Search Results (20,501)

A sub-micron two-dimensional displacement sensor based on a segment of multi-core fiber is presented in this paper. Light at the wavelengths of 1520 nm, 1530 nm, and 1540 nm was introduced separately into three cores of a seven-core fiber (SCF). They were independently transmitted in their respective cores, and after being emitted from the other end of the SCF, they were irradiated onto the end-face of a single-mode fiber (SMF). The SMF received light at three different wavelengths, the power of which was related to the relative position between the SCF and the SMF. When the SMF moved within a two-dimensional plane, the direction of displacement could be determined based on the changes in power at different wavelengths. As a benefit of the high sensitivity of the spectrometer, the sensor could detect displacements at the sub-micron level. When the SMF was translated in 200 nm steps over a range from 5.2 μm to 6.2 μm, the sensitivities at the wavelengths of 1520 nm, 1530 nm, and 1540 nm were 0.34 dB/μm, 0.40 dB/μm, and 0.36 dB/μm, respectively. The two-dimensional displacement sensor proposed in this paper offers the advantages of high detection precision, simple structure, and ease of implementation. Full article

Presently, on-orbit calibration methods have several problems, such as low calibration accuracy and broken traceability links, so an urgent need exists to unify traceable and high-precision on-orbit radiometric calibration loads as benchmarks for cross-transfer radiometric calibration. Considering the deficiencies of current on-orbit calibration, this paper proposes adjusting the size of the variable diaphragm at the entrance pupil and the integration time to attain large dynamic attenuation, converting the radiometric calibration into absolute geometric calibration of the attenuation device, and realizing a self-calibrating real entrance pupil imaging spectrometer (SCREPIS) that can be directly used to view the Earth and the Sun and quickly obtain apparent reflectance data. An initial structural design method based on the distance between individual mirrors is proposed according to the instrument design requirements. The design of a real entry pupil image-side telecentricity off-axis three-reflector front optical system with a 7° field of view along the slit direction, a 3.7 systematic F-number, and a 93 mm focal length is finally realized, and the system image plane energy is verified to change proportionally to the variable diaphragm area. Finally, the front system and rear Offner optical system are jointly simulated and optically designed. The system provides instrumental support for cross-calibration and theoretical support and a technical basis for planning space-based radiation references. Full article

Accurate estimation of chlorophyll content is essential for understanding the growth status and optimizing the cultivation practices of Ginkgo, a dominant multi-functional tree species in China. Traditional methods based on chemical analysis for determining chlorophyll content are labor-intensive and time-consuming, making them unsuitable for large-scale dynamic monitoring and high-throughput phenotyping. To accurately quantify chlorophyll content in Ginkgo seedlings under different nitrogen levels, this study employed a hyperspectral imaging camera to capture canopy hyperspectral images of seedlings throughout their annual growth periods. Reflectance derived from pure leaf pixels of Ginkgo seedlings was extracted to construct a set of spectral parameters, including original reflectance, logarithmic reflectance, and first derivative reflectance, along with spectral index combinations. A one-dimensional convolutional neural network (1D-CNN) model was then developed to estimate chlorophyll content, and its performance was compared with four common machine learning methods, including Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF). The results demonstrated that the 1D-CNN model outperformed others with the first derivative spectra, achieving higher CV-R² and lower RMSE values (CV-R² = 0.80, RMSE = 3.4). Furthermore, incorporating spectral index combinations enhanced the model’s performance, with the 1D-CNN model achieving the best performance (CV-R² = 0.82, RMSE = 3.3). These findings highlight the potential of the 1D-CNN model in strengthening the chlorophyll estimations, providing strong technical support for the precise cultivation and the fertilization management of Ginkgo seedlings. Full article

The scroll disc is a critical functional component of the scroll compression mechanism, and its machining precision and quality directly impact the performance and longevity of the compressor. Current machining methods for scroll profiles face challenges in simultaneously achieving wide applicability, high precision, and high efficiency. This paper addresses issues related to unsmooth toolpaths of machine tool axes and high acceleration in the rotary axis during redundant degrees of freedom scroll profile machining. This paper proposes a toolpath optimization method for redundant axes, with optimization objectives focused on reducing the counts of directional changes in the linear axes and smoothing the trajectories of all axes. Experimental results demonstrate that the proposed method offers higher machining efficiency compared to traditional polar coordinate machining. Full article

In this study, a multiscale model is developed through secondary development (UMAT and UEXTERNALDB) in Abaqus with the objective of simulating the thermal deformation process with dynamic recrystallization behavior. The model couples the finite element method (FEM) with the multiphase field model (MPFM), thereby establishing bidirectional coupling between macroscopic mechanical behavior and microstructural evolution. A comparison between the single-element hot compression simulation and experimental results demonstrates that the model accurately simulates both the macroscopic mechanical behavior and microstructural evolution during the thermal deformation process, thereby exhibiting high precision. Simulations of the reduction pretreatment (RP) process under different reduction amounts and billet surface temperatures demonstrate that increasing the reduction amount and billet surface temperature significantly enhances both plastic deformation and the volume fraction of dynamic recrystallization in the billet core. This results in the closure of core voids and the refinement of the core microstructure, thereby providing valuable guidance for the development of optimal reduction pretreatment (RP) processes. Full article

Droplet quality in drop-on-demand (DoD) Electrohydrodynamic (EHD) inkjet printing plays a crucial role in influencing the overall performance and manufacturing quality of the operation. The current approach to droplet printing analysis involves manually outlining/labeling the printed dots on the substrate under a microscope and then using microscope software to estimate the dot sizes by assuming the dots have a standard circular shape. Therefore, it is prone to errors. Moreover, the dot spacing information is missing, which is also important for EHD DoD printing processes, such as manufacturing micro-arrays. In order to address these issues, the paper explores the application of feature extraction methods aimed at identifying characteristics of the printed droplets to enhance the detection, evaluation, and delineation of significant structures and edges in printed images. The proposed method involves three main stages: (1) image pre-processing, where edge detection techniques such as Canny filtering are applied for printed dot boundary detection; (2) contour detection, which is used to accurately quantify the dot sizes (such as dot perimeter and area); and (3) centroid detection and distance calculation, where the spacing between neighboring dots is quantified as the Euclidean distance of the dot geometric centers. These stages collectively improve the precision and efficiency of EHD DoD printing analysis in terms of dot size and spacing. Edge and contour detection strategies are implemented to minimize edge discrepancies and accurately delineate droplet perimeters for quality analysis, enhancing measurement precision. The proposed image processing approach was first tested using simulated EHD printed droplet arrays with specified dot sizes and spacing, and the achieved quantification accuracy was over 98% in analyzing dot size and spacing, highlighting the high precision of the proposed approach. This approach was further demonstrated through dot analysis of experimentally EHD-printed droplets, showing its superiority over conventional microscope-based measurements. Full article

Organic field-effect transistors (OFETs) require ultra-precise electrical measurements due to their unique charge transport mechanisms and sensitivity to environmental factors, yet commercial semiconductor parameter analysers capable of such measurements are prohibitively expensive for many research laboratories. This study introduces a novel, cost-effective, and portable setup for high-precision OFET characterisation that addresses this critical need, providing a feasible substitute for conventional analysers costing tens of thousands of dollars. The suggested system incorporates measurement, data processing, and graphical visualisation capabilities, together with Bluetooth connectivity for local operation and Wi-Fi functionality for remote data monitoring. The device consists of a motherboard and specialised cards for low-current measurement, voltage measurement, and voltage generation, providing comprehensive OFET characterisation, including transfer and output characteristics, in accordance with IEEE-1620 standards. The system can measure current from picoamperes to milliamperes, with voltage measurements supported by high input resistance (>100 MΩ) and a voltage generation range of −30 V to +30 V. This versatile and accessible approach greatly improves the opportunities for future OFET research and development. Full article

Precision machining technology has received significant attention from researchers and engineers. With the increasing complexity of product designs and continuous advancements in high-tech industries, the precision requirements for manufacturing are constantly escalating. For researchers who are new to precision machining, conducting experiments directly on commercial equipment is resource-intensive and does not accommodate diverse working scenarios. Therefore, designing a generalized precision machining experimental test platform is particularly important. This paper presents a practical plan to construct such a platform, integrating key components such as a gantry-type Cartesian coordinate robot, a 2D rotary table, a 2D precision slide stage, a galvanometer, and a telecentric lens. The platform serves as a test environment for verifying the feasibility of various precision machining control algorithms. It not only demonstrates the desired stability and scalability but also offers a user-friendly operational interface via the LabVIEW front panel. This facilitates simple and efficient experimental operations, providing an effective and reliable environment for testing precision machining control algorithms. Full article

The combined complexity of wind turbine systems and harsh operating conditions pose significant challenges to the accuracy of operational data in Supervisory Control and Data Acquisition (SCADA) systems. Improving the precision of data cleaning for high proportions of stacked abnormalities remains an urgent problem. This paper deeply analyzes the distribution characteristics of abnormal data and proposes a novel method for abnormal data cleaning based on a classification processing framework. Firstly, the first type of abnormal data is cleaned based on operational criteria; secondly, the quartile method is used to eliminate sparse abnormal data to obtain a clearer boundary line; on this basis, the Random Sample Consensus (RANSAC) algorithm is employed to eliminate stacked abnormal data; finally, the effectiveness of the proposed algorithm in cleaning abnormal data with a high proportion of stacked abnormalities is verified through case studies, and evaluation indicators are introduced through comparative experiments to quantitatively assess the cleaning effect. The research results indicate that the algorithm excels in cleaning effectiveness, efficiency, accuracy, and rationality of data deletion. The cleaning accuracy improvement is particularly significant when dealing with a high proportion of stacked anomaly data, thereby bringing significant value to wind power applications such as wind power prediction, condition assessment, and fault detection. Full article

Background/Objectives: The role of artificial intelligence (AI) in dentistry is becoming increasingly significant, particularly in diagnosis and treatment planning. This study aimed to assess the sensitivity, specificity, accuracy, and precision of AI-driven software in analyzing dental panoramic radiographs (DPRs) in patients with permanent dentition. Methods: Out of 638 DPRs, 600 fulfilled the inclusion criteria. The radiographs were analyzed by AI software and two researchers. The following variables were assessed: (1) missing tooth, (2) root canal filling, (3) endodontic lesion, (4) implant, (5) abutment, (6) pontic, (7) crown, (8) and sound tooth. Results: The study revealed very high performance metrics for the AI algorithm in detecting missing teeth, root canal fillings, and implant abutment crowns, all greater than 90%. However, it demonstrated moderate sensitivity and precision in identifying endodontic lesions and the lowest precision (65.30%) in detecting crowns. Conclusions: AI software can be a valuable tool in clinical practice for diagnosis and treatment planning but may require additional verification by clinicians, especially for identifying endodontic lesions and crowns. Due to some limitations of the study, further research is recommended. Full article

The advent of precision agriculture has highlighted the necessity for the careful determination of crop phenology at increasingly smaller scales. Although remote sensing technology is extensively employed for the monitoring of crop growth, the acquisition of high-precision phenological data continues to present a significant challenge. This study, conducted in Youyi County, Shuangyashan City, Heilongjiang Province, China, employed time-series spectral index data derived from Sentinel-2 remote sensing images to investigate methodologies for the extraction of pivotal phenological phases during the primary growth stages of maize. The data were subjected to Savitzky–Golay (S-G) filtering and cubic spline interpolation in order to denoise and smooth them. The combination of dynamic thresholding with slope characteristic node recognition enabled the successful extraction of the jointing and tasseling stages of maize. Furthermore, a comparison of the extraction of phenophases based on the time-series curves of the NDVI, EVI, GNDVI, OSAVI, and MSR was conducted. The results showed that maize exhibited different sensitivities to the spectral indices during the jointing and tasseling stages: the OSAVI demonstrated the highest accuracy for the jointing stage, with a mean absolute error of 3.91 days, representing a 24.8% improvement over the commonly used NDVI. For the tasseling stage, the MSR was the most accurate, achieving an absolute error of 4.87 days, with an 8.6% improvement compared to the NDVI. In this study, further analysis was conducted based on maize cultivation data from Youyi County (2021–2023). The results showed that the maize phenology in Youyi County in 2021 was more advanced compared to 2022 and 2023, primarily due to the higher average temperatures in 2021. This study provides valuable support for the development of precision agriculture and maize phenology monitoring and also provides a useful data reference for future agricultural management. Full article

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO₃), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties. Full article

Photopolymerization-based three-dimensional (3D) printing techniques such as stereolithography (SLA) attract considerable attention owing to their superior resolution, low cost, and relatively high printing speed. However, the lack of studies on improving the mechanical properties of 3D materials highlights the importance of delving deeper into additive manufacturing research. These materials possess considerable potential in the medical field, particularly for applications such as anatomical models, medical devices, and implants. In this study, we investigated the enhancement of mechanical strength in 3D-printed photopolymers through the incorporation of potassium titanate powder (K₂Ti₈O₁₇), with a particular focus on potential applications in medical devices. The mechanical strength of the photopolymer containing potassium titanate was analyzed by measuring its flexural strength, hardness, and tensile strength. Additionally, poly(ethylene glycol) (PEG) was used as a stabilizer to optimize the dispersion of potassium titanate in the photopolymer. The flexural strengths of the printed specimens were in the range of 15–39 MPa (Megapascals), while the measured surface hardness and tensile strength were in the range of 41–80 HDD (Hardness shore D) and 2.3–15 MPa, respectively. Furthermore, the output resolution was investigated by testing it with a line-patterned structure. The 3D-printing photopolymer without PEG stabilizers produced line patterns with a thickness of 0.3 mm, whereas the 3D-printed resin containing a PEG stabilizer produced line patterns with a thickness of 0.2 mm. These findings demonstrate that the composite materials not only exhibit improved mechanical performance but also allow for high-resolution printing. Furthermore, this composite material was successfully utilized to print implants for pre-surgical inspection. This process ensures the precision and quality of medical device production, emphasizing the material’s practical value in advanced medical applications. Full article

The 2015 Tianjin Port chemical explosion highlighted the severe environmental and structural impacts of industrial disasters. This study presents an Adaptive Weighted Coherence Ratio technique, a novel approach for assessing such damage using synthetic aperture radar (SAR) data. Our method overcomes limitations in traditional techniques by incorporating temporal and spatial weighting factors—such as distance from the explosion epicenter, pre- and post-event intervals, and coherence quality—into a robust framework for precise damage classification. This approach effectively captures extreme damage scenarios, including crater formation in inner blast zones, which are challenging for conventional coherence scaling. Through a detailed analysis of the Tianjin explosion, we reveal asymmetric damage patterns influenced by high-rise buildings and demonstrate the method’s applicability to other industrial disasters, such as the 2020 Beirut explosion. Additionally, we introduce a technique for estimating crater dimensions from coherence profiles, enhancing assessment in severely damaged areas. To support structural analysis, we model air pollutant dispersal using HYSPLIT simulations. This integrated approach advances SAR-based damage assessment techniques, providing rapid reliable classifications applicable to various industrial explosions, aiding disaster response and recovery planning. Full article

Mildew in maize kernels is typically caused by various fungi, necessitating prompt detection and treatment to minimize losses during harvest and storage. In this study, a deep learning YOLOv5s algorithm based on machine vision technology was employed to develop a maize seed surface mildew detection model and to enhance its portability for deployment on additional mobile devices. To guarantee the fruitful progression of this research, an initial experiment was conducted on maize seeds to obtain a sufficient number of images of mildewed maize kernels, which were classified into three grades (sound, mild, and severe). Subsequently, a maize seed image was extracted to create an image of a single maize seed, which was then divided to establish the data set. An enhanced YOLOv5s–ShuffleNet–CBAM model was ultimately developed. The results demonstrated that the model achieved with an mAP50 value of 0.955 and a model size of 2.4 MB. This resulted in a notable reduction in the model parameters and calculation amount while simultaneously enhancing model precision. Furthermore, K-fold cross-validation demonstrated the model stability, and Grad-CAM validated the model effectiveness. In the future, the proposed lightweight model in this study can be applied to other crops in the context of portable or online inspection systems, thus advancing effective and high-quality agricultural applications. Full article