Search Results (701)

Despite the rapid expansion of unmanned aerial vehicle (UAV) applications across precision agriculture, logistics, infrastructure inspection, disaster response, and aerial surveying, objective and quantitative hardware evaluation criteria for UAV components remain insufficiently developed. This paper proposes quantitative key performance indicators (KPIs) for thirteen core hardware subsystems, including airframe and propulsion, battery and power supply, flight control, wireless communication, imaging (camera), Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) positioning, thermal management, acoustic and vibration characteristics, AI-based autonomous flight, electromagnetic compatibility (EMC), cybersecurity, and reliability and environmental qualification, together with LiDAR payload evaluation criteria. International standardization activities by 3GPP (Release 15/17), IEEE (1936–1958 series), American society for photogrammetry and remote sensing (ASPRS), and national regulatory frameworks are synthesized to define measurable performance metrics and recommended test methods for each subsystem. An integrated KPI matrix maps application-domain-specific performance targets—encompassing surveying (real-time kinematic (RTK) horizontal accuracy ≤ 2 cm root-mean-square error (RMSE), ground sample distance (GSD) ≤ 2 cm/px), infrastructure inspection (LiDAR payload up to 8 kg, beyond visual line-of-sight (BVLOS) latency ≤ 140 ms), and logistics delivery (payload ≥ 2 kg, precision landing ≤ 50 cm)—demonstrating that no universal platform can simultaneously satisfy all domain requirements. A fuzzy-AHP weighting procedure and inter-subsystem coupling analysis are introduced to address size, weight, and power (SWaP) trade-off relationships that purely additive scoring models cannot capture. The proposed evaluation framework is intended to contribute practically to UAV standardization, certification, and quality management across the full design–procurement–operation lifecycle. Full article

With advances in computer vision and modern surveying technologies, intelligent inspection systems and automatic recognition methods are increasingly used in highway tunnel maintenance. However, existing mobile inspection methods still struggle to balance high-speed operation, fine-crack recognition, and comprehensive assessment of multiple defects. This study proposes an automatic recognition and quantitative assessment method for multiple visible defects in highway tunnels based on a vehicle-mounted multisensor inspection system. The system integrates high-resolution imaging, infrared illumination, 3D laser scanning, mileage positioning, and high-speed data storage, enabling continuous full-section data acquisition at speeds up to 80 km/h. A structural-feature-constrained mileage correction strategy is developed to reduce accumulated localization errors. For crack analysis, a multilevel framework combining two-stage CNN screening, cascaded segmentation, crack trajectory tracking, and subpixel edge extraction is established for crack recognition and 0.1 mm-level width measurement. Water leakage and spalling are extracted through visible–infrared image fusion and adaptive boundary refinement, while cross-sectional deformation is calculated using 3D tunnel axis reconstruction, point-cloud filtering, and cross-section fitting. Field tests and controlled experiments demonstrate that the system can rapidly identify, locate, and quantify multiple tunnel defects, providing a practical reference for intelligent tunnel inspection and maintenance. Full article

Metallic probe carriers are commonly used in magnetic flux leakage (MFL) inspection to support sensing elements and maintain lift-off, but a conductive carrier located near the sensor can act as an active electromagnetic boundary. This study investigates the carrier-induced waveform reconstruction caused by such a conductive near-field boundary. A theoretical model is developed to describe the induced current, secondary magnetic field, and relaxation-related downstream memory generated when the carrier moves through a non-uniform leakage field. Transient finite-element simulations are used to examine the effects of carrier material, scanning speed, and concave carrier geometry. Compared with the air reference, aluminum and copper carriers produce stage-dependent waveform reconstruction, including valley modification, peak modulation, feature-position shift, and trailing-side extension. The quantitative waveform-deviation indicators increase with increasing speed and are further regulated by carrier geometry. Experimental results based on repeated magnetic response events confirm amplitude suppression, non-zero residual after amplitude matching, response broadening, and enhanced trailing asymmetry. These results demonstrate that the metallic probe carrier is not an electromagnetically transparent holder but an active near-field conductive boundary that should be considered in probe-carrier design and MFL signal interpretation. Full article

Professional UAV thermal imaging systems are widely used for inspection, environmental monitoring, search and rescue, agriculture, and technical diagnostics. However, their cost limits their use in education, preliminary field screening, rapid prototyping, and low-resource applications. This study evaluates a minimum-cost indirect UAV thermal sensing workflow based on a DJI Mini 4K consumer drone, a lightweight Servo King9000 smartphone, and a UTi260M smartphone-connected infrared thermal camera. In the proposed configuration, the smartphone displayed and recorded the thermal stream, while the onboard RGB camera of the UAV recorded the smartphone-displayed infrared video during flight. The aim was not to develop a radiometric UAV thermal imaging platform, but to determine whether such a low-cost configuration can provide qualitative presence/absence indication of clear thermal hotspots and to identify its operational limits. The system was experimentally assessed under no-payload and payload conditions, daylight and nighttime illumination, and several low-altitude operating heights. Additional motor-region thermal observations were performed using a UTi260T handheld thermal camera under loaded and unloaded operating conditions. The complete UAV–payload configuration had a measured mass of approximately 340 g, corresponding to an effective added payload of 91 g and a payload-to-UAV mass ratio of 36.5%. Payload operation reduced near-ground flight endurance from approximately 25 min to 14 min 40 s. The maximum observed motor-region temperature increased from 24.9 °C under unloaded operation to 42.0 °C under loaded operation, while motor thermal asymmetry increased from 4.8 °C to 7.6 °C. Nighttime and low-glare operation improved the readability of the smartphone-displayed thermal stream, with the most practical usability observed at approximately 10–20 m. The results show that the proposed workflow is feasible only for short-range qualitative thermal screening and clear hotspot presence/absence indication. The UAV-recorded video should not be interpreted as direct thermal data, but as an RGB recording of a smartphone display showing thermal information. Therefore, the workflow is not suitable for quantitative temperature measurement, radiometric thermal mapping, or accurate thermal shape delineation. The main operational limits are payload mass, suspended-load oscillation, display readability, reduced endurance, motor-region thermal loading, sensitivity to payload alignment, and the absence of raw radiometric data. Direct UTi260M smartphone-recorded thermal frames were additionally used for pixel-size-assisted qualitative verification of practical reference thermal targets, including a human-sized target and a vehicle-sized target, at selected low-altitude operating heights. Full article

This study proposes a deep learning-based framework for the automated classification of photovoltaic solar cells as defective or normal using electroluminescence (EL) imaging. A balanced dataset containing 20,400 EL images, comprising 10,200 defective and 10,200 normal solar cells, was used for model development and evaluation. To reflect practical inspection requirements, cracked and broken cells were combined into a single defective category, resulting in a binary classification task. The dataset includes both monocrystalline and polycrystalline solar cells, which were analyzed together within a unified classification framework to improve applicability to real-world photovoltaic systems. To ensure a fair and unbiased evaluation, dataset partitioning was performed prior to any preprocessing or augmentation operations, and each image was assigned exclusively to the training, validation, or test subset. Data augmentation was applied only to the training set, eliminating the possibility of data leakage. Four state-of-the-art deep learning architectures, EfficientNet-B2, ConvNeXt-Tiny, MaxViT-T, and ResNet-50, were trained and evaluated under identical experimental conditions using the same preprocessing pipeline, training strategy, and dataset split. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, confusion matrices, and explainability-based activation and attention heat maps. All evaluated models achieved classification accuracies exceeding 98%, demonstrating strong capability for EL-based defect detection. EfficientNet-B2 achieved the highest numerical performance, reaching 99.31% accuracy, 0.9931 F1-score, and 0.9987 ROC-AUC. MaxViT-T exhibited similarly strong performance with rapid convergence and balanced class-wise metrics, while ConvNeXt-Tiny and ResNet-50 also produced highly reliable results. Heat map visualizations revealed that EfficientNet-B2 and MaxViT-T concentrated their attention more precisely on defect regions such as cracks and fractures, providing visual interpretability in addition to quantitative performance. The results demonstrate that modern deep learning architectures can accurately and reliably detect photovoltaic cell defects from EL images under a unified binary classification framework. Furthermore, explainability techniques enhance the transparency of model predictions, supporting the practical deployment of intelligent inspection systems for photovoltaic manufacturing and maintenance applications. Full article

This study addresses the absence of a structured and reproducible framework for evaluating road infrastructure project quality under decentralized procurement systems. A quantitative approach was employed using project-level data from 96 completed road infrastructure projects and questionnaire responses from 50 stakeholders. Infrastructure quality was measured using an Infrastructure Quality Index (IQI) derived from objective technical inspection data, while perceived risk factors were assessed through Likert-scale questionnaires. Multiple regression analysis was used to estimate the relationship between risk factors and infrastructure quality, and statistically significant predictors were incorporated into a risk-based evaluation model following ISO 31000 principles. The results indicate that Procurement Governance Risk, Contract Compliance Risk, Project Supervision Risk, Resource Availability Risk, and Sociocultural Risk significantly influence infrastructure quality, whereas Contractor Capacity Risk and Contractor Workload Risk were not statistically significant. A Composite Risk Index was subsequently developed by integrating likelihood and consequence dimensions through weighted aggregation and explicit classification thresholds. The proposed framework provides a transparent and reproducible tool for project evaluation, contractor performance assessment, and risk-informed decision-making. The findings highlight the importance of strengthening governance, supervision, and contract compliance to improve infrastructure quality in decentralized construction environments. Full article

Driven by orchard labor shortages and rising demand for intelligent harvesting, automated apple picking requires a balance between conformal enveloping and slip-resistant stability. To reduce damage and slippage caused by fragile skins, variable morphologies, and motion disturbances, this study proposes a multi-joint pneumatic flexible apple-picking hand with adjustable circumferential configuration. Based on structural configuration determining grasping stability, six apple-morphology-based finger-base supports were designed. Parametric analysis of soft gripper cavities identified an isosceles trapezoidal profile as the best configuration. Using the Yeoh constitutive model, an equivalent joint model for conformal gripping was developed, and genetic algorithm (GA) optimization selected the four-joint design as the preferred configuration. Static finite element simulations determined an operating pressure of 20.32 kPa. Grasping stability was quantified by relative slip displacement in rigid–flexible coupled dynamic simulations. Among the tested support configurations within 60–110°, the 90° bracket produced the most stable slip response under vertical and horizontal disturbances. Thin-film pressure tests showed an asymmetric but stable three-finger load-sharing pattern. Field trials in a high-density dwarf spindle orchard achieved an 83.98% harvesting success rate. After 72 h of cold storage, no obvious surface browning, epidermal abrasion, or compression marks were observed during visual inspection. This assessment was limited to visible external damage and did not include quantitative evaluation of internal bruising, firmness degradation, flesh browning, or long-term storage quality. These results demonstrate stable grasping performance and low visible external damage under the tested conditions. Full article

Scanning acoustic microscopy is a useful non-destructive imaging technique for semiconductor inspection, providing acoustic contrast without physical sectioning. However, the selection of an ultrasound transducer for high-quality imaging is not determined by the operating center frequency alone. The focusing condition, represented by the F-number, also plays a critical role in determining the lateral resolution. In this study, the combined effects of the center frequency and F-number on lateral resolution were investigated using wafer-based test samples. Focused ultrasound transducers with different center frequencies were used to image a striped resolution target for quantitative lateral resolution analysis. In addition, a custom-fabricated silicon wafer containing void-mimicking patterns was also imaged for qualitative evaluation. The results show that a higher frequency does not necessarily guarantee better lateral resolution. In fact, a lower-frequency transducer with tighter focusing showed greater image quality compared to a higher-frequency transducer with a larger F-number. These findings indicate that both frequency and F-number should be jointly considered when selecting ultrasound transducers for semiconductor inspection. This wafer-based evaluation provides practical guidance for optimizing imaging conditions in scanning acoustic microscopy, according to target feature size and inspection requirements. Full article

The increasing adoption of virtual reality for industrial tasks such as virtual assembly, inspection, and operator training necessitates a standardized approach for evaluating and selecting appropriate hardware. This paper addresses this need by introducing a comprehensive methodology to assess the usability of commercially widespread virtual reality headsets specifically for industrial applications with hand-held controllers. We conducted a large-scale comparative study involving five leading headsets (HTC VIVE Pro 1 and 2, HTC VIVE XR Elite, Meta Quest Pro, and Meta Quest 3) and 60 demographically balanced participants. The evaluation was based on a protocol of 15 distinct tasks designed to measure performance in near and far-field object manipulation, interaction fidelity, visual clarity, ergonomics, and long-term comfort. By combining quantitative Key Performance Indicators with subjective user feedback and rigorous inferential statistical analysis, our findings reveal significant performance disparities among the devices. The results demonstrate that, while certain headsets excel in high-precision tracking for assembly tasks, others offer superior comfort, visual quality, and ease of use for inspection and prolonged sessions. Ultimately, this study concludes that no single headset is universally superior; the optimal choice is highly task-dependent. The proposed methodology provides a robust, evidence-based framework to guide industries in making informed virtual reality hardware selections tailored to their specific needs. Full article

Ports combine clusters of operational buildings, shared energy infrastructure, and structurally critical assets requiring coordinated management to ensure safety and efficiency. Nevertheless, existing Digital Twin (DT) frameworks for building energy management rarely integrate Structural Health Monitoring (SHM) with energy performance assessment, while port-specific implementations remain scarce. This paper presents a pre-operational energy-aware DT architecture for port building clusters, structured in a unified five-layer framework integrating three capabilities: (i) EGMS/InSAR-based SHM screening with planned in situ sensing and computer-vision inspection workflows; (ii) smart metering and measurement and verification (M&V) protocols aligned with ISO 50001/50015 and IPMVP standards; and (iii) weighted multi-criteria prioritization considering structural condition, energy saving potential, service continuity, and cost. The framework is applied to the Port of Formia (Italy), a brownfield district comprising nine buildings (3371 m²), 16 high-mast lighting towers, shore power infrastructure, and 90 kWp of planned photovoltaics. In the absence of operational metering, energy and carbon values are reported as bounded ex-ante scenario estimates, not as verified performance outcomes. The analysis estimates photovoltaic generation of 116–137 MWh/year and lighting retrofit savings of 31.5–36.8 MWh/year; the related carbon values are treated as gross grid-displacement upper bounds pending measured self-consumption and export data. A four-phase validation roadmap with quantitative acceptance criteria supports the transition from feasibility assessment to verified performance. Full article

Integration of LiDAR and thermal sensing has become increasingly important in robotics, infrastructure diagnostics, environmental monitoring, and autonomous perception systems. LiDAR sensors provide accurate three-dimensional geometric information but do not directly capture thermal properties of observed objects, whereas thermal cameras provide temperature distributions without explicit spatial structure. Fusion of both sensing modalities enables thermally augmented 3D scene reconstruction and spatial localization of temperature anomalies. This paper presents a practical LiDAR–thermal fusion framework for three-dimensional localization of heat sources using an Ouster OS1 LiDAR sensor and a FLIR A70 thermal camera. The proposed framework includes intrinsic thermal-camera calibration, extrinsic LiDAR–thermal calibration, multimodal data synchronization, projection of LiDAR points onto the thermal image plane, and assignment of temperature values to spatial points. Additionally, a dedicated thermally distinguishable calibration target is proposed to enable reliable multimodal feature extraction under low-contrast LWIR imaging conditions. The developed framework was experimentally validated using real radiometric thermal data and LiDAR point clouds acquired under laboratory conditions. Quantitative evaluation demonstrated reprojection errors below 1 pixel and a mean hottest-point localisation error of approximately 4.1 cm at a distance of 12.3 m. The results confirm that accurate spatial localisation of thermal anomalies can be achieved using a geometry-based multimodal fusion approach without relying on computationally expensive learning-based methods. The proposed framework emphasises practical deployment, deterministic calibration, and applicability in scenarios where limited training data or constrained computational resources make learning-based approaches difficult to apply. The proposed system may be applied to building energy diagnostics, industrial inspection, technical infrastructure monitoring, and robotic perception systems that require reliable spatial localisation of heat sources under real measurement conditions. Full article

This study presents the diagnostic and conservation work carried out on the Roman mosaic of the South cubiculum in the Latomia dei Niccolini (Marsala, western Sicily). The mosaic, decorated with polychrome tesserae featuring a kantharos motif, presented severe structural damage, including fractures, subsurface voids, and progressive material loss. To assess the causes of deterioration and design an effective conservation strategy, an integrated approach combining non-invasive geophysical and 3D survey methods was applied. Ground-penetrating radar (GPR) was selected as the main diagnostic tool because it allows high-resolution subsurface imaging while preserving the integrity of the fragile mosaic surface. By utilizing high-frequency 2 GHz antennas and complementary video inspection, a significant subsurface cavity beneath the mosaic preparation layer was successfully mapped, determining its critical relationship with the main diagonal surface fracture. Simultaneously, laser scanning and close-range photogrammetry enabled the creation of accurate 3D models supporting both documentation and restoration planning. The conservation concluded with surface cleaning, mortar consolidation, and the successful structural detachment and relocation of the compromised section onto a lightweight support for future museum display. The findings demonstrate that integrating 3D digital and geophysical data provides a quantitative, low-risk roadmap for preserving highly vulnerable archaeological floorings, moving beyond qualitative technical documentation to establish a replicable preservation framework. Full article

This study proposes a vision-based framework for automated inspection and quality grading of beef steaks by integrating fat marbling distribution analysis and lean-meat color evaluation. In frozen beef products, surface frost often generates specular reflections that resemble both fat and lean regions, thereby reducing segmentation accuracy. To address this challenge, a sequential and interpretable analytical framework is developed. First, homomorphic filtering is applied to suppress frost-induced illumination artifacts, followed by curvelet transform combined with square-ring filtering to separate fat and lean regions based on their multi-scale and directional characteristics. For marbling analysis, the convex hull, skeleton, and principal axis of the steak are extracted, and a chi-square goodness-of-fit test is performed within eight predefined regions to quantitatively evaluate marbling distribution uniformity and identify localized fat accumulation. For lean-meat evaluation, RGB color features are extracted and classified using a Support Vector Machine (SVM) to determine redness levels. The resulting marbling and color information are subsequently integrated through a weighted grading strategy to estimate the final quality grade. Experimental results demonstrate a fat detection rate of 92.68%, a false-positive rate of 4.97%, and a correct classification rate of 94.09% for fat segmentation, while the SVM-based lean-meat color classifier achieves an accuracy of 96.67%. Furthermore, the proposed grading framework attains an overall grading accuracy of 90.38%, showing strong agreement with human evaluation. Full article

The article deals with non-destructive methodologies for assessing and preventing corrosion of polymer-coated underground pipelines, advanced corrosion-barrier coating systems based on extruded three-layer high-density polyethylene (3LPE), corrosion control strategies for buried oil, gas, and water transmission infrastructures, and mechanisms and engineering approaches for corrosion prevention and mitigation. The quality assurance of newly polymer-coated underground pipelines, following construction (installation and backfilling), is vital for evaluating the polymer coating quality state and the efficiency of passive anti-corrosion protection, aimed at reducing corrosion risks and prolonging the pipeline’s service life. The evaluation relies on the coating average specific electrical resistance and the presence of coating defects (number, total area, and distribution) of inspected pipeline sections. In this study, based on extensive real data obtained from testing of newly installed underground water and oil/gas pipeline networks (60 projects with a total pipeline length of 260 km) with various technical characteristics, Drainage Test and DCVG (Direct Current Voltage Gradient) complementary non-destructive indirect methods have been investigated to determine the quality level and identify the location and severity of defects in polyolefin (polyethylene) coatings. The novel concepts and criteria were defined: the quantitative criteria for average specific electrical resistance are established; in addition, a new parameter related to the specific coating defects ratio is introduced, which has been shown to correlate with the criteria for the average specific electrical resistance of the polymer coating and consumed electrical current; finally, following DCVG measurements of the 3LPE coating system, a novel degree of relative defect sizes (%IR) for repairs has been suggested. The innovative and comprehensive approach can support the efforts of regulatory quality assurance, design, maintenance, safety, and research communities to ensure the long-term integrity and sustainability of underground polymer-coated steel pipelines. Full article

South Korea is a highly import-dependent food economy and therefore offers a useful case for examining how an integrated national food control system can be built under trade openness, limited domestic agricultural capacity and changing consumer risk perceptions. This article presents a structured narrative review, rather than a causal impact evaluation, of South Korea’s transition from multi-agency food safety regulation toward an integrated, risk-based food control system. The review is organized through the FAO/WHO national food control system framework and maps Korean legal, institutional and operational evidence onto six analytical dimensions: legal foundations, institutional coordination, risk-based official controls, import supervision, traceability and recall, and risk communication. Examples of embedded risk-analysis principles include the Positive List System for pesticide residues with a default limit of 0.01 mg/kg for substances without a Korean MRL, inspection orders and risk-ranked import controls, barcode-linked recall blocking through the Hazardous Food Sales Prevention System, and public disclosure of unsafe directly purchased overseas products. Quantitative evidence is used descriptively: Korea’s agricultural and food imports reached USD 45.3 billion in 2024, hepatitis A notifications fell from 17,598 in 2019 to 3989 in 2020 after the salted-clam outbreak, and MFDS reported that 12 of 544 overseas direct-purchase products tested in the first half of 2020 contained restricted substances. These indicators suggest improvements in coordination and crisis response capacity, but they do not prove that institutional integration alone reduced foodborne disease incidence. The review finds that South Korea’s model is strongest in institutional consolidation, import-oriented technical standards and digital recall communication, while key challenges remain in small-business compliance burden, scientific independence, data transparency, cross-border e-commerce and novel foods such as cell-cultured food ingredients. Full article