Search Results (116)

We present some numerical results on piston and tilt detection by using the Young experiment with Genetic Algorithms (GAs). We have simulated the cophasing of a flat surface by following the experimental setup and the mathematical model for Optical Path Difference (OPD) in the Young experiment to characterize piston and tip–tilt misalignment images in the order of a few nanometers, considering diffraction effects and random noise of 5%. Thus, the best fitness obtained by the genetic algorithm is considered as a determining factor to decide a complete error measurement because the proposed algorithm is capable of extracting the values of piston and tilt separately, regardless of which error is present or both. As a result, we have developed a study on piston detection from (0.001, 10) mm with a tilt present in the same pattern from (0, $\lambda /2$) by using GAs embedded in a computational application. Full article

Rapid full-field surface topography measurement for large-scale wafers remains challenging due to limitations in speed, system complexity, and scalability. This work presents a interferometric system based on thin-film interference for high-precision wafer profiling. An optical flat serves as the reference surface, forming a parallel air-gap structure with the wafer under test. A large-aperture collimated beam is introduced via an off-axis parabolic mirror to generate high-contrast interference fringes across the entire field of view. Once the wafer is fully illuminated, topographic information is directly extracted from the fringe pattern. Comparative measurements with a commercial interferometer show relative deviations below $3\%$ in bow and warp, confirming the system’s accuracy and stability. With its simple optical layout, low cost, and robust performance, the proposed method shows strong potential for industrial applications in wafer inspection and online surface monitoring. Full article

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

This study assessed and compared the shear bond strength of self-adhesive and self-etching resin cements for indirect aesthetic restorations to dentin. Four different materials, lithium disilicate ceramics, zirconia ceramics, polymethyl methacrylate (PMMA) composites, and hybrid materials, were used for indirect restorations cemented to dentin. The null hypothesis was that there would be no differences in shear bond strength between the investigated materials. Eighty extracted human molars were used. Eighty dentin specimens with a flat surface were prepared and randomly distributed in groups of 10 (n = 10). From each material (Cerasmart 270, Initial LiSi Blok, Katana ZR Noritake, and Crowntec Next Dent), 20 blocks were made and cemented to the dentin samples. Half of the blocks from each material were cemented to dentin using self-etching resin cement (Panavia V5), and the other half using self-adhesive resin cement (i-CEM). After the specimens were prepared, a laboratory test was conducted to evaluate the shear bond strength. The fracture type was determined using a light microscope, and SEM confirmed the results. The results were statistically analysed. All materials cemented with self-etching cements (Panavia V5) showed statistically higher shear strength values than those cemented with self-adhesive resin cement (i-CEM). In the specimen groups where self-adhesive cement (i-CEM) was used, Cerasmart 270 bonded statistically better. A statistical difference was found between all groups of materials cemented with self-etching cement. The Initial LiSi Block showed the strongest bond, followed by Katana Zr Noritake, Crowntec NextDent, and Cerasmart 270. Adhesion fracture to dentin was observed for all groups cemented with i-CEM. This study highlights the superior performance of self-etching cements in terms of shear bond strength. 10-Methacryloyloxydecyl dihydrogen phosphate (10-MDP), a functional monomer, was found to enhance adhesion strength significantly. However, using self-adhesive cements was associated with a weaker bond to dentin, highlighting the importance of the right cementing agent in restorative dentistry. Full article

The surface topography of the rolls used in skin-pass cold rolling determines the surface finish of rolled sheets. In this sense, work rolls can be intentionally textured to produce certain topographical features on the final sheet surface. The maskless electrochemical texturing method (MECT) is a potential candidate for industrial-scale application due to its reduced texturing cost and time when compared to traditional texturing methods. However, there are few studies in the literature that address the MECT method applied to the topography control of cold rolling work rolls. The present work aims to analyze the viability of surface texturing via MECT of work rolls used in skin-pass cold rolling. In this study, we first investigated how texturing occurs for tool steel using flat textured samples to facilitate the understanding of the dissolution mechanisms involved. In this case, a specially designed texturing chamber was built to texture flat samples extracted from an actual work roll. The results indicated that the anodic dissolution involved in tool steel texturing occurs preferentially in the metallic matrix around the primary carbides. Then, we textured a work roll used in pilot-scale rolling tests, which required the development of a special prototype to texture cylindrical surfaces. After texturing, the texture transfer from the work roll to the sheets was investigated. Rolling tests showed that the work roll surface textured with a dimple pattern generated a pillar-shaped texture pattern on the sheet surface, possibly due to a reverse extrusion mechanism. Full article

Optical coherence tomography (OCT) employs light to acquire high-resolution 3D images and is widely applied in fields such as ophthalmology and forensic science. A popular technique for visualizing the top view (en face) is to slice it with flat horizontal plane or apply statistical functions along the depth axis. However, when the target appears as a thin layer, strong reflections from other layers can interfere with the target, rendering the flat-plane approach ineffective. We apply Otsu-based thresholding to extract the object’s foreground, then use least squares (with Tikhonov regularization) to fit a polynomial curve that describes the sample’s structural morphology. The surface is then used to obtain the latent fingerprint image and its residues at different depths from a translucent tape, which cannot be analyzed using conventional en face OCT due to strong reflection from the diffusive surface, achieving FSIM of 0.7020 compared to traditional en face of 0.6445. The method is also compatible with other signal processing techniques, as demonstrated by a thermal-printed label ink thickness measurement confirmed by a microscopic image. Our approach empowers OCT to observe targets embedded in samples with arbitrary postures and morphology, and can be easily adapted to various optical imaging technologies. Full article

Visual geolocalization for aerial vehicles based on an analysis of Earth observation imagery is an effective method in GNSS-denied environments. However, existing methods for geographic location estimation have limitations: one relies on high-precision geodetic elevation data, which is costly, and the other assumes a flat ground surface, ignoring elevation differences. This paper presents a novel aerial vehicle geolocalization method. It integrates 2D information and relative depth information, which are both from Earth observation images. Firstly, the aerial and reference remote sensing satellite images are fed into a feature-matching network to extract pixel-level feature-matching pairs. Then, a depth estimation network is used to estimate the relative depth of the satellite remote sensing image, thereby obtaining the relative depth information of the ground area within the field of view of the aerial image. Finally, high-confidence matching pairs with similar depth and uniform distribution are selected to estimate the geographic location of the aerial vehicle. Experimental results demonstrate that the proposed method outperforms existing ones in terms of geolocalization accuracy and stability. It eliminates reliance on elevation data or planar assumptions, thus providing a more adaptable and robust solution for aerial vehicle geolocalization in GNSS-denied environments. Full article

Background/Objectives: A material incompatibility has been established between self-etching adhesives and amine-containing dual-cure resin composite materials used for core buildups. This study aims to compare the dentin bond strength of several amine-containing and amine-free core materials using self-etching adhesives with different pHs. Methods: Extracted human molars were mounted in acrylic and ground flat with 320-grit silicon carbide paper. Next, 520 specimens (n = 10/group) were assigned to a dual-cure core buildup material group (10 amine-containing, 2 amine-free, and 1 reference light-cure only bulk fill flowable composite) and assigned to a self-etching adhesive subgroup (pH levels of approximately 1.0, 3.0, and 4.0). Within 4 h of surface preparation, the adhesive corresponding to the specimen’s subgroup was applied and light-cured. Composite buttons for the assigned dual-cure core material of each group were placed using a bonding clamp apparatus, allowed to self-cure for 2 h at 37 °C, and then unclamped. An additional group with one adhesive (pH = 3.0) was prepared in which the dual-cure core materials were light-cured. The bonded specimens were stored in water at 37 °C for 24 h. The specimens were mounted on a testing clamp and de-bonded in a universal testing machine with a load applied to a circular notched-edge blade at a crosshead speed of 1 mm/min until bond failure. The maximum load divided by the area of the button was recorded as the shear bond strength. The data was analyzed via 2-way ANOVA. Results: The analysis of bond strength via 2-way ANOVA determined statistically significant differences between the adhesives, the core materials, and their interaction (p < 0.01). There was a general trend in shear bond strength for the adhesives, where pH 4.0 > 3.0 > 1.0. The amine-free core materials consistently demonstrated higher shear bond strengths as compared to the other core materials when chemically cured only. Light-curing improved bond strength for some materials with perceived incompatibility. Conclusions: The results of this study suggest that an incompatibility can exist between self-etching adhesives and dual-cure resin composite core materials. A decrease in the pH of the utilized adhesive corresponded to a decrease in the bond strength of dual-cure core materials when self-curing. This incompatibility may be minimized with the use of core materials formulated with amine-free chemistry. Alternatively, the dual-cure core materials may be light-cured. Full article

Precise point positioning–real-time kinematic (PPP-RTK) enables users to achieve rapid centimeter-level absolute positioning accuracy within a few epochs. The interpolation of ionospheric delay corrections at the user end, extracted from reference stations, constitutes a key aspect of the process, which depends not solely on the precision of the interpolation model. This study investigates the recommended number of selected reference stations and proposes a method to mitigate the potential loss of observations due to missing ionospheric corrections. According to the experimental results, the number of reference stations should be determined based on the reference network size. Under normal conditions (terrain is relatively flat and the atmospheric conditions are inactive) where reference stations are approximately evenly distributed in all directions, and using low-order surface interpolation model, for networks with 50 km spacing, four or five reference stations are recommended, while for 100 km networks, six or seven stations are enough to calculate precise corrections. Adding more stations beyond these thresholds provides limited improvement in interpolation accuracy and increases the communication load. In addition, an interpolation basis recovery algorithm is proposed to preserve otherwise excluded satellite observations through intelligent handling of correction data gaps at individual reference stations. Experimental validation demonstrates that the recovered ionospheric delay corrections obtained through the algorithm deviate from the ground-truth interpolated values of no more than ±1 cm, an accuracy level deemed adequate for PPP-RTK applications. Furthermore, approximately 3% of the observations, which would otherwise have been discarded due to the missing corrections from a specific reference station, are retained by the algorithm. Full article

To improve the traction performance of mobile mechanisms on soft ground, such as paddy fields, tidal flats, and swamps, a mallard (Anas platyrhynchos) foot was adopted as a bionic prototype to explore the influence and contribution of the plantar morphology of the toes and webbing on the anti-subsidence function during its locomotion on wet and soft substrates and to apply this to the bionic design of high-traction wheel grousers. A handheld three-dimensional laser scanner was used to scan the main locomotion postures of a mallard foot during ground contact, and the Geomagic Studio software was utilized to repair the scanned model. As a result, the main three-dimensional geometric models of a mallard foot during the process of touching the ground were obtained. The plantar morphology of a mallard foot was divided into three typical parts: the plantar irregular edge curve, the lateral webbing surface, and the medial webbing surface. The main morphological feature curves/surfaces were extracted through computer-aided design software for the fitting and construction of a mathematical model to obtain the fitting equations of the three typical parts, and the mathematical model construction of the plantar irregular morphology of the mallard foot was completed. In order to verify the sand-fixing and flow-limiting characteristics of this morphological feature, based on the discrete element method (DEM), the numerical simulation of the interaction between the plantar surface of the mallard foot and sand particles was carried out. The simulation results show that during the process of the mallard foot penetration into the loose medium, the lateral and medial webbing surfaces cause the particles under the foot to mainly move downward, effectively preventing the particles from spreading around and significantly enhancing the solidification effect of the particles under the sole. Based on the principle and technology of engineering bionics, the plantar morphology and movement attitude characteristics of the mallard were extracted, and the characteristics of concave middle and edge bulge were applied to the wheel grouser design of paddy field wheels. Two types of bionic wheel grousers with different curved surfaces were designed and compared with the traditional wheel grousers of the paddy field wheel. Through pressure-bearing simulation and experiments, the resistance of different wheel grousers during the process of penetrating into sand particles was compared, and the macro–micro behaviors of particle disturbance during the pressure-bearing process were analyzed. The results show that a bionic wheel grouser with unique curved surfaces can well encapsulate sand particles at the bottom of the wheel grouser, and it also has a greater penetration resistance, which plays a crucial role in improving the traction performance of the paddy field wheel and reducing the disturbance to the surrounding sand particles. This paper realizes the transformation from the biological model to the mathematical model of the plantar morphology of the mallard foot and applies it to the bionic design of the wheel grousers of the paddy field wheels, providing a new solution for improving the traction performance of mobile mechanisms on soft ground. Full article

This paper presents a simulation approach for mobile robots designed to operate within networks intended for emergency response scenarios. The simulation component is part of a broader and more complex system architecture focused on enhancing communication efficiency and operational coordination within robotic networks. This study leverages virtualization and robotic simulation technologies to develop a controlled environment in which the behavior and coordination of mobile robots can be analyzed and validated under simulated emergency conditions. To achieve this, a virtual machine was configured to host a ROS2 and Gazebo-based simulation environment. Custom packages were developed to enable the dynamic instantiation of mobile robots and the integration of essential sensing and control functionalities. The simulation process was carried out in two stages: initially, a single mobile robot was deployed and evaluated; subsequently, the configuration was extended to support a second robot, enabling multi-agent interaction within the simulated environment using flat surfaces. The proposed architecture demonstrates the potential for scalable deployment and simulation of mobile robotic instances. As a future direction, the authors aim to extend the system by optimizing data extraction from the simulation environment and implementing ROS2 microservices to facilitate secure and efficient communication with a centralized server deployed within a Kubernetes cluster. This integration will enable real-time coordination and data exchange between simulated agents and backend services, forming the foundation for a robust, distributed robotic system tailored to emergency operations. Full article

Smart construction technology integrates artificial intelligence, Internet of Things, UAVs, and building information modeling to improve productivity and quality in construction. In road embankment earthworks, ground compaction quality is critical for structural stability and maintenance. This study proposes a methodology combining UAV photogrammetry with intelligent compaction quality management systems to evaluate surface flatness and compaction homogeneity in real-time. High-resolution UAV images were used to generate digital elevation models, from which surface roughness was extracted using terrain element analysis and fast Fourier transform. Local terrain changes were interpreted through contour gradient, outline gradient, and tangential gradient curvature analysis. Field tests were conducted at a pilot site using a vibratory roller, followed by four compaction quality assessments: plate load test, dynamic cone penetration test, light falling weight deflectometer, and compaction meter value. UAV-based flatness analysis revealed that, when surface flatness met the standard, a strong correlation was observed, with results from conventional field tests and intelligent compaction data. The proposed method effectively identified poorly compacted zones and spatial inhomogeneity without interrupting construction. These findings demonstrate that UAV-based terrain analysis can serve as a nondestructive real-time monitoring tool and contribute to automated quality control in smart construction environments. Full article

The semi-automated extraction of flat surface landforms was carried out, merging a GIS tools application and a geomorphic analysis. The study focuses on seven areas in southern Italy, characterized by different physical landscapes, using a 5 m resolution digital elevation model (DEM). The GIS application allowed the selection of polygonal areas of flat surfaces from diverse arrays of landforms and was implemented using a filtering process to minimize noises. Subsequently, the mean elevation and mean slope of the detected surfaces were extracted and visualized using scatter plots, which helped in determining the elevation ranges and average slope angles for various flat-floored and terraced surfaces. To enhance the reliability of the results, a detailed geomorphic analysis and field survey were conducted to differentiate between fluvial and marine flat surfaces across different physical landscapes. This comprehensive approach included statistical analyses and comparisons with the existing literature to validate the identified flat surfaces, ensuring the accuracy and reliability of the semi-automated extraction procedure. The integration of GIS technology with field investigations not only streamlines the detection of flat landforms but also contributes to a deeper understanding of their geomorphic characteristics, ultimately enhancing geomorphic analysis efficiency. Full article

Water is one of humanity’s most fundamental needs. The demand for freshwater rises in tandem with population expansion. Only 0.01 percent of freshwater is available as surface water in lakes, wetlands, and rivers. As a result, the only choice is to extract water from the oceans. Desalination is an effective option for this. This study focused on the multi-objective design optimization, fabrication, and thermal evaluation of an integrated desalination system combining a solar still with a flat plate collector (SS-FPC). The study investigated the trade-off between two competing objectives: maximizing the efficiency of the SS-FPC system while minimizing its total cost. A numerical code is written in MATLAB to simulate the influence of changing design parameters (DPs) on the SS-FPC performances. The optimal SS-FPC design, offering low costs and a high thermal efficiency, was identified using the multi-objective colonial competitive algorithm (MOCCA). This design was subsequently fabricated and experimentally evaluated under the climatic conditions of Unaizah in Al Qassim, Saudi Arabia. The optimal numerical results were compared with both the literature values and experimental measurements. The comparison revealed strong agreement with the experimental data, with a maximum relative error of 4%. Moreover, the obtained results indicate that the optimized SS-FPC design is capable of achieving a 31% increase in efficiency and a 49% reduction in total cost relative to those reported in the literature. Full article

The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the grasping force based on individual mushroom sizes. To address this, we propose an improved method that integrates visual feedback to dynamically adjust the execution end, enhancing cut precision. Our approach enhances YOLOv8n-seg with Star Net, SPPECAN (a reconstructed SPPF with efficient channel attention), and C2fDStar (C2f with Star Net and deformable convolution) to improve feature extraction while reducing computational complexity and feature loss. Additionally, we introduce a mask ownership judgment and merging optimization algorithm to correct positional offsets, internal disconnections, and boundary instabilities in grasping area predictions. Based on this, we optimize grasping parameters using an improved centroid-based region width measurement and establish a region width-to-PWM mapping model for the precise conversion from visual data to gripper control. Experiments in real-situation settings demonstrate the effectiveness of our method, achieving a mean average precision (mAP50:95) of 0.743 for grasping area segmentation, a 4.5% improvement over YOLOv8, with an average detection speed of 10.3 ms and a target width measurement error of only 0.14%. The proposed mapping relationship enables adaptive end-effector control, resulting in a 96% grasping success rate and a 98% qualified cutting surface rate. These results confirm the feasibility of our approach and provide a strong technical foundation for the intelligent automation of enoki mushroom cutting systems. Full article