Jaw Functional Orthopedics and Cranoficial Growth
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Multi frequency weak signals detection based on under-sampling bistable stochastic resonance
No full textSignal detection has a wide range of practical applications. Compared with traditional weak signal detection, the nonlinear effect of noise in the bistable system is typical and easy to extend, so bistable stochastic resonance detection technique has a wider applicability. In this paper, the multi-frequency weak signals detection based on under-sampling bistable stochastic resonance is studied. Based on the theory of bistable stochastic resonance, a simulation model is established to simultaneously detect multiple low-frequency weak signals. The spectrum of input and output signals is obtained through numerical simulation calculation, and the system characteristics are analyzed. When the large parameter signal does not meet the adiabatic approximation theory, the appropriate sampling coefficient can be selected to directly under-sample the input signal to make it conform to the conditions, and then the scale inverse transformation can be carried out. Finally, combined with the simulation analysis, the correctness of the under-sampling bistable stochastic resonance system to detect the multi-frequency weak signals is verified, the sampling rate is reduced, and the complexity is effectively decreased
Cross domain fault diagnosis method based on MLP-mixer network
No full textThe quality of rolling bearings determines the safety of mechanical equipment operation, and bearings with more precise structures are prone to damage due to excessive operation. Therefore, cross domain fault diagnosis of bearings has become a research hotspot. To better improve the accuracy of bearing cross domain fault diagnosis, this study proposes two models. One is a cross domain feature extraction model constructed using a mixed attention mechanism, which recognizes and extracts high-level features of bearing faults through channel attention and spatial attention mechanisms. The other is a bearing cross domain fault diagnosis model based on multi-layer perception mechanism. This model takes the feature signals collected by the attention mechanism model as input to identify and align the differences between the source and target domain features, facilitating cross domain transfer of features. The experimental results show that the mixed attention mechanism model has a maximum accuracy of 97.3 % for feature recognition of different faults, and can successfully recognize corresponding signal values. The multi-layer perception model can achieve the highest recognition accuracy of 99.5 % in bearing fault diagnosis, and it can reach a stable state when it iterates to 26, and the final stable loss value is 0.28. Therefore, the two models proposed in this study have good application value
Research on high-speed railway vehicle emergency braking control based on rail surface foreign matter identification
No full textIn order to improve the operation stability of high-speed railway vehicles, an emergency braking control method for high-speed railway vehicles based on the identification of foreign matters on the track surface is proposed. The dynamic frame scanning method is used to build the image acquisition of abnormal objects on the high-speed rail track surface, and the visual sensor and vibration sensor are used to identify and process the features of foreign objects on the high-speed rail track surface. According to the abnormal feature extraction results, the command signal in case of foreign objects is transmitted to the terminal command library of the braking control system. According to the transfer switch of the emergency braking system to display the main emergency disposal output states such as the train emergency disposal information reception, emergency disposal level, emergency disposal type and other information, the joint control method of contact pressure distribution and non-uniform heat flux is adopted, and the joint control method of control system, drive system, disc friction system and signal acquisition system is adopted. The emergency braking control module is established to realize the emergency braking control of high-speed railway vehicles according to the feature recognition results of foreign matters on the rail surface. The test results show that the control module is less affected by the vehicle wheelbase characteristics, track structure characteristics and other factors when using this method for high-speed railway vehicle emergency braking control, and has a strong ability to identify the characteristics of foreign matters on the track surface, which improves the stability and real-time performance of emergency braking
An efficiency calculation model for ball screws by accounting for lead errors
No full textTransmission efficiency is a pivotal indicator, providing a comprehensive view of the overall performance of a ball screw. While extensive research has predominantly focused on computing transmission efficiency across various operating conditions, the factors influencing the variability have often been overlooked. This study introduces an innovative method for computing transmission efficiency, which considers lead error, drawing on deformation coordination theory and load distribution. Multiple ball screws of varying precision grades underwent rigorous testing to quantify lead errors. Subsequently, each screw was matched with an identical set of nuts to measure the respective transmission efficiencies. Experimental results reveal a linear correlation between lead error and transmission efficiency when both lead error and uneven ball load distribution in ball screws are considered. The relative error between the calculated transmission efficiency results and experimental values for ball screws of different precision grades falls within the range of 0 % to 7.42 %, confirming the validity of the proposed model in this paper
A mathematical model and microcontroller-based method for measuring dielectric permittivity and discharge characteristics with Arduino ATmega 328: a case study in a physics laboratory
No full textA microcontroller-based measuring instrument and a new mathematical model are used to investigate capacitor permittivity and dielectric materials' charge-discharge characteristics. In this study, a prototype capacitive permittivity measurement apparatus for dielectric materials was carefully developed using an Arduino microcontroller, a resistor, and a capacitor. The experimental setup comprises a capacitor-resistor circuit, wherein a 5-volt power supply sourced from the microcontroller interfaces with a computer. During the charging process, a comprehensive evaluation of model-data alignment was performed, yielding values of 0.54252156, 0.9951, and 111.2508701 for the sum of squares error (SSE), the coefficient of determination (R-squared), and the sum of squares total (SST), respectively. Similarly, the analysis extended to the discharging process, unveiling values of 5.10174756, 0.962805684, and 137.1647082 for SSE, R-squared, and SST, respectively. These findings confirm the accuracy of the microcontroller that was programmed by incorporating a model in precisely measuring the relative permittivity of dielectric materials and capacitance values, with an R-squared value above 0.95 following capacitor literature benchmarks. The novelty of this study is that this configuration enabled the precise assessment of both permittivity and the charge-discharge characteristics of the dielectric materials within the capacitor. This methodology made it possible to accurately measure the permittivity and charge-discharge characteristics of dielectric materials within a capacitor. The scientific significance of this research lies in its ability to provide a carefully developed instrument capable of investigating the permittivity of dielectric materials and capacitor capacitance measurements. Scholars, international engineering communities, and academics can use this technological breakthrough to advance research into dielectric material properties and capacitor characteristics
Gabor wavelet transform combined with area CNN in appearance intelligent detection of stayed cables
No full textThe major structure for transmitting forces in a cable-stayed bridge is the stay cable. If the stay cable system fails, the whole bridge will collapse. At present, there is no very mature technical means for the detection of bridge cables. The most common method is for workers to perform regular manual inspection and replacement of cables. Although the defect condition of the cable can be detected to a certain extent, the real-time monitoring of its health cannot be realized. Therefore, the research used the median filter, mean standardization method and Retinex algorithm to preprocess the image data. And after extracting the stay cable picture’s macroscopic characteristics using the Gabor wavelet transform, it implemented image segmentation using the OTSU approach. The obtained feature images were applied to the Faster Regional Convolution Neural Network (RCNN) recognition model to detect microscopic defects. During the research, the training efficiency of traditional Convolution Neural Network (CNN) was not high. Therefore, Faster RCNN built a cable defect recognition model. Based on this, the research built an intelligent detection model for apparent defects of stay cables based on Gabor wavelet transform and improved RCNN. Through the experimental analysis, the model built for the study has a recognition accuracy rate of 94.14 %, which can achieve dynamic bridge health monitoring and instantly identify the condition of the stay cables, and maintain the safety of the bridge
Trajectory planning and control algorithm of industrial robot manipulator
No full textAs the industrial robot task becomes more complex, the difficulty of trajectory planning and tracking control of manipulator is gradually increasing. To minimize the vibration during the manipulator motion and improve the planning accuracy, the method of quintic polynomial combined with non-uniform B-spline interpolation is studied for joint space (JS) planning. The trajectory tracking system is easily affected by friction nonlinearity and parameters. So a JS trajectory tracking controller based on based on fuzzy neural network (FNN) is designed. Through simulation experiments, the curve obtained by the planning method studied is smoother and the planning error is minimum. The maximum position error is 0.09 rad, and the speed error is not more than 0.1 rad/s. The controller performance test results under different parameters show that the W^, c^, κ^ parameter in FNN can be adjusted in real time, and the value will not affect the performance of the controller. The fluctuation range of trajectory error of different joints is within ±0.2×10-5rad, which indicates that the performance of AFNNC controller studied is better. And its response time is the shortest and its robustness is better when the load changes suddenly
Experimental tests and modeling of H2S-CO2-brine systems – a case study
No full textA geochemical study aimed to determine the impact of CO2 and H2S, mixtures on the representative formation rocks from the Dębowiec Fm. and Paralic series of the Upper Silesian Coal Basin, and the adjacent Małopolska Block (Poland) was performed. In the way of experiments and hydrochemical modeling the following goals were achieved: determination of the impact of acid gases on the mineralogical composition and porosity, and the assessment of mineral trapping capacity of cap rocks. Dissolution of skeletal grains, as the dominant process (the most distinct in carbonates and chlorite) was determined by means of SEM analysis in all of the samples. The increase in porosity at the injection stage, depending on the mineralogy of samples was caused by the decomposition of calcite and siderite or ankerite (Dębowiec Fm.), daphnite, clinochlore, and siderite (Paralic series) and hematite, ankerite, dolomite (Małopolska Block). After 10 000 years of simulated storage, the total porosity decreased in the cap rocks by several percent points, mainly due to precipitation of saponite, muscovite, gibbsite, phlogopite and dawsonite, in favor of the rock insulating properties. Among the secondary minerals enabling the trapping of CO2 and S in simulated storage there were observed: dolomite and pyrite (Dębowiec Fm.), dolomite, calcite and pyrite (Paralic series) and siderite, anhydrite, pyrite (Małopolska Block). Maximum calculated mineral-trapping capacity, calculated based on the results of kinetic modeling, reached 43.4 kgCO2/m3 and 44.9 kgS/m3 for CO2+ H2S co-injection into the Dębowiec Fm. Miocene rock
Sustainability assessment of CO2 valorisation routes for Latvia: LCA, S-LCA and LCCA
No full textTo initiate and maintain the European Green Deal transformative policies, an evident-based multi-sectoral forecasting model needs to be timely and effectively deployed. The overall decarbonisation solutions proposed in this research can be defined as regional CO2 “value spots”– areas in regions where CO2 can be directly (CO2-based new products) or virtually (change in planning and implementation) utilised for the development of high-added value products, ensuring decarbonisation of rural areas, as well as promoting economic growth of the regions. Within the framework of this work, three scenarios for using carbon dioxide are analysed – its use in methanol production, cement production and open-air algae ponds. The analysis aims to assess the potential environmental impacts of CO2 utilisation and consider the impact on the environment, human health, labour rights, working conditions, social equity, and other social factors, as well as costs and economic sustainability. LCA provides a decision-making platform to understand the mid-term and long-term environmental effects of CO2 valorisation scenarios according to the ISO Standard 14044 standard requirements. Sensitivity analysis is performed to exclude high input data uncertainties (if any) and identify model behaviour factors. Effects of CO2 valorisation scenarios on social endpoints (well-being of stakeholders) are identified via S-LCA based on multi-regional input/output methods of qualitative and quantitative generic data. The S-LCA include health and safety, cultural heritage, and governance impact categories covering the interests of such stakeholder groups as workers, the local community, society, and consumers. Cost-effectiveness of CO2 valorisation scenarios is performed. Regional valorisation scenarios are assessed and benchmarked via regional development sustainability indicators. A comparative assessment of core indicators is performed
Characterization of 3D-Radar images of pavement devoid damage based on FDTD
No full textAccurate judgement of devoid damage information by 3D-Radar is an effective way of repairing damage in nondestructive pavements. In order to systematically analyse the characteristics of devoid damage under nondestructive pavements in 3D-Radar response. In this study, the 3D-Radar response to devoid damage of different sizes, locations and moisture contents was quantified by FDTD orthorectified simulations. Data acquisition of the pre-buried devoid damage on site was carried out using 3D-Radar, compared with the orthorectified simulation results and numerical analysis. The detection effect was also verified by relying on the project. The results show that the radar wave characteristics of the devoid damage are obvious. Different colour and waveform image characteristics in B-Scan in the presence and absence of water at the location; the size of the devoid also has an impact on the image characteristics. It depends on the footprints and size of the devoid. It creates “upward-convex”, “down-concave” and straight features; the presence of the devoid characteristics in the 3D-Radar mapping will enhance the confidence of the devoid identification through field tests and engineering verification