Search Results (38,589)

Tool wear detection is essential for machining quality control and predictive maintenance, but conventional inspection is often manual, time-consuming, and operator-dependent. Existing learning-based visual methods still face challenges in jointly achieving reliable wear-type recognition, accurate wear-region localization, and quantitative wear-width measurement under shop-floor imaging conditions. To address these issues, this study proposes MaskLenNet, a query-based instance segmentation and length prediction network for solid carbide end-milling tool diagnosis. MaskLenNet combines a Swin Transformer backbone, query-based instance-mask prediction, wear-oriented attention, and a key-point head that directly estimates the maximum wear-land width (VB). Evaluation uses 234 images from 54 physical tools under a tool-disjoint split, so different rotations of one tool cannot occur in both training and evaluation sets. On the held-out test set, MaskLenNet achieves 96.52% matched-instance classification accuracy, 95.75% foreground instance mIoU, and a VB mean absolute error of 0.010214 mm. Relative to BEiT-Base, the gains are 3.04 and 3.60 percentage points in accuracy and mIoU, respectively. These results demonstrate promising performance within the evaluated acquisition system; they do not establish equivalence to microscopy or generalization to other machines, optics, workpiece materials, or sites. Full article

Corrosion remains one of the principal causes of degradation in metallic structures across a wide range of industrial sectors. Although visual inspection is routinely employed for preliminary corrosion assessment, its effectiveness depends heavily on operator experience and subjective interpretation. This work introduces CorrQuant, a web-based computer vision platform designed to transform qualitative corrosion images into quantitative measurements of corrosion extent and morphology. The proposed methodology processes images acquired with conventional mobile devices and integrates geometric calibration using a reference coin, perspective correction, adaptive image enhancement through Contrast Limited Adaptive Histogram Equalization (CLAHE), multi-descriptor feature extraction, and consensus-based corrosion segmentation. The detected corrosion regions are subsequently quantified to determine corrosion area, surface coverage, spatial distribution, morphological descriptors, and corrosion intensity maps. The methodology was verified using an aluminum specimen with a known corrosion area of 143 mm² under both controlled illumination and optical stress-test conditions. Under standard acquisition conditions, corrosion-area estimation accuracies exceeding 90% were achieved. Additional evaluations under red illumination, fisheye, blur, and kaleidoscope distortions demonstrated that the proposed framework is considerably more sensitive to degradation of local image information than to variations in illumination spectrum. These results demonstrate the robustness of the proposed multi-descriptor voting strategy while defining the operational limits of the platform under challenging image acquisition conditions. Full article

To address the demand for efficient and automated removal of surface crack defects in special steel bars, this paper proposes a grinding path planning method based on hybrid ACO–SA optimization. A geometric model of surface cracks is established, and the key factors influencing grinding efficiency are analyzed. Crack defects are represented as line segments with defined start and end coordinates, which effectively preserves defect continuity and overcomes the limitations of scattered point-based representations. To enhance optimization performance and avoid premature convergence, a task-oriented grinding path planning framework based on a hybrid ant colony optimization (ACO) and Simulated Annealing (SA) strategy is developed, combining the global search capability of ACO with the local optimization strength of SA. Experimental results for 10 m long special steel bars containing 100 randomly distributed cracks demonstrate that the proposed method achieves an average planning time of 9.4 s and a maximum path fluctuation of 324 mm, ensuring both computational efficiency and path stability. Validation using real surface defect data further confirms the effectiveness and practicality of the proposed approach for industrial grinding applications. The proposed method provides an effective solution for automated grinding of surface cracks in special steel bars and offers practical potential for automated defect repair in the steel manufacturing industry. Full article

The growing complexity of urban mobility requires datasets that integrate dynamic traffic observations with meteorological, geometric, and urban-context information. This study presents MUTra-CDMX, a multisource urban traffic dataset covering a 14.72 km section of the Insurgentes Sur corridor in Mexico City. Traffic data were obtained from TomTom at five-minute intervals for 20 consecutive road segments from 1 November 2024 to 28 February 2025. Hourly meteorological data were retrieved from Meteosource, while segment-level geometry, topology, signalized locations, and nearby points of interest were derived from TomTom metadata and OpenStreetMap. The primary analytical file contains 691,200 segment–timestamp records and 12 variables describing traffic and free-flow conditions, meteorological information, derived operational indicators, and reconstruction status. Of these records, 682,264 are original observations and 8936 are reconstructed segment–timestamp combinations, identified by the Boolean variable is_imputed. Technical validation confirmed complete temporal coverage, preservation of original traffic observations, consistent weather alignment, and reconstruction performance through artificial masking. Predictive utility was evaluated through chronological travel-time forecasting under a leakage-controlled protocol. At the 30 min horizon, XGBoost achieved a mean absolute error of 12.84 s, a root mean squared error of 37.91 s, and a coefficient of determination ($R^2$) of 0.771, outperforming a persistence baseline. MUTra-CDMX supports congestion analysis, imputation studies, spatiotemporal modeling, and travel-time forecasting. Full article

Mammalian orthoreoviruses (MRVs) are segmented, double-stranded RNA viruses that infect a broad range of mammalian hosts, including humans. Although MRVs have been detected in wastewater in parts of Southeast Asia, they have not previously been reported in U.S. wastewater. Using the VirCapSeq-VERT pan-viral sequencing assay, we identified MRV type 2 (MRV-2) in a wastewater sample collected in March 2024 from the United States Air Force Academy. Complete genome sequences were recovered for all 10 segments. Phylogenetic analyses showed that the virus clustered most closely with an MRV isolate recovered from a big brown bat (Eptesicus fuscus) in Pennsylvania, with additional genomic similarity to a second bat-derived isolate from Nebraska. The MRV sequence signal declined rapidly in subsequent wastewater samples and was nearly undetectable two weeks later. These findings represent the first reported detection of MRV in U.S. wastewater and demonstrate the utility of pan-viral wastewater surveillance for identifying uncommon viruses with potential public health relevance. Continued genomic and epidemiologic surveillance will improve understanding of MRV circulation and zoonotic transmission in North America. Full article

Arduino-based creepmeters utilizing a Linear Variable Differential Transformer (LVDT) and ultrasonic sensors were fabricated to monitor displacement changes along the creeping segment of the West Valley Fault (WVF) in southeastern Metro Manila, Philippines. Along with a custom-assembled, Arduino-based rain gauge, these instruments were initially intended to prevent data gaps during the COVID-19 pandemic when commercial data recorders experienced operational downtime. However, they have since proven to be cost-effective alternatives for determining short-term slip rates and monitoring displacement variations driven by episodic and seasonal precipitation changes. The LVDT creepmeter provides higher accuracy for displacement and slip rate determination. Conversely, the ultrasonic creepmeter is better suited for tracking abrupt displacement changes and, to some extent, longer-term displacement trends as it is more sensitive to environmental conditions. Deploying low-cost monitoring instruments in active fault regions bridges critical data gaps and improves the understanding of creep triggers and mechanisms. Although vertical creep occurs along pre-existing tectonic features of the WVF creeping segment, our creepmeter monitoring reveals sustained, accelerated creep within its southern portion. This localized movement is driven primarily by nontectonic forces—chiefly groundwater extraction, with episodic and seasonal precipitation influences. Consequently, this implies a continued ground rupture hazard and the potential for induced seismicity. Full article

During the fabrication of prestressed concrete beams, the quality and positional accuracy of the laid corrugated ducts (or prestressing ducts) directly influence the load-bearing capacity and durability of the beams. However, traditional manual inspection is inefficient, highly subjective, and difficult to achieve full coverage. To address this problem, this paper proposes an automated detection method that integrates improved YOLOv11-based pose estimation, robust curve fitting, and image stitching techniques. The method automatically identifies duct positions and evaluates laying quality. By incorporating the SE channel attention mechanism and the SPPFCSPC multi-scale pooling module, the SC-YOLOv11 model is developed, which significantly enhances the detection accuracy of slender corrugated pipe key points in environments with dense rebar occlusion. The RANSAC algorithm is employed to fit curves to the predicted key points, effectively suppressing the influence of outliers. Furthermore, the SIFT algorithm is used for precise stitching of drone-captured segmented images, which are then transformed into a unified front orthographic coordinate system of the entire box girder via perspective transformation, enabling accurate reconstruction of the corrected 2D layout of corrugated ducts across the full beam. Ablation experiments using 5-fold cross-validation demonstrate that SC-YOLOv11 improves mAP₅₀ and mAP_50–95 by 2.6% and 1.2%, respectively, with statistical significance (paired t-test, p < 0.01). The model achieves a per-image inference time of 6.37 ms, with 4.34 M parameters and 8.1 GFLOPs, meeting real-time requirements. In a 30 m prefabricated box girder field application, the measured section trajectory fitting curves of the corrugated ducts were compared with the design alignment, successfully identifying two abnormal locations where the laying deviation exceeded the allowable threshold. Cross-validation with on-site inspector records shows that over 92% of the measurement points agree within ±10 mm. This method achieves a fully automated analysis chain from key point detection and curve fitting to deviation quantification, providing an efficient, non-contact, and traceable intelligent tool for quality control of bridge prestressed systems. Full article

Low-emission zones (LEZs) require cities to define policy rules, predict effects before deployment, and verify outcomes afterwards, yet mobility, emissions, meteorology, exposure, data governance, and public communication are commonly handled in separate systems. This paper presents envair360, a Physical Intelligence architecture and a four-stage, evidence-gated LEZ methodology connecting project definition, baseline feasibility, digital-twin design, deployment, and verified impact closure. A design-science method is combined with an operational case study of Cartagena, Spain, because the research object is both a socio-technical artefact and a context-dependent municipal deployment. The technology chain is selected to bridge complementary scales and functions: SUMO for link- and vehicle-level traffic, WRF and CHIMERE for meteorology and regional chemistry, MUNICH and street-canyon parameterisation for computationally tractable street resolution, model-output calibration anchored to measurements, and FIWARE/NGSI-LD for governed context exchange. The manuscript distinguishes city observations, peer-reviewed component validation, demonstrated platform capabilities, and policy or engineering targets. A Murcia component study reports lower hourly than daily agreement after deep-learning calibration (NO${}_2$: $r=0.79$ hourly and $0.94$ daily; O${}_3$: $r=0.85$ hourly and $0.97$ daily), illustrating the importance of temporal aggregation and transfer limits. Digitisation of the prior Madrid ozone-density figure indicates modal shifts of approximately +32.0 and +27.8 source-axis units at two stations; the supplied source does not permit a numerical NO${}_x$ bias estimate. A separate six-city export audit covers 24,384 records and 4064 street segments and demonstrates a common model-output schema, not predictive validation. In Cartagena, project documentation reports elevated PM${}_{10}$/PM${}_{2.5}$, urban heat and solar-radiation stress, and a plausible role for dry-climate dust resuspension, supporting a superblock-oriented LEZ proposal with a long-term 30% vehicular CO${}_2$ reduction target. The paper’s specific contribution is the governed orchestration, evidence taxonomy, quality gates, reproducible lineage, explicit policy-scenario representation, and portable city-onboarding protocol; it does not claim that the individual scientific models, the Cartagena deployment, or the cited project targets originated in this manuscript. Full article

Image-based analysis offers a scalable way to examine indoor environmental design, but semantic segmentation studies often stop at pixel-level recognition and provide limited design-oriented quantification. This study proposes the Interior Design Element Segmentation and Spatial Quantification Framework (IDESQ Framework) to convert indoor scene images into measurable spatial design indicators. Using ADEChallengeData2016, an indoor subset containing eight scene categories was constructed, and the original ADE semantic labels were re-mapped into twelve interior environmental design element categories. U-Net, DeepLabv3+, PSPNet, and SegFormer-B0 were evaluated under the same annotation system. DeepLabv3+ achieved the highest performance, with a mean Intersection over Union (mIoU) of 0.494, Pixel Accuracy of 0.755, and Mean Accuracy of 0.661 on the internal test split; on the independent ADE validation subset, its mIoU was 0.495. The predicted masks were then used to calculate area proportions, furniture density, functional facility ratio, soft decoration ratio, decorative object ratio, greenery ratio, visual complexity, and spatial distribution features. The quantified results showed scene-dependent patterns, including a high spatial envelope ratio and low visual complexity in corridors, higher functional facility ratios in kitchens and bathrooms, and richer decorative composition in living rooms. Ground-truth–prediction (GT–Pred) consistency analysis showed that scene-level aggregation improved agreement between prediction-derived and GT-derived indicators, with a Pearson correlation of 0.960 for the eight main indicators. These results indicate that IDESQ can support automated and interpretable comparison of indoor design element composition and spatial patterns across scene types. Full article

Martian landslide segmentation is challenging because annotated samples are limited and landslide deposits can have weak boundaries, heterogeneous textures, and visual similarity to crater rims and canyon walls. This study evaluates a terrain-residual and boundary-assisted network (TRB-Net) on the locally available MMLSv2 train/validation/test split. TRB-Net combines RGB texture with digital elevation model (DEM), slope, thermal inertia, and grayscale information through terrain-residual fusion, an atrous spatial pyramid pooling decoder, and auxiliary boundary supervision. The compact evaluation checkpoint, using a validation-selected threshold of 0.55, achieves an mIoU of 0.8060, foreground IoU of 0.7502, F1-score of 0.8573, precision of 0.8473, and recall of 0.8676 with 5.255 million parameters. In same-split comparisons, DeepLabV3+ obtains the highest overlap scores, while TRB-Net provides competitive segmentation and an explicit architecture for tracing how terrain and boundary cues enter the prediction. These results apply only to the local MMLSv2 split; geographically isolated and large-area Martian mapping performance were not evaluated. Full article

Microprocessor-controlled knee prostheses (MPKs) face limited accessibility in resource-constrained environments due to high implementation costs and excessive power consumption associated with complex actuators. This study examines the technical feasibility of a low-cost electronically controlled pneumatic knee (ECPK) that combines structural mechanics with minimal electronic control. A functional decoupling strategy was implemented: stance-phase stability is provided by passive kinematic locking of a four-bar linkage over the near-extended stance range, while a lightweight feedforward controller driven by a single joint-axis Hall sensor segments the gait cycle continuously, updates its speed estimate once per step, and adjusts the valve only for swing-phase damping. From the stance duration of the preceding steps, this controller presets the pneumatic valve orifice to compensate for mechanical response delays, so that link rotation speed is regulated semi-actively without powered actuation. System integration and control viability were evaluated in a single-case feasibility study (N = 1), in which the ECPK was compared within subject with a commercial mechanical prosthesis after a 4-week adaptation period. Despite a 400 g distal mass penalty, the semi-active control algorithm was associated with a smaller increase in step-length asymmetry at the highest speed tested. Furthermore, net oxygen cost was lower with the ECPK during high-speed walking. Because the conditions were compared at unmatched self-selected speeds and the ECPK condition reached a respiratory exchange ratio (RER) of 1.13, this observation is hypothesis-generating. Coupling passive four-bar stance stability with minimal electronic swing regulation is therefore a viable engineering basis for accessible prostheses, and the present study establishes its technical feasibility rather than its clinical effectiveness. Full article

Accurate pavement crack segmentation is essential for structural health monitoring, yet existing methods often face a trade-off between segmentation accuracy and computational efficiency. To address this issue, a novel teacher–student framework, termed RTCS-T and RTCS-S, is proposed. The teacher network RTCS-T is constructed based on the Swin Transformer to capture long-range dependencies and multi-scale contextual information. To further enhance crack representation, a strip refinement module is introduced to model directional structural features, while a cascaded atrous spatial pyramid pooling module is employed to improve multi-scale feature aggregation. Based on the teacher network, a lightweight student model RTCS-S is developed by using depthwise separable convolutions to achieve efficient inference. In addition, a foreground-aware and boundary-aware knowledge distillation strategy is introduced to guide the transfer of structural and contextual information from the teacher to the student. Experiments on the Crack500, DeepCrack, and CFD datasets demonstrated competitive performance against representative segmentation models. On CFD, RTCS-S achieved an F1 Score of 0.7514 and an mIoU of 0.7962. Notably, RTCS-S required only 1.82 M parameters and 1.13 GFLOPs and achieved a model inference speed of 680 FPS on an RTX 4090 GPU. When deployed on an RDK X5 edge-computing platform, the complete pipeline achieved an end-to-end throughput of 34 FPS, with an average latency of approximately 29.4 ms and peak memory consumption of 1.8 GB. These results demonstrate that the proposed framework provides an efficient solution for automated pavement crack detection and shows strong potential for practical road inspection applications. Full article

This paper proposes an intelligent analysis method of rock sheet based on the segment anything model (SAM) with zero samples, which combines small sample training with deep learning to realize high-precision automatic identification and segmentation of rock minerals, and then converts the segmentation results into vectorized data by using image processing technology to construct the numerical model of rock minerals, and ultimately realizes rock sheet from image identification to physical and mechanical research. The results show that the SAM-based zero-sample segmentation method can accurately and efficiently identify different mineral components in multi-component complex rock flakes. Numerical simulation results show that the numerical model of rock minerals generated by the method can effectively reflect the microstructural characteristics of rocks and accurately predict their mechanical behaviors, and the resulting elastic modulus matches well with the existing literature data, with a relative error of only 3.4%, suggesting that the proposed method provides reasonable predictive capability for rock mechanical behavior. Compared with the traditional measurement methods, this method realizes the automation and intelligence of rock thin-section analysis and enhances the adaptability to different rock samples, providing an efficient tool means for geological exploration, petroleum engineering, and geotechnical research. Full article

Multi-threshold image segmentation is a common technique in computer vision and image analysis. However, segmentation quality suffers greatly as the number of thresholds increases, particularly for color image segmentation tasks. To address this challenge, this paper proposes a modified reptile search algorithm (MRSA) based on Otsu and Kapur objective functions. Firstly, an RSA algorithm is developed by combining an adaptive weight factor and elite-guided learning to improve segmentation performance. Secondly, an RGB channel symmetric cooperation mechanism is introduced to exchange information among color channels. Thirdly, a repair mechanism is designed to maintain the structural symmetry of solutions throughout the optimization process. We conduct extensive experiments on the BSD500 benchmark color images under different threshold levels and compare MRSA with an improved bald eagle search algorithm (IBES), enhanced Giza pyramids construction algorithm (GGPC), multi-mechanism artificial lemming algorithm (MALA), and RSA. The experimental results demonstrate that the proposed MRSA algorithm achieves superior segmentation performance in terms of objective function values, region covering, peak signal-to-noise ratio, structural similarity index measure, and feature similarity index, and it exhibits excellent results even at high threshold levels. Full article

Automatic Dependent Surveillance–Broadcast (ADS-B) is a key source of air traffic surveillance data, but its open broadcast mechanism and possible trajectory-data inconsistencies may reduce surveillance reliability. This study proposes a Short-window Temporal Latent-Consistency Network (STLC-Net) for window-level anomaly detection in real-world ADS-B surveillance data. Raw ADS-B messages are transformed into physically interpretable kinematic features and segmented into short sliding windows to capture local motion inconsistencies. STLC-Net combines LSTM-based temporal encoding with an encoder–decoder–encoder latent-consistency mechanism, and fuses reconstruction and latent-consistency discrepancies to produce anomaly scores. Experimental results show that STLC-Net achieves a PR-AUC of 0.952540, precision of 1.0, recall of 0.882682, and F1-score of 0.937685 under highly imbalanced conditions. Ablation studies further confirm the effectiveness of short-window representation, temporal modeling, and latent-consistency learning. These results indicate that STLC-Net provides an effective and physically interpretable framework for detecting rare kinematic anomalies in ADS-B trajectory surveillance data. Full article