Search Results (595)

Autonomous headland operations require reliable boundary perception and timely behavioral decisions in unstructured field environments. This study proposes a perception-and-decision framework for autonomous agricultural machinery. A CA-SP-DeepLabV3+ semantic segmentation model is developed by integrating Coordinate Attention, Strip Pooling, and a combined Boundary–Dice loss to enhance the continuity and localization of weak and elongated headland boundaries. To facilitate deployment on resource-constrained computing platforms, structured pruning and knowledge distillation are further applied to compress the segmentation network. A stereo-vision-based three-dimensional boundary reconstruction method is then used to estimate the vehicle-relative distance to the headland boundary. Based on the estimated distance, tractor–implement parameters, operating speed, and selected turn type, the decision module calculates the required headland width and distance thresholds for deceleration and turn preparation. Experimental results show that the compressed segmentation model achieved IoU values of 92.55% and 96.53% for the headland and field classes, respectively, while reducing the number of parameters and FLOPs by 28.48% and 48.43% compared with the uncompressed model. Across four representative headland environments and reference distances ranging from 10 to 30 m, the relative ranging errors ranged from 1.30% to 7.03%, and the total processing latency remained below 45.5 ms under the evaluated conditions. The decision experiments further showed that the proposed method generated headland-width plans and deceleration thresholds consistent with the geometric and kinematic constraints of different tractor–implement combinations and operating conditions. These results support the feasibility of the proposed framework for boundary perception, distance estimation, and behavioral decision support under the evaluated conditions. Full article

This paper presents a stage-complete programmable-logic architecture for pairwise monocular structure from motion using calibrated, pre-undistorted 1920 × 1080 video. It integrates streaming feature extraction, Block-RAM-backed Top-K selection, spatial-bucket matching, two-pass essential-matrix estimation and pruning, fixed-point pose recovery, and triangulation with point-coordinate output. Bounded feature storage, local correspondence search, and mixed floating- and fixed-point arithmetic support the complete pairwise chain without processor-side geometry computation. The complete VCU118/XCVU9P design was synthesized, placed, routed, and compiled to a bitstream in Vivado 2026.1, meeting setup and hold timing with $+0.031$ ns and $+0.010$ ns slack, respectively. It uses 12.09% of the device’s logic lookup tables and 32.94% of its Block RAM tiles. Controlled numerical tests characterize the operating domain of the geometry stages. The cycle-based model estimates a 13.13 ms geometry-back-end subtotal at 1000 matches; 60 fps is the architectural input target. To the authors’ knowledge, within the directly comparable literature surveyed, this is the first reported stage-complete mapping of the listed pairwise chain entirely to programmable logic. Full article

Rapid advances in genomics and artificial intelligence require breeding courses to integrate quantitative-genetic theory with genome-scale analysis and breeding decisions. We developed a Plant Intelligent Breeding Teaching Platform using Python (version 3.12.7) and PyQt5 (version 5.15.11), integrating quality control, population-structure analysis, GWAS, genomic prediction, and cross-design simulation. The demonstration used 898 individuals of black poplar (Populus nigra L.), with nine-year diameter at breast height as the phenotype and 50,537 SNPs retained after quality control and LD pruning from approximately 30× whole-genome resequencing. The platform supports adjustable QC parameters, PCA, genomic relationship analysis, GEMMA-based linear mixed-model GWAS, and six genomic-prediction models evaluated by five-fold cross-validation. Pearson correlations ranged from 0.489 to 0.574, with RF performing best. Known female/male information and additive GBLUP were used for cross prediction. In 64 Biological Sciences students, self-reported mastery increased by 26.6–71.9 percentage points across five modules (exact McNemar tests, p < 0.001). The platform provides an integrated environment for teaching the workflow from genomic data to breeding decisions. Full article

Deep learning models have achieved remarkable progress in computer vision tasks, but constructing efficient models still requires substantial expert experience and computational resources. Pre-trained model reuse provides a practical way to reduce model generation cost; however, existing methods still face challenges in feature alignment, structural integration, and resource-constrained search when stitching heterogeneous architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). To address these issues, this paper proposes Multi-Pretrained Model Stitching for Automatic Generation (MPMS-AG), an automatic generation framework for deep learning models based on heterogeneous pre-trained model stitching. MPMS-AG decomposes heterogeneous pre-trained models into reusable neural blocks and formulates model stitching as a sequential decision-making problem. Specifically, it uses hierarchical feature extraction and Radial Basis Function Centered Kernel Alignment (RBF-CKA) to quantify functional similarity between heterogeneous blocks, introduces adaptive block partitioning and hybrid clustering to reduce the search space, and adopts a Generalized Advantage Estimation (GAE)-based Actor–Critic strategy with a Single-Shot Network Pruning (SNIP)-based zero-shot proxy reward to search for stitching paths under parameter and floating point operations (FLOPs). Under the current CIFAR-10 experimental protocol, MPMS-AG generates trainable hybrid architectures with competitive classification performance across homogeneous stitching, heterogeneous cross-architecture stitching, and lightweight model stitching scenarios. The ablation results provide descriptive evidence that hybrid clustering and reinforcement learning-based search contribute to the observed performance. These findings support the feasibility of MPMS-AG for automatic generation of customized deep learning models from heterogeneous pre-trained model libraries within the evaluated setting. Full article

In power transmission systems, insulator strings typically exhibit a distinct periodic geometric symmetry. However, defects such as breakages and flashovers disrupt this symmetry, producing subtle, orientation-sensitive asymmetric patterns that traditional isotropic convolution kernels often struggle to capture. To tackle the challenge of identifying these symmetry-breaking defects, we propose the Direction-aware You Only Look Once version 11 (DA-YOLOv11), a lightweight detection framework designed for asymmetric, direction-aware feature learning. By integrating the PaddlePaddle Lightweight Convolutional Network (PP-LCNet) into the backbone, the model preserves essential structural features while minimizing computational redundancy. We also introduce the Insulator-oriented Simple Parameter-Free Attention Module (INS-SimAM), a direction-aware attention mechanism that employs an asymmetric spatial statistical strategy to adaptively enhance defect-related signals within non-uniform regions. Additionally, the Adaptive Numerically Stable Normalized Wasserstein Distance (ANS-NWD) loss models tiny defects as 2D Gaussian distributions, leveraging their mathematical symmetry to ensure stable regression. Evaluations on a custom dataset and three public benchmarks (IDID, CPLID, UPID) show that the final pruned DA-YOLOv11 achieves an 88.95% mean Average Precision (mAP50) on the core dataset, outperforming the baseline while reducing the parameter count by 34.36%. This work provides a robust solution for detecting geometric symmetry-breaking in complex power inspection scenarios. Full article

Financial markets exhibit complex nonlinear dynamics driven by interactions between firm-specific characteristics and macroeconomic conditions, requiring robust mathematical models capable of capturing structural changes and temporal heterogeneity. This study proposes an explainable machine learning framework that combines regime-switching analysis and predictive modeling to investigate the long-term resilience of firms listed in the BIST 100 Index. Structural breaks in monthly return and volatility series are first detected using the Pruned Exact Linear Time (PELT) algorithm, enabling the classification of firm trajectories into resilient and fragile market regimes. A Regime Persistence Score is then introduced to quantify the proportion of time each firm remains in the resilient state. This score serves as the response variable in a Random Forest model that evaluates the influence of firm-specific financial indicators and macroeconomic variables, including exchange rates, producer price inflation, and commercial loan interest rates. In addition, a forward-looking classification model estimates the probability that firms will transition into a fragile regime within the subsequent six months. The proposed framework achieves an AUC of 0.706 and demonstrates stable predictive performance under alternative regime definitions, penalty parameters, cost functions, and cross-validation strategies. Explainability analysis derived from Shapley Additive Explanations (SHAP) data shows book-to-market ratio and sensitivities to inflation and interest rate changes as the most important components of enduring resilience. The proposed methodology provides an interpretable mathematical framework for regime detection, nonlinear time-series modeling, and decision support in sustainable financial systems, offering practical value for risk assessment and resilience-oriented portfolio management. Full article

Two-stage oriented detectors achieve high localization accuracy in synthetic aperture radar (SAR) ship detection, but their large backbones, feature pyramids, proposal modules, and heavy region of interest (RoI) heads hinder deployment. Existing lightweight SAR ship detectors typically use one-stage frameworks that lack proposal-level refinement for precise rotated localization. This paper presents a dual-teacher knowledge distillation and pruning (DTKDP) framework for lightweight oriented SAR ship detection. DTKDP introduces learnable gates into convolutional, normalization, and linear layers to prune convolutional channels and RoI-head neurons. Rotated proposal alignment (RPA) distills teacher and student predictions in a shared teacher-generated rotated proposal space, while a dual-teacher scheme combines classification and regression guidance from a homogeneous main teacher with complementary classification cues from a heterogeneous auxiliary teacher. Experiments on the SAR Ship Detection Dataset (SSDD) and Rotated Ship Detection Dataset in SAR Images (RSDD-SAR) show that DTKDP reduces the parameters of Oriented Region-based Convolutional Neural Network (Oriented R-CNN) and RoI Transformer equipped with ResNet-50 backbones by 87.5–91.8% and their floating-point operations (FLOPs) by 75.6–79.9%. In terms of average precision (AP) and mean average precision (mAP), the resulting Oriented R-CNN-slim and RoI Transformer-slim retain accuracy close to their full-scale counterparts. Relative changes across AP₅₀, AP₇₅, mAP_50:75, and mAP_50:95 range from a 2.38% decrease to a 0.65% improvement. Compared with RTMDet-tiny, they improve all four metrics on both datasets by 0.52–27.55% and consistently surpass representative distillation methods, demonstrating a favorable accuracy–efficiency trade-off. Full article

Tomato pruning robots are limited by the absence of accurate shear simulation models for tomato stems. Conventional homogeneous discrete element models are incapable of characterizing the multi-layer heterogeneous structure of tomato stems, which gives rise to significant simulation deviations. With Zheza No. 8 tomato stems as research objects, this study measured intrinsic and contact parameters through physical tests and constructed a three-layer bonded discrete element model to simulate the epidermis, xylem and pith with particles of different sizes. Bonding parameters were calibrated using a two-level factorial design, steepest ascent test and Box–Behnken design with shear force as the evaluation index. The optimal parameters reduced simulation errors by 75.8% and 43.7% relative to traditional and pre-optimized models, and accurately reproduced the double-peak shear behavior. The model accuracy improves with xylem maturation, providing a dependable simulation method for the optimization of tomato pruning robots. Full article

Existing architectural backdoors embed malicious logic directly into model structures to persist after clean training, but they typically rely on handcrafted trigger patterns vulnerable to preprocessing and lack the mechanism to dynamically update attack targets. Addressing these limitations, this paper proposes ArchLock, a dynamic-target architectural backdoor framework designed exclusively for stateful inference deployments that maintain persistent mutable state across requests. ArchLock comprises two core components: a correlation-based statistical trigger detector that utilizes local red-green channel Pearson correlations as the activation signal, thereby reducing dependence on absolute pixel values and exhibiting theoretical invariance to linear transformations; and an Adaptive Confidence Calibration module, which maintains a persistent memory buffer to enable post-deployment target switching through a two-phase protocol. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-100 demonstrate that ArchLock achieves a dynamic target success rate exceeding 90% while preserving clean accuracy. Furthermore, the method exhibits robustness against fine-tuning and pruning and evades three behavioral detectors under the stated stateful deployment assumptions. This work highlights the security risks of mutable architectural components in stateful inference services while explicitly discussing the limitations of the proposed approach under stateless deployment and aggressive preprocessing conditions. Full article

Accurate stereo visual–inertial measurement is essential for mobile robots operating in Global Navigation Satellite System (GNSS)-denied and structurally complex environments. In stereo visual–inertial odometry (VIO), pose and trajectory outputs depend strongly on the point measurements delivered by the visual front end before sensor-fusion update. In sparse-texture but structurally regular scenes, tracked point features may exhibit poor persistence, uneven spatial distribution, and local tracking noise, even when informative line structures are present. Existing point–line VIO methods can improve positioning accuracy by introducing line landmarks or line residuals, but they usually modify the estimator state, measurement model, and Jacobian treatment. We present a scene-adaptive line-aware visual measurement conditioning method for stereo VIO front ends with point-measurement updates. The method uses 2-D image-line segments as lightweight structural priors and applies bounded normal-direction conditioning to reliable point measurements before a fixed-interface VIO back-end update. A sparse pruning safeguard removes only highly inconsistent long-lived tracks under strong structural support, while a scene-level confidence gate attenuates the intervention when line evidence is weak or unstable. The method is instantiated and evaluated in an S-MSCKF pipeline. On the reported EuRoC MAV sequences, it reduces the sequence-averaged absolute trajectory error (ATE) RMSE by approximately 13% relative to S-MSCKF, with 3–27% reductions on machine-hall sequences. On three real-world robot measurement sequences with an RTK-aided inertial reference, the mean Sim(2)-aligned planar position error decreases from 8.72 m to 7.31 m, and the mean yaw error decreases from $8.02°$ to $6.76°$; an additional scale-preserving SE(2) evaluation reveals sequence-dependent planar behavior and residual metric-scale sensitivity. Candidate-level stereo-consistency diagnostics show subpixel mean and 95th-percentile image-domain perturbations without systematic vertical-stereo bias, while the final reliability-weighted primary-view update is analytically bounded by approximately 0.221 pixels in the reported implementation. Runtime profiling reports an average front-end time of 33.34 ms on the tested CPU platform, close to the 33.3 ms frame period of the 30 Hz stereo input, although the $\mu +3\sigma$ runtime of 47.23 ms exceeds a strict frame-by-frame 30 Hz budget. These results suggest that line-aware front-end conditioning can improve visual measurement quality in structured stereo visual–inertial sensing without modifying the evaluated back-end interface. Full article

Occupant-centric building operation requires timely information on occupant states and local indoor conditions, but fixed sensors provide limited spatial coverage, and wearables depend on user participation. This study develops a Robot Operating System 2 (ROS 2)-based wheeled mobile sensing platform for buildings. The main contribution is the integration of autonomous mapping and navigation, target approach, multisensor occupant-data acquisition, lightweight edge-based human detection, and return-to-dock operation on a Raspberry Pi 5. During operation, the robot patrols indoor locations, detects and approaches occupants, collects human and environmental data, uploads the data to a server, and returns to the charging dock. To support concurrent perception and navigation, YOLOv5n was compressed using structured channel pruning and multi-scale feature distillation. The compressed model reduced parameters and computation by approximately 53% and 61%, increased inference throughput from 7.5 to 16.5 frames per second, and enabled the robot to complete all 60 controlled trials across five locations and three postures. This study does not quantify HVAC energy savings or carbon-emission reductions. Instead, it validates a mobile sensing and edge-perception layer for future server-side thermal comfort inference, demand-responsive HVAC control, and evaluation of building energy and carbon performance. Full article

Reliable identification and behavioural tracking of individual fish are increasingly required in aquaculture monitoring and ecological conservation, yet such tasks often rely on extremely limited image samples. Individual-level fish recognition remains challenging because subtle inter-individual variations in stripe patterns, spots and body markings are difficult to distinguish, while annotated datasets are scarce and costly to construct. To address these challenges, this study proposes Squeeze-and-Excitation Feature-Fusion Window Transformer (SEFFwin), a lightweight vision Transformer derived from Swin Transformer V2 for ultra-fine-grained fish recognition under extreme few-shot conditions. Through structured pruning, the SEFFwin backbone contains approximately 11.30 million parameters, excluding the task-specific classification layer. To support evaluation, we construct Koi-fish-3, a dedicated few-shot benchmark consisting of three individual koi categories, with only two training images and 100 validation images per class. Transfer learning, data augmentation and knowledge distillation are further incorporated to improve model optimisation under severely limited supervision. Experimental results show that SEFFwin achieves 92.0% top-1 accuracy on Koi-fish-3, outperforming representative lightweight baselines while maintaining high computational efficiency. Post-hoc interpretability analysis indicates that SEFFwin consistently attends to biologically meaningful stripe and spot patterns and can weakly localise class-specific regions. These findings demonstrate that SEFFwin provides an accurate and efficient solution for individual fish recognition under extreme data scarcity, offering practical value for intelligent aquaculture and ecological monitoring, while also providing insights into compact Transformer design for low-resource fine-grained recognition. Full article

Fruit-tree pruning has to keep tree structure under control while continually renewing fruiting wood, and many decisions still depend on practical experience. This narrative review synthesizes 92 English-language publications from 1983 to 2026, identified through Google Scholar and backward citation tracking. As orchard systems become more regular and mechanization expands, mechanical pruning has been used to control canopy edges. Vision and 3D sensing are now being used to identify branches and locate pruning points, while robotic systems are beginning to attempt selective pruning. This review looks at the literature at canopy and branch scales. Mechanical canopy pruning is effective for quickly treating regular canopy profiles, but field trials show that regrowth, follow-up manual pruning, and crop-load management can change later yield and economic outcomes. Branch-level automation has moved from 2D recognition to 3D reconstruction, pruning-point generation, and small-scale robotic trials. The harder questions are increasingly about branch function and continuous whole-tree operation. For pruning automation, machine speed or a successful cut is only part of the result; post-pruning tree responses and longer-term production also have to be considered. Multi-temporal tree records, branch-function information, continuous operation, and long-term field testing deserve more attention. Full article

Urban rail transit infrastructure inspection increasingly relies on Light Detection and Ranging (LiDAR) due to its capability for efficient and high-precision 3D data acquisition. However, robust rail tread segmentation in ballastless metro environments remains challenging due to boundary leakage, interference from geometrically similar structures, and the heavy dependence of existing methods on Red-Green-Blue (RGB) imagery, trajectory priors, or template matching. To address these limitations, this study proposes a lightweight rail tread extraction framework for ballastless track LiDAR point clouds based on multi-stage filtering and curvature-guided region growing. First, intensity thresholding and cloth simulation filtering are leveraged to prune tunnel walls, track beds, and other large-scale non-target structures, thereby reducing computational overhead. Subsequently, local normal vectors and curvature features are estimated via Principal Component Analysis (PCA). A curvature-ranked seed selection strategy and a dual-constrained region growing mechanism, integrating normal consistency and curvature thresholds, are then introduced to suppress excessive growth near rail boundaries and enhance regional homogeneity. Experimental results on field data collected from Shanghai Metro Line 10 demonstrate that the proposed method achieves a recall of 92.23%, a precision of 95.32%, and an F1-score of 93.7%, outperforming conventional Euclidean clustering and standard region growing algorithms. Compared with deep learning approaches, the proposed framework requires no large-scale annotated training data and is independent of RGB information or trajectory priors, making it better suited for lightweight engineering deployment in practical urban rail transit maintenance. Full article

Fuel design based on distillation fractions is crucial for advancing fuel development and optimizing combustion systems. However, the chemical diversity and broad boiling-point distribution of full-range fuels pose significant challenges for kinetic modeling. In this study, a volatility-aware, multi-component kinetic mechanism was developed for simulating the combustion of various distillation fractions from an FRF in diesel engines. The mechanism comprises 261 species and 860 reactions. Unlike conventional surrogate mechanisms designed primarily for a single fuel or narrow distillation range, the proposed framework simultaneously represents the major hydrocarbon classes, ignition quality, and boiling-point distribution of FRF. The surrogate palette includes n-pentane, n-heptane, n-decane, n-dodecane, n-hexadecane, heptamethylnonane, 1-methylnaphthalene, iso-octane, methylcyclohexane, decalin, toluene, tetralin, and 1,2,4-trimethylbenzene. These components were selected to reproduce the molecular structures, ignition characteristics, and distillation behavior of the target fuel fractions. The mechanism was further refined through targeted replacement of the toluene sub-mechanism using updated hydrogen-abstraction and benzyl-radical oxidation reactions, followed by a fuel-oriented five-stage reduction strategy involving reaction-pathway-based pruning, DRGEP reduction, isomer lumping, sensitivity/ROP refinement, and targeted rate optimization. The resulting mechanism provides reasonable predictions of ignition delay, laminar flame speed, and species profiles for pure components, surrogate fuels, and real gasoline, jet, and diesel fuels. Coupled with a three-dimensional CFD model, the reduced mechanism also reproduces the main combustion phasing, pressure-rise process, and peak in-cylinder pressure of a diesel engine at 500 and 800 r/min over the investigated intake-temperature range. Although discrepancies remain in the low-temperature/negative-temperature-coefficient regime and in the quantitative prediction of the peak apparent heat-release rate, the mechanism provides a unified and practical framework for linking FRF distillation characteristics with chemical reactivity and engine-level combustion behavior. It therefore offers a foundation for designing tailored fuels from distillation fractions for operation in extreme environments. Full article