the bio-inspired flying robot. The wings of the vehicle are vector-controlled independently on both sides, enhancing the maneuverability and robustness of the system. Unique flight control strategy enables the aircraft to have multiple flight modes such as fast forward flight, sharp turn and hovering. The aerodynamics of the prototype is analyzed via the lattice Boltzmann method of computational fluid dynamics. The chordwise flexible deformation of the wing is implemented via designing a segmented rigid model. The clap-and-peel mechanism to improve the aerodynamic lift is revealed, and two air jets in one cycle are shown. Moreover, the dynamics experiment for the novel vehicle is implemented to investigate the kinematic parameters that affect the generation of thrust and maneuver moment via a 6-axis load cell. Optimized parameters of the flapping wing motion and structure are obtained to improve flight dynamics. Finally, the prototype realizes controllable take-off and flight from the ground. Full article

Overtaking situations are commonly encountered in maritime navigation, and the overtaking process involves various risk factors that significantly contribute to collision incidents. It is crucial to conduct research on the maneuvering behaviors and decision-making processes associated with ship overtaking. This paper proposes a method based on the analysis of ship maneuvering performance to investigate overtaking behaviors in navigational channels. A relative motion model is established for both the overtaking and the overtaken vessels, and the inter-vessel distance is calculated, taking into account the psychological perceptions of the ship’s driver. A decision-making model for ship overtaking is presented to provide a safety protocol for overtaking maneuvers. Applying this method to overtaking data from the South Channel shows that it effectively characterizes both the permissible overtaking space and the driver’s overtaking desire. Additionally, it enables the prediction of optimal overtaking timing and strategies based on short-term trajectory forecasts. Thus, this method not only offers a safe overtaking plan for vessels but also provides auxiliary information for decision making in intelligent ship navigation. Full article

Current research on ground-effect unmanned aerial vehicles (UAVs) predominantly centers on numerical aerodynamic optimization and stability analysis in the ground effect, leaving a significant gap in the thorough examination of flight performance through flight tests. This study presents the design of a vertical takeoff and landing (VTOL) ground-effect UAV, featuring a vector motor configuration. The control system utilizes a decoupled strategy based on position and attitude, enabling stable altitude control in the low-altitude ground-effect region. Comprehensive flight tests were conducted to evaluate the UAV’s flight stability and energy consumption in the ground-effect region. The results reveal that the ground-effect UAV successfully performed rapid takeoff maneuvers and maintained stable forward flight in the designated ground-effect region. In the span-dominated ground-effect region, a significant 33% reduction in flight current was observed, leading to a corresponding 33% decrease in total power consumption compared to flight conditions outside the ground effect. These findings highlight a substantial improvement in flight performance under the influence of ground effect. The real-time flight data produced by this system provides valuable insights for optimizing the design of VTOL ground-effect UAVs. Full article

To address the mission planning challenge for agile satellites in dense point target observation, a clustering strategy based on an ant colony algorithm and a heuristic simulated genetic annealing optimization algorithm are proposed. First, the imaging observation process of agile satellites is analyzed, and an improved ant colony algorithm is employed to optimize the clustering of observation tasks, enabling the satellites to complete more observation tasks efficiently with a more stable attitude. Second, to solve for the optimal group target observation sequence and achieve higher total observation benefits, a task planning model based on multi-target observation benefits and attitude maneuver energy consumption is established, considering the visible time windows of targets and the time constraints between adjacent targets. To overcome the drawbacks of traditional simulated annealing and genetic algorithms, which are prone to local optimal solution and a slow convergence speed, a novel Simulated Genetic Annealing Algorithm is designed while optimizing the sum of target observation weights and yaw angles while also accounting for factors such as target visibility windows and satellite attitude transition times between targets. Ultimately, the feasibility and efficiency of the proposed algorithm are substantiated by comparing its performance against traditional heuristic optimization algorithms using a dataset comprising large-scale dense ground targets. Full article

In this study, we explore the work of breathing (WoB) experienced by aviators during the Anti-G Straining Maneuver (AGSM) to improve pilot safety and performance. Traditional airflow models of WoB fail to adequately distinguish between breathing rate and inspiratory frequency, leading to potentially inaccurate assessments. This mismatch can have serious implications, particularly in critical flight situations where understanding the true respiratory workload is essential for maintaining performance. To address these limitations, we used a non-sinusoidal model that captures the complexities of WoB under high inspiratory frequencies and varying dead space conditions. Our findings indicate that the classical airflow model tends to underestimate WoB, particularly at elevated inspiratory frequencies ranging from 0.5 to 2 Hz, where resistive forces play a significant role and elastic forces become negligible. Additionally, we show that an increase in dead space, coupled with high-frequency breathing, elevates WoB, heightening the risk of dyspnea among pilots. Interestingly, our analysis reveals that higher breathing rates lead to a decrease in total WoB, an unexpected finding suggesting that refining breathing patterns could help pilots optimize their energy expenditure. This research highlights the importance of examining the relationship between alveolar ventilation, breathing rate, and inspiratory frequency in greater depth within realistic flight scenarios. These insights indicate the need for targeted training programs and adaptive life-support systems to better equip pilots for managing respiratory challenges in high-stress situations. Ultimately, our research lays the groundwork for enhancing respiratory support for aviators, contributing to safer and more efficient flight operations. Full article

This paper proposed an innovative guidance strategy, denoted as NMPC-IM, which integrates the Insensitive Mechanism (IM) with Nonlinear Model Predictive Control (NMPC) for Unmanned Aerial Vehicle (UAV) pursuit-evasion scenarios, with the aim of effectively intercepting maneuvering targets with consideration of input constraints while minimizing average energy expenditure. Firstly, the basic principle of IM is proposed, and it is transformed into an additional cost function in NMPC. Secondly, in order to estimate the states of maneuvering target, a fixed-time sliding mode disturbance observer is developed. Thirdly, the UAV’s interception task is formulated into a comprehensive Quadratic Programming (QP) problem, and the NMPC-IM guidance strategy is presented, which is then improved by the adjustment of parameters and determination of maximum input. Finally, numerical simulations are carried out to validate the effectiveness of the proposed method, and the simulation results show that the NMPC-IM guidance strategy can decrease average energy expenditure by mitigating the impact of the target’s maneuverability, optimizing the UAV’s trajectory during the interception process. Full article

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms. Full article

This paper presents a study on aircraft engine identification using real-time 3D LiDAR point cloud segmentation technology, a key element for the development of automated docking systems in airport boarding facilities, known as jetbridges. To achieve this, 3D LiDAR sensors utilizing a spinning method were employed to gather surrounding environmental 3D point cloud data. The raw 3D environmental data were then filtered using the 3D RANSAC technique, excluding ground data and irrelevant apron areas. Segmentation was subsequently conducted based on the filtered data, focusing on aircraft sections. For the segmented aircraft engine parts, the centroid of the grouped data was computed to determine the 3D position of the aircraft engine. Additionally, PointNet was applied to identify aircraft engines from the segmented data. Dynamic tests were conducted in various weather and environmental conditions, evaluating the detection performance across different jetbridge movement speeds and object-to-object distances. The study achieved a mean intersection over union (mIoU) of 81.25% in detecting aircraft engines, despite experiencing challenging conditions such as low-frequency vibrations and changes in the field of view during jetbridge maneuvers. This research provides a strong foundation for enhancing the robustness of jetbridge autonomous docking systems by reducing the sensor noise and distortion in real-time applications. Our future research will focus on optimizing sensor configurations, especially in environments where sea fog, snow, and rain are frequent, by combining RGB image data with 3D LiDAR information. The ultimate goal is to further improve the system’s reliability and efficiency, not only in jetbridge operations but also in broader autonomous vehicle and robotics applications, where precision and reliability are critical. The methodologies and findings of this study hold the potential to significantly advance the development of autonomous technologies across various industrial sectors. Full article

Air exchange in pressurized water pipelines is an essential but complex aspect of pipeline modeling and operation. Implementing effective air management strategies can yield numerous benefits, enhancing the system’s energy efficiency, reliability, and safety. This paper comprehensively evaluates an irregular profile pipeline filling procedure involving air-release through an air valve. The analysis includes real-time data tests and numerical simulations using Computational Fluid Dynamics (CFD). A Digital Twin model was proposed and applied to filling maneuvers in water installations. In particular, this research considers an often-overlooked aspect, such as filling a pipe with an irregular profile rather than a simple straight pipe. CFD simulations have proven to capture the main features of the transient event, which are suitable for tracking the air-water interface, the unsteady water flow, and the evolution of the trapped air pocket. Thus, they provide thorough and reliable information for real-time operational processes in the industry, focusing on the filling pressure and geometry of the air-valve hydraulic system. Additionally, this study provides details regarding the application of an efficient Digital Twin CFD approach, demonstrating its feasibility in optimizing the filling procedure in pipes with irregular profiles. Full article

With the proliferation of Unmanned Aerial Vehicle (UAV) technology, the demand for effective collision avoidance technology has intensified. The DAIDALUS algorithm, devised by NASA Langley Research Center under the Detect-and-Avoid (DAA) framework, provides conflict prevention bands for remotely piloted UAVs navigating in intricate airspace. The algorithm computes the bands in two distinct phases: Conflict and Recovery. The formal model for both phases has been established and implemented through iterative programming approaches. However, the mathematical model remains incomplete. Therefore, based on the model, this paper proposes the mathematical model for the two phases of the horizontal track conflict prevention algorithm. Firstly, Cauchy’s inequality is proposed to formulate the model that addresses trajectory conflicts considering the UAV non-instantaneous maneuvering dynamics model, and then a prudent maneuvering strategy is designed to optimize the model for the recovery phase. Finally, the execution procedure of the algorithm within the two-stage mathematical model is also detailed. The results demonstrate that the proposed model achieves a higher precision in the preventive bands, implements an effective collision avoidance strategy, and consistently aligns with the DAIDALUS model while offering a larger buffer time or distance. This work theoretically validates the formal model of the DAIDLAUS algorithm and provides insights for further refinement. Full article