Search Results (562)

Detecting anomalies in electrical equipment and improving maintenance efficiency are critical for ensuring operational safety, reliability, and sustainability. To address the structural limitations of conventional manual and visual inspection methods, this study developed an object-recognition-based automated damage diagnosis system for lightning rods and insulators (ADS-LI), which enabled non-contact and fully automated diagnosis of lightning rods and insulators. ADS-LI employs a dual-module architecture. The first module precisely detects lightning rods and insulators using the PointRend algorithm applied to drone-acquired aerial imagery. The second module is a formula-based diagnostic model that quantitatively determines structural anomalies using the geometric attributes of the detected objects. Specifically, anomalies in lightning rods are identified by analyzing variations in inclination derived from center-coordinate shifts ($\Delta x$), while insulator anomalies are evaluated based on the mask area conservation ratio ($r$). The performance of ADS-LI was validated using 90 independent test datasets, achieving a 0.89 F1-score and 99% overall accuracy. These results demonstrate that ADS-LI effectively automates labor-intensive diagnostic tasks that previously relied on skilled experts. Furthermore, by quantifying anomaly detection criteria, it ensures consistency and reproducibility for diagnostic outcomes. This study is also expected to contribute, in the long term, to the transition of elevated electrical installations toward a sustainable maintenance regime. Full article

Unmanned Aerial Vehicle (UAV)-based photogrammetric reconstruction is a key step in geometric digital twinning of bridges, but ensuring the quality of the reconstruction data through the planning of measurement configurations is not straightforward. This research investigates an approach for quantitatively evaluating the impact of different methodologies and configurations of UAV-based image collection on the quality of the collected images and 3D reconstruction data in the bridge inspection context. For an industry-grade UAV and a consumer-grade UAV, paths for image collection from different Ground Sampling Distance (GSD) and image overlap ratios are considered, followed by the 3D reconstruction with different algorithm configurations. Then, an approach for evaluating these data collection methodologies and configurations is discussed, focusing on trajectory accuracy, point-cloud reconstruction quality, and accuracy of geometric measurements relevant to inspection tasks. Through a case study on short-span road bridges, errors in different steps of the photogrammetric 3D reconstruction workflow are characterized. The results indicate that, for the global dimensional measurements, the consumer-grade UAV works comparably to the industry-grade UAV with different GSDs. In contrast, the local measurement accuracy changes significantly depending on the selected hardware and path-planning parameters. This research provides practical insights into controlling 3D reconstruction data quality in the context of bridge inspection and geometric digital twinning. Full article

Underground roadway support is a critical component for ensuring safety in mining operations. In recent years, with the rapid advancement of intelligent technologies, computer vision-based automatic rock bolt detection methods have emerged as a promising alternative to traditional manual inspection. However, the underground mining environment inherently suffers from severely insufficient lighting. Images captured on-site often exhibit problems such as low overall brightness, blurred local details, and severe color distortion. To address the problem, this study proposed a novel low-light image enhancement algorithm, PromptHDR. Based on Transformer architecture, the algorithm effectively suppresses color distortion caused by non-uniform illumination through a Lighting Extraction Module, while simultaneously introducing a Prompt block incorporating a Mamba mechanism to enhance the model’s contextual understanding of the roadway scene and its ability to preserve rock bolt details. Quantitative results demonstrate that the PromptHDR algorithm achieves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index scores of 24.19 dB and 0.839, respectively. Furthermore, the enhanced images exhibit more natural visual appearance, adequate brightness recovery, and well-preserved detailed information, establishing a reliable visual foundation for the accurate identification of rock bolts. Full article

We establish a general, device-oriented procedure to extract absolute pump-band metrics from room-temperature UV–Vis (ultraviolet–visible) absorbance—including the absorption coefficient α(λ), per-active-ion cross-section ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}\left(\lambda \right)$, the effective per-active-ion absorption cross section ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}\left(\lambda \right)$ and derivative-based line-shape descriptors. As a representative case study, the procedure is applied to nanocrystalline Er³⁺/Ho³⁺:Y₃Ga₅O₁₂ over the 350–700 nm spectral range. After baseline correction and line-shape inspection assisted by the numerical second derivative of the absorbance, we extract conservative peak positions and the full width at half maximum across the visible 4f–4f manifolds. At the technologically relevant pump wavelengths near 406 nm (Er-addressing) and 473 nm (Ho-addressing) bands, resulting absorption coefficients are α = 0.313 ± 0.047 cm⁻¹ and α = 0.472 ± 0.071 cm⁻¹, respectively. The corresponding per-active-ion ${\sigma }_{\mathrm{e}\mathrm{f}\mathrm{f}}$ of (3.62 ± 0.54) × 10⁻²² cm² and (5.46 ± 0.82) × 10⁻²² cm², referenced to the measured optical path length L = 0.22 ± 0.03 mm (approximately 15% propagated relative uncertainty; explicit 1/L rescaling). Cross sections are reported per total active-ion density (Er³⁺ + Ho³⁺). The spectra exhibit Stark-type substructure only partially resolved at room temperature; the second derivative highlights hidden components, and we report quantitative descriptors (component count, mean spacing, curvature-weighted prominence, and pump detuning) that link line-shape structure to absolute pump response. These device-grade metrics enable rate-equation modelling (pump thresholds, detuning tolerance), optical design choices (path length, single/multi-pass or cavity coupling), and host-to-host benchmarking at 295 K. The procedure is general and applies to any rare-earth-doped material given an absorbance spectrum and path length. Full article

Objectives: This in vitro study aimed to evaluate the effects of three framework orientation (FO) positions on an SLM building platform (Horizontal [H], Diagonal-45° [D45°], Diagonal-60° [D60°]) and two designs (with [B] or without [NB] stabilizing bars) on the fitting accuracy of digitally fabricated Co-Cr RPD frameworks. Materials and Methods: A custom RPD framework CAD was performed on a 3D-printed resin-model of an edentulous maxilla with three geometric tooth forms. A Co-Cr alloy was processed via SLM processing into 24 framework specimens, divided into three FO groups (n = 8: H, D45°, D60°) and two subgroups each (n = 4: B, NB). Qualitative/quantitative fit-evaluation was assessed using virtual framework-to-model seating and a custom digital protocol with GOM Inspect software (2018-Hotfix5, Rev.115656). Mean fitting distances were calculated from 220 equidistant points per specimen. Statistical comparisons were performed using ANOVA-on-ranks, Kruskal–Wallis multiple comparisons, and Bonferroni adjustment. Results: FO Sub-Group medians (Q1, Q3: 25% and 75% Quartiles) (mm) were: H/NB 0.150 (0.140, 0.164), H/B: 0.136 (0.121, 0.152), D45°/NB: 0.230 (0.219, 0.241), D45°/B: 0.144 (0.137, 0.154), D60°/NB:0.238 (0.232, 0.247), D60°/B: 0.171 (0.166,0.176). Pairwise comparisons indicated the following statistically significant (p < 0.05) FO Sub-Group differences: H/B-D45°/NB, H/B-D60°/NB, D45°/B-D45°/NB, D45°/B-D60°/NB, H/NB-D45°/NB, H/NB- D60°/NB. Conclusions: Horizontal orientation improved RPD fit accuracy regardless of bar presence. D45° accuracy is enhanced by stabilizing bars, while D60° accuracy is unaffected by bar addition. Full article

Coarse aggregate shape plays a critical role in determining surface performance and durability in pavement systems. Traditional manual shape inspection is laborious and subjective, especially for bulk aggregates in overlapped state. In this work, we propose an automated digital image-based evaluation method for stacked coarse aggregates, combining preprocessing (grayscale conversion, histogram equalization, Gaussian filtering), segmentation, and contour reconstruction via the Graham scan convex hull algorithm. Morphological parameters such as equivalent ellipse major/minor axes, area, and perimeter are then extracted to compute individual particle shape factors. To assess batch-level quality, shape factor standard deviations (σ) and mean shape factors were computed from 50 aggregate images. Comparison with manual measurement results shows mean relative errors below 15%. Our analysis reveals a strong correlation between σ and overall shape quality: lower σ indicates more uniform geometry, while higher σ suggests greater irregularity. Based on experimental data, we define three σ-based categories: excellent (σ ≤ 0.32), good (0.32 < σ ≤ 0.42), and poor (σ > 0.42). This σ-driven evaluation framework enables rapid, quantitative, and objective assessment of aggregate morphology in practical aggregate production and pavement quality control. Full article

Laser Speckle Imaging (LSI) is a non-invasive optical technique used to assess biological activity by detecting dynamic variations in speckle patterns. These patterns exhibit statistical symmetry in static regions, while biological activity induces symmetry breaking that can be captured through the Graphic Absolute Value of Differences (GAVD), producing the activity map $I_{GAVD}$. This work evaluates the effect of four contrast enhancement algorithms: Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Multiscale Morphological Contrast Enhancement (MMCE), and Multiscale Top-Hat Transform with an Open-Close Close-Open (OCCO) filter, applied to intermediate LSI images, with the final activity map used for quantitative evaluation. Each method represents a distinct enhancement paradigm: HE and CLAHE are histogram-based techniques for global and local contrast adjustment, whereas MMCE and OCCO-MTH are morphological approaches that emphasize structural preservation and local detail enhancement. The dataset consisted of images of Phaseolus vulgaris (SP) and Lactuca sativa (SL) seeds. Evaluation was conducted through expert visual inspection and quantitative analysis using contrast, entropy, spatial frequency (SF), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and contrast improvement ratio (CIR). All metrics were computed on $I_{GAVD}$ activity maps, which reflect bioactivity through the disruption of statistical symmetry. Non-parametric statistical tests (Friedman, aligned Friedman, and Quade) revealed that CLAHE and MMCE significantly improved image quality compared to the original images ($p<0.05$). Among the evaluated algorithms, CLAHE increased global contrast by approximately 25% and entropy by 6% relative to the original speckle frames, enhancing the visibility of bioactive regions. MMCE achieved the highest bioactivity contrast ratio (CIR = 0.64), while OCCO-MTH provided the best structural fidelity (SSIM = 0.91) and noise suppression (PSNR = 30.7 dB). These results demonstrate that suitable contrast enhancement can substantially improve the interpretability of LSI activity maps without altering acquisition hardware. This finding is particularly relevant for experimental applications aiming to maximize information quality without modifying acquisition hardware. Full article

Surface inspection of aero-engine blades is critical for aero-engine production and maintenance. However, composite materials like titanium alloys and superalloys, as well as thermal barrier coatings on blades, exhibit distinct optical reflection properties, while their complex curved surfaces cause severe image reflections leading to overexposure, underexposure, edge blurring and reduced measurement accuracy. To solve this, we propose ELANet, a deep-learning-based multi-exposure image fusion method with DenseNet as the backbone. Its key innovations include two parts: first, an Efficient Channel Attention mechanism to capture reflection feature differences between substrate and coating, prioritizing resource allocation to anti-reflection channels; second, an Ultra-Lightweight Subspace Attention Mechanism with only one-fifth the parameters of traditional spatial attention that adaptively assigns weights to local features based on curved surface reflection laws, enhancing edge and detail extraction while reducing computational cost. The Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism synergistically address exposure and blurring issues. Validated against 12 mainstream methods via 9 quantitative metrics, ELANet achieves state-of-the-art performance: MEF-SSIM reaches 0.9472, which is 1.3% higher than the best comparative method, PSNR reaches 21.48 dB, which is 2.2 percent higher than the second-best method, and the average processing time is 0.48 s. Ablation experiments confirm the necessity of the Efficient Channel Attention and Ultra-Lightweight Subspace Attention Mechanism. This method effectively supports high-precision blade inspection. Full article

Background: Sustainable sanitation underpins Sustainable Development Goal (SDG) 6.2, which mandates safe, equitable services and the elimination of open defecation by 2030. Rural South African communities continue to face significant Water, Sanitation and Hygiene (WASH) challenges driven by economic, environmental and governance constraints. Methods: An explanatory sequential mixed-methods design was conducted in King Sabata Dalindyebo Local Municipality, Eastern Cape. Quantitative data comprised household surveys (n = 246) and structured observations of VIP latrines (n = 50). Qualitative data were gathered from 20 semi-structured interviews with community representatives and four focus groups (n = 32). Results: While 63% of households owned VIP latrines, only 22% of the inspected facilities were in good working condition and 20% were abandoned; 58% required major maintenance. Major barriers to sustainable sanitation included limited financial capacity, structural damage related to a high-water table, gendered safety risks, and low community engagement in sanitation planning and maintenance. Conclusions: Achieving SDG 6.2 in rural South Africa requires co-productive governance that integrates infrastructure maintenance with community leadership. Recommended actions include delegated WASH committees, targeted subsidies for vulnerable households, routine gender and safety audits, and enforcement of environmental protection measures to secure long-term sanitation sustainability. Full article

In the context of accelerating digitalization, reliable object reconstruction represents a key prerequisite for developing accurate and functional digital twins. This study introduces a unified evaluation methodology designed to assess and compare optical 3D scanning technologies in terms of geometric accuracy, data completeness, and model consistency. The framework integrates all essential stages of digital reconstruction—from data acquisition to quantitative validation—ensuring reproducibility and comparability of results across different optical systems. To verify its applicability, two optical principles, photogrammetry and structured-light scanning, were implemented on the autonomous mobile robot MiR100. The reference CAD model in a 1:1 scale served as the ground-truth geometry for all analyses. Evaluation procedures included visual inspection, dimensional measurements, and statistical error analysis performed in MeshLab, CloudCompare, and MATLAB. The results confirmed that photogrammetry provides high-quality textural detail but suffers from geometric noise and scale drift (relative error > 10%), whereas structured-light scanning delivers more stable and metrically accurate results. In particular, the scanner mode achieved the highest precision, with a mean deviation of 17.4 mm, RMSE of 26.8 mm, and relative error of 7.6%. The proposed methodological framework thus establishes a reproducible basis for evaluating 3D reconstruction accuracy and supports the integration of optimized digital models into digital twin environments. Full article

This study applies to a virtual commissioning (VC) workflow with discrete-event simulation in WITNESS Horizon to diagnose and improve an automated brushless stator assembly line. A validated model of the full route—Stator Assembly Machine (SAM), Linear Transport System (LTS), Winding Machine (WM), Terminal Welding Machine (TWM), Inspection Machine (IM) and Electric Tester (ET)—was executed over a one-shift horizon (28,800 s). We compared the baseline configuration with an optimized scenario that retrieved robot tasks and refined LTS routing. Key performance indicators (KPIs) were resource utilization (Busy/Idle/Blocked) and completed operations. The results are quantitative and specific. Blocking at the SAM interface collapsed from 73.32% to 0% at PressPosition and from 80.64% to 0% at Robot2. LTS transitioned from 97.46% Blocked to 0%, with the share of Move/Running increasing to 14.76% (from ~0%). Line output—measured as completed assemblies at SAM—increased from 368 to 425 units per shift (+15.5%). Similar gains were recorded at other stations (e.g., WM1: 351 → 424 operations, +20.8%). These changes reflect the removal of the primary transfer bottleneck and a more balanced utilization across stations. The study demonstrates that VC can deliver actionable commissioning guidance. By quantifying where blocking occurs and testing alternative control strategies in a virtual environment, it is possible to raise throughput while maintaining stable operation. The modeling approach and metrics are reusable for related electromechanical assembly lines. Full article

The manual inspection of electric water heater enamel is inefficient and unreliable, a challenge stemming from the tank’s narrow (approx. 50 mm) aperture that creates extremely dim, non-uniform lighting. Existing enhancement algorithms struggle with such complex industrial imagery. To address this, we propose an integrated hardware-software system: the three-axis Image Acquisition Robot (IAR) and Interactive Visualization Enhancement Software (IVES). Using this system, we constructed and released the first Heater Tank Inner Wall (HTIW) dataset, containing 900 real-world images. We further introduce jar-RetinexNet, a Retinex-based network featuring a Feature Preservation Attention Module (FPAM), a Cascaded Channel-Spatial Attention Module (CSAM) for precise decomposition, and a Random Affine Generation (RAG) module for generalization. Experiments show that jar-RetinexNet significantly outperforms state-of-the-art methods, achieving the best no-reference quantitative scores on our HTIW dataset: a BRISQUE of 25.4457 and a CLIPIQA of 0.3160. Full article

Short-pitch rail corrugation is commonly found at curves or resilient track structures of the metro system, causing fatigue failure of key components of the train–track system. Currently, rail corrugation is detected via routine inspections during the possession period, with the compromise between inspection efficiency and data accuracy. A newly proposed indirect diagnosis method for rail corrugation has been proposed. Rail corrugation dynamic characteristics and location can be quantitatively identified by measuring train vehicle interior noise and vibration response of a running train under the normal operation conditions, without requiring track access, together with transfer functions—including receptance and accelerance of the wheel–rail system. This indirect method has been applied to and tested on a rail track section with severe corrugation at curves. Results from the indirect diagnosis method are then compared against direct rail roughness measurement using a standard Corrugation Analysis Trolley. Good agreements of peak magnitudes and corresponding frequency bands have been achieved. The indirect method has been successfully validated and can be used to assist track maintenances. Full article

Based on the inspection results of existing structures, this study conducts a safety evaluation of buildings adjacent to a 17.2–23.2 m deep metro station. Stratum loss induced by the deformation of the foundation pit retaining structure leads to displacement and stress redistribution in the surrounding strata, which in turn triggers displacement and deformation of adjacent existing structures. Numerical models were established to quantitatively assess the impacts of cover-and-cut top-down construction on adjacent structures, predict surface settlements during construction through numerical simulation, and formulate control measures to prevent foundation pit safety accidents. This research focuses on the influence mechanism of each construction stage of the cover-and-cut top-down method in Changchun Metro on the settlement patterns of surrounding soil and adjacent buildings, and puts forward targeted recommendations regarding monitoring, construction practices, and emergency early warnings. During the excavation and support of the station’s main foundation pit, the maximum peripheral surface settlement reached −9.804 mm, with a maximum horizontal deformation of −4.345 mm. For adjacent buildings, the maximum structural settlement was −4.243 mm, horizontal deformation 0.929 mm, and inclination rate 0.0107‰—all deformation indices remained within safe thresholds. The findings provide empirical data and technical references for safety assessment and risk control of existing structures adjacent to deep foundation pit engineering. Full article

Automated Optical Inspection (AOI) of Printed Circuit Boards (PCBs) suffers from scarce labeled data and frequent domain shifts caused by variations in camera optics, illumination, and product design. These limitations hinder the development of accurate and reliable deep-learning models in manufacturing settings. To address this challenge, this study systematically benchmarks three Parameter-Efficient Fine-Tuning (PEFT) strategies—Linear Probe, Low-Rank Adaptation (LoRA), and Visual Prompt Tuning (VPT)—applied to two representative foundation vision models: the Contrastive Language–Image Pretraining Vision Transformer (CLIP-ViT-B/16) and the Self-Distillation with No Labels Vision Transformer (DINOv2-S/14). The models are evaluated on six-class PCB defect classification tasks under few-shot (k = 5, 10, 20) and full-data regimes, analyzing both performance and reliability. Experiments show that VPT achieves 0.99 ± 0.01 accuracy and 0.998 ± 0.001 macro–Area Under the Precision–Recall Curve (macro-AUPRC), reducing classification error by approximately 65% compared with Linear and LoRA while tuning fewer than 1.5% of backbone parameters. Reliability, assessed by the stability of precision–recall behavior across different decision thresholds, improved as the number of labeled samples increased. Furthermore, class-wise and few-shot analyses revealed that VPT adapts more effectively to rare defect types such as Spur and Spurious Copper while maintaining near-ceiling performance on simpler categories (Short, Pinhole). These findings collectively demonstrate that prompt-based adaptation offers a quantitatively favorable trade-off between accuracy, efficiency, and reliability. Practically, this positions VPT as a scalable strategy for factory-level AOI, enabling the rapid deployment of robust defect inspection models even when labeled data is scarce. Full article