Jaw Functional Orthopedics and Cranoficial Growth
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Multi-scale rheological properties of municipal solid waste fly ash-asphalt mastic materials
No full textIn order to promote the resource utilization of the byproducts of municipal solid waste incineration in asphalt pavement materials, this study selected different types of waste incineration fly ash as fillers and prepared waste fly ash-asphalt mastic materials. The Brookfield viscosity test was used to investigate the variation in apparent viscosity of the waste fly ash-asphalt mastic at different temperatures. The dynamic shear rheological test was employed to study the effects of fly ash content on the viscoelastic properties of asphalt under different frequencies. The low-temperature bending beam rheological test was used to analyze the changes in creep stiffness and creep rate of the waste fly ash-asphalt mastic. Based on this, the rotating film oven aging test was conducted to investigate the mass loss and softening point increment of the waste fly ash-asphalt mastic. The results indicated that the small particle size and developed pore structure of fly ash contributed to the adsorption of asphalt components, enhancing the volume of the mastic. As the fly ash content increased, its specific surface area also increased, further promoting the increase in the viscosity of the asphalt mastic. Under low-temperature conditions, the asphalt mastic became more prone to hardening and brittleness, which resulted in poorer low-temperature cracking resistance, consistent with the ductility results
Predictive vibration diagnostics of helicopter rotating units in field conditions
No full textCurrent helicopter onboard monitoring systems are not effective enough, and most helicopters do not even have them. As helicopter unit state is not clearly known, it is subject of preventive maintenance. To maintain helicopters predictively reducing repair costs and time, the techniques are required that allow diagnosing the operating units in field conditions for all helicopters. This work is aimed to practically check the Vibropassport techniques allowing the predictive maintenance on the operating helicopter. The Vibropassport using high-order models considers the spatial vibrations in a wide frequency range, applies diagnostic parameters at the normalized scale, and allows diagnostics using a single field inspection. The main finding of the work is that the Vibropassport-based system adapted to a specific helicopter type allows the detailed diagnostics of the engines, gearboxes and transmissions based on the data of a single test in field conditions. Applicability of the Vibropassport system in field conditions was demonstrated on an operating helicopter, and the vibration-based diagnostics estimates whether the unit state complies with the thresholds common for a wide range of similar units. The Vibropassport-based system makes the predictive maintenance possible, reducing costs of maintenance and repair for helicopters, including those without onboard monitoring systems
Numerical evaluation of horn geometry influence on VHCF ultrasonic test parameters
No full textThe study presents a numerical investigation of the influence of horn geometry on the performance of a 20 kHz ultrasonic testing system used for Very High Cycle Fatigue (VHCF) applications. The system, comprising an aluminium horn and duplex 2205 steel specimen, was evaluated using COMSOL Multiphysics. Four horn geometries – conical, stepped-conical, exponential, and stepped-cylindrical – were analysed to compare their ability to amplify displacement and stress under varying input voltages. Findings indicate the importance of horn geometry in achieving optimal resonance and mechanical amplification, offering valuable insight for the design of efficient ultrasonic fatigue testing systems
Dual-aggregation feature compilation network for urban traffic object detection and pedestrian pose estimation
No full textWith the increasing complexity of urban transportation systems, object detection and pedestrian pose estimation play a crucial role in intelligent traffic management and autonomous driving technologies. However, existing feature compilation networks are often designed for single tasks and perform poorly in small object detection and high occlusion pedestrian pose estimation tasks. To address the above issues, this paper proposes an efficient feature compilation network with Dual-aggregation, compatible with both object detection and pedestrian pose estimation. This network adopts a transfer learning-like training strategy in the feature extraction network, using a micro-complex convolution structure during training to bring the training results as close as possible to global optimization. During inference, a single simple convolution is used to inherit the training results, improving the model performance while ensuring model lightweight. The feature fusion employs a global-local dual aggregation structure, simultaneously considering multi-scale global and local features. Additionally, we use multiple public datasets to create a hybrid dataset under various scenarios to validate the robustness of the network. The experiments show that the proposed method outperforms existing mainstream methods in detection accuracy for urban object detection and pedestrian pose estimation tasks, especially demonstrating better robustness in complex urban traffic scenarios
Simulation analysis and safety performance assessment of a novel am opening barrier for highway
No full textTo address the need for quick opening, easy mobility, and convenient maintenance of barrier in highway central medians, a novel Am rotatable open barrier has been designed. Based on guardrail safety performance evaluation standards, a finite element model of the vehicle-guardrail interaction is established for collision simulations to validate the adequacy of the new guardrail structure. In the meantime, full-scale vehicle crash tests are conducted to assess the safety performance of the proposed open guardrail. The results demonstrate that safety performance metrics, including vehicle post-collision acceleration, maximum dynamic inclination, maximum lateral dynamic deformation, and displacement extension values, meet standard requirements in both simulations and real-world validations. Additionally, the vehicle doesn’t penetrate, overturn, or ride over the barrier, and no rollover occurred. This indicates that the newly designed barrier not only fulfills the functions of quick opening and easy mobility but also provides excellent blocking and guiding capabilities, contributing to the enhanced safety and operational efficiency of highway service
Use of fragility curves to assess the seismic vulnerability of soft rock tunnels: a review
No full textDue to their distinct geotechnical and structural features, soft rock tunnels pose serious issues because of their seismic sensitivity. These tunnels, often constructed in formations with lower shear strength and higher deformability, are particularly susceptible to damage during earthquakes. Fragility curves, which graphically represent the probability that a structure may sustain damage up to or beyond a particular threshold as a function of seismic intensity, are essential tools for evaluating the seismic resilience of these infrastructures. This research looks closely at the use of fragility curves to assess the seismic vulnerability of soft rock tunnels. Exploring the fundamental concepts and methodologies involved in constructing fragility curves, including seismic hazard analysis, structural modeling, damage state definition, data collection and statistical analysis is looked at first. The review highlighted the integration of soft rock characteristics such as strength and deformation properties into the fragility assessment process. Key developments in the topic are covered such as how machine learning and Bayesian inference might improve the precision and usefulness of fragility curves. The paper identified key findings such as the high sensitivity of fragility curves to geotechnical properties and seismic intensity levels and emphasized the importance of accurate data collection and model calibration. Important gaps in seismic risk evaluations are filled by integrating cutting-edge methodologies, such as Bayesian inference and real-time machine learning models that clarify the seismic behaviour of soft rock tunnels in the real world. For the purpose of strengthening earthquake-resistant infrastructure in earthquake-prone areas, engineers, scholars and policymakers are given practical insights
A novel problem and algorithm for solving permuted cordial labeling of corona product between two graphs
No full textThis study has come up with a new application of permuted cordial labeling initiated by two graphs based on their corona product, furthering the cause of a better comprehension of and research into specific types of graphs. The Permuted cordial labeling construction for the corona product of graphs consisting of paths, cycles, second power of a path and second power of cycle graphs may facilitate the consideration of the properties and structures of the graphs. It helps us to study its topological properties, connectivity images, symmetries and other properties
LSGAN-Transformer life prediction method for rolling bearings under few samples
No full textAiming at the problem that it is difficult to obtain a large amount of data for bearings with complex working conditions, which leads to the inability to accurately predict their life, a rolling bearing life prediction method based on few samples, LSGAN-Transformer, is proposed. A dropout layer is added to the LSGAN generator to avoid the overfitting phenomenon that often occurs during few-sample training. The normalization of each layer in the traditional Transformer model is moved forward to the input of the decoder and encoder submodules before the residual network, forming a direct gradient path from input to output, avoiding the problem of excessive expected gradient near the output layer that often occurs in the traditional Transformer network. Verification on the PHM2012 dataset and the XJTY-SY dataset shows that the MAE and RMSE of the proposed method are greatly improved; compared with other common prediction models, the MAE and RMSE of the proposed method are improved by 30.61 % and 35.93 % respectively
Small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN)
Get PDFIn actual industrial environments, equipment failures often occur sporadically during operation, resulting in insufficient labeled data for training. To address the issues of difficult feature extraction and poor generalization caused by insufficient data in small-sample fault diagnosis, a small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN) is proposed. Firstly, dual convolution kernels are employed to extract signal features, with the large kernel capturing low-frequency components and the small kernel extracting additional features to enhance the network's expressiveness. Secondly, the channel attention mechanism adaptively adjusts the feature responses of each channel, enabling the network to focus on the most informative and relevant features while suppressing unimportant ones. Finally, the Temporal Convolutional Network (TCN) is utilized to capture dependency features within long time series, further improving the model's ability to process sequential data. Experimental results demonstrate that the DCK-CAM-TCN model significantly outperforms traditional Convolutional Neural Networks (CNNs) and other comparison models in small-sample scenarios. The results indicate the significant advantages of the DCK-CAM-TCN model in small-sample fault diagnosis
Fuzzy dynamic self-tuning based linear active disturbance rejection control for PMSM speed control
No full textIn this paper, a novel control approach, namely fuzzy dynamic self-tuning-based linear active disturbance rejection control (FDS-LADRC), is proposed for the speed loop system of permanent magnet synchronous motors (PMSMs). Specifically, a control framework based on the linear active disturbance rejection control (LADRC) is presented. Fuzzy dynamic self-regulators are developed to enable simultaneous adaptive adjustments of both the controller and observer parameters. Additionally, the stability analysis is provided. A series of numerical simulations demonstrates that FDS-LADRC achieves superior adaptivity, transient performance, disturbance rejection capability, and anti-noise ability under various operating conditions. For instance, during no-load startup, compared with the traditional LADRC, nonlinear active disturbance rejection control (ADRC), a variant of FDS-LADRC named IT2FDS which utilizes interval type-2 fuzzy systems as fuzzy dynamic self-regulators, a state-of-the-art fractional-order ADRC with fuzzy self-tuning (FSFOADRC), and sliding mode control (SMC), FDS-LADRC reduces overshoot by 10.82 %, 13.55 %, 7.36 %, 5.53 %, and 3.94 %, respectively, and shortens settling time by 0.0132 s, 0.0076 s, 0.0139 s, 0.0009 s, and 0.0156 s, respectively. Finally, corresponding real-world experiments are conducted to validate the effectiveness and superiority of FDS-LADRC