In this study, the optimal driving frequency was derived through finite element analysis (FEA) to optimize the developed piezoelectric ultrasonic medical devices(PUMD) for bone surgery. The core of the PUMD is the piezoelectric ceramic (PZT), which is a vibrator that generates vibration energy. The piezoelectric ceramic shows the maximum current value with respect to the input voltage at the resonance frequency, which generates the maximum mechanical vibration. In the past, various studies have been conducted related to the analysis of PUMD, but most of the research so far has been limited to free vibration analysis. However, in order to derive the accurate resonant frequency, the initial stress generated by bolt tightening in the bolt-clamped Langevin type transducer (BLT) must be considered. In this study, after designing a PUMD, the driving performance according to the bolt tightening value was analyzed through FEA, and this was experimentally verified. First, the resonance mode and frequency response were confirmed through modal and harmonic analysis at 20-40 kHz, which is known as the optimal driving frequency band of PUMD for bone surgery. In addition, the design of the PUMD was confirmed by checking the mechanical behavior of the tip and the piezoelectric ceramic at the resonant frequency. Consequentially, the characteristic evaluation was performed, and it was confirmed that the resonant frequency result derived through the FEA was reasonable. Through this study, we presented a more rational FEA method than before for BLT transducers. We expect that this will shorten the time and cost of developing a PUMD, and will enable the development of more stable and high-quality products.
A difference in subgrade settlement between two rails of a track manifests as lateral differential subgrade settlement. This settlement causes unsteadiness in the motion of trains passing through the corresponding area. To illustrate the effect of lateral differential subgrade settlement on the dynamic response of a vehicle-track coupling system, a three-dimensional vehicle-track-subgrade coupling model was formulated by combining the vehicle-track dynamics theory and the finite element method. The wheel/rail force, car body acceleration, and derailment factor are chosen as evaluation indices of the system dynamic response. The effects of the amplitude and wavelength of lateral differential subgrade settlement as well as the driving speed of the vehicle are analyzed. The study reveals the following: The dynamic responses of the vehicle-track system generally increase linearly with the driving speed when the train passes through a lateral subgrade settlement area. The wheel/rail force acting on a rail with a large settlement exceeds that on a rail with a small settlement. The dynamic responses of the vehicle-track system increase with the amplitude of the lateral differential subgrade settlement. For a 250-km/h train speed, the proposed maximum amplitude for a lateral differential settlement with a wavelength of 20 m is 10 mm. The dynamic responses of the vehicle-track system decrease with an increase in the wavelength of the lateral differential subgrade settlement. To achieve a good operation quality of a train at a 250-km/h driving speed, the wavelength of a lateral differential subgrade settlement with an amplitude of 20 mm should not be less than 15 m. Monitoring lateral differential settlements should be given more emphasis in routine high-speed railway maintenance and repairs.
KSII Transactions on Internet and Information Systems (TIIS)
/
제17권12호
/
pp.3330-3344
/
2023
This paper proposes an antenna performance prediction model in the autonomous driving radar manufacturing process. Our research work is based upon a challenge dataset, Driving Radar Manufacturing Process Dataset, and a typical AutoML machine learning workflow engine, Pycaret open-source Python library. Note that the dataset contains the total 70 data-items, out of which 54 used as input features and 16 used as output features, and the dataset is properly built into resolving the multi-output regression problem. During the data regression analysis and preprocessing phase, we identified several input features having similar correlations and so detached some of those input features, which may become a serious cause of the multicollinearity problem that affect the overall model performance. In the training phase, we train each of output-feature regression models by using the AutoML approach. Next, we selected the top 5 models showing the higher performances in the AutoML result reports and applied the ensemble method so as for the selected models' performances to be improved. In performing the experimental performance evaluation of the regression prediction model, we particularly used two metrics, MAE and RMSE, and the results of which were 0.6928 and 1.2065, respectively. Additionally, we carried out a series of experiments to verify the proposed model's performance by comparing with other existing models' performances. In conclusion, we enhance accuracy for safer autonomous vehicles, reduces manufacturing costs through AutoML-Pycaret and machine learning ensembled model, and prevents the production of faulty radar systems, conserving resources. Ultimately, the proposed model holds significant promise not only for antenna performance but also for improving manufacturing quality and advancing radar systems in autonomous vehicles.
The ride quality has become a key component of not only design but car selling as the technology developed and the requirement of passengers grew up. Thus car industry invests lots of time and cost for the higher ride quality. The evaluation of the ride quality mainly is expressed by subjective element that drivers felt. In this paper, instead of the original transfer path analysis method, relatively new methods such as OPAX(Operational path analysis with eXogeneous inputs) and OTPA(operational transfer path analysis) are used for analyzing the main hindrance element of ride quality. With those new method, contribution rate of all paths that the vibration propagate along analyzed after driving test on the roads having different characteristic. The comprehensive hindrance elements of ride quality are deduced from the contribution rate and the improve experiment by changing one of hindrance elements for higher ride quality.
This research was thereby made to study and analysis ecological, landscape, and stability qualities of roadside cut Slopes in expressways and national highways that went through revegetation works. Also, evaluation items and framework for integrated assessment of plant structure and restored state for the maintenance were suggested. Following is the summary of the result. firstly, for stabilization of introduced native plant species on roadside cut slops, assessment on the post-project environmental management through long-term monitoring and maintenance works must be made. It signifies a form of authentic recovery and restoration by the native plants. second, current evaluation framework on the test beds is well managed with clear evaluation standards and table while its method of assessing the post management with only test bed results lacks conformity with this study site. Newly developed evaluation framework on the post construction sites is estimated to be used as assessment standards on any roadside construction sites. Third, after site investigation distinction was observed among different revegetating construction types in the long-term perspective. Due to the different duration time of vegetation state between seed-spray measures(degraded in time) and layer-spray measure(maintained), the use of coarse straw-mat mulching work or Coir net was suggested in long term maintenance of cut slopes. forth, segmentation of post environmental assessment is organized into three large categories of "ecological", "stabile", and "landscape" qualities regarding post restoration quality and stability of slope through native plants with which categories marks 50, 30, 20 points of rates. fifth, components of the post environmental assessment were segmented in twelve categories driving results from former experimental construction and newly focused items on site. In the future, it is possible to propose a customized assessment method considering the location and location of construction work if the research on the application of post-environment assessment methods for road construction surface recording is conducted systematically.
PURPOSES : The purpose of this research is to analyze the characteristics of panels that affect the evaluating results of riding quality and to evaluate the appropriateness of roughness management criteria based on ride comfort satisfaction. METHODS : In order to analyze the influence of panel characteristics of riding quality, 33 panels, consisting of civilians and experts, were selected. Also, considering the roughness distribution of the expressway, 35 sections with MRI ranging from 1.17 m/km to 4.65 m/km were selected. Each panel boarded a passenger car and evaluated the riding quality with grades from 0 to 10, and assessed whether it was satisfied or not. After removing outlier results using a box plot technique, 964 results were analyzed. An ANOVA was conducted to evaluate the effects of panel expertise, age, driving experience, vehicle ownership, and gender on the evaluation results. In addition, by using the receiver operating characteristics (ROC) curve, the MRI value, which can most accurately evaluate the satisfaction with riding quality, was derived. Then, the compatibility of MRI was evaluated using AUC as a criterion to assess whether the riding quality was satisfactory. RESULTS : Only the age of the panel participants were found to have an effect on the riding quality satisfaction. It was found that satisfaction with riding quality and MRI are strongly correlated. The satisfaction rate of roughness management criteria on new (MRI 1.6 m/km) and maintenance (MRI 3.0 m/km) expressways were 95% and 53%, respectively. As a result of evaluating the roughness management criteria by using the ROC curve, it was found that the accuracy of satisfaction was the highest at MRI 3.1-3.2 m/km. In addition, the AUC of the MRI was about 0.8, indicating that the MRI was an appropriate index for evaluating the riding quality satisfaction. CONCLUSIONS : Based on the results, the distribution of the panels' age should be considered when panel rating is conducted. From the results of the ROC curve, MRI of 3.0 m/km, which is a criterion of roughness management on maintenance expressways, is considered as appropriate.
For the optimum design of pile foundations, the determination of allowable load should take both the material strength and the geotechnical characteristics of the site into consideration. Through survey of previous construction records in Korea, it was found that most of the design is overconservative and in some cases the design load was higher than the constructed quality. Proper analysis making use of WEAP and monitoring of pile driving by PDA have been proved to solve most of the prevailing problems. In addition, the time effect in the evaluation of pile bearing capacity has the must significant effect and should not be ignored in the pile design.
Agricultural environmental indicators (AEIs) are useful tool for evaluating environmental performance induced by agri-environmental policy measures. General and specific criteria have been set to assess the linkages between policy measures and environmental states. In addition, a number of specific AEIs such as nutrient balance indicators and farm management indicators have been posit to review environmental performance associated with agri-environmental policy measures. The proposed environmental subjects encompass soil quality, qualities of underground and surface water, water resource preservation, species and genetic diversity, diversity for wildlife habitats, and agricultural landscapes. The developed AEIs may contribute to establishment or adjustment of environmental targets and ex-ante or ex-post evaluation for environmental performance associated with policy measures. In addition, the AEIs may be useful to consider introduction of new agri-environmental measures and enhance policy efficiency by assessing environmental performance, considering specific locality, and harmonizing support measures.
Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.
In recent years, environmental damage to urban area becomes serious problem due to the exhaust emissions by increasing the number of vehicle . Especially, diesel particulate matters(DPM) are hazardous air pollutant s to human health and environment. The reduction technologies of exhaust emissions from diesel engines are improvement of engine combustion, fuel quality and development of diesel exhaust aftertreatment. In this study, a diesel oxidation catalyst(DOC) that is one of diesel exhaust aftertreatments was made for performance evaluation . It was tested for NA and turbocharged engine by D-13 mode that currently be used for regulation driving test mode in Korea Scanning mobility particle sizer (SMPS) was used for the analysis of the particle size distribution with and w/o DOC. As the results , for NA and tubochartged engine, CO, THC, DPM was respectively reduced 85.7, 40.7,3.3% and 79.1, 53.1, 11.6% by DOC. Test results of particle size distribution was showed that particle number is 107 ~108per ㎤ , 2 $\times$105 ~5$\times$105$\mu\textrm{g}$/㎥ for weight concentration and 100~200nm for particle mean size in diesel engine and there is no effect to reduce the particle concentration by the DOC.
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