Browse Topic: Optics

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Associations between Vehicle Safety Systems and Pilon Fracture Severity Are Revealed by Using a Novel Objective Quantitative Fracture Energy Metric09-14-03-001310/26/2026
Pilon fractures are a severe lower-extremity injury sustained in motor vehicle crashes, characterized by high-axial loading that drives the talus into the tibial plafond and frequently results in poor long-term functional outcomes and post-traumatic osteoarthritis (PTOA). Despite advances in surgical management, clinical complications remain frequent in 20–50% of cases in literature, which highlights the need to identify modifiable upstream risk factors in the motor vehicle crash environment. The Abbreviated Injury Scale (AIS) is the predominant tool for crash injury severity analysis, and it classifies all closed pilon fractures uniformly as moderate (AIS 2) regardless of comminution or imparted energy and poorly predicts long-term functional outcomes. An existing fracture energy severity metric quantifies the acute mechanical energy delivered to bone during fracture and has been linked to PTOA development. This study represents one of the first multivariate applications of the fracture energy metric to crash data, examining how occupant demographics, crash kinematics, and vehicle safety systems relate to pilon fracture severity. Fifty belted occupants with Orthopaedic Trauma Association (OTA) Type B or Type C pilon fractures were identified through the Crash Injury Research and Engineering Network (CIREN) database. Fracture fragment surface models were generated from clinical computed tomography (CT) and used to compute fracture energy severity through a custom MATLAB script. A generalized linear model with a gamma log link was fit using seven predictors: age, delta-v, knee bolster airbag (KBAB) deployment, fracture loading mechanism, biological sex, osteoporosis, and body mass index (BMI). KBAB deployment and pure axial compression loading were both significantly associated with higher fracture energy (p = 0.011 and p = 0.002), and age approached an inverse association (p = 0.086). These findings establish the fracture energy metric as a viable tool for crash injury analysis and suggest that certain vehicle safety system characteristics warrant further investigation as modifiable factors influencing pilon fracture severity.
Northrup, Samuel, Bangert, L. Garrett, Armstrong, William, Stitzel, Joel D., Martin, R. Shayn, Dibbern, Kevin, Anderson, Donald D., Weaver, Ashley A., Collins, Caitlyn J.
Coking Inhibition Characteristics of Dimethyl Disulfide during Supercritical Pyrolysis of n-Decane2026-99-299310/19/2026
Inhibition of coking from pyrolysis of hydrocarbon fuel is critical for ensuring the safe operation of high-speed aircraft. In this work, the inhibitory effects of dimethyl disulfide (DMDS) on coke during the supercritical pyrolysis of n-decane were investigated. Experiments using an electrically heated micro-reactor tube were conducted to study the influences of presulfidation and cofeeding methods with varying DMDS concentrations. The pyrolysis products were analyzed via gas chromatography, while pressure drop and coke mass were measured. The composition and morphology of coke were further characterized by energy-dispersive spectroscopy and scanning electron microscopy. The results show that the influence of DMDS on coke formation depends on its application method and concentration. At a DMDS concentration of 500 ppm, the coke inhibition rate reached 85.5% with presulfidation and 81.7% with cofeeding. However, with increasing DMDS concentration beyond 500 ppm, the amount of coke formation increases. Coke formation during supercritical pyrolysis of n-decane primarily involves metal-catalyzed coking and bulk-phase coking processes. The H2S and HS radicals generated from pyrolysis of DMDS react with transition metals such as Fe and Ni, thereby inhibiting the formation of metal-catalyzed filamentous coke. The presulfidation process forms a sulfide layer earlier than the cofeeding process, more effectively suppressing the generation of filamentous coke. However, excessively high DMDS concentrations can lead to its decomposition into sulfur-containing radicals such as thiol radicals (CH3S·), which incorporate into the coke matrix, enhance its reactivity, and promote coke formation. This work offers valuable insights for the application of DMDS as a coke inhibitor in endothermic hydrocarbon fuels.
Liao, Yan, Yao, Yixuan, Yang, Can, Huang, Qin, Chen, Yu
An Adaptive Cloud Prediction Network for Supporting Space-Ground Laser Communication Links2026-99-298210/19/2026
Space-ground laser communication links are highly susceptible to interruptions caused by cloud cover. Reliable dynamic scheduling of ground stations and seamless link handover, therefore, requires accurate short-term cloud-layer forecasts. However, cloud evolution is inherently chaotic, and atmospheric conditions exhibit evident seasonal non-stationarity. These factors weaken the prediction stability of existing models and make it difficult to maintain consistent accuracy over extended forecasting horizons. To address these challenges, this paper proposes KFMNet, an adaptive cloud prediction network that integrates Koopman theory with frequency-domain Mamba modeling. Unlike conventional frameworks that mainly process cloud features in the spatial domain, KFMNet embeds a linear state-space modeling mechanism into the frequency-domain feature evolution process. Specifically, an adaptive spectral gating mechanism is used to capture global cloud distribution patterns in the frequency domain, thereby reducing the computational burden associated with high-resolution image sequences. Meanwhile, a Koopman seasonal gating module introduces monthly prior knowledge into the network, enabling the model to adapt to atmospheric drift across seasonal transitions. Within the encoder-decoder backbone, KFMNet jointly incorporates temporal modeling, frequency-domain representation, and physical consistency constraints, providing a more stable framework for short-term cloud prediction. We evaluate KFMNet on multi-season cloud images collected over Nanjing and Beijing. The climate profiles of these two cities are markedly different. Under these conditions, KFMNet achieves competitive accuracy in cloud segmentation and shows a better ability to handle seasonal and regional shifts than several reference models. In essence, our method adapts well to changing atmospheric conditions and provides a new cloud prediction approach for space-ground laser link assurance. This method combines physical interpretability with the actual needs of real-world deployment.
Wu, Xibing, Meng, Xiangxu, Zhong, Yun, Li, Wenfeng, Zhao, Kanglian
Low-Cost Ground Station Network Siting for Geostationary Space-to-Ground Laser Communication2026-99-295110/19/2026
The deployment of multi-link ground station networks for geostationary space-to-ground laser communication is constrained not only by local weather conditions but also by infrastructure accessibility and engineering cost. Existing ground station siting studies have mainly emphasized cloud-related meteorological indicators and site diversity, while giving insufficient attention to deployment-oriented cost factors. As a result, meteorologically favorable solutions may still be expensive to implement in practice. To address this issue, this paper investigates the low-cost siting problem of a ground station network under a target network availability constraint and proposes a two-stage method for a multi-link, single-telescope scenario. Under the adopted assumption, each ground station can provide at most one available link at any given time, so the number of available network links is equal to the number of clear-sky available stations. The network availability is therefore defined as the probability that at least a required number of links are simultaneously available. Using half-hourly cloud mask data from the Himawari 8 geostationary meteorological satellite for the year 2023, site-neighborhood cloud metrics and inter-site weather correlation metrics are constructed. A data-center clustering and distance-decay model is then introduced to build an infrastructure impact cost field. On this basis, nationwide optimization is carried out through candidate construction and final subset selection. A baseline method without data-center factors is also evaluated under the same total-cost definition. The results show that, under the same target network availability requirement, the proposed method can effectively reduce total deployment cost. This indicates that incorporating data-center factors during siting improves the engineering tradeoff of multi-link geostationary laser communication ground station networks and provides a reproducible route for low-cost planning under service availability constraints.
Zhang, Chi, Zhao, Juntong, Li, Wenfeng, Zhao, Kanglian
Lightweight Compression Networks for Efficient RGBD-IR Imagery Transmission on Resource-Constrained Edge UAV Platforms2026-99-297810/19/2026
Due to the limitation of unstable wireless links and resource-constrained edge computing device, it is challenging to transmit the unmanned aerial vehicles (UAVs) captured images with large data volume in real-time. To address this issue, this paper proposes a lightweight hybrid compression method named Jpeg Plus Plus Network (JppNet) for efficient multimodal image transmission. Different from existing methods that mainly focus on RGB image compression for offline storage and often use large networks for coding and reconstruction, the proposed JppNet focuses more on edge deployment under limited computing resources. At the encoder side, JPEG is first used to perform high-ratio compression on the RGB, D, and IR channels. Then, a lightweight convolutional encoder network extracts supplementary binary features for progressive transmission. At the decoder side, a medium-scale reconstruction network fuses the supplementary code with the JPEG reconstruction to restore image details. To verify the effectiveness of the proposed method, this paper built a multimodal data acquisition prototype and collected 20,000 high-quality RGBD-IR image samples for training and testing. Experimental results show that JppNet combines the high coding efficiency of JPEG with the detail recovery ability of convolutional neural networks. With low computational cost, JppNet obtains much lower reconstruction errors than JPEG on the RGB, D, and IR channels, especially at very low compression ratios. This supports the efficient transmission of UAV captured multimodal images under limited communication conditions.
Pi, Haoran, Ao, Yile, Li, Zhi, Tang, Gang, Jiang, Bowu
OpenClaw-Enabled Real-Time Diagnosis of Inter-Satellite Links in Decentralized Satellite Network Emulation2026-99-291510/19/2026
With the continued expansion of low Earth orbit (LEO) satellite constellations, inter-satellite links (ISLs) have become a critical infrastructure for network connectivity and end-to-end forwarding capability. Existing studies often show a gap between the physical layer and the system layer: physical factors such as link budget, pointing error, and Doppler mismatch are frequently simplified, while link performance analysis is difficult to validate in a closed loop using a real protocol stack. In addition, dynamic topology and time-varying link states make fault detection and localization highly complex.To address these problems, this paper constructs a decentralized satellite network emulation platform and proposes a decentralized addressing and connectivity verification method for full inter-satellite link interfaces. At the physical layer, an optical inter-satellite link (OISL) model is developed by incorporating free-space path loss, Doppler mismatch, and BER-FEC-PLR mapping, with its dynamic evolution driven by orbital parameters. At the system layer, based on a one-satellite-one-container architecture, OpenClaw is introduced to perform link detection and log analysis, thereby generating diagnostic reports.Experimental results show that the proposed method supports interface-level link detection, identifies dynamic link faults such as transient impairments caused by Doppler mismatch in real time, and generates corresponding diagnostic reports.
Zhao, Jiangtong, Luo, Jiangtao, Zhang, Jinshuo, Ran, Yongyi
A Three-Axis Attitude Measurement Method for Key Spaceborne Components Based on Dual-Path Autocollimation2026-99-291810/19/2026
Aiming to address the attitude deviations of spaceborne optical payloads caused by the coupled effects of extreme on-orbit environments, as well as the limitations of existing on-orbit measurement algorithms, including reference error propagation and the neglect of prism attitude variations, this paper proposes a three-axis attitude measurement method for key spaceborne components based on dual-path autocollimation. The proposed method constructs a two-level spatial attitude measurement chain and employs a low-thermal-expansion polyhedral prism as a unified central reference, thereby transforming the discrete measurement of multiple instruments into a transfer solution relative to a common reference. Focusing on the first-level measurement chain, a dual-path autocollimation measurement method is derived to solve the three-axis attitude angles of the remote prism. The proposed method does not rely on the actual satellite attitude reference and can directly monitor the absolute attitude deviations of spaceborne instruments, thereby laying a foundation for attitude measurement of distributed spaceborne instruments. Analysis of typical layout angles shows that a 90° orthogonal configuration enables complete decoupling of the three attitude angles, whereas a 60° acute-angle configuration is more compatible with the limited space available on a satellite and achieves partial decoupling. Moreover, when the included angle between the two autocollimators lies within 30° to 90°, the solution exhibits favorable stability, meeting the requirements of high-precision spaceborne attitude measurement.
Shi, Jian, Liu, Juncai, Tan, Jiubin
A Fuzzy Initiative-Yielding Mechanism for Space-Air-Ground Cooperative Communication2026-99-294110/19/2026
Space-Air-Ground Integrated Network (SAGIN) emerges as an appealing framework for implementing pervasive connections among diverse layers of space, air, and ground. The scheme relies on satellite deployment for extensive space coverage, Unmanned Aerial Vehicle (UAVs) acting as agile aerial relay stations, and ground robots for performing the final task assignment. Nevertheless, integrating heterogeneous nodes with varying mobility characteristics poses significant challenges: satellite operations are restricted by orbital dynamics, ground robot movements are restricted by topography constraints, whereas UAVs demonstrate flexibility and can establish line-of-sight connections with both satellites and ground robots. Current solutions usually adopt UAVs merely as passive relay nodes, causing inefficient adaptation mechanisms and excessive communication overhead when responding to dynamic perturbations or blocked connections. To overcome the aforementioned challenge, this paper proposes a novel fuzzy initiative-yielding coordination approach assigning two opposite behaviors. Initiative is assigned to UAVs in order to facilitate coordinated operations between layers, whereas Yielding refers to the adaptive response of satellites and ground robots. Contrary to previous research where behavioral assignments follow strict threshold rules, our fuzzy inference considers various parameters such as link quality, node state, and task priority, providing flexible behavioral assignments. Our predictive approach incorporates the Exponentially Weighted Moving Average (EWMA) technique to perform proactive failure avoidance. Based on five comprehensive experiments with 30 independent runs each, our approach obtains similar task completion rates (84.4%), but exhibits significantly lower communication overhead by up to 11 to 147 times compared to baseline methods. Furthermore, the proposed behavioral assignments avoid any coordination conflict (0 vs. 30.8), reduce completion time by 10.1%, maintain robust performance under 20% rule reduction, scale to 150-node mega-constellation scenarios, and exhibit higher fault tolerance under dynamic environments (81.5% vs. 79.8% of CBBA under high stress conditions).
Bao, Jie, Zhang, Yuping, Zhang, Ronghao
EAU-Net: An Edge-Aware Enhanced Attention U-Net for Robust SAR Near-Shore Segmentation2026-99-291010/19/2026
Synthetic Aperture Radar (SAR) provides important all-weather images. These images help researchers observe the sea surface. However, separating the coast from the ocean in these images is hard. Near-shore areas face three main issues. The first issue is coherent speckle noise. The second issue is geometric distortion caused by the radar’s side-looking geometry. The third issue is feature aliasing caused by multiple scattering from coastal buildings. To solve these problems, this paper proposes the Enhanced Attention U-Net (EAU-Net). This model is a deep learning framework. It adds SAR scattering priors directly into the learning process. The architecture has three main parts. First, the encoder includes an adaptive local signal-to-noise ratio (SNR) module. This module filters multiplicative speckle dynamically. It stops the network from mistaking noise for real features. Second, the network bottleneck uses a lightweight Transformer block. This block learns global shape priors. It helps the network find the continuous coastline even when radar shadows cover parts of the image. Third, the decoder uses an adaptive edge refinement mechanism. A composite edge-aware loss guides this mechanism. It fixes rough edges and corrects pixel boundaries. This step removes jagged artifacts. Tests on a near-shore dataset show the reliability of this method. EAU-Net achieves a Dice Similarity Coefficient (DSC) of 0.9601 and an Intersection over Union (IoU) of 0.9234. The Average Symmetric Surface Distance (ASSD) is 5.7501 pixels. This paper compares EAU-Net with classical convolutional networks, global-attention architectures, and vision foundation models. The results show that EAU-Net preserves boundary precision better. It outperforms state-space models like VM-UNet and large-scale zero-shot models like SAM-Med2D. EAU-Net uses physical inductive biases to separate coastal building interference from marine targets. This provides a realistic foundation for downstream tasks like autonomous ship detection.
Bai, YuanJie, Zhong, Sheng
Graphene Solar Sail for LEO Deorbiting Based on Experimental Light Propulsion Modeling2026-99-292610/19/2026
Graphene materials have recently been shown to produce measurable propulsion when exposed to light, raising the possibility of using them to enhance solar sail performance for satellite deorbiting. The pendulum-based experiments on graphene samples under controlled laser illumination inside a vacuum chamber and a quantitative model that describes the propulsion force as a function of laser power, illuminated area, incident angle, and ambient pressure were carried out. The measured pressure dependence and its shift with sample characteristic size point to Knudsen forces as the dominant mechanism. Orbital dynamics simulations incorporating this model show that a graphene-based sail can deorbit a 100 kg satellite from 660 km altitude roughly 21% faster than a conventional membrane sail of the same area, because the graphene configuration combines atmospheric drag with light-induced propulsion in a hybrid decay process.
Zhu, Haidong, Liu, Xiang, Cai, Guoping
Aerospace & Defense Technology: Oct 202626AERP1010/8/2026
Terabit Optical Communications in Space: Engineering High-Speed Networks for Data Centers in Orbit Mild Hybrids: A Current-Gen Bridge to Electrifying Military Ground Vehicles The Integrated and Flexible Approach to Large-Scale Metrology: How Integrating Laser Trackers and Modular Arms Creates a 'Unified Nervous System' The Defense Industry Continues to Meet CMMC Phase II Requirements Despite 60-Day Pause In Space, No One Can Hear You Stream AMD to Bring UCIe Connectivity to Select Versal Adaptive SoCs How Special Interface Gages Provide Microscopic Precision for Designing Ballistic Missiles The pristine precision needed to certify the nation's underwater nuclear deterrent is anchored on the talents of civilian scientists at a Navy laboratory in Southern California. Rapidly Testing Deployable Systems Sandia National Laboratories is evaluating portable security architectures that combine radar, drones, ground sensors, cameras and wireless communications networks into a single rapidly deployable system for remote and austere environments. Testing focuses on sensor integration, resilient communications and edge-based data processing to improve situational awareness while operating within real-world bandwidth constraints. Tailored Surface Vibrations to Improve Aerodynamic Performance in Passenger Jets University of Colorado Boulder researchers have demonstrated two new phononic flow-control concepts, super-resonance and scatterless interference, that use engineered subsurface vibrations to interact with boundary-layer airflow across a broader range of turbulence frequencies than previously possible. By delaying flow instabilities without altering a wing's external shape, the technology could reduce aerodynamic drag and improve fuel efficiency in passenger aircraft. New Type of Attack Can Slip Past the Defenses in Your Computer's Processor MIT researchers found a way to exploit a split-second gap in chip security - and used it to acquire a Linux system's password file. Army Tests Safer Electrical Connectors for Ground Vehicle Readiness U.S. Army DEVCOM Ground Vehicle Systems Center engineers are evaluating cadmium- and hexavalent chromium-free electrical connectors designed to reduce hazardous material exposure while maintaining corrosion protection, electrical performance, and long-term vehicle sustainment.
Robust Fiber Optics for Ground Combat Vehicles: Unlocking the Bandwidth for the Mission2026-01-75419/22/2026
The U.S. Army’s Modular Open Systems Approach (MOSA) is driving data-centric vehicle architectures that demand higher bandwidth, faster decision loops, and greater cross-platform interoperability. Despite these needs, stakeholders hesitate to adopt fiber optics because of perceived fragility, field-retrofit FOD risk, and soldier-handling concerns. This paper characterizes common fiber failure modes in ground-vehicle environments and demonstrates how system-level ruggedization, connector design, and qualified components mitigate those risks. Drawing on demonstrated experience with hardened optics, connector-integrated transceivers, sealed media converters, and rugged cabling, we summarize practical architecture and field maintenance procedures that enable reliable fiber deployment in vehicles. Results show that, with appropriate component selection, installation practices, and built-in diagnostics, fiber optics can provide a robust, maintainable backbone for future ground vehicle networks.
Kouns, Heath, Simms, Marc, Weiss, Evan, Breedlove, Joe
View Translation: Geometry-Guided Latent Diffusion for Cross-View Image Synthesis on Ground Vehicles2026-01-75019/22/2026
Synthesizing novel camera views is important for autonomous ground vehicles, with applications in surround-view monitoring, occlusion recovery, and training data augmentation. We present View Translation, a geometry-guided latent diffusion framework that generates a target camera view from a source image, relative camera pose, and an available target-view depth prior. The method combines three components: a Vector Quantized Variational Autoencoder for compact latent encoding, a depth-based warping module that projects the source image into the target view to provide geometric guidance, and a ControlNet-augmented denoising UNet conditioned on source appearance, relative pose, and an auxiliary Image-Depth fusion network. Evaluated on KITTI and a simulated off-road dataset, our method achieves competitive FID while improving LPIPS and PSNR over baseline approaches, supporting cross-view synthesis for ground vehicle perception.
Mayekar, Omkar, Aiyetigbo, Mary, Salvi, Ameya, Samak, Tanmay, Samak, Chinmay, Desjardins, Brendan, Smereka, Jonathon, Brudnak, Mark, Krovi, Venkat, Luo, Feng, Li, Nianyi
Scene-Level Multi-View Instance Consistency Using Multi-Feature Vision Fusion for Synthetic Ground Vehicle Environments2026-01-75029/22/2026
Maintaining consistent object identities across multiple camera viewpoints is a critical challenge in synthetic perception environments used for autonomous ground vehicle evaluation. This paper presents a scene-level multi-view instance consistency framework that integrates OpenUSD scene composition, Omniverse Replicator synthetic-data generation, and a multi-feature vision fusion pipeline. The proposed approach combines semantic embeddings from CLIP, patch-level descriptors from DINOv2, geometric correspondences from LoFTR, mask-derived shape invariants using Hu moments, and relative-position priors to associate object instances across views, including visually identical objects. A compact composite scoring function fuses these complementary cues to achieve robust cross-view identity assignment while preserving OpenUSD asset modularity through grouped-prim support. Synthetic experiments across 120 multi-camera scenes demonstrate improved Top-1 Match Accuracy and Identity Consistency Rate, with reduced ID-switch occurrences compared to single-cue baselines. The framework supports scalable, repeatable, and traceable digital engineering workflows for defense-oriented perception evaluation.
Bhattacharya, Sambit, Nakamoto, Kyle
Change Detection Filtering with Visual Language Models2026-01-75089/22/2026
Intelligence, surveillance and reconnaissance (ISR) often require review of significant quantities of video. While machine vision is used to flag objects for human review, too many flags are generated. Integrating newer methods like Open-Vocabulary Object Detection (OVOD) that support zero shot detection, but with significantly lower accuracy only make the problem worse. This work addresses the utility of OVOD in ISR missions by focusing only what has changed between successive runs through an environment. A Vision Language Model (VLM) compares current observations against a registered “cleared” baseline to focus only on what has changed. Testing across three distinct environments, and using either monocular camera phones or RGB-D equipped vehicles, demonstrates that integrating change detection can automatically remove as much as 80% of unchanged objects without impacting recall.
Martinson, Eric, Fishta, Igri
Parametric Screening of Hybrid Powertrain Architectures for High-Energy Laser Vehicles: Application to Foxhound and Boxer2026-01-75529/22/2026
The US Army and several NATO allies have committed funds for directed energy weapons including high energy lasers (HEL), which require substantial electrical power. Silent Mobility and Silent Watch requirements mandate hybridization, which also provides the electrical capacity needed for a HEL. This paper introduces a HEL framework of classes A-E based on target types and fluence physics. It estimates installed HEL mass and power demand by class, and applies rapid powertrain screening to hypothetical hybrid variants of the UK vehicles Foxhound and Boxer. Results show that HELs up to Class B (60 kW) and D (300 kW) laser output can be supported with minimal powertrain modifications by Foxhound and Boxer respectively, and upgrades to support Class C (150 kW) and E (500 kW) are containable within the payload of each vehicle. A rapid methodology is presented to determine what powertrain architecture is needed to support a given HEL.
Salis, Rupert Tull
Multimodal Digital-Twin Simulations for Off-Road Autonomous Vehicles2026-01-75509/22/2026
Research in autonomous driving has largely focused on structured, on-road environments in densely populated urban areas, leaving off-road autonomy comparatively underexplored despite its importance for applications such as search and rescue, agriculture, and defense. Progress in developing solutions for off-road autonomy is heavily constrained by the high cost, safety risks, and limited coverage of real-world off-road data collection, particularly for rare terrain-induced failure cases. To address these challenges, we introduce a large-scale simulation platform for off-road autonomous driving generated in the Unity3D engine. Our simulator provides indefinite data from multiple RGB cameras, LiDAR, GPS, and IMU sensors to support research in long-duration applications. Our platform also facilitates research in risk-aware planning through dynamic weather, lighting, and annotations for sequence-level failures like collisions and weather-induced sensor blockages. By combining long-duration temporal coverage, multimodal sensing, and procedurally generated terrain diversity, our simulator facilitates progress for systematic evaluation of learning-based autonomous driving models in unstructured, safety-critical environments.
Ross, Timothy, Boone, Julia, Afghah, Fatemeh
An Assessment of Biomanufacturing in Forward Deployed Locations2026-01-75579/22/2026
Biomanufacturing uses microorganisms to produce chemicals or materials of interest, much like a brewery uses fermentation by yeast to produce the alcohol in beer. Biomanufacturing relies upon synthetic biology to reprogram yeast or other microorganisms to produce something of greater value, such as fuel, food, or pharmaceuticals. Industrial biomanufacturing has made significant advances and the products it can deliver include reactive coatings and textiles, sensors, optical materials that can bend light, and new therapeutics such as antimicrobials and vaccines. The convergence of synthetic biology, robotics, and artificial intelligence is opening the way to produce materials never before possible in the commercial market. These same technologies create the opportunity for the miniaturization of this technology to fit into ever more compact spaces, bringing forward deployment of these mini-factories closer and closer to the point of need.
Ahern, Brooke, Crumbley, Annie, Walker, Anne, Grodecki, Joseph
Development of a UAV Platform for Remote Inspection and Visualization2026-01-75119/22/2026
This paper details the development of an intelligence and inspection platform consisting of an attritable sub-250g UAV, a ground control station, and a visualization interface for users. The UAV architecture combines onboard obstacle detection and avoidance along with simultaneous localization and mapping to have full autonomous navigation inside of complicated GPS-denied environments. The ROS 2-to-Unreal Engine data pipeline allows for sensor fusion, data cleansing, and initial analysis as well as creation of a high-fidelity real-time 3D digital twin. The visualization interface allows users to easily identify critical features and turn data into intelligence to support decision making by soldiers and first responders.
Lee, Yeen K., Bainard, Sean, Shaughnessy, Michael, Bolger, Matt, Koepp, R. Tucker, Salehzadeh, Roya, Mallory, Stephen, Mynderse, James A., Guillen, Pedro, Hernandez, Margarita
Clemson Off-Road Dataset (CORD): A Multimodal Benchmark for Autonomy beyond the Flat-World2026-01-75189/22/2026
While autonomous perception has matured within the structured confines of urban roadways, it remains brittle when confronting the chaotic, non-rigid terrain of the natural world. This paper introduces the Clemson Off-Road Dataset, a high-fidelity, multimodal dataset engineered to bridge this gap by challenging standard “flat-world” assumptions. Featuring 2.90 TB of sensor data, the dataset captures a diverse spectrum of unstructured environments, ranging from the transitional trails of CU-ICAR and the day/night lighting dynamics of TN3 to the unstructured wilderness of Camp Daniels and the novel coastal scenery of Edisto Island. Distinguishing itself from existing forest-centric benchmarks, the Clemson Dataset provides a first-of-its-kind focus on coastal data, featuring unique adversarial conditions such as extreme solar glare, loose sand, and shifting tide lines. The data is collected aboard a Polaris RZR Pro R 4, a high-performance platform integrated with a sensor suite designed to perceive physics beyond geometry. Alongside 360° HD camera coverage, 3D LiDAR, and Radar, we integrate Cubert Ultris Hyperspectral imaging and Prophesee EVK4 Event-based vision to enable material classification and high-dynamic-range motion tracking. To overcome the bottleneck in ground truth generation, we used our “AI LabelMate,” a context-aware semi-automated annotation agent that fuses Vision-Language Models (Florence-2) with SAM2 to generate 6331 pixel-perfect annotated frames using a specialized off-road ontology and a human-in-the-loop pipeline. We establish performance baselines using Oneformer for semantic segmentation and used SalsaNext for lidar point clouds labelling. Available in both raw ROS2 bag and extracted standard formats, this Dataset serves as a pivotal testing ground for the next generation of robust autonomous systems.The dataset of this paper is available upon request to the Virtual Prototyping of Autonomy-Enabled Ground Systems (VIPR-GS) Center.
Patil, Ashish, Gupta, Prakhar, Bhosale, Mayuresh, Mukwaya, Arthur, Jegede, Akinbobola, Mikulski, Dariusz, Mwakalonge, Judith, Jia, Yunyi
Bullfrog: AI Powered Counter Unmanned Aerial System2026-01-75199/22/2026
The proliferation of small unmanned aircraft systems (sUAS) presents an asymmetric threat to ground maneuver forces operating in contested and gray-zone environments. The Bullfrog Autonomous Weapon Station (AWS) addresses this operational gap through a passive, AI-powered counter-UAS system employing computer vision and machine learning for autonomous detection, tracking, classification, and engagement. Field testing at Technology Readiness Experimentation (T-REX) 26-1 demonstrated 100% probability of defeat against Group 1 UAS targets with a mean engagement time of 6 seconds and 10 rounds per kill at ranges exceeding 160 meters. Operating in both autonomous and human-in-the-loop modes, Bullfrog achieved 99.45% operational availability while leveraging service-common M240B weapons and Modular Open Systems Architecture for rapid integration with Joint All-Domain Command and Control (JADC2) networks. At $300,000 per unit with $10 cost-per-engagement, Bullfrog demonstrates operational relevance, speed-to-field, and alignment with Army and Marine Corps autonomy priorities.
Cunningham, Jason, Clark, Alex
Real-Time Road Intelligence through a Hazard Authentication Model2026-24-00279/21/2026
Advanced Driver Assistance Systems (ADAS) are evolving beyond onboard perception. The ability to dynamically map and share temporary road hazards is important for connected and autonomous driving, but authenticating the data is critical. An in-vehicle hazard recognition layer performs real-time video analysis and geotagging on embedded platforms. Real-time video is analyzed for road hazards—such as potholes, construction zones, waterlogging, fallen trees, roadside accidents, and debris—using onboard cameras and lightweight computer vision models. This metadata is sent to a cloud-based aggregation layer to validate hazard reports. It is of paramount importance to validate hazard reports originating from diverse sources, regardless of their accuracy. This paper presents a mathematical approach to determine an overall Hazard Confidence Score (HCS) based on data received from diverse sources. A unique hazard authentication model is introduced to quantify the credibility of each hazard report using six validation metrics.
Bose, Souvik
3D Dynamic Object Detection with Multi-Modal Temporal Fusion2026-99-11179/11/2026
This research aims to address the critical challenge of accurately detecting and estimating the state of dynamic objects in autonomous driving. Traditional 3D object detection methods often struggle with motion perception, particularly in velocity estimation, due to the lack of information in single frame perception. We propose a novel framework that enhances the BEV representation with temporal modeling. The core of our method is a two-stage temporal fusion process. First, we align historical BEV features to the current coordinate frame to eliminate the interference of ego-motion. Subsequently, a dedicated temporal fusion encoder, architected with residual connections and a Feature Pyramid Network, refines the aligned multi-frame BEV features to capture complex motion patterns and improve multi-scale object representation. This approach directly tackles the problem of motion decoupling. By aligning features, we disentangle object motion from ego-motion. The temporal fusion encoder then mitigates the positional ambiguity of moving objects in the fused BEV space, a common issue in simple feature concatenation, leading to more robust detection. We built a dataset following the structure of the nuScenes dataset, using data collected from an autonomous driving simulation platform. The evaluation results on our simulation dataset demonstrate that the proposed temporal module achieves a 13.0% improvement in NDS score and a substantial 29.7% reduction in velocity error (mAVE). These results demonstrate that our temporal fusion strategy effectively enhances 3D detection accuracy in dynamic scenarios.
Shao, Mengjia, Li, Wei, Bai, Jie, Zhu, Shaoxiong, Xu, Chenjie
Site Selection Method for Solid Waste Processing Plant of Highway Reconstruction and Expansion Project2026-99-15869/11/2026
The highway reconstruction and expansion project is accompanied by the generation of a large amount of construction solid waste. The unreasonable site selection of solid waste processing plants will increase the social, environmental, and economic burden. Taking a highway reconstruction and expansion project in Guangdong Province as an example, this study uses the combination of the analytic hierarchy process and the layer superposition method to extract the influencing factors of site selection, such as geological conditions, natural conditions, hydrological conditions, traffic conditions, and resource conditions, according to relevant specifications, and uses the analytic hierarchy process to quantify each influencing factor. From the relevant research data, official public information, and other channels, we comprehensively collected the data of topography, climate, geology, land use planning, and other aspects of Guangzhou and Dongguan along the project. With the help of buffer analysis tools and overlay analysis tools of GIS software, the optimal decision results were determined. The research results show that using this method to analyze the site selection of the relying project, the factory site selection should be located in Wangniudun Town near the project line, which has comprehensive advantages. The site selection method of a solid waste processing plant for an expressway reconstruction and expansion project proposed in this paper comprehensively considers the influence of 10 sub-factors on the site selection, and has been successfully applied to the site selection decision of an expressway reconstruction and expansion project in Guangdong Province. The final site selection result is more professional and objective than the previous site selection method, which effectively solves the site selection problem of a solid waste processing plant under the influence of economic factors, social factors, municipal factors, and environmental factors.
Zhang, Yuping, Yang, Ming, Zeng, Siqing, Long, Hao, Liu, Yuanqing, Zhao, Qiu
Nc5-PCS Based Heterogeneous Point Cloud Matching for Roads2026-99-15339/11/2026
Point cloud registration represents a fundamental task in geospatial informatics and 3D computer vision, aiming to align heterogeneous point clouds through rigid transformation estimation. While Super-4PCS serves as an efficient coarse registration method, it exhibits limitations when handling large-scale datasets, planar-distributed point clouds, and scenarios with unknown scale differences. To overcome these challenges, this paper proposes the Nc-5PCS (Neighborhood-constrained 5-Point Congruent Sets) algorithm. Nc-5PCS first performs approximate scale estimation through concavity-convexity similarity analysis within coarse overlap regions, addressing the inherent scale limitation in 4PCS-based approaches. Subsequently, the algorithm employs 3D Harris feature point extraction to significantly reduce data volume while preserving critical geometric characteristics. The core innovation lies in designing a non-coplanar 5-point basis with a corresponding hash-based retrieval mechanism, effectively resolving the feature degradation problem caused by coplanar 4-point bases. Furthermore, normal vector angular constraints are incorporated to enhance consensus evaluation during correspondence selection, substantially improving registration accuracy. Experimental validation demonstrates that Nc-5PCS achieves a point-to-point RMS error of ≤ 0.227 m, outperforming Super-4PCS to provide superior initial alignment for subsequent ICP refinement.
Liu, Lei, Yu, Keguang, Li, Xinyi, Sun, Guangde, Zhao, Xinyuan, Zhu, Dongni, Fan, Yabo, Guo, Shihao
Analysis of Traffic Capacity at the Intersection of Xiyou Road and Qianshan Road in Hefei City2026-99-15519/11/2026
With the advancement of urbanization and the popularization of automobiles, the traffic load on urban roads is becoming increasingly heavy, resulting in many traffic problems. Road intersections serve as crucial linchpins in the urban transportation grid, wielding considerable influence over the overall traffic capacity of a city’s road network. Enhancing intersection efficiency and cutting down on delays stand at the heart of tackling urban congestion challenges. This study zeroes in on the crossroads where Xiyou Road intersects with Qianshan Road in Hefei City. Employing hands-on observation and photographic documentation, the research examines traffic flow and signal configurations during the peak demand period (7:30-8:30). The analysis evaluates traffic capacity and utilization rates for through, left-turn, and right-turn lanes at this intersection. Findings reveal that the right-turn lane at the southern entrance and the left-turn lanes at both northern and eastern entries show relatively low saturation levels, while the saturation of other lanes is greater than or close to 1. Therefore, this intersection does not have sufficient capacity. The actual traffic operation at the intersection, particularly during peak traffic times, is analyzed to identify the reasons for congestion Finally, improvement plans for optimizing traffic organization at intersections are proposed, such as optimizing signal timing schemes and transforming traffic channelization. Simulation analysis using VISSIM shows a 9.34% reduction in total intersection parking time, a 34.26% decrease in average queue length, and an 8.12% reduction in average vehicle delay. These results provide a reference for future optimization work, including intersection signal timing and channelization.
Wang, Yanmei, Wang, Chen, Fu, Ziyue, Meng, Xianglong
DisReID: Single Domain Generalizable Person Re-ID Method for Highway Construction & Operation2026-99-15309/11/2026
With the advancement of computer vision technologies and the widespread deployment of video surveillance systems, traffic safety and the development of intelligent highways have been significantly enhanced. As a key component of the intelligent video analysis module in smart highways, person re-identification (re-ID) addresses critical challenges, including cross-segment tracking of pedestrians illegally using emergency lanes, multi-camera joint searches for lost persons in service areas, and trajectory tracing of individuals involved in traffic accidents. These functions directly support the core goals of "safety assurance and efficient service" for smart highways. However, due to the complexity of the application scene, its generalization to unseen environments remains a core challenge. This problem is formally studied under the setting of Single-Domain Generalizable Person Re-identification (SDG re-ID), which aims to train a model on a single source domain that can perform well on arbitrary unseen target domains. To handle this issue, this paper proposes a novel Disentangled Augmentation re-ID Framework (DisReID) that disentangles and augments both structure and style. Specifically, DisReID consists of two modules: Structure-aware Viewpoint Simulation (SVS), a novel pre-processing technique that simulates cross-camera perspective changes by perspective transformation, diversifying geometric structure without harming identity semantics; and Style-Dominant Frequency Perturbation (SFP), which selectively focuses on the style-dominant frequencies and applies perturbation to enable controllable style augmentation while preserving structure cues. Furthermore, to alleviate the BN-induced domain bias, we introduce a simple yet effective test-time adaptation strategy, termed Cluster Fine-tuning (CF), that performs unsupervised clustering on target-domain features to assign pseudo-labels and subsequently fine-tunes the model, enhancing adaptability to unseen domains. Extensive experimental results on four public datasets demonstrate that our DisReID achieves superior generalization performance compared to the state-of-the-art methods. This work provides key technical support for the large-scale application of re-ID in smart highways, advancing the goal of "full-domain perception and intelligent collaboration".
Pan, Hong, Yu, Fangying
Ultrasonic Frequency-Domain Reverse-Time Migration Imaging Incorporating Element Directivity Correction2026-99-14199/11/2026
The finite width of ultrasonic array elements results in a non-uniform angular radiation pattern of elastic waves in solids, deviating from the ideal point-source assumption commonly adopted in reverse-time migration (RTM). This angular radiation non-uniformity produces a crack tip-dominated imaging amplitude with weak crack flank representation, manifesting as a pronounced depth-dependent amplitude imbalance along vertically oriented defects. As a result, cracks may be misinterpreted as point reflectors, which compromises the reliability of characterization in ultrasonic nondestructive testing (NDT). This study proposes an ultrasonic frequency-domain reverse-time migration (FDRTM) imaging method incorporating element directivity correction. A longitudinal-wave directivity model in solids is formulated in the frequency domain and normalized at each frequency to ensure consistent scaling during multi-frequency stacking. During backward wavefield reconstruction using full matrix capture (FMC) data, the angular-dependent energy distribution associated with the receiving direction is explicitly corrected, rebalancing the angular energy distribution in the reconstructed wavefield. Defect imaging is then performed using a frequency-domain cross-correlation imaging condition, followed by stacking over frequency and normalization. Validation experiments were conducted on an artificially manufactured vertical crack in a 7075 aluminum alloy specimen. The results indicate that, relative to conventional RTM, the proposed method reduces crack tip dominance and enhances the relative visibility and continuity of crack flanks. Compared with conventional RTM, the peak imaging amplitude increases by 22.44%, and the amplitude at a depth of 6.8 mm is enhanced by 24.39%. In addition, the proposed method outperforms the total focusing method (TFM) in crack profile continuity and the relative visibility of crack flanks. The results confirm that incorporating element directivity correction into frequency-domain RTM mitigates depth-dependent amplitude imbalance and restores crack flank visibility, thereby improving the reliability of crack defect characterization in ultrasonic NDT.
Chen, Si, Zhang, Yifeng, Ma, Tengfei, Xu, Zheng, Jiang, Jiansheng, Gao, Jiaqi
A Maritime Occluded Ship Detection and Tracking Method Based on Improved YOLOv8 Fused with AIS Trajectory2026-99-15499/11/2026
Typical maritime monitoring scenarios are usually constrained by factors such as multi-scale ship density, frequent motion overlap, and limited viewing angle of shore-based cameras. These challenges often lead to trajectory interruptions and identity mismatches in target detection and multi-target tracking tasks. In order to solve these problems, this study proposes a ship occlusion detection and tracking method based on the improved YOLOv8 model and further integrates an automatic identification system (AIS) trajectory reasoning. The method builds a unified perception framework with enhanced detection architecture, multi-source data fusion, and behavioral reasoning capabilities. First, in the target detection module, the improved SEConv structure is introduced into the YOLOv8 trunk network to address challenges caused by small-scale variations and severe occlusion in maritime scenes. The ReLU activation function in SEConv is replaced by the Swish activation function to enhance the nonlinear feature representation. In addition, the optimized SEConv is embedded in the C2f structure, and the convolutional block attention module (CBAM) attention mechanism is introduced to enhance the sensitivity of the model to the occlusion area. Next, for multi-target tracking, ByteTrack is used as the basic tracking framework. AIS trajectory data is introduced as auxiliary input to compensate for trajectory losses caused by occlusion. Finally, experimental results on the SeaShips public dataset and the self-built occlusion reference dataset show that the improved YOLOv8 detector achieves stable mAP gains in mild, moderate, and severe occlusion scenarios. The AIS enhanced tracking system improves the multi-target tracking accuracy (MOTA) and identification F1 score (IDF1) by about 6.3% and 8.1%, respectively, and the average occlusion reconstruction error is controlled within 1.4 seconds. The proposed method effectively enhances the perception ability of ships in complex occlusion environments and verifies the feasibility and superiority of the strategy of combining visual detection with AIS data assistance.
Guan, Keping, Chen, Miao, Zhou, Yue
An Improved Multi-Object Vehicle Detection Algorithm Based on YOLOv11s2026-99-11299/11/2026
For object detection in complex road situations, such as inadequate detection performance and difficulties caused by vehicle occlusion and cluttered environments, this paper pursues a YOLOv11s-based object detection framework. The algorithm successfully designed a novel PEConv module. This module integrates a partial convolutional network with an efficient multi-head attention mechanism. Through a Split operation, the input image is divided into locally enhanced channels and original channels. The locally enhanced channels undergo partial convolution and feature weight allocation via the efficient multi- head attention mechanism for feature extraction. Finally, these channels are fused with the original channels before undergoing convolution. This approach preserves the original features while minimising feature loss caused by the series of operations. Therefore, the PEConv module is based on a partially convolutional network and efficient multi-head attention. It improves the detection ability by precisely giving more weight to small objects and occluded parts with augmented partial channel attention and original channel fusion. This study further enhances the model’s detection precision and improves its performance in addressing small target vehicles and severe occlusion issues by refining and upgrading the original C3K2 architecture. The LSBlock is integrated into the original model’s bottleneck structure, replacing the traditional 3x3 convolution to create the C3K2 - LSBlock module. Experimental results show that on the UA - DETRAC dataset, compared with the original YOLOv11s, the optimized YOLOv11s has improved the original mAP @ 50 by 3.4%, reaching 61.3%, and improved the original mAP @ 50: 95 by 2%, which verifies the correctness of it.
Chen, Yulin, Wang, Yini, Wang, Jianwei, Zhang, Xin
A Coaxiality Measurement Method Applicable to the Three Holes of an Engine2026-99-12269/4/2026
To address the measurement challenges posed by the large diameters and wide spans of engine crankshaft holes, cylinder holes, and camshaft holes, as well as the abnormal laser measurement data caused by oil film adhesion and local protrusion structures on their inner walls, this paper proposes a non-contact coaxiality measurement method based on a laser displacement sensor (LDS) and a threshold-modified least squares ellipse fitting (TMLSE) algorithm. The method first preprocesses the data through median filtering and then employs an adaptive distance threshold based on maximum inter-class variance to eliminate outliers caused by oil film scattering and protrusions, achieving robust fitting of the cross-sectional centers. Subsequently, the datum axis is established using the least squares midline method, ultimately enabling the evaluation of coaxiality error. In the simulation experiments, a dual-hole model with an inner diameter of 100 mm and a spacing of 700 mm was simulated, where the right hole was translated by 1.5 mm to introduce coaxiality error. Each cross-sectional point cloud included 10% protrusion points (protrusion height of 10 mm) along with random noise and data loss simulating the effects of oil film. To assess the robustness of the method, a systematic analysis was conducted on the influence of laser incidence angles [80°,89°]on measurement accuracy. The results show that across different incidence angles, the maximum deviation between the coaxiality error obtained by the TMLSE method and the theoretical value is only 0.1582 mm, which is significantly better than that of the RANSAC, MZC, and MIC methods. Meanwhile, TMLSE demonstrates stable and efficient computational speed. The simulation verifies that this method offers higher fitting accuracy and robustness under both optical interference and variations in installation angle, making it suitable for the precision measurement of key engine hole systems.
Yin, Jiakuo
Molecular Dynamics Simulations of Microstructural Evolution in Pure Titanium under Laser Shock Peening2026-99-12859/4/2026
Impacts of laser shock peening (LSP) on the evolution characteristics of microstructure in commercially pure α-phase titanium (α-Ti) are explored by molecular dynamics (MD) simulations of high strain-rate compression. The EAM potential (Zhou potential) is selected for its ability to capture the evolution of microstructures. Considering the LSP-induced peak plasma pressure, the strain rate during the simulated shock compression process is set at 10^9 s-1 to replicate the LSP process. The stress-strain curve of the α-Ti under high strain-rate compression is obtained. The maximum equivalent stress reaches 3.6 GPa, consistent with the theoretically calculated value. The simulation results reveal that mechanical twins (MTs) are activated at a strain of 3%. The number of mechanical twins increases and eventually stabilizes, forming a network structure throughout the grains. In the meantime, numerous partial dislocations are generated adjacent to the grain boundaries. The dislocation density also increases with strain and dislocation reactions occur. Moreover, grain refinement is identified. The grain size is refined from the initial ~ 8 nm to ~ 4 nm in the polycrystalline α-Ti. Twinning, together with dislocation-mediated plasticity, drives the refinement of grain size. Gradients of twin density, dislocation density, and grain size density are induced by LSP on the surface of α-Ti. This study comprehensively investigates how LSP influences the evolution of microstructures by MD simulations. It develops an innovative numerical strategy that offers a foundation for elucidating the underlying mechanisms of LSP.
Zhao, Congshan, Zhang, Linbing, Xu, Yidi, He, Jianye, Fang, Jing, Li, Zezhou, Ruestes, Carlos J., Cheng, Xingwang
The Interconnected Battlefield: Optimizing Systems Beyond Standard D3899926AERP09_019/1/2026
Modern defense platforms are no longer isolated systems. They are high-bandwidth nodes in a joint network spanning air, land, sea, space and cyber domains, and the connectors holding it all together are being pushed to their limits. The increasing prevalence of high-speed Ethernet in defense is pushing the boundaries of the tried and true circular D38999 connector. Military jets are becoming increasingly interconnected with edge computing devices, sensors, communication equipment and cameras via high-speed Ethernet. This is being replicated in other military assets, such as armored ground vehicles, artillery and command centers, as we move closer to the envisioned interconnected battlefield. The industry standard D38999, which has been around for decades, is starting to show its limitations when it comes to high-speed data signals.
Enhancing Laser Processing Head Service and Quality Assurance: How Advanced Laser Diagnostics Transform Service Operations and Product Development for High-Power Laser Applications26AERP09_029/1/2026
In the rapidly evolving arena of high-power laser technology, precision and reliability are becoming increasingly important. The increasing powers in laser applications into the 10's of kW and, in some cases, 100's of kW, present additional challenges when applied to internal laser system components. For Precitec, respected globally for their advanced laser processing heads, delivering consistent performance to customers is both a challenge and a necessity. This case study explores how Precitec's U.S. operation has integrated laser measurement technology into its workflow; a non-contact beam characterization system significantly elevates the company's service, quality assurance, and development processes for high-power laser applications.
Multi-Scale Investigation of Burn-Through Mechanisms during In-Service Welding of Pipeline Steel2026-99-04098/28/2026
The core challenge of in-service welding repair of oil and gas pipelines is the risk control of burn-through. Current research primarily focuses on macroscopic phenomena, lacking a systematic multi-scale analysis of burn-through mechanisms and their dynamic evolution. Existing criteria are primarily based on qualitative experience, and widely accepted quantitative safety assessment standards have yet to be established. Furthermore, insufficient understanding of multi-scale damage failure mechanisms and weak theoretical foundations have become bottlenecks in this field. This study targets X65 pipeline steel and combines in-service welding experiments with in-situ scanning electron microscope tensile tests to elucidate the formation mechanism of burn-through from a multi- scale perspective. The results show that during in-service welding, the remaining wall thickness of the pipeline continuously decreases with the welding process, ultimately resulting in burn-through holes. On one hand, the welding arc drives the expansion of the hole; on the other hand, the internal pressure of the medium further enlarges the hole, leading to the expulsion of water and rapid pressure loss in the pipeline. Notably, the fusion zone behind the maximum melt depth is subject to high temperatures, which reduces strength and degrades plasticity, exhibiting significant plastic strain, making it a high-risk area for burn-through instability. Before instability occurs, this region shows evident grain coalescence, with plastic deformation primarily occurring through dislocation slip; when the difficulty of activating slip systems increases, twinning deformation may be induced, and large twin grains rarely develop cracks. Strain concentration and crack initiation are more likely to occur between grains with significant orientation differences.
Wang, Bangyu, Qiao, YingJie, Li, Dong, Xu, ShiHang
Effect of Copper Valence and Content in Copper Red Glaze on Its Color Appearance2026-99-04208/28/2026
Copper red glazes have received considerable attention due to their perfect decorative effects and vivid coloration. This paper selected four different formula copper red glazes with different colors of bright red, violet blue, dark red, and gray blue as the samples. Based on the analysis of the colorimeter, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and scanning electron microscope (SEM), a possible coloration mechanism was proposed to explain the variation of glaze colors. The results indicated that the glaze layers were mainly composed of amorphous phases with few quartz diffraction peaks and mainly presented a granulous structure and phase separation. Increasing the content of CaO could cause color changes of the glaze due to the high ionic potential of calcium ions, which could form a unique feature in the glaze melt. In addition, a small amount of calcium phosphate could greatly change the color of the glaze. The phase separation structure of the blue samples was more obvious than that of the red samples, with a phase separation size of less than 100 nm. The formation of droplet phase separation structure in the glaze could lead to Rayleigh scattering and Mie scattering, which made the color of copper red glazes blue and opacified. Increasing the content of Cu0 and decreasing Cu+ could weaken the structural color, which contributes to a* value of the glaze changing from 26.93 to 22.93. At the same content of Cu0, the higher the ratio of Cu+ /Cu2+ is, the less a* value of the glaze is. Finally, the existence of CuSiO3 in the glaze could also make the blue color.
Ding, Erbao, Yang, Mengli, Liu, Nannan, Li, Yabo, Zheng, Ruimiao, Xu, Yan
Anti-splash System of Sprinkler Based on Millimeter Wave Radar and Machine Vision Fusion2026-99-03238/14/2026
In view of the problems that it is difficult to accurately control the spraying area of the mining sprinkler, and the resource waste caused by the mis-spraying material stacking area, as well as the failure of traditional radar monitoring in the complex electromagnetic environment, this paper proposes an anti-splashing system for the mining sprinkler. By combining millimeter wave radar and visual recognition fusion technology, the overall scheme of the anti-splash system is proposed. Then the control simulation of the whole system is carried out. The results show that the problem of poor control in traditional sprinkler operation can be effectively solved, and the sprinkler area can be adjusted intelligently. Finally, in order to verify the accuracy of the algorithm used in this paper, different algorithms are used for comparative experimental verification. The results show that the Modified YOLOv4 algorithm has a high accuracy of 98.75 %, which has good applicability and provides a theoretical basis for subsequent research.
Hou, Lin
Study on Visible-Light-Driven Photocatalytic Degradation of Tetracycline Hydrochloride by g-C 3 N 4 /CoFe 2 O 4 Composite Photocatalysts2026-99-10078/7/2026
g-C₃N₄, a metal-free semiconductor photocatalyst, demonstrates remarkable potential, but its practical application in pollutant degradation is significantly limited by the rapid recombination of photogenerated electron-hole pairs and low photocatalytic efficiency. To address this, a series of magnetic recyclable g-C₃N₄/CoFe₂O₄ composite photocatalysts with different CoFe₂O₄ doping ratios were innovatively designed and prepared via thermal polymerization, sol- gel, and combined with ultrasonic and heat treatment processes. The novelty of this composite design lies in the effective integration of magnetic CoFe₂O₄ with g-C₃N₄ through a heterojunction structure. It substantially boosts the absorption of visible light. Concurrently, it effectively fosters the separation and mobility of photo-induced charge carriers. The composite materials were systematically characterized by X-ray diffraction, thermogravimetric analysis, scanning electron microscopy with energy-dispersive X-ray spectroscopy, photoluminescence spectroscopy, and ultraviolet-visible diffuse reflectance spectroscopy. Using tetracycline hydrochloride as the target pollutant, the photocatalytic activity of the composites was evaluated under visible light irradiation, and the effects of initial concentration, catalyst dosage, and the influence of solution pH on degradation efficiency were also examined. The results indicated that the composite with a CoFe₂O₄ to g-C₃N₄ mass ratio of 1:3 (denoted as 3-CN/CFO) exhibited the optimal performance: a TCH degradation rate of 80.29 % within 105 minutes and a total organic carbon removal rate of 61.63 %. After five consecutive cycling experiments, the degradation efficiency remained above 70 %, demonstrating good reusability and stability. The performance improvement is attributed to the formation of heterojunctions in the composite, which effectively facilitates charge separation, inhibits carrier recombination, and enhances visible light absorption. Furthermore, the inherent magnetism of the composite permits efficient recovery, streamlining its integration into practical applications. Toward the purification of antibiotic-contaminated water, this research proposes a viable method for fabricating highly effective and recyclable photocatalysts.
Hua, Longjun, Chai, Tian, Wang, Yiming, Zhang, Jing, He, Ting
Experimental Study on the Combined Effects of Swirl and Inlet Offset on the Flow through an Automotive Catalyst Monolith Substrate03-19-04-00198/7/2026
Maldistributed flow within an automotive catalyst can cause reduced conversion efficiency, high pressure loss, and premature deactivation. However, packaging constraints often result in uneven flow distribution between the monolith channels, thus compromising design and, inevitably, performance of the device. Flow uniformity may be improved by the introduction of swirl upstream of the catalyst assembly, and in turbocharged applications the residual swirl from the turbine can serve that purpose. Indeed, low swirl has been shown to provide favorable flow uniformity in the monolith substrate in an axisymmetric flow setup. However, the automotive exhaust aftertreatment setups are seldom axisymmetric, and the combined effects of inlet swirl and offset on the flow profile through a monolith substrate are unknown. To address this gap, this study provides the first systematic experimental characterization of the coupled influence of inlet swirl and packaging-relevant inlet offset on flow development and uniformity in a sudden expansion catalyst assembly. Particle image velocimetry (PIV), wall pressure measurements, and hot-wire anemometry (HWA) are combined to link the upstream separation and recirculation structures to the velocity distribution downstream of the monolith. The results reveal a previously unreported swirl-dependent sensitivity to geometric asymmetry: under no-swirl and moderate-swirl conditions, flow uniformity is robust to inlet offset, varying by no more than 1.4%, whereas at low swirl the offset reduces uniformity by up to 8% at high mass flow rate. Increasing mass flow rate reduces uniformity by up to 15%, while swirl improves uniformity by up to 19% relative to axial flow. These findings demonstrate that improvements observed for swirl in axisymmetric assemblies cannot be assumed to transfer directly to offset geometries. Swirl intensity and inlet alignment must instead be considered as coupled design variables. The measurements also provide a benchmark dataset for validating computational fluid dynamics simulations before their application to production-type systems.
Rusli, Ijhar, Aleksandrova, Svetlana, Medina, Humberto, Benjamin, Stephen F.
How Airbus is Developing Artificial Intelligence for Future Cockpits26AERP08_118/1/2026
Computer vision, automated landing and embedded AI for tomorrow's cockpits. Airbus, Toulouse, France At the VivaTech forum in June, Airbus showcased a demonstration highlighting the use of computer vision to enhance automated landing procedures and operational efficiency. The “Vision Landing Application” utilizes artificial intelligence to analyze runway features in real-time using onboard cameras. The goal of this research is to create an additional and independent positioning source to guide pilots and/or their aircraft reliably, opening up the perspective of bringing autoland (fully automated landing procedure) capabilities to airports that lack advanced ground infrastructure. While the technology is still in the research phase and far from commercial certification, this technical exploration aligns directly with Airbus' global roadmap for Smart Automation. Airbus already has a head start, since it has already conducted numerous research projects during the last decade, which have led to the demonstrator at Airbus' stand at this year's show.
Microstructure and Mechanical Properties of Vacuum Laser Welded Joints in 42CrMo Thick Plate2026-99-01327/31/2026
Vacuum laser welding trials were carried out on 42CrMo steel, a material widely utilized in the defense sector. By employing a 30 kW fiber laser system, complete penetration welds were successfully produced on 20 mm thick 42CrMo steel plates. The resulting joints displayed satisfactory surface quality on both the top and bottom sides, with no evident defects such as cracks or porosity. A comprehensive analysis of the joint microstructure and mechanical properties was conducted. Findings reveal that the weld zone (WZ) is predominantly composed of lath martensite, accompanied by minor quantities of plate martensite, organized as columnar crystals. The joints demonstrated high tensile strength at ambient temperature, with fracture consistently occurring within the base metal (BM). Microhardness measurements indicated higher values within the weld relative to the base metal, and no pronounced softening was detected in the heat-affected zone (HAZ). Additionally, the joints exhibited commendable impact toughness, suggesting overall superior mechanical performance.
Shi, Haicheng, Zhang, Guoyu, Li, Wuhong, Cao, Dongxu, Liu, Tianlei
Micro-Groove Fabrication and Simulation Analysis on PEEK Surface under UV Nanosecond Laser Irradiation2026-99-02257/31/2026
This study investigates the interaction mechanism between ultraviolet nanosecond pulsed lasers and polyetheretherketone (PEEK). By integrating finite element simulations with experimental validation, the work explores the laser microtexturing characteristics of PEEK surfaces and evaluates the influence of microtextures on the material’s surface biocompatibility. First, the interaction between the laser and the PEEK polymer was analyzed, and a laser ablation model was established using the COMSOL Multiphysics simulation platform. Using finite element simulation, the effects of spot overlap ratio were investigated by adjusting the average laser power, while the influence of single-pulse energy on the ablation characteristics of the PEEK surface was examined by varying the scanning speed. Subsequently, ultraviolet nanosecond laser processing experiments were conducted on planar PEEK microtextures based on the simulation results. Taking surface microgrooves on PEEK as representative structures, the variations in groove depth and width under different combinations of laser parameters were analyzed. The parameters, including average laser power, scanning speed, and repetition frequency, were optimized to identify processing conditions that yield stable depth and width, along with good surface flatness. Finally, experiments have initially verified that the microtextured PEEK surface may improve biocompatibility and regulate surface wettability to a certain extent.
Wu, Yifan, Wang, Xiaohui, Han, Yujie, Jin, Shuo
A Stress Relief Method Based on a Hybrid Process of Shot Peening and Laser Directed Energy Deposition with the Same Materials2026-99-02327/31/2026
Laser directed energy deposition (LDED) is widely used in various fields due to its fine forming structure and superior performance. However, the characteristics of the hot forming process result in significant residual tensile stress in the formed materials, which affects the capability and useful life of the mechanism accessory. The hybrid manufacturing technology of shot peening (SP) and LDED has a significant influence on the elimination of defects and the improvement in microstructure of formed materials and reducing residual stress, but it also has limitations. To solve the problems, such as the introduction of powders and the difficulty in recycling and classification when using heterogeneous materials for shot peening in hybrid processes, this paper proposes a method of strengthening with the same material, establishes a thermal shot peening simulation model for the hybrid process, and conducts experimental verification. The research finds that the average generated stress of SP in hybrid manufacturing technology is -215.6 MPa, and the thickness of the strengthening layer is about 40 μm. The subsequent hot forming process will eliminate part of the induced stress by SP on the previous deposition, but the deposited stress on the surface is reduced compared with that in the single process. The hybrid manufacturing technology of SP and LDED, based on the same material, effectively utilizes the residual heat from the forming process, providing feasibility for engineering applications.
Zhang, Xiaoyu, Zhang, Min, Li, Dichen, Jiang, Yunfeng, Chen, Xinjin, Fei, Ya, Hu, Ying, Liu, Yuyang
Discussion on Methods for Studying Material Fatigue Life: Taking Aluminum Alloy as An Example2026-99-01217/31/2026
Fatigue design is a key common quality technology for improving the quality control capability of China’s automotive products. The fatigue of materials is a multi-scale damage evolution process. Characterizing and processing the large number of three-dimensional defects inside the material, which have different shapes and distributions, and predicting the material’s lifespan based on the cross-scale damage evolution mechanism, is one of the key technologies for fatigue optimization design. This paper discusses the research methods for the fatigue life of aluminum alloy materials. Firstly, based on the staged fatigue damage experiments, the three-dimensional defect features are obtained through CT scanning and reconstruction, and a defect characterization and processing method based on k-d tree and multi-scale feature pyramid is established to accurately represent the topological and geometric relationships of non-uniformly distributed three-dimensional defects. Secondly, a mathematical model for the evolution of micro-damage and macro-cracks is constructed, and the cross-scale transformation of defects is achieved through hierarchical and recursive methods, revealing the cross-scale evolution mechanism of fatigue damage in aluminum alloy materials. Finally, a remaining life prediction model based on defect information and feature weights is established through the support vector regression algorithm (SVR). This research method can provide technical support for the fatigue life optimization design application of lightweight materials such as aluminum alloys.
Zhang, Liangxia, Niu, Zhijun, Cheng, Fangfang, Chen, Hao, Yang, Yali
Design and Optimization of Hollow Rotating Traveling Wave Ultrasonic Motor Based on Laser Beam Expander2026-99-01547/31/2026
To meet the critical need for rapid response and miniaturization in laser beam expander drive systems, this study proposes an innovative actuation solution based on a hollow rotary traveling-wave ultrasonic motor. By thoroughly analyzing the optical adjustment mechanism of laser beam expanders and the electromechanical coupling behavior of ultrasonic motors, the motor structure was systematically optimized. Using a multiphysics coupling approach, the performance of stators fabricated from three distinct materials was compared, and parametric optimization was conducted. Experimental verification confirms that the developed ultrasonic motor precisely matches the load characteristics of beam-expanding optics while fulfilling the stringent requirements for both fast response and compact design. This research provides a reference for the miniaturization drive of high-precision optical systems, with promising applications in space optics and precision instrumentation.
Qiu, Haihui, Niu, Chuanhu, Xiao, Zhong, Xu, Zhangfan, Li, Jialiang, Pan, Song
Study on the Contact Stress Distribution and Calculation Method of Cylindrical Roller Bearings Considering Clearance Changes2026-99-01247/31/2026
The distribution of contact stress in roller bearings has a significant impact on operational performance and safety. Firstly, we established a bearing clearance change model that combines interference fit and thermal expansion effects. Then, we studied the clearance changes of key parameters’ influence under different operating conditions. Using Hertz contact theory, we analyze the nonlinear coupling relationship between clearance changes, load distribution, and contact stress. Through MATLAB analytical calculations, load and stress distribution contour maps were obtained under typical operating conditions, which provided theoretical support for bearing optimization design and reliability analysis. The result depicts that an increase in interference fit and temperature difference leads to clearance decrease, triggering a redistribution of contact stress. As clearance decreases, the maximum contact stress exhibits a nonlinear growth trend. To further enhance engineering practicality, this paper uses the MATLAB platform to develop a visualization of digital image processing software. The software enables interactive analysis throughout the entire process of clearance input, stress calculation, and graphical display.
Pang, Yiqing, Cai, Hongbin, Ren, Siyang
Research on Welding Deformation of the Car Body Based on 3D Laser Scanning2026-99-08217/30/2026
This study aims to verify the accuracy and stability of a system used for measuring and analyzing the welding deformation of vehicle bodies under different welding parameters. A 3D laser scanner was employed to capture the surface topography data of the vehicle’s front deck before and after welding. In order to determine the welding deformation, PolyWorks software was utilized for deformation analysis, which processed the 3D scanning data and compared the post-welding data set. A dedicated vehicle body welding deformation measurement system was developed, including hardware configuration and software development. The BP neural network algorithm was adopted to predict the welding deformation, and the results indicated that the deviation between the predicted values and the average experimental measurements was less than 10%. This confirmed the practicality of the BP neural network in predicting welding deformation and highlighted its effectiveness in technical support for the optimization of welding parameters and deformation control in automotive manufacturing.
Li, Lina, Zhang, Yiqi, Sun, Hongchang, Wei, Xiezhen
Design of Telescope Support Truss Based on Topology Optimization and Size Optimization2026-99-08487/30/2026
The primary mirror support truss of large-aperture segmented telescopes, serving as a critical load-bearing component of the optical system, has its structural stability directly determining the optical imaging quality. This paper adopts a collaborative design method integrating topology optimization and size optimization to address issues, including excessive weight and unreasonable stiffness distribution in traditional support truss designs. First, based on the topology optimization theory of the Solid Isotropic Material with Penalization variable density method, topology optimization was performed on the initial truss structure using finite element simulation software, with the volume fraction as a constraint and the objective of maximizing structural stiffness to determine the optimal material distribution model. Subsequently, the truss structure was reconfigured based on the topology optimization results. Finally, the cross-sectional dimensions of the truss members were selected as optimization variables, and size optimization was performed using the NSGA-II multi-objective optimization algorithm with the objectives of minimizing structural weight and minimizing weighted compliance, while considering constraints such as stress and displacement. The results show that the optimized support truss achieves a 3.9% reduction in weight and a 35.47% decrease in elastic strain energy. This effectively meets the high-precision and lightweight design requirements for telescope support structures and provides a feasible technical solution for the design of large-aperture telescope support trusses.
Tan, Deli, Guo, Liquan, Gao, Dedong, Liu, Chuanjie, Dai, Xiaodong, Huang, Lei
Integrating 4D Millimeter-Wave Radar with YOLOv5-Monster for Distance Measurement2026-99-08537/30/2026
To address the challenges of binocular vision ranging under complex environmental conditions—such as illumination variations, occlusion, and textureless regions, which result in unreliable and non-robust performance—this paper proposes a multi-source heterogeneous sensor fusion ranging method integrating 4D millimeter-wave radar with the YOLOv5-Monster framework. This method is capable of overcoming the issue of limited ranging accuracy in monocular or binocular vision algorithms under non-ideal imaging conditions. This study achieves high-precision spatial perception through the following specific pipeline: First, Zhang’s calibration method is used to obtain the intrinsic and extrinsic parameters of the binocular camera, and stereo rectification is performed on the raw images. Next, a lightweight YOLOv5 network is employed for object detection, while a high-performance Monster network is utilized to generate dense disparity maps, thereby accomplishing initial depth estimation. To mitigate the inherent depth estimation errors of vision-only systems, 3D point cloud data from a 4D millimeter-wave radar is further introduced. By applying a Kalman filter algorithm, the millimeter-wave radar point cloud and visual outputs are fused, achieving spatiotemporal synchronization and optimal state estimation across modalities and effectively correcting biases in visual ranging. Experimental results show that within the full range of 4 to 150 meters, the relative error of the proposed method remains below 5%. Specifically, the relative errors are 1.25% (absolute error: 0.05 m) at 4 meters, 1.40% at 5 meters, 2.99% at 75 meters, and 4.91% at 150 meters. Compared with the vision-only Monster-YOLOv5 baseline method, the relative error at 150 meters is reduced from 13.16% to 4.91%, representing an accuracy improvement of over 60%. Meanwhile, in terms of long-distance error control, the proposed method significantly outperforms traditional stereo matching approaches such as SGBM+YOLOv5 and BM+YOLOv5, reducing errors by more than 20 percentage points. These results verify that deep multi-modal fusion can enhance environmental adaptability and measurement reliability, providing a high-precision and highly robust solution for distance estimation in intelligent perception systems, which holds important theoretical and engineering significance.
Li, Fugai, Xie, Yuwen, Su, Hao, Liu, Donglei, Wu, Qiong
Three-Dimensional Transient Thermal-Fluid Coupling Numerical Simulation of the Molten Pool in Aluminum Alloy Laser Welding2026-99-08547/30/2026
Laser welding technology for aluminum alloy electrode and busbar connections: addressing challenges in battery module assembly. In this work, a CFD framework was built in ANSYS Fluent using a Gaussian rotating heat-source representation, while a VOF approach was used to capture the transient gas–liquid interface in deep-penetration welding. A three-dimensional, transient, thermal-fluid coupled numerical model of the dual-layer heterogeneous aluminum alloy laser deep penetration weld pool was established concurrently with laser deep penetration welding experiments. Results indicate: Peak flow velocities in the weld pool during welding are concentrated along the weld centerline, with flow vectors predominantly directed axially along the weld. Once a quasi-steady keyhole regime is established, vaporization-induced recoil pressure becomes the primary driver governing melt circulation. The liquid metal first impinges on the pool bottom along the keyhole wall and then recirculates upward near the pool boundary, producing strong vortical motion. These findings are intended to support parameter selection and process optimization for laser welding of layered dissimilar aluminum components used in battery tab–busbar assemblies.
Lv, Wenjun, Wu, Yan
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