Browse Topic: Imaging and visualization

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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
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
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
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
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.
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
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
Construction and Verification of Munich EV Driving Cycle Considering Road Slope2026-99-08697/30/2026
The driving cycle is the basic model of certification of vehicle fuel consumption and emissions, or calibration of powertrains. Standard regulatory driving cycles, such as WLTC, in general assume flat roads during their generation and fail to take into account the strong effect that road gradients have on vehicle operation and driving energy consumption. Such a shortcoming, then, leads to gross mismatches in adaptability when used for urban environments with typical hilly topography. To solve this problem, in this paper, we proposed a method for building driving cycles that consider the impact of slope with actual driving data. Initially, high-precision onboard data collectors were used to generate a total sum of 21, 350 km of driving data from the Munich area, thus creating a diversified driving data set with details such as vehicle speed, slope, and environmental information. Subsequently, joint probability distributions of “speed-acceleration” and “slope-slope change rate” are proposed by using the Micro-trip Method, and a novel chi-squared test algorithm is used to obtain a higher fidelity of urban driving cycle representative of typical conditions. Results of the driving cycle results show that the driving cycle built was close to the actual kinematics, indicating a deviation of less than 5%, and can capture the average uphill characteristic of 1.7%, which is quite well represented. Finally, in fact, validation of whole vehicle environmental chamber tests further demonstrates that the energy consumption prediction error of the developed driving cycle is just 2.2%, much lower than 19.1% error of WLTC. It highlights the importance of considering slope parameters in improving the accuracy of energy consumption calibration for an EV operating on complex slope terrains. Furthermore, it underscores that converting the real-world driving data into lab-based driving cycles can reduce the cost and time of actual road tests for Chinese companies going to the overseas markets, thereby offering support for the international marketing strategy of a global database.
Tian, Lichen, Jiang, Ping, Gao, Wang, Liang, Yongkai, Ma, Kunqi, Yu, Hanzhengnan
Research on Clustering Data-Driven Intelligent Perception Algorithm for Micro Mobile Robots2026-99-18437/17/2026
To address the challenges faced by micro flapping-wing flying robots in visual navigation—specifically, the large volume of visual information and the difficulty in transforming it into usable intelligent visual data—this paper proposes a clustering-based data-driven approach for directional and image perception. The aim is to enable intelligent visual navigation for flapping-wing robots. The proposed method performs clustering analysis on gyroscope data from the flapping-wing robot to extract directional features. Simultaneously, it applies clustering techniques to visual images captured by the robot to identify intelligent features such as edges. This approach enables the robot to acquire multiple optimized perceptual data types, thereby enhancing the behavior control system. Through the use of clustering analysis, the method not only improves the effectiveness of visual navigation but also extracts features related to visual targets and environmental information, providing technical support for visual target tracking. The experimental platform consists of a flapping-wing robot equipped with an onboard camera, and the proposed clustering-driven visual image perception approach has been experimentally validated. Experimental results demonstrate the high feasibility and effectiveness of the method in practical applications. The main contributions of this study lie in two aspects: (1) a clustering-driven visual image perception method for flapping-wing robots, and (2) a clustering-based approach for identifying posture and behavioral patterns of flapping-wing flying robots.
Li, Zixuan, Ding, Wei, Zhang, Feng, Song, Min, Liu, Zhaoming, Miao, Lei, Liu, Haotian, Bai, Ning, Tian, Shen, Cui, Long, Wang, Hongwei
Multi-Frame Centroid Extraction Method for Optical Navigation During the Approach Phase of Small Body2026-99-18517/17/2026
Autonomous optical navigation is one of the important navigation methods for the small bodies approach phase. To improve optical navigation performance during the approach phase to a small body, this paper presents a method for extracting the target centroid from sequential optical images. The process begins with fitting a minimum enclosing ellipse to the detected contours in each frame to obtain an initial estimate of the centroid. Building upon this, edge corner points across adjacent images are matched using normalized cross-correlation, and their displacement is tracked using optical flow techniques. The observed pixel trajectories are analyzed, and a predictive model of pixel motion is formulated based on the geometric relationship between the detector and the small body. By combining the directly extracted centroids with the predicted motion of key pixels, a fusion strategy is developed to improve the reliability of the centroid estimation. Finally, numerical simulation results demonstrate that the method significantly improves the accuracy of centroid extraction, thereby enhancing the overall performance of optical navigation during approach operations.
Liu, Jing, Zhu, Shengying
Aircraft Optoelectronic Payload Transfer Alignment and Dynamic Error Compensation Algorithm Research2026-99-18497/17/2026
This paper proposes a multi-source dynamic error compensation algorithm for the transfer alignment of airborne optoelectronic payloads. This method addresses performance limitations of micro-inertial navigation systems (micro-INS) in complex dynamic environments, specifically those arising from accumulated device noise and the inability to perform static alignment due to installation errors. The algorithm’s core is the Extended Kalman Filter (EKF) technology. By constructing a “velocity + attitude” matching model between the UAV’s master inertial navigation system (MINS) and the optoelectronic payload’s slave inertial navigation system (SINS), it leverages high-precision MINS navigation information to correct SINS errors. Utilizing a 21-dimensional state space equation and measurement equation, the algorithm achieves real-time estimation and compensation of various errors, including attitude misalignment angles, sensor biases, installation errors, and flexure deformation. Simulation results demonstrate significant alignment accuracy improvement. Post-lever arm effect compensation, velocity errors are stably controlled within 0.01 m/s. Concurrently, flexure deformation angle compensation substantially reduces misalignment angle fluctuations across all directions, enhancing system stability and maintaining low misalignment angles. These findings validate the proposed error compensation strategy’s effectiveness.
Zhang, Lu, Li, Mao, Wang, Shiyong, Lei, Chao
Few-Shot Semantic Segmentation Method for Crater Detection on Small Bodies2026-99-18527/17/2026
Craters are the primary landmarks used for visual navigation in missions exploring small celestial bodies. However, obtaining high-quality, annotated crater data is often challenging due to limited imaging conditions and strict mission constraints. Conventional semantic segmentation models struggle with limited data and are challenging to train effectively. To overcome this limitation, this study introduces a few-shot segmentation approach for crater detection on small celestial bodies. Our method includes a prototype representation module that constructs class-level prototypes to quickly associate crater regions with their semantic features. This paper also designs an iterative learning module that gradually improves the segmentation output, helping the model better capture detailed edges and structures. Tests on a simulated few-shot dataset demonstrate that our method provides reliable and accurate crater segmentation, achieving a mean intersection-over-union (mIoU) of 88.7, outperforming traditional fully supervised methods.
Li, Shuai, Zhu, Shengying
Drop Impact Phenomena: An Experimental Study of Isooctane Interaction with Dry Walls and Liquid Films2026-01-50157/13/2026
In this study, high-speed back-illuminated imaging and laser-induced fluorescence (LIF) methods were employed to investigate the impingement behavior of millimeter-sized single isooctane drops on a dry solid wall and various liquid films, including isooctane and glycerol solution films of different concentrations. Various fuel spray impingement scenarios in gasoline direct injection engines were examined. High-speed back-illuminated imaging was primarily used to examine the impact of fuel drops on a dry wall and a fuel film of the same composition as the drops. The LIF method was used to examine the impact of fuel drops on the glycerol solution film, allowing for the distinction between fuel drops and the glycerol solution film. The impingement behavior varied depending on the Weber number of the incident drop and the wall condition. When fuel drops impacted the solid dry wall vertically, they spread into a circular liquid film. The outer edge of the liquid film folded and bulged, and upon reaching the maximum spreading diameter, it maintained equilibrium and did not retract. When isooctane fuel drops impacted the isooctane film, they broke and splashed, with thinner films producing stronger splashes. Additionally, the Weber number of the fuel drops significantly influenced the crown shape and splashing after impact. The impingement behavior of fuel drops on the glycerol solution film was also investigated, focusing on the liquid film morphology after impact. Based on the experimental data, empirical correlations were established between the critical Weber numbers for transitions among different crown morphologies and the dimensionless film thickness under varying film viscosities.
Yang, Tian, Lu, Lili, Guo, Zongwei, Song, Enzhe, Yao, Chong, Ning, Yilin, Ke, Yun
Validation and Exploration of Multimodal Deep-Learning Camera–LiDAR Calibration Models2026-01-50377/13/2026
This paper presents an innovative study in exploring, evaluating, and implementing deep-learning architectures for the calibration of multimodal sensor systems. The aim of this paper is to leverage the use of sensor fusion to achieve dynamic, real-time alignment between 3D LiDAR and 2D camera sensors. Static calibration methods are tedious and time-consuming, which is why we propose utilizing conventional neural networks (CNNs) coupled with geometrically informed learning to solve this issue. We leverage the foundational principles of extrinsic LiDAR–camera calibration tools such as RegNet, CalibNet, and LCCNet by exploring open-source models that are available online and compare our results with their corresponding research papers. Requirements for extracting these visual and measurable outputs involved tweaking source code, fine-tuning, training, validation, and testing of each of these frameworks for equal comparisons. This approach aims to investigate which of these advanced networks produces the most accurate and consistent predictions. Through a series of experiments, we reveal some of their shortcomings and areas for potential improvements. We find that LCCNet yields the best results among all the models that we validated.
Karramreddy, Venkat Sai Raxit, Mitchell, Liam
Research on the Design of Lane Line Recognition and Deviation Warning System Based on Machine Vision2026-99-05667/10/2026
In order to reduce traffic accidents caused by cars straying from lanes, a lane line recognition and deviation warning system based on machine vision is designed. It mainly includes image preprocessing, lane line detection, and the design of a deviation warning model. “In this study, an ROS-based intelligent vehicle-mounted camera is adopted for road image collection. To reduce the computational load of data processing while guaranteeing the algorithm’s accuracy and reliability, grayscale conversion and region of interest (ROI) extraction are implemented to finish the image preprocessing stage. Additionally, a fusion strategy of global and local thresholds is introduced to enhance both the operational speed and detection accuracy of the algorithm” use the Canny operator for the edge feature extraction; and complete the fitted lane lines with the improved Hough transform. Finally, based on the Kalman filter and camera viewpoint conversion coefficient algorithm, the lane line offset is detected in real time, and the deviation is judged in combination with the monitoring interface. Simulation experiments show that the system is able to effectively recognize the lane line and judge the deviation status under the condition of setting the offset threshold of 70 pixels, which significantly improves the accuracy and real-time performance of the lane deviation warning and provides effective technical support for reducing traffic accidents.
Wang, Xufeng, Zhang, Chunshu, Wang, Yan, Chen, Yihui, Ji, Rui
Missile Pose Measurement Based on ArUco Markers2026-99-05767/10/2026
This paper presents a monocular vision-based system for high-precision missile pose measurement using ArUco markers and Perspective-n-Point (PnP) algorithms. By deploying 6 × 6 ArUco markers on a cylindrical missile mock-up, the system establishes 3D-2D correspondences between structured-light-scanned models and camera images to solve the PnP problem. The proposed approach integrates optimized ArUco marker recognition — leveraging adaptive thresholding, contour simplification, and grid-based validation — with the Efficient PnP (EPnP) algorithm to achieve real-time pose estimation. Experimental validation demonstrates angular accuracy of ± 0.3° in roll/pitch/yaw and positional accuracy of ± 2 mm within a 2 m range under controlled conditions. The system exhibits robustness against partial occlusions and motion blur, with degraded performance (± 1.2°, ± 5 mm) in extreme scenarios. Key innovations include a streamlined marker detection pipeline and adaptive pose refinement using Levenberg-Marquardt optimization. This work provides a cost-effective, non-contact solution for flight tests, with potential applications in weapon separation testing.
Wang, Ruiyang, Zhang, Chaofan
Development of Graphene Filled Polypropylene Nanocomposite for Aerospace Application2026-26-07396/1/2026
Polypropylene, a commodity plastic, is the semi-crystalline thermoplastics widely used in high volume for general purpose application. Polypropylene is the macro molecules of soft and weak backbone, which by reinforcement of fillers in different forms such as fiber, spheroids, nanotubes, flakes, etc., can influence its mechanical, thermal, electrical, creep resistance, and flame resistance properties for use in aerospace applications. Currently, polycarbonate and nylon plastics are used in aerospace applications, however, they are expensive compared with polypropylene. In this thesis, efforts are put to study the effect of reinforcement fillers in the properties of polypropylene composite, primarily the mechanical and flammability properties. The matrix element, polypropylene co polymer and reprocessed polypropylene blended in equal ratio, are coupled with the dispersing phases such as graphene, mica, fumed silica, and polydimethylsiloxane polymer. Effect of graphene as reinforcing filler at different weight % to polypropylene composite’s properties are studied and compared with that of the neat polypropylene. Effect of coupling agent, Aminopropyltriethoxysilane (APTES), on mineral fillers and Polydimethylsiloxane polymer (PDMS) used for crosslinking with the polypropylene matrix is also studied and compared using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) techniques.
Govindaraju, Parthasarathy
Lightweight Magnesium Nanocomposites with Enhanced Wear Resistance for Landing Gear Application2026-26-07516/1/2026
This study systematically evaluated the wear resilient performance of AZ61 magnesium alloy reinforced with 15 wt.% SiC and diverse amounts of multi-walled carbon nanotubes (MWCNTs) under dry sliding circumstances adopting pin-on-disc apparatus (ASTM G99). To identify the influence of factors like sliding speed (SS) (1-3 m/s), axial load (AL) (10-30 N), and MWCNT concentration (0-3 wt.%) that affect tribological performance, experiments were developed using a Central Composite Design (CCD) under Response Surface Methodology (RSM). SEM micrographs revealed a dispersion optimum near 2 wt.% MWCNT, where CNTs anchor to SiC and bridge the α-Mg matrix, while 3 wt.% shows agglomerates and micro-voids. Findings showed that wear loss (WL) and friction coefficient (CoF) was greatly amplified by increasing AL owing to localized heating and contact stresses. A compacted tribolayer was formed by increasing SS, which decreased WL but marginally raised the CoF. At low AL (10 N), SS (2.09 m/s), and 2.12 wt.% MWCNT, the wear resistance was significantly improved by improving load transfer and creating a lubricating carbon-rich coating, resulting in a decreased WL of 0.006 g. The CoF persisted within the range of 0.19 to 0.28. Agglomeration of MWCNTs caused increased WL and CoF when the MWCNT content is increased above 2 wt.%. Worn-surface microscopy at the optimum showed fine wear tracks and a continuous carbon/oxide glaze, evidencing a lubricious CNT-rich third-body film, whereas high AL/low MWCNT produced deep grooves and delamination.
Senthilkumar, N.
Automating Compliance Workflows in Aerospace Systems: A Scalable Framework for Specification Review and Requirement Mapping2026-26-07146/1/2026
Compliance verification in aerospace systems often relies on labor-intensive workflows that demand extensive manual effort to produce structured review documentation and requirement matrices. These processes can span dozens of hours per review, are vulnerable to inconsistencies due to non-standardized annotations, and depend heavily on individual interpretation of fragmented technical sources. With a growing backlog of review tasks and a steady influx of new requests, the need for scalable automation has become increasingly important. This study presents a modular automation framework designed to streamline compliance assessments through intelligent document parsing, requirement extraction, and matrix generation. The system integrates optical character recognition, computer vision, and natural language processing techniques to process both scanned and digital documents. By digitizing data across multiple hardware configurations and automating extraction from diverse technical records, the framework enables consistent evaluation across a broad spectrum of requirement categories. Automation scripts and standardized templates facilitate rapid population of compliance matrices, reducing manual hand-offs and minimizing reliance on specialized expertise. Implementation led to a 39% reduction in turnaround time and a 62% increase in monthly throughput, demonstrating measurable efficiency gains in the compliance review process. This framework exemplifies how intelligent automation can drive operational efficiency, deliver measurable cost savings, and pioneer data-driven and scalable innovation in aerospace compliance engineering.
Mirani, Harsh, Baviskar, Yash G.
Commercial Satellite Manufacturing Scales Up as LEO Demand Drives New Production Lines26AERP06_046/1/2026
Space vehicle and satellite development programs are driving demand for new small- and medium-sized satellites across commercial and defense imaging, data collection, and other space-based applications.
Edge Computing Advances Enable Smarter Spacecraft and Satellites in Orbit26AERP06_026/1/2026
As satellites take on more onboard processing - from Earth imaging to autonomy - spacecraft computing designers are pushing for higher performance under tight thermal and radiation constraints. Here's how suppliers are approaching heat removal, radiation mitigation and production-scale space-grade computing for LEO and beyond.
Building Magnetically Responsive Robots and MaterialsTBMG-552686/1/2026
Under a microscope, a bouquet of lollipop-like structures, each smaller than a grain of sand, waves gently in a petri dish of liquid. Suddenly, they snap together, like the jaws of a Venus flytrap, as a scientist waves a small magnet over the dish. What was previously an assemblage of tiny passive structures has transformed instantly into an active robotic gripper.
Six Trends Shaping Motion System Design for Point-of-Care Imaging ApplicationsTBMG-553356/1/2026
Medical imaging technology is advancing rapidly, bringing new opportunities and challenges for machine designers. Systems that once required dedicated hospital rooms and significant floor space are becoming more compact, faster, mobile and capable of delivering increasingly detailed clinical insight. From advanced CT scanners to imaging platforms integrated with surgical robotics, imaging equipment is evolving toward more point-of-care (POC) solutions to meet rising expectations for diagnostic accuracy, procedural guidance and operational efficiency.
Computational Analysis of Platform Motion and Wave Effects on the Aerodynamics and Wake of the IEA 22 MW Floating Wind Turbine2026-99-17355/22/2026
This study investigates the unsteady aerodynamic response, wake evolution, and vortex dynamics of an ultra-large floating offshore wind turbine (FOWT) under coupled motion–wave conditions. A high-fidelity aero–hydrodynamic CFD model is employed for the IEA 22 MW reference turbine. Platform pitch and surge motions are prescribed via sinusoidal functions, and wave conditions are independently introduced by considering two representative sea states (H = 4 m and 7 m) and a no-wave case. Results show that pitch and combined pitch–surge motions significantly amplify unsteady aerodynamic effects, increasing peak power from 81.1 MW (P5S0) to 92.6 MW (P5S5), with periodic negative power output and severe dynamic stall. Under strong motion, waves further raise peak power to 93.4 MW (H7P5S5), indicating a coupled amplification effect. Dynamic stall is mainly triggered by pitch motion, expanding in scope and duration with motion amplitude; wave effects on stall remain limited. Platform motion also enhances wake recovery by increasing inflow shear and turbulence, leading to higher turbulent kinetic energy (TKE) and a reduced velocity deficit (ΔŪ). Waves compress the low-speed wake core and reduce ΔŪ from 0.248 (no-wave case) to 0.204 under H7 conditions at x/D = 3.0, with the effect being particularly evident under combined motion. Vortex visualization reveals that platform movement leads to vortex merging, ring thickening, and deflection, with combined motion creating the strongest mixing. Wave-generated vortices interact with tip vortices near the surface, becoming more intense under larger wave heights. In general, platform motion is the main factor in FOWT unsteady aerodynamics, while waves have secondary but cooperative effects by changing inflow structures and aiding wake recovery. This study offers theoretical support and engineering guidance for aerodynamic design optimization and wind farm layout of next-generation ultra-large floating offshore wind turbines.
Xie, Bin, Sun, Haiying, Chen, Ye
Adaptive Speed-Regulated Impedance Control for Robotic Ultrasound Scanning: Reducing Constant-Force Tracking Errors2026-99-07515/15/2026
Robotic ultrasound scanning technology is a research hotspot in the field of medical imaging, and can achieve standardized and high-precision data acquisition. However, large force tracking errors occur during scanning, especially in complex human tissues, which can severely degrade image quality and diagnostic accuracy. Therefore, we propose an adaptive speed-regulated impedance control strategy to address this challenge, which innovatively combines the spline real-time interpolation and impedance control for constant force tracking. Firstly, the discrete ultrasound scanning paths are fitted to generate a smooth and synchronized interpolation trajectory. Then, the speed of the reference trajectory is adjusted in real time based on the Taylor formula to reduce the force tracking error. Experimental verification was conducted, and the results showed that the force tracking error increases with the increase of trajectory speed. In addition, at high speeds (e.g., 10 mm/s), the mean/variance of the force tracking error of the proposed method (0.3067N/0.2784) is reduced by 31.1%/37.4% respectively compared with the mean/variance of the traditional impedance control (0.4452N/0.4448), fully demonstrating the effectiveness of the proposed control strategy.
Min, Kang, Zhang, Le, Shi, Yudong, Fang, Jin, Mo, Hangjie, Li, Xiaojian
Cabbage Cleaning Device Based on Fischertechnik Model2026-99-07345/15/2026
This article focuses on the problem of high labor cost, low processing efficiency and poor automation of the existing equipment in the postharvest processing of Chinese cabbage. It will design and produce an automated Chinese cabbage processing method called Smart Fresh Pack. Root removal, leaf removal, washing, loading, weighing, packaging and labeling functions were integrated, and smart dexterous intelligence was applied to core concepts and this can be used in the bulk production scenario of supermarkets in the city and countryside Compared with traditional assembly line equipment, obvious advantages in terms of structure, function and processing capacity: Key innovations include: Low-pressure air jet cleaning replaces water washing, which prevents a second contamination and weighing error due to surface moisture; pneumatic gripper and multi-DOF robotic arms combine to package and dynamically weigh simultaneously, streamlining these tasks; machine vision relies on an SSD-MobileNetV2 visual model with Sobel edge detection to locate and identify wilted leaves; and pairing with a multi-threaded control structure for millisecond level closed-loop response. I used Fischertechnik models to build and simulate, checking whether the motion logic of this design is reasonable, whether the stresses are safe, and whether the airflow cleaning is effective. This machine finishes the complete processing of one cabbage just within one minute, its modular and its maintainance and scalability aspects are also there, it gives small and medium size agricultural entities a low cost but also very effective clean vegetable processing route, this is truly good for making progress with the auto, standard and green developments within agric prd processing.
Chen, Yuhui, Zhang, Yixuan, Ruan, Jia, Zhu, Huayun, He, Lianzheng, Zhao, Ping
Design of a Novel Intelligent Monitoring Platform for Low and Medium Altitudes2026-99-07395/15/2026
This paper presents the design of a novel intelligent monitoring platform for low and medium altitudes, aiming to offer a new solution for the development of intelligent equipment operating in this airspace. Current monitoring tasks are primarily performed by fixed-wing and multi-rotor UAVs, but these platforms face significant technical bottlenecks in flight endurance and monitoring precision. This research aims to address these deficiencies. The platform is based on a small-scale unmanned airship featuring a semi-rigid, hybrid lift-body structure. Improvements were made upon the traditional ellipsoidal hull; the hull profile was optimized using a geometric superposition method, introducing an aerodynamic camber line with a maximum camber (m) of 4% to enhance aerodynamic performance at small angles of attack. In terms of its energy system, the platform is powered by a purely electric energy system composed of solar panels and batteries; solar energy is used during the day, while surplus energy is stored in the batteries for night operations, thereby effectively extending flight endurance. For intelligent monitoring, the platform integrates an intelligent recognition and positioning system based on machine vision, which is deployed on a Jetson Nano and utilizes a YOLO11 instance segmentation model. The team has experimentally proven that the platform can effectively achieve intelligent monitoring in low and medium altitude airspace. Furthermore, the structural integrity of the gondola and the aerodynamic advantages of the modified hull have also been verified via simulation analysis. This work provides a new design concept for intelligent monitoring equipment. The platform can also be applied to scenarios such as forest fire prevention, precision agriculture, and long-term ecological monitoring, offering a new solution for the design of unmanned intelligent monitoring equipment.
Song, Ziang, Gao, Wenxuan, Cao, Xiaochuan, Zheng, Xing, Zhao, Chong
Diffusion Framework with Cross-Modality Fusion Perception for Autonomous Driving in Urban Traffic2026-99-07495/15/2026
End-to-end autonomous driving in urban environments faces three core challenges. First, camera and LiDAR sensor heterogeneity causes cross-modal perception inconsistencies and sensor fusion instability. Second, diffusion models suffer from training instability due to scale variance and distribution changes, which limits generalization. Third, traditional trajectory decoders lack structured interaction with semantic elements, thereby undermining planning rationality. To address these issues, CMFPNet introduces an integrated framework with three key modules. The HGCF-Backbone integrates LiDAR and camera features using channel focus, deformable cross-focus, and state space modeling to enhance semantic alignment. The NST module maps physical trajectories to normalized space, employing truncated diffusion sampling for stable generation in just 2–4 steps. The NDA models trajectory generation as a semantic narrative, utilizing a six-stage semantic attention flow incorporating BEV context, interactive dynamics, and self-states. Experiments on the NAVSIM dataset demonstrate CMFP Net’s superiority over existing baselines, showing outstanding generalization and trajectory stability in challenging scenarios. Notably, the truncated sampling strategy achieves an 8–10× acceleration during inference while maintaining decision accuracy and reducing computational costs. CMFPNet provides a scalable, semantically consistent solution for diffusion-based autonomous driving with significant potential in both research and practical deployment.
Qu, Yanwei, Mo, Hangjie
Design and Implementation of an Intelligent Mobile Waste Classification and Collection Robot System2026-99-07025/15/2026
To address the growing demand for waste management, improve the efficiency and accuracy of waste classification, reduce costs, promote environmental protection and circular economy development, and solve environmental pollution and resource waste problems through technological innovation. This paper proposes an intelligent mobile waste classification and collection robot system. The system consists of a picking mechanical arm subsystem, a waste classification and collection subsystem, a self-moving chassis subsystem, and a solar tracking power generation subsystem. The picking mechanical arm subsystem actively collects waste through a mechanical arm combined with machine vision technology and deposits it into the waste classification device, while the waste classification and collection subsystem completes functions such as classification, compression, collection, and dumping, utilizing a navigation and positioning-driven chassis to achieve autonomous waste collection, simultaneously employing an AI (Artificial Intelligence) interactive voice broadcast device for waste classification promotion. The operation and control of each subsystem are fed back to the client through remote network connection devices, achieving “unified network management.”
Xia, Ying, Zhu, Huabing, Jia, Ruitong, He, Yifan, Hou, Wentao, Fu, Shaozao, Lin, Jiaoyang
Design and Research of an Elderly Mobility Assist System Based on Intelligent Power-Assistance Device2026-99-07375/15/2026
The aging of the population has been a key issue worldwide, with mobility and fall of the elderly an important problem to be solved. In this paper, we propose an elderly mobility assist system based on the intelligent power-assisted device consisting of an assistive cane and an intelligent companion. It has the functions of standing support after falling, daily support and on-site rest. The assistive cane adopts a two-stage expansion mechanism of crank and slider structure, which forms a stable triangular support after unfolding, so that the patient can stand safely. The intelligent companion platform is driven by drive wheels, equipped with pushrod motors and vacuum suction devices, it can automatically approach the user and form an stable support column when the cane is in the out-of reach range; the control system is designed by combining microcontroller, camera object recognition, wristband remote control, to realize automatic steering and autonomous navigation at differential speed. The overall design satisfies the requirements of safety and strength through mechanical verification and stress analysis. The proposed system can help the elderly people to recover from falls better and enhance their independence and safety in their daily walks.
Yu, Chenxi, Wang, Longyi, Zhu, Huayun, Dong, Yan, Mi, Ruixue, Zhu, Lihong
Gear Coatings for Loss of Lubrication: System Level Transmission Experiment within VIPERF-0082-2026-00405/5/2026
The Army requires rotorcraft drive systems to operate for 30 minutes following a loss of lubrication event to make an emergency landing. Coatings research has shown great promise for loss of lubrication, but coating repeatability and quality control is a primary hurdle. The Army partnered with Acree Technologies via a Small Business Innovation Research (SBIR) effort to develop an optimized gear coating for loss of lubrication. The research culminated in a system level transmission experiment that maintained flight relevant torque and speed through a helicopter gearbox without oil for three hours. The authors decided to shutdown the experiment for inspection after three hours of operation without oil because the temperature and vibration signals maintained steady state conditions without signs of failure. Teardown analysis showed the transmission gear surfaces did not scuff, scanning electron microscope analysis showed coating remained on the gear teeth, and cross-sectional SEM analysis showed a measurable coating thickness remaining on the gear teeth after three-hours of operation without oil.
Riggs, Mark, Pomplon, William, Fetty, Jason, Milligan, Ryan, Woods, Ron, Wong, Kelvin, Matzke, Caleb, Jacques, Kelly, Hood, Adrian
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