Search Results (42)

Precise measurement of particle concentration in dilute gas–solid two-phase flows is challenging due to low particle loading and stochastic particle motion, which lead to weak signals and detection blind zones. This study develops a swirling-flow-enhanced triboelectric nanogenerator (SF-TENG) using active flow field regulation to enhance particle–wall interactions. Through CFD optimization of guide vane geometry, the SF-TENG achieved a nearly twenty-fold increase in short-circuit current compared to non-swirling configurations. The swirling flow exhibited a particle-size-dependent enhancement mechanism. For fine particles, the improvement was mainly attributed to an increased collision ratio. For coarse particles, it resulted from enhanced charge transfer per single impact. The swirling flow continuously improved the reliability and sensitivity of detection across all particle sizes. These findings provide valuable insights for designing highly sensitive, self-powered flow meters with minimized blind zones for gas–solid monitoring. Full article

This paper investigates ship–pier collision risk caused by yaw deviation in inland bridge waterways. The proposed framework is conceived for fixed auxiliary thruster installation in bridge areas, rather than retrofitting shipboard propulsion systems. A proactive intervention scheme is developed based on state estimation and short-horizon prediction. A Kalman filter is used for state fusion and short-horizon motion prediction. Yaw events are detected via a threshold rule with consecutive-decision logic. An extended state observer (ESO) is adopted to estimate lumped disturbances and model uncertainties. A fuzzy self-tuning PID law is then applied to generate thruster commands for closed-loop corrective control. Numerical simulations suggest that, relative to rudder-only recovery, thruster-assisted intervention yields improved restoration behavior, reduced lateral deviation accumulation, and increased minimum clearance to bridge piers under the tested conditions. Additional tests with cross-current disturbances indicate that the risk-triggered scheme with ESO-based compensation can maintain stable recovery and a higher safety margin. The proposed approach provides an engineering-oriented pathway to extend bridge-area risk management from warning-level assessment to executable control intervention. Full article

Although underwater gliders are highly energy-efficient platforms capable of long-duration and large-scale ocean observation, their lack of self-propulsion requires external assistance for recovery upon mission completion. In harsh and dynamic marine environments, reliably detecting the glider and accurately estimating its three-dimensional position are critical to ensuring the recovery operations are safe and efficient. This paper proposes a perception framework based on deep learning to detect underwater glider hulls and estimate their three-dimensional relative positions using camera–sonar multi-sensor fusion. This approach integrates a hierarchical convolutional neural network (CNN) vision encoder and a transformer-based architecture to estimate the glider’s spatial location and heading direction simultaneously. The hierarchical CNN encoder extracts multi-level, semantically rich visual features, thereby improving robustness to visual degradation and environmental disturbances common in underwater settings. Additionally, the transformer incorporates a variable mixture-of-experts (vMoE) mechanism that adaptively allocates expert networks across layers, enhancing representational capacity while maintaining computational efficiency. The resulting pose estimates enable precise, collision-free ROV navigation for automated recovery and onboard sensor inspection tasks. Experimental results, including ablation studies, validate the effectiveness of the proposed components and demonstrate their contributions to accurate glider hull detection and three-dimensional localization. Overall, the proposed framework provides a scalable, reliable perception solution that allows for the safe, autonomous recovery of underwater gliders with an ROV in realistic ocean environments. Full article

The high rate of bike commuting around the globe has greatly transformed the mode of transportation in cities, but the high speeds of motorized cycling have contributed to a high rate of serious road trauma. Although conventional helmets offer necessary passive structural protection, they do not consider the most important aspect of the emergency response, which is the Golden Hour the time frame during which medical intervention can have the most significant impact. This paper is a development and validation of an autonomous, low-cost smart helmet architecture that is programmed to operate in real-time to detect accidents and autonomously inform the operator of accidents. The system is built up of an ESP32 microcontroller with a multi-modal sensor package, which comprises an inertial measurement unit (IMU), force-impact sensors, and MQ-3 alcohol sensors to conduct proactive safety screening. To overcome the single threshold limitation of unreliable systems, a time-windowed sensor-fusion algorithm was applied in order to distinguish between normal riding dynamics and bona fide collisions. This reasoning involves concurrent cues of high-G inertial rotations and physical impacting features over a time window of 500 ms to reduce spurious activations. The architecture of the system is completely self-sufficient and employs an in-built GPS-GSM module to send the geographical location through SMS without the need to have a smartphone connection. The prototype was also put through 150 experimental tests, with some conducted in laboratories, and real-world running tests in diverse terrains. The findings reveal an accuracy in detection of 93.7, a false positive rate (FPR) of 2.6 and a mean emergency alert latency of 2.8 s. In addition, it was found that structural integrity was confirmed at ECE 22.05 impact conditions using Finite Element Analysis (FEA), with a safety factor of 1.38. These quantitative results mean that the proposed system is an effective way to address a cultural shift between passive structural protection and active rescue intervention as a statistical and computationally efficient safety measure of modern micro-mobility. Full article

Laser-induced breakdown spectroscopy (LIBS), limited by matrix effects, self-absorption in complex samples, and ambient atmospheric influences, still requires further improvement in detection sensitivity and signal stability. In this work, the excitation beam of LIBS is modulated into an optical vortex by an optical phase element, and optical vortex-induced LIBS is used to detect and analyze coal samples. Building on the uniform annular intensity distribution and orbital angular momentum (OAM) carried by the optical vortex, it is anticipated that spectral signal intensity can be enhanced by improving plasma ablation efficiency, reducing shielding effects, and increasing electron collision frequency, thereby reducing signal uncertainty and enhancing LIBS analytical performance. For the first time, a classification model combining principal component analysis (PCA) and support vector machine (SVM) is developed, integrating optical vortex-induced LIBS technology with machine learning. Using the PCA-SVM model, optical vortex-based LIBS attained a coal classification accuracy of 95%, significantly higher than the 88% achieved with Gaussian beams, thereby markedly improving classification performance for complex matrix samples. These results demonstrate that optical vortex-induced LIBS possesses strong potential for efficient detection of samples with complex matrices. Full article

To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be computationally expensive for real-time applications or lack the precision needed to handle both rotational and translational movements, leading to issues such as misidentifying static elements as dynamic obstacles and generating false positives. In this paper, we propose a novel approach that integrates an event camera-based perception pipeline with an ego-motion compensation algorithm to accurately compensate for both rotational and translational UAV motion. An enhanced warping function, integrating IMU and depth data, is constructed to compensate camera motion based on real-time IMU data to remove ego motion from the asynchronous event stream, enhancing detection accuracy by reducing false positives and missed detections. On the compensated event stream, dynamic obstacles are detected by applying a motion aware adaptive threshold to the normalized mean timestamp image, with the threshold derived from the image’s spatial mean and standard deviation and adjusted by the UAV’s angular and linear velocities. Furthermore, in conjunction with a 3D Artificial Potential Field (APF) for obstacle avoidance, the proposed approach generates smooth, collision-free paths, addressing local minima issues through a rotational force component to ensure efficient UAV navigation in dynamic environments. The effectiveness of the proposed approach is validated through simulations, and its application for UAV navigation, safety, and efficiency in environments such as warehouses is demonstrated, where real-time response and precise obstacle avoidance are essential. Full article

Infrared imaging technology demonstrates significant advantages in aviation safety monitoring due to its exceptional all-weather operational capability and anti-interference characteristics, particularly in scenarios requiring real-time detection of aerial objects such as airport airspace management. However, traditional infrared target detection algorithms face critical challenges in complex sky backgrounds, including low signal-to-noise ratio (SNR), small target dimensions, and strong background clutter, leading to insufficient detection accuracy and reliability. To address these issues, this paper proposes the AFK-YOLO model based on the YOLO11 framework: it integrates an ADown downsampling module, which utilizes a dual-branch strategy combining average pooling and max pooling to effectively minimize feature information loss during spatial resolution reduction; introduces the KernelWarehouse dynamic convolution approach, which adopts kernel partitioning and a contrastive attention-based cross-layer shared kernel repository to address the challenge of linear parameter growth in conventional dynamic convolution methods; and establishes a feature decoupling pyramid network (FDPN) that replaces static feature pyramids with a dynamic multi-scale fusion architecture, utilizing parallel multi-granularity convolutions and an EMA attention mechanism to achieve adaptive feature enhancement. Experiments demonstrate that the AFK-YOLO model achieves 78.6% mAP on a self-constructed aerial infrared dataset—a 2.4 percentage point improvement over the baseline YOLO11—while meeting real-time requirements for aviation safety monitoring (416.7 FPS), reducing parameters by 6.9%, and compressing weight size by 21.8%. The results demonstrate the effectiveness of dynamic optimization methods in improving the accuracy and robustness of infrared target detection under complex aerial environments, thereby providing reliable technical support for the prevention of mid-air collisions. Full article

To address the problems of low coverage rate and low detection accuracy in UAV-based aircraft skin defect detection under complex real-world conditions, this paper proposes a method combining a Greedy-based Breadth-First Search Coverage Path Planning (GB-CPP) approach with an improved YOLOv11 architecture (INN-YOLO). GB-CPP generates collision-free, near-optimal flight paths on the 3D aircraft surface using a discrete grid map. INN-YOLO enhances detection capability by reconstructing the neck with the BiFPN (Bidirectional Feature Pyramid Network) for better feature fusion, integrating the SimAM (Simple Attention Mechanism) with convolution for efficient small-target extraction, as well as employing RepVGG within the C3k2 layer to improve feature learning and speed. The model is deployed on a Jetson Nano for real-time edge inference. Results show that GB-CPP achieves 100% surface coverage with a redundancy rate not exceeding 6.74%. INN-YOLO was experimentally validated on three public datasets (10,937 images) and a self-collected dataset (1559 images), achieving mAP@0.5 scores of 42.30%, 84.10%, 56.40%, and 80.30%, representing improvements of 10.70%, 2.50%, 3.20%, and 6.70% over the baseline models, respectively. The proposed GB-CPP and INN-YOLO framework enables efficient, high-precision, and real-time UAV-based aircraft skin defect detection. Full article

The in situ emulsification synergistic self-profile control system has wide application prospects for efficient development on offshore oil reservoirs. During water flooding in Bohai heavy oil reservoirs, random emulsification occurs with superimposed Jamin effects. Effectively utilizing this phenomenon can enhance the efficient development of offshore oilfields. This study addresses the challenges hindering water flooding development in offshore oilfields by investigating the emulsification mechanism and key influencing factors based on oil–water emulsion characteristics, thereby proposing a novel in situ emulsification flooding method. Based on a fundamental analysis of oil–water properties, key factors affecting emulsion stability were examined. Core flooding experiments clarified the impact of spontaneous oil–water emulsification on water flooding recovery. Two-dimensional T1–T2 NMR spectroscopy was employed to detect pure fluid components, innovating the method for distinguishing oil–water distribution during flooding and revealing the characteristics of in situ emulsification interactions. The results indicate that emulsions formed between crude oil and formation water under varying rheometer rotational speeds (500–2500 r/min), water cuts (30–80%), and emulsification temperatures (40–85 °C) are all water-in-oil (W/O) type. Emulsion viscosity exhibits a positive correlation with shear rate, with droplet sizes primarily ranging between 2 and 7 μm and a viscosity amplification factor up to 25.8. Emulsion stability deteriorates with increasing water cut and temperature. Prolonged shearing initially increases viscosity until stabilization. In low-permeability cores, spontaneous oil–water emulsification occurs, yielding a recovery factor of only 30%. For medium- and high-permeability cores (water cuts of 80% and 50%, respectively), recovery factors increased by 9.7% and 12%. The in situ generation of micron-scale emulsions in porous media achieved a recovery factor of approximately 50%, demonstrating significantly enhanced oil recovery (EOR) potential. During emulsification flooding, the system emulsifies oil at pore walls, intensifying water–wall interactions and stripping wall-adhered oil, leading to increased T2 signal intensity and reduced relaxation time. Oil–wall interactions and collision frequencies are lower than those of water, which appears in high-relaxation regions (T1/T2 > 5). The two-dimensional NMR spectrum clearly distinguishes oil and water distributions. Full article

To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article

This study proposes a GPU-accelerated Position-Based Dynamics (PBD) system for realistic and interactive cloth simulation in Extended Reality (XR) environments, and comprehensively evaluates its performance and functional capabilities on standalone XR devices, such as the Meta Quest 3. To overcome the limitations of traditional CPU-based physics simulations, we designed and optimized highly parallelized algorithms utilizing Unity’s Compute Shader framework. The proposed system achieves real-time performance by implementing efficient collision detection and response handling with complex environmental meshes (RoomMesh) and dynamic hand meshes (HandMesh), as well as capsule colliders based on hand skeleton tracking (OVRSkeleton). Performance evaluations were conducted for both single-sided and double-sided cloth configurations across multiple resolutions. At a 32 × 32 resolution, both configurations maintained stable frame rates of approximately 72 FPS. At a 64 × 64 resolution, the single-sided cloth achieved around 65 FPS, while the double-sided configuration recorded approximately 40 FPS, demonstrating scalable quality adaptation depending on application requirements. Functionally, the GPU-PBD system significantly surpasses Unity’s built-in Cloth component by supporting double-sided cloth rendering, fine-grained constraint control, complex mesh-based collision handling, and real-time interaction with both hand meshes and capsule colliders. These capabilities enable immersive and physically plausible XR experiences, including natural cloth draping, grasping, and deformation behaviors during user interactions. The technical advantages of the proposed system suggest strong applicability in various XR fields, such as virtual clothing fitting, medical training simulations, educational content, and interactive art installations. Future work will focus on extending the framework to general deformable body simulation, incorporating advanced material modeling, self-collision response, and dynamic cutting simulation, thereby enhancing both realism and scalability in XR environments. Full article

The rapid advancement of wearable electronics and the Internet of Things (IoT) has driven the demand for sustainable power sources to replace conventional batteries. In this study, we developed a high-performance, lead-free triboelectric nanogenerator (TENG) using methylammonium tin chloride (MASnCl₃) perovskite–poly(methyl methacrylate) (PMMA) composite films. MASnCl₃ was synthesized via an anti-solvent-assisted collision technique and incorporated into a flexible PMMA matrix to enhance dielectric properties, thereby improving triboelectric output. The optimized 10 wt% MASnCl₃–PMMA composite-based TENG exhibited a maximum output voltage of 525 V, a current of 13.6 µA, and of power of 2.5 mW, significantly outperforming the many halide perovskite-based TENGs. The device demonstrated excellent pressure sensitivity, achieving 7.72 V/kPa in voltage detection mode and 0.2 μA/kPa in current detection mode. The device demonstrated excellent mechanical stability and was successfully used to power a small electronic device. The findings highlight the potential of halide perovskite–polymer composites in developing eco-friendly, efficient mechanical energy harvesters for next-generation self-powered electronics and sensor applications. Full article

Marine anchor buoys, as fixed-point profile observation platforms, are highly susceptible to the threat of ship collisions. Installing cameras on buoys can effectively monitor and collect evidence from ships. However, when using a camera to capture images, it is often affected by the continuous shaking of buoys and rainy and foggy weather, resulting in problems such as blurred images and rain and fog occlusion. To address these problems, this paper proposes an improved YOLOv8 algorithm. Firstly, the polarized self-attention (PSA) mechanism is introduced to preserve the high-resolution features of the original deep convolutional neural network and solve the problem of image spatial resolution degradation caused by shaking. Secondly, by introducing the multi-head self-attention (MHSA) mechanism in the neck network, the interference of rain and fog background is weakened, and the feature fusion ability of the network is improved. Finally, in the head network, this model combines additional small object detection heads to improve the accuracy of small object detection. Additionally, to enhance the algorithm’s adaptability to camera detection scenarios, this paper simulates scenarios, including shaking blur, rain, and foggy conditions. In the end, numerous comparative experiments on a self-made dataset show that the algorithm proposed in this study achieved 94.2% mAP50 and 73.2% mAP50:95 in various complex environments, which is superior to other advanced object detection algorithms. Full article

The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall. Full article

Mosquitoes’ self-generated air movements around their antennae, especially at the wing-beat frequency, are crucial for both obstacle avoidance and mating communication. However, the characteristics of these air movements are not well clarified. In this study, the air movements induced by wing tones (sound generated by flapping wings in flight) around the antennae of a mosquito-like model (Culex quinquefasciatus, male) are investigated using the acoustic analogy method. Both the self-generated wing tone and the wing tone reflected from the ground are calculated. Given that the tiny changes in direction and magnitude of air movements can be detected by the mosquito’s antennae, a novel method is introduced to intuitively characterize the air movements induced by the wing tone. The air movements are decomposed into two basic modes (oscillation and revolution). Our results show that, without considering the scattering on the mosquito’s body, the self-generated sound wave of the wing-beat frequency around the antennae mainly induces air oscillation, with the velocity amplitude exceeding the mosquito’s hearing threshold of the male wingbeat frequency by two orders of magnitude. Moreover, when the model is positioned at a distance from the ground greater than approximately two wing lengths, the reflected sound wave at the male wingbeat frequency attenuates below the hearing threshold. That is, the role of reflected wing tone in the mosquito’s obstacle avoidance mechanism appears negligible. Our findings and method may provide insight into how mosquitoes avoid obstacles when their vision is unavailable and inspire the development of collision avoidance systems in micro-aerial vehicles. Full article