Search Results (1,648)

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Head-mounted eye-tracking systems play a critical role in virtual reality, human–computer interaction, and clinical applications, yet achieving both high angular accuracy and precise 3D gaze position estimation with low-cost hardware remains challenging. This paper proposes a lightweight, training-free geometric 3D gaze tracking framework for binocular 3D gaze tracking using consumer-grade hardware, which leverages stereo geometric triangulation and a simplified physiological eye model to achieve robust 3D gaze estimation, requiring only standard infrared cameras and dichroic mirrors without additional specialized hardware. The method was evaluated in controlled indoor conditions with 30 participants, where it achieved an angular error ranging from 1.1° to 2.82° and a 3D gaze position error below 13.24 mm. Compared to two state-of-the-art academic non-deep-learning methods, the proposed framework delivers competitive angular accuracy while significantly reducing 3D position error, outperforming the baselines by 34% to 56% in depth estimation precision. These results demonstrates that the proposed geometric framework is a practical and effective solution for high-precision 3D gaze tracking on low-cost hardware, suitable for both research and consumer applications. Full article

(1) Background: Intervertebral Disc Disease (IVDD) represents one of the most frequently encountered neurological disorders in canine patients and often necessitates surgical decompression by means of hemilaminectomy. Objective methods for evaluating postoperative inflammatory response and tissue healing remain limited. This study aimed to evaluate temporal changes in local cutaneous temperature and thermal patterns in dogs undergoing hemilaminectomy for thoracolumbar intervertebral disc extrusion using Infrared Thermography; (2) Methods: Fifteen dogs diagnosed with Hansen type I thoracolumbar intervertebral disc extrusion were included. Diagnosis was established through neurological examination and computed tomography. Thermographic assessment of the thoracolumbar region (T11–L3) was performed preoperatively (Day 0), 24 h postoperatively (Day 1), and 7 days after surgery (Day 7) using an FLIR E50 thermal camera. Mean cutaneous temperature and thermal distribution patterns were analyzed. Statistical evaluation was performed using Repeated Measures ANOVA and the Friedman test; (3) Results: Significant temporal differences in local cutaneous temperature were identified between the evaluated time points (p < 0.001). Thermographic assessment demonstrated progressive modifications in thermal distribution throughout the postoperative period. No postoperative complications, including seroma formation, wound dehiscence, or fistula development, were observed; (4) Conclusions: Infrared thermography enabled identification of significant postoperative thermal changes following hemilaminectomy and may represent a useful complementary non-invasive method for postoperative monitoring of tissue healing in dogs. Full article

Laser Powder Bed Fusion (LPBF) is a complex manufacturing process that depends on precise control of printing parameters and melt pool geometry, which directly influence defect formation and final part quality. This study evaluated the reliability of a simplified thermal conduction-based melt pool model by combining post-process metallographic analysis with in situ dual-wavelength infrared thermal imaging. Experimental data were obtained through single-track printing on 316L, IN625, and CoCr alloys across a wide range of parameters. The simulated melt pool length showed strong agreement with thermal camera measurements (R²_adj > 0.78), while the width showed moderate but consistent correlation (R²_adj > 0.52). For melt pool depth, the model systematically deviated due to its inability to capture keyhole melting, although a strong linear correlation was still observed (R²_adj > 0.86). Cross-validation between metallographic measurements and thermal imaging revealed only a 6–9% discrepancy, confirming the reliability of both methods and the potential of dual-wavelength cameras for industrial process monitoring. Overall, the model proves to be a reliable tool for predicting melt pool surface geometry specifically within the conduction melting regime, while its predictive capability degrades significantly in the keyhole regime, where simulated peak temperatures reach up to 7000 °C and melt pool depth errors escalate due to the disregard of recoil pressure, liquid and vapor dynamics. Full article

The influence of internal substrate geometry on thermal stability during Laser Directed Energy Deposition Repair (DED-R) remains insufficiently understood, particularly for components containing internal cavities and cooling channels. This study investigates the thermal response of solid (Alpha), blind-hole (Bravo), and channeled (Charlie) AISI 316L substrates using dual infrared thermography, transient finite element modeling, and Short-Time Fourier Transform (STFT)-frequency-domain analysis. Despite substantial differences in internal heat-dissipation pathways, all substrate configurations exhibited similar peak surface temperatures (~1700–2100 °C), indicating that conventional temperature monitoring alone is insufficient to distinguish geometry-dependent melt-pool behavior. To address this limitation, a Spectral Entropy Index (SEI) derived from STFT analysis was proposed to quantify thermal stability. The channeled substrate exhibited the lowest entropy value (H_s = 0.172), compared with the solid (H_s = 0.181) and blind-hole (H_s = 0.183) configurations, indicating a more ordered and predictable thermal response. Furthermore, distinct variations in the spectral stability shadow revealed geometry-dependent oscillatory behavior that was not observable from thermal histories. Finite element simulations showed good agreement with experimental measurements in conduction-dominated regions (RMSE ≈ 46 °C), whereas deviations were observed within the melt-pool region (~250–310 °C), highlighting the increasing influence of fluid-flow phenomena not captured by the conduction-based model. The results demonstrate that internal substrate architecture primarily influences melt-pool stability through frequency-domain thermodynamics rather than significant changes in peak temperature. The proposed STFT method provides a quantitative approach for monitoring thermal stability and assessing the feasibility of L-DED repair over complex internal geometries. Full article

Background: Excessive heat during implant osteotomy adversely affects bone healing and osseointegration. It is essential to carefully study the effects of drill systems, motors, and irrigation methods on intraosseous temperature. We aim to analyze the predictors of intraosseous temperature variation during osteotomy in bovine bone blocks using the Helix GM (Titanium) and Zi Compact (Zirconium) drill systems in conjunction with three different surgical motors and irrigation conditions using a linear regression model. Materials and Methods: An in vitro experimental study was conducted at the Periodontology and Oral Implantology Laboratory of the Universidad Nacional Federico Villarreal. A total of 120 bovine rib bone blocks (1.5 cm) were prepared using a standardized osteotomy protocol involving lance drills and ∅ 2 mm and ∅ 3 mm helical drills from the Zi and Helix GM compact kits (Neodent, Curitiba, Brazil). Irrigation was performed with chlorhexidine 0.12% + CPC 0.05% at ambient temperature (21 °C). Osteotomies were executed with two surgical motors (Coxo and Driller) at 1200 rpm and 35 Ncm torque. The intraosseous temperature was recorded in real time via a calibrated Fluke TiS55+ (Fluke, Everett, WA, USA) infrared thermographic camera and validated using a probe thermometer. Statistical analyses used Stata 17, applying descriptive measures, t-tests, and linear regression at 95% confidence for reliability. Results: Osteotomies without irrigation consistently resulted in slightly higher intraosseous temperatures. The Helix GM system, with the ∅ 3 mm drill and Driller motor, produced a final temperature of 29.3 °C ± 2.0. The Zi system with the lance drill and drill motor produced a maximum temperature of 32.7 °C ± 2.3. Irrigation was successful, and the elevated temperatures after irrigation were close to the surgical room temperature of 21–23 °C. Linear regression analysis showed that the drill motor produced a statistically significant decrease in temperature (−2.29 °C; 95% CI: −4.36 to −0.21; p = 0.031) while the lance drill with no additional irrigation produced a statistically significant increase in temperature (0.24 °C; 95% CI: 0.06 to 0.42; p = 0.009). Conclusions: The absence of irrigation during osteotomy significantly increased the intraosseous temperature, potentially compromising bone integrity. The use of irrigation, especially with the Driller motor, demonstrates a protective thermal effect. Full article

Infrared focal plane array detectors produce column stripe noise due to inter-detector response variations. Existing single-frame correction methods operate exclusively on the degraded infrared image and cannot reliably distinguish column noise from genuine vertical scene structures. With the increasing availability of co-registered visible-light cameras in modern electro-optical/infrared payloads, we propose to exploit the visible image as a structural guide for infrared destriping. Through a cross-modal correlation analysis, we show that the structural correspondence between RGB and infrared images is spatially non-uniform, motivating a selective rather than uniform fusion strategy. Based on this observation, we propose CMSP (Cross-Modal Scene Prior), a lightweight single-frame denoising architecture that selectively applies RGB guidance where it is beneficial. The proposed AdaptiveSPADE module blends RGB-guided modulation with standard instance normalization through a learned per-pixel confidence map, while a dual-path output head separately estimates pixel-wise residuals and column-constant stripe patterns. Evaluated on three public RGB–IR datasets, CMSP achieves 51.91 dB PSNR on M3FD, outperforming the best baseline by 5.79 dB with only 638 K parameters. A downstream evaluation on real stripe noise demonstrates that CMSP not only removes artifacts but also preserves the fine structures critical for infrared small target detection. Ablation studies confirm that adaptive gating more than doubles the benefit of RGB guidance compared to uniform modulation, and prevents degradation when cross-modal alignment is weak. Full article

Forest conversion from natural forests to secondary forests and plantations has significantly altered wildlife habitats in subtropical regions. However, the drivers of disparities in bird and mammal multidimensional diversity between these forest types remain poorly understood. We analyzed a four-year camera-trapping dataset to compare the taxonomic, functional, and phylogenetic diversity and community structure of birds and mammals. Our results indicated that forest conversion impacts biodiversity differently across taxa. Birds exhibited higher taxonomic and phylogenetic diversity in secondary forests than in plantations. A similar pattern was also evident for birds among different stand types. However, mammals exhibited considerable taxonomic diversity but showed higher phylogenetic diversity and structure in secondary forests. A similar pattern was also evident for mammals among different stand types. Beta diversity revealed significant differences in bird taxonomic composition and mammal phylogenetic composition between secondary and plantation forests. Furthermore, elevation primarily influenced bird taxonomic diversity, phylogenetic diversity and structure in secondary forests, whereas mammal functional diversity, phylogenetic diversity and structure were more sensitive to elevational changes in both secondary and plantation forests. These findings reveal that birds and mammals respond distinctively to forest conversion. We emphasize that management strategies must be group-specific. For birds, we recommend prioritizing the preservation of secondary forests as biodiversity refugia and transforming structurally simplified plantations into complex habitats by retaining legacy trees and native understory vegetation. For mammals, conservation should prioritize landscape-scale connectivity by protecting continuous forest corridors along altitudinal gradients. Practically, this requires restricting further fragmentation of high-altitude habitats and restoring native vegetation in degraded corridors to facilitate dispersal and maintain the phylogenetic integrity of mammal communities. Full article

Research on human motion representation commonly investigates portable, wearable, and ergonomic sensing systems. Cameras, infrared sensors, and inertial measurement units (IMUs) are widely used to reproduce and validate human movement. Known limitations persist, including increased error during slow movements, the gimbal lock effect in Euler space, and the requirement for one sensor per joint. The objective of this work is twofold: first, to characterize the measurement accuracy of a commercial IMU sensor (BWT901BLE) under controlled conditions using a fixed-arm pendulum model that replicates the single-degree-of-freedom planar kinematics of elbow flexion–extension, comparing angular position, angular velocity, and angular acceleration outputs against a video-based reference system; and second, to describe and publish a complete data processing pipeline—from raw sensor readings to real-time biomechanical motion visualization within OpenSim—demonstrated through upper limb motion recordings from 6 participants, whose data were used to generate motion files and estimate muscle fiber lengths and activation patterns within OpenSim. Regarding sensor characterization, experiments compared sensor data against the video-based reference. The inter-sensor angular position mean error was 0.765° (100 Hz) and 0.445° (200 Hz); angular velocity mean error was 0.124°/s (100 Hz) and 0.277°/s (200 Hz). Direct Euler angle measurements outperformed quaternion-to-Euler conversion (mean RMSE 5.69° vs. 53.1° at 100 Hz; 5.08° vs. 41.8° at 200 Hz). Angular velocity showed the highest agreement with the video-based reference (mean RMSE 0.60 rad/s at 100 Hz and 0.43 rad/s at 200 Hz; mean R = 0.982 and 0.991). Raw accelerometer output showed negligible correlation with the video-based angular acceleration reference (mean R ≈ 0.00–0.05); however, acceleration derived from angular velocity differentiation achieved high accuracy (mean RMSE 4.43 rad/s² at 100 Hz and 3.06 rad/s² at 200 Hz; mean R = 0.976 and 0.989). Regarding the OpenSim integration, the real-time visualization pipeline achieved an effective frame rate of 40–50 fps with an estimated end-to-end latency of 35–50 ms, and the recorded motion data were used to estimate muscle fiber lengths and activation patterns through OpenSim’s analysis tools. These findings confirm that angular velocity is the most reliable output of this sensor class. Full article

Simulations of intermediate-mass black holes (IMBHs) in dwarf galaxies within 10 Mpc that host bright nuclear star clusters (NSCs), prime candidates for IMBH formation, using the High Angular Resolution Monolithic Optical and Near-infrared Integral (HARMONI) field spectrograph on the Extremely Large Telescope, probe black hole formation in the early universe. Our approach combines observed surface-brightness profiles from the Hubble Space Telescope (HST), synthetic stellar population spectra, and Jeans Anisotropic Modeling (JAM) for stellar dynamics. Mock HARMONI observations were generated with the HSIM simulator and analyzed in a Bayesian framework to infer IMBH masses down to 0.5% of the NSC mass. In this work, we extend these simulations by constructing improved stellar mass models using SimCADO to simulate imaging with the Multi-AO Imaging Camera for Deep Observations (MICADO). The MICADO data are jointly analyzed with HARMONI kinematics via JAM to reassess IMBH masses and uncertainties. This combined framework enables us to examine how variations in the NSC inner surface-brightness slope influence IMBH mass estimates, providing tighter constraints on low-mass black holes and advancing models for IMBH detection in NSCs. Full article

This study evaluates the feasibility, repeatability, and temporal consistency of a low-cost long-wave infrared (LWIR) thermal imaging workflow for in-vehicle driver monitoring under realistic operating conditions. Two participants were monitored during three independent 60 min driving sessions each. Facial thermal observations were obtained using a consumer-grade mobile LWIR camera operated through a smartphone application environment. Forehead-region temperature data were extracted from a manually positioned region of interest (ROI), including center-point, mean, maximum, and minimum temperature values. Geometric validation was first performed under stationary vehicle conditions in order to confirm forehead-ROI visibility and stability across multiple head orientations and posture variations. Subsequent dynamic sessions were used to evaluate cross-session repeatability and temporal behavior of sampled ROI-based thermal metrics. The results show that the facial thermal patterns remained structurally consistent across repeated sessions, while the sampled temperature trajectories exhibited generally smooth behavior without evidence of progressive within-session instability over the 60 min recordings. Although minor inter-session offsets were observed, normalized analysis confirmed preservation of the relative temporal dynamics. The findings indicate that the examined low-cost LWIR workflow can provide sufficiently stable and repeatable facial thermal observations for feasibility-level driver monitoring analysis under realistic in-vehicle conditions. The contribution of this work lies in a structured validation methodology combining geometric validation, cross-session repeatability, and temporal consistency assessment as a methodological foundation for future thermal-based driver monitoring applications. Full article

To investigate simultaneously both the effect of fin cross-sectional shape on heat transfer and the influence of different polymeric materials, test samples were manufactured by 3D printing in the form of bushings with attached radial fins of varying cross-sections. Through the research undertaken, the aim was to obtain information regarding the length of the fin at which a certain temperature is reached; therefore, the length that ensures efficient heat transfer to the external environment. Dedicated testing equipment was designed and built to test the thermal transfer in fins made of three different materials (polylactic acid (PLA)-based materials, i.e., standard PLA, PLA with carbon black (protopasta), and PLA with graphene (prografen)) and, respectively, with different sizes and shapes of the cross-section (circular, square, equilateral triangular, and rectangular). The experimental results were mathematically processed to develop empirical models that illustrate both the direction and the intensity of the influence of the input factors on the fin length at which a specific temperature is reached. Under certain conditions, radial components with a circular cross-sectional area of 20 mm² showed significant differences depending on the polymer type. For the polylactic acid material, this length was 42% higher than for prografen and 25% higher than for protopasta. Full article

Assessing methane (CH₄) emissions is critical for mitigating the environmental impact of greenhouse gases (GHGs). Optical Gas Imaging (OGI) technologies have emerged as effective tools for gas leak detection and quantification; however, their performance under varying environmental conditions and gas compositions remains insufficiently understood. In particular, previous studies have largely overlooked the combined effects of ambient temperature variations and Carbone. Dioxide (CO₂) interference on both detection sensitivity and quantification accuracy. This study addresses this gap by systematically investigating the influence of temperature and CO₂ on methane detection and quantification using an infrared OGI camera coupled with real-time analysis software. Logistic regression was employed to model detection probability under two temperature conditions at a fixed distance of 45 m. Results show that at 30 °C, the system achieved 50% and 90% detection probabilities at 5.51 and 11.30 kg/h, respectively. In contrast, at −7 °C, detection reliability decreased due to increased false positives, preventing the establishment of robust detection thresholds. However, methane quantification accuracy improved under colder conditions, with 90% of leaks correctly quantified compared to 60% in warmer environments. Furthermore, the presence of CO₂ completely inhibited methane detection and quantification. These findings demonstrate the significant and previously underexplored impact of temperature and gas composition on OGI performance, highlighting critical limitations in real-world applications and providing new insights for improving methane monitoring strategies. Full article

To elucidate the mammalian community structure and interspecific relationships within the alpine ecosystem of the Qinghai–Tibet Plateau, this study was conducted in the Selin co National Nature Reserve for Black-necked Cranes, Tibet. Based on infrared camera monitoring data collected from June 2023 to July 2024, we analyzed mammalian species diversity and their spatial association patterns. A total of 150 infrared cameras were deployed, of which 128 were effectively retrieved, yielding 13,301 effective camera-trap days and 31,170 photographs of mammals. In total, 21 mammal species were recorded, belonging to 5 orders, 9 families, and 17 genera. The species accumulation curve approached an asymptote, indicating adequate sampling effort. Relative abundance analysis showed that Bharal (Pseudois nayaur) was the dominant species (RAI = 13.72), followed by Plateau Pika (Ochotona curzoniae) (RAI = 8.44), Moupin Pika (Ochotona thibetana) (RAI = 5.93), and Red Fox (Vulpes vulpes) (RAI = 5.50), while Snow Leopard (Panthera uncia) exhibited a moderate abundance level (RAI = 3.69). Significant differences in species diversity were observed among habitat types. Alpine meadow and meadow–desert ecotone exhibited higher diversity indices, whereas alpine desert and alpine bare rock habitats showed lower diversity. Interspecific association analysis identified 30 significant species pairs (p < 0.05), among which positive associations accounted for 93.3% and negative associations for 6.7%. The constructed association network comprised 16 nodes and 30 edges, with Chiru (Pantholops hodgsonii), Snow Leopard, and Red Fox serving as key hub species. Predator–prey pairs exhibited clear spatial coupling, while positive associations among herbivores mainly reflected shared utilization of similar habitat resources. The association structure varied across habitats, being most complex in alpine meadow, whereas no significant associations were detected in alpine desert. Overall, the mammalian community in this region is characterized by “low species richness and high endemism,” with interspecific relationships dominated by positive associations. Habitat heterogeneity plays a critical role in shaping the structure of the association network. These findings provide a scientific basis for biodiversity conservation and alpine ecosystem management on the Qinghai–Tibet Plateau. Full article

In a context of greenhouse-gas-reduction for climate-change mitigation, Optical Gas Imaging (OGI) is cited by US and EU regulations as a key technology for detecting methane leaks in the oil and gas industry. The paper outlines the principles of OGI, covering specificity of both high-performance cooled cameras and cost-effective thermal infrared uncooled cameras. It explains camera design, the optical-radiometric theory of contrast and sensitivity, and provides a comprehensive description of the key performance indicators (KPIs) such as NETD, NECL, and MDLR; together with parameters that influence them. These theoretical concepts are supported by measurements taken under laboratory conditions and outdoors, with wind and complex scenes. Finally, video-processing methods for visualizing gas leaks are presented, showing how they increase visual sensitivity and reduce the user’s cognitive load. Full article