Jaw Functional Orthopedics and Cranoficial Growth
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Fabrication and characterization of interdigital transducer structures as temperature sensors by two-photon lithography
No full textContinuous developments in the field of 3D printing techniques and equipment have enabled their usage in the field of electronics structures, circuits, and device fabrication in addition to many other fields. This advancement has enabled the potential fabrication of sensors using silicon-based micro or even Nanoelectronics. Currently, the manufacturing and packaging of such devices and structures are heavily reliant on lithography, which can be slow and can involve substantial processing requirements. In this paper, a temperature-sensing Interdigital Transducer (IDT) structure was designed and fabricated using Direct Laser Writing (DLW) based on Two-Photon Lithography (TPL), which is a high-resolution 3D printing technology. The TPL in a positive photoresist was combined with the physical vapor deposition method and the lift-off process to create gold IDT microstructures. The developed sensing structures were characterized using a network analyzer to determine the resonance frequency and its dependence on the temperature changes. The results showed that the IDT structures exhibit a linear response toward the changes in temperature with an average sensitivity of 0.123 MHz/°C. The most important advantage in producing the IDT structure with the additive manufacturing technique is that a very small-sized structure is produced error-free and efficiently
Application of unsupervised identification of dissolved gases in transformer oil based on spin coating film making process
No full textAddressing the issues of low efficiency and uneven collection of dissolved gases in transformer oil leading to overfitting and poor performance of identification models, we propose a novel film-making process that integrates Gaussian process and unsupervised pre-classification to enhance the recognition efficiency of dissolved gases in transformer oil. This method not only forms a thinner and more uniform separation layer, significantly improving degassing performance and collection efficiency, but also addresses the problems of insufficient data labeling and sample imbalance by introducing the K-means++ clustering algorithm and pseudo-random integration technology, thereby enhancing model robustness and generalization ability. Moreover, the designed Gaussian Process Multi-Classification (GPMC) method employs probabilistic interpretation for result presentation, which increases the accuracy of fault identification. Experimental results show that under consistent starting conditions, the RCC and ARI indicators of our pre-classification method are close to 0.8, with the test set’s recognition rate exceeding 80 %, while the GPMC method misclassified only 2.4 % of the cases in the 1800-case dataset. These improvements make our method particularly effective for handling uncertainties and imbalances in dissolved gas cases in transformer oil, showcasing its potential for practical applications
Enhancing loess deformation resistance using waste tire rubber particles
Get PDFLoess, characterized by its large pore structure and vertical joints, is prone to collapsible deformation upon moisture infiltration and significant settlement under load, threatening the stability of buildings and infrastructure. This study systematically investigates the effects of rubber particle size (10, 20, 40, and 100 mesh), content (0 %, 5 %, 10 %, 15 %, and 20 % by volume), moisture content, and freeze-thaw cycles on the deformation properties of loess. This systematic investigation distinguishes itself by using waste tire rubber particles as the sole amendment to elucidate both the individual and coupled effects of these factors. Results demonstrate that incorporating rubber particles significantly reduces the compression coefficient of loess, with optimal compressibility achieved at a 5 % rubber particle content and 40 mesh particle size. The collapsibility coefficient is minimized at a 20 mesh particle size with the same 5 % content. Moisture content significantly influences deformation behavior, with both high and low levels increasing the compression and collapsibility coefficients. The study also reveals that rubber particle-loess mixtures exhibit superior freeze-thaw resistance, with smaller increases in deformation coefficients after multiple freeze-thaw cycles compared to remolded loess. The particle size and content of rubber particles are identified as the most important factors influencing the compressibility and collapsibility of loess. This research provides specific guidelines for optimizing rubber particle size and content, controlling moisture levels, and evaluating freeze-thaw impacts to enhance the engineering performance of loess. The findings offer a scientific basis for sustainable waste tire management and advance the application of rubber particles in geotechnical engineering
A vision-based deep learning approach for non-contact vibration measurement using (2+1)D CNN and optical flow
Get PDFThis paper introduces a proof-of-concept vision-based deep learning approach for vibration measurement, proposing a factorized (2+1)D Convolutional Neural Network (CNN) model to predict four vibration metrics: acceleration, velocity, displacement, and frequency, with a focus on rigid body motion. Unlike conventional neural network models that primarily focus on frequency prediction alone, this approach uniquely enables the simultaneous estimation of four critical vibration metrics, offering a comprehensive and cost-effective alternative to traditional contact-based sensors such as accelerometers. The framework relies on the visibility of a training fiducial marker, eliminates the need for calibration in controlled settings, enhancing scalability across specific environments. A curated dataset was generated using a controlled experimental setup comprising a single object in a lab-scale environment, augmented synthetically to enhance frequency diversity. An optical flow-based preprocessing algorithm synchronized motion features in recorded video inputs with measured vibration labels, improving measurement accuracy. The proposed model achieved an average Mean Absolute Percentage Error (MAPE) of 7.51 %, with acceleration predictions exhibiting the lowest error at 4.84 % and displacement the highest at 8.80 % across varying brightness levels and object-camera distances. Techniques such as Region of Interest (ROI) cropping and multi-section frame extraction were implemented to reduce computational complexity while further enhancing accuracy. These results highlight the framework’s potential for non-invasive vibration analysis, though its generalizability is limited by the single-object dataset. Future work will expand the dataset, integrate multi-sensor inputs, explore marker-less tracking methods, and enable real-time deployment for predictive maintenance and structural health monitoring
Small targets detection in low-resolution remote sensing images based on super-resolution joint optimization
Get PDFWhile convolutional neural networks have driven remarkable progress in remote sensing object detection, persistent challenges remain in detecting small targets within low-resolution imagery due to their limited pixel representation and feature degradation during hierarchical downsampling. To address this, this study proposed the joint super-resolution and detection network (JSRDN), which synergistically optimizes SR reconstruction through task-specific detection feedback, significantly enhancing small target recognition in LR remote sensing imagery. Firstly, generator in generative adversarial network incorporates improved residual blocks, enabling enhanced perception of complex deep-level features in the SR reconstruction process. Then, a perceptual loss function is introduced into the adversarial training process, which captures perceptual discrepancies in high-level features between reconstructed images and original HR references. After that, an edge-enhancement network is designed to dynamically detect edges in intermediate features restored by the generator, prioritizing edge influence across network layers to generate discriminative features for target recognition. Furthermore, the JSRDN implements detection-driven feedback by backpropagating object recognition loss through the generator, enforcing the super-resolution process to prioritize detection-salient feature recovery. Evaluated on 64×64 low-resolution COWC datasets, JSRDN achieves 0.1819 dB peak signal-to-noise ratio (PSNR) and 7.18 % average precision (AP) improvements over the deep residual dual-attention network (DRDAN), with ablation studies and visualizations confirming its balanced optimization of reconstruction fidelity and detection-oriented feature learning. This technology can provides valuable support for small target measurement and opens new opportunities in the field
Establishing the natural frequency of oscillations of a continuous system with a concentrated mass in aviation and manufacturing engineering
Get PDFThe uneven transportation of products in the working bodies (trays) of one-mass vibrating conveyors with inertial exciters prompted the conduct of a study aimed at establishing the natural frequency of oscillations of a continuous system, namely, a long-dimensional body with distributed parameters in the form of a beam with a rigidly fixed concentrated mass. Using the Krylov-Duncan functions, the differential equation of movement of the beam was solved, taking into account the concentrated mass and the boundary conditions at its ends. The system of equations made it possible to analytically establish the natural frequency of such a continuous system. This approach was tested to establish the natural oscillation frequency of a glider, demonstrating its versatility for use in various industries. An analysis of the obtained results was carried out, and conclusions were drawn
Structural damage detection by progressive continuous wavelet transform and singular value decomposition of noisy mode shapes
No full textFor decades, damage identification based on structural mode shapes has been a popular research topic. While mode shapes provide valuable spatial structural information, the sensitivity to localized damage remains limited. In contrast, modal curvature exhibits high sensitivity to local damage, enabling precise damage localization. However, its susceptibility to environmental noise poses a significant limitation. To this end, a novel damage identification method is proposed by integrating continuous wavelet transform (CWT) and singular value decomposition (SVD). First, the CWT is applied to structural mode shapes for generating continuous wavelet coefficients. Subsequently, the SVD is performed on these coefficients, yielding new damage indicator termed as the singular image of continuous wavelet coefficients (SICWC). The SICWC enhances damage sensitivity and localization accuracy by suppressing noise-induced global trends in structural mode shapes. The effectiveness of proposed method is validated through numerical simulations of a cantilever beam under noisy conditions, as well as experimental detection of a cracked beam using mode shapes acquired via a scanning laser vibrometer. The results demonstrate that SICWC effectively mitigates the limitations of traditional damage detection methods based on mode shape and curvature
Development of a flexible piezoresistive sensor prototype using resin doped with magnetically oriented nanoparticles
No full textHigh-performance flexible piezoresistive sensors are highly useful in areas such as biomedicine, soft robotics, and pressure change detection technology. However, they require complex designs and advanced manufacturing methods. In this study, the design and fabrication of a flexible piezoresistive sensor using a flexible resin matrix doped with magnetically oriented iron nanoparticles is presented. The sensor consists of a flexible polymer resin matrix as substrate, reinforced with iron nanoparticles in different concentrations (0.5 %, 0.7 % and 1 % by weight), oriented by a magnetic field during the manufacturing process. The nanoparticles significantly enhance the piezo-resistive properties of the sensor, increasing its sensitivity and electrical conductivity under compressive loads. The sensor demonstrated high sensitivity under loads greater than 100 N in samples with concentrations of 0.7 % and 1 % of nanoparticles, and exhibited stability during cyclic testing, demonstrating durability. Additionally, stability tests showed excellent durability in repeated load cycles. Scanning Electron Microscopy (SEM) and Confocal Laser Scanning Microscopy (CLSM) confirmed the effective alignment and distribution of the nanoparticles within the matrix, enhancing conductivity. This flexible piezoresistive sensor doped with nanoparticles has great potential for future applications in technologies such as soft robotics and electronic skins, where high sensitivity and durability in pressure detection are required
Dynamic performance analysis of 1000 MW double reheat steam turbine foundation
Get PDFIn recent years, power equipment has been developing towards low-carbon, high-efficiency, and green environmental protection. The double reheat unit has been increasingly employed in power plants due to its advantages of low energy consumption and less pollution. As a core component of power plants, the dynamic performance analysis of the steam turbine foundation is essential for ensuring the overall safety of double reheat unit. For this reason, the dynamic performance of a steam turbine foundation is investigated based on the engineering background of frame-type reinforced concrete foundations of 1000 MW double reheat steam turbine set in a power plant. The solid finite element model of the steam turbine foundation is first established by using ANSYS software, along with a detailed description of foundation information and modelling methodology. Subsequently, the dynamic characteristic and response analyses of the steam turbine foundation are performed to evaluate its dynamic performance, respectively. The results indicate that the 1000 MW steam turbine foundation demonstrates satisfactory dynamic performance. Within the operating speed range, the transverse, longitudinal, and vertical vibration displacements of the foundation bearings and columns remain below 20 μm, while the vibration velocity does not exceed 3.8 mm/s, both of which comply with relevant specifications. Moreover, enhancing the stiffness of the fifth and sixth beams, along with increasing the cross-sectional area of columns C3 and C4 on the steam turbine foundation, should be considered to mitigate its vibration responses and thus improve its dynamic performance. The research findings can serve as a reference for the type selection and optimization design of 1000 MW double reheat steam turbine foundations
Study on the variation mechanism of non-linear stiffness of rubber O-ring
Get PDFO-ring dampers can be used as vibration-damping elements for short-life, low-cost engines, and the selection of a suitable rubber superelastic-viscoelastic ontological model to study their stiffness and damping is an important prerequisite for determining their vibration-damping characteristics. The superelastic-viscoelastic constitutive model consists of two models, superelastic and viscoelastic, in which the superelastic model reflects the static characteristics of the O-ring. Therefore, it is the basis of the study of dynamic characteristics to carry out the research on the static stiffness of the O-ring and to select an accurate superelastic model to describe its deformation and recovery characteristics under different working conditions. Based on the fact that the O-ring is in a small deformation range in the damper and the applicability of finite element simulation, the Mooney-Rivilin superelastic constitutive model is selected in this paper. Establish a three-dimensional finite element model of the O-ring damper, focusing on the analysis of the effect of temperature on the O-ring material properties and damper structure, to reveal the mechanism of non-linear stiffness change of the O-ring damper. At the same time, the accuracy of the hyperelastic model is verified by the test method, which lays a foundation for the study of the dynamic stiffness and damping characteristics of the O-ring. The results show that in the pre-compression state, there is a large contact pressure between the O-ring and the inner and outer rings of the damper. The contact pressure increases linearly during the compression process, and the stiffness of the O-ring changes linearly. In the non-pre-compression state, the contact pressure is 0, the contact pressure increases nonlinearly during the compression process, and the stiffness of the O-ring shows obvious nonlinear characteristics. In addition, the static stiffness of the O-ring increases with the increase of pre-compression amount, increases with the increase of material hardness, and decreases with the increase of temperature. The above research provides a reference for selecting the appropriate O-ring material size and installation conditions in the project to ensure that the O-ring can effectively withstand pressure during use