According to data gathered by the Korean National Police Agency, 1,028,777 vehicle-to-vehicle traffic accidents and 125,597 vehicle-to-pedestrian traffic accidents occurred in 2020. To decrease the occurrence possibility of traffic accidents, advanced driver-assistance system (ADAS), which provides assistance in regions that drivers do not recognize, have attracted increasing interest. Among the representative functions of ADAS, autonomous emergency braking (AEB) is a valuable system for accident prevention and mitigation. In this study, to evaluate AEB by using dual cameras, actual vehicle tests were conducted. In addition, the theoretically calculated values, results obtained using dual cameras, and results obtained using measuring instruments were compared and analyzed. The result values using the proposed theoretical formula and those of the actual vehicle test using the measurement instruments were compared and analyzed. The minimum and maximum error rates were 0.11% and 4.45%, respectively. For results obtained using dual cameras and measurement instruments, the minimum and maximum error rates were 1.80% and 8.89%, respectively. In the development stage of ADAS especially when developing AEB system, the use of the theoretical formula and dual cameras can decrease the cost burden compared to that associated with testing.
To date, the spring stiffness of resilience pads was mostly evaluated based on conventional (site measurement and laboratory tests) methods. Most studies in the past analyzed the effects of the deterioration of resilience pads on track damage. To examine the deterioration of resilience pads, evaluations were conducted based on laboratory tests using site measurements and samples were collected from the site, or based on loading tests using special equipment. such as TSS. However, no methodology was proposed to prove the theoretical equations of Zimmermann which compute the reaction force at the rail support point. Hence, this study aimed to prove that the reaction force increased if spring stiffness at the rail support point increased; this was achieved by using a pressure sensor according to the theoretical equations of Zimmermann. Furthermore, we aimed to propose a method to evaluate the spring stiffness of resilience pads to predict the extent of deterioration of the pads based on the increase in the pressure measured by a pressure sensor.
Unlike the winding structure of existing three-phase single winding motors, the winding structure of the dual winding motor (DWM) contains a master part and two slave parts. Thus, when the master part fails, it can be driven using the remaining slave part. It is applicable to various electric parts driving the integrated electric brake (IEB) system; thereby securing the high reliability of vehicle parts. However, in the existing DWM, there is an overheating problem owing to the increase in current because it operates with half the motor during the faulty mode. Therefore, a compensation method for the increase in current in a faulty mode was employed by increasing the stacking length of the DWM. However, although it solves the overheating problem of the DWM in the faulty mode, the motor output performance and braking performance of the IEB system are degraded in the normal mode because of the change in the motor control parameters. Thus, in this paper, we propose a DWM with winding switching (WS) to simultaneously solve the DWM overheating problem in the faulty mode and the DWM’s performance degradation problem in the normal mode. The output performance of the DWM with an increased stack length and the previously developed base model are compared to verify the performance of the proposed DWM with WS. As a result of the comparison, unlike the DWM with an increased stack length, in which the maximum speed is reduced by 20.5% compared to the existing base model during quick braking in the normal mode, the proposed DWM with WS has no performance degradation. In other words, it is confirmed that the proposed DWM with WS effectively solves the overheating problem during faulty mode, while simultaneously solving the performance degradation problem of the IEB system during the normal mode.
Liquefied Natural Gas (LNG) is one of the major renewable energy sources and is stored and carried in a storage tank that is designed following international standards. Since LNG becomes highly unstable when it encounters oxygen in the air, a leakage from an LNG storage tank can cause a catastrophic industrial accident. Thus, the inspection of LNG storage tanks is one of the priorities to be completed before LNG is stored in a storage tank. Recently, the usage of Phased Array Ultrasonic Testing (PAUT) has been gradually increasing as the risks of RT emerge. PAUT has some obstacles to overcome in order to substitute RT, such as efficiency and accuracy. Specifically, the cost issue must be addressed. Therefore, many attempts to combine PAUT with Artificial Neural Networks (ANN) have been made. PAUT provides many types of 2D images of the inspected weldment. The S-scan is one of the 2D images provided by PAUT, and it displays the cross-sectional view of the specimen with a single transducer. The inspectors examine the S-scan image and other provided images of PAUT to detect, classify and size the flaw that exists in the weldment so that the decision of whether the inspected weldment with the flaw is acceptable can be made. Nowadays, most of the previous research on PAUT and ANN focuses on detecting and classifying the flaws in B-scan or S-scan images. However, the last step to determine the flaws’ acceptability is not yet covered. In this study, the flaw acceptance criteria of PAUT in various international standards are listed. EXTENDE CIVA is used to create the PAUT S-scan images. The S-scan images are labeled with the listed acceptance criteria. Then, they are used in Mask R-CNN training. After the training, some new S-scan images with flaws are used to test the performance, and this showed 96% precision and 87% recall. With the algorithm, the acceptability of a flaw in a weldment can be determined efficiently and it will reduce the burden of PAUT usage and reduce the time required for a full-length inspection.
Here, the capability of the Bat algorithm optimized extreme learning machines ELM (Bat-ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (i) using air temperature (Ta) as input for predicting Tw and (ii) using Ta and the periodicity (i.e., day, month and year number). River Tw calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat-ELM accurately predicts the Tw and surpassed all other models with coefficient of correlation (R) values ranging within the limits of 0.973 to 0.981, and the Nash-Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat-ELM using the periodicity by enhancing its ability to estimate river Tw.
Urban railway sleeper floating track (STEDEF) reduces the block vibration transmitted to the subgrade structure by structurally separating the sleeper and the concrete bed, using a rubber boot and a resilient pad. Recently, the replacement of rubber boot material (SBR) after long-term wear and tear has become of utmost importance because of durability problems such as deformation, tearing, and abrasion. This study investigates rubber boots—a component of the urban railway sleeper floating track—to resolve these concerns and proposes the material and shape of a novel rubber boot. The proposed rubber boot reduces the maximum displacement and strain by more than 83% and 90%, respectively, compared with the existing rubber boots. In addition, the results of numerical analysis and indoor tests show that type 3 rubber boots can prevent displacement and stress generation in rubber boots.
Over the past 25 years, maintenance processes for the wooden sleeper turnouts of sleeper floating tracks (known as STEDEF) have been insufficient, lacking accurate maintenance history and failure cause analyses. Currently, damaged parts are repeatedly replaced without an analysis of the cause of their failure. Therefore, it has been difficult to predict potential damages due to the lack of maintenance history data on the replaced items and the timing of the turnout component. This study proposes a turnout maintenance plan using the failure mode and effects analysis (FMEA) technique to analyze the turnout maintenance history data for a period spanning 10 years. The maintenance damage type of the turnout was classified to confirm the cause of damage and the damage frequency. Additionally, components that could lower the occurrence score were selected based on a risk analysis of the turnover. Therefore, it was possible to reduce the risk occurrence score by improving the size of the turnout top plate and the number of bolts.
In the present paper, we propose a new approach for monthly streamflow prediction based on the extreme learning machine (ELM) and the metaheuristics Bat algorithm (BAT-ELM). The performances of the BAT-ELM were compared to those of ELM, support vector regression (SVR), Gaussian process regression (GPR), multilayer perceptron neural network (MLPNN), and the generalized regression neural network (GRNN). The proposed models have been applied using data from three hydrometric stations located in the Cheliff Basin, Algeria. It was shown that the BAT-ELM was more satisfactory than the standalone models. The numerical results showed that the BAT-ELM achieved the highest numerical performances with correlation coefficient and Nash-Sutcliffe efficiency ranging from 0.927 to 0.973 and from 0.846 to 0.944, respectively, much higher than the values obtained using the MLPNN, GRNN, SVR, GPR and ELM approaches, respectively. Obtained results demonstrate that the BAT-ELM presents interesting alternative algorithm for predicting high extreme streamflow data
This study entailed performance tests to confirm the bond performance of the proposed new repair material and the pressurization effect of the developed mechanical pressurizing equipment. The physical property changes of the new repair material were reviewed by varying the mixing ratio of high aluminate cement (HAC)-mixed mortar. Strength tests were performed according to the mixing ratios of polymer and silica fume to improve the bond performance. To improve water retention, the mixing ratios of the cellulose and nylon fibers were adjusted, and the change in water retention was measured. The proposed repair material mixing ratio yielded the best performance when pressure was applied to the repair surface. Comparing the existing repair materials and the new repair material prepared by adjusting the ratios of HAC-mixed mortar, cellulose fiber, redispersible powder resin, and other factors confirmed that the new repair material has a high bond strength.
Various hybrid approaches combined the different deep learning and machine learning models with evolutionary optimization algorithms have improved the accuracy of streamflow forecasting problem. In this article, three deep learning models were investigated for streamflow forecasting with various lag-times at both stations (i.e., Sidi Aich and Ponteba Defluent), Algeria. Also, a machine learning (i.e., feedforward neural network (FFNN)) model was implemented to compare the forecasting accuracy of deep learning models. The particle swarm optimization (PSO) algorithm was combined to determine the hyperparameters (i.e., model structure) automatically based on adaptive moment estimation (ADAM) algorithm. The addressed two-stage hybrid models were assessed and evaluated by root mean square error (RMSE), signal-to-noise ratio (SNR), and Nash-Sutcliffe efficiency (NSE) statistical indices. Evaluating all models explained that the GRU Ⅱ two-stage hybrid model (RMSE = 35.241 m3/sec, SNR = 0.5159, and NSE = 0.7337 at Sidi Aich and RMSE = 11.074 m3/sec, SNR = 0.3600, and NSE = 0.8703 at Ponteba Defluent) was found to produce more accurate results compared to the ERNN, LSTM, and FFNN two-stage hybrid models during testing phase for forecasting streamflow.
The Olympic legacy framework was proposed by the International Olympic Committee; however, it has not yet been discussed much in academia. This study identified a set of key dimensions and items out of 7 dimensions and 39 items from the Olympic legacy framework, along with the weight of each dimension and item. Based on the judgments of 12 Korean Olympic experts collected via the Delphi-analytical hierarchal process method, the results indicate that social development through sport is the most significant dimension, followed by economic value and brand equity, and urban development. The results also reveal that the most crucial of the 39 items are health and well-being benefits from the practice of recreational sport and physical activity from the social development through sport dimension, while the intangible cultural heritage of Olympism from the culture and creative development dimension was considered the least important. The results provide useful insight for evaluating the Olympic legacy framework for host or candidate cities and countries, as well as the International Olympic Committee.
Object The aim of this study was to explore the learning experiences of student nurses’ simulation-based community visit and understand these experiences in detail. Method This study followed Colazzi’s phenomenological research method. Nineteen participants were divided into three teams and participated in focus group interviews. The research question was as follows: “How was your experience with the simulated home-visit nursing?” Results The study results uncovered four essential themes: “burden of community nursing simulation-based learning,” “solving the problems faced by patients with dementia through teamwork,” “home-visiting nursing skills learned through physical practice,” and “community nursing competency growth.” Conclusion The study results provide a basis for developing a community nursing curriculum with effective evaluation and management of community home-visiting nursing education using simulation.
The increasing development of underground infrastructure has led to the deformation of subway box structures and surrounding roadbeds, eventually resulting in cracks. Therefore, it is necessary to predict damage through effective analysis and evaluation techniques. This study examined the correlation between displacement behavior and damage in a subway box structure and proposed an analysis technique to predict the damage location and scale of a structure by comparing the results from visual inspection, on-site measurement, and numerical analysis. The proposed technique can be used to compute the external boundary conditions that may induce major deformations in a subway box structure, and to predict and evaluate the members and locations where the damage may occur. In addition, we confirm that the damaged location and scale in a subway box structure can be determined, and that the maintenance of a subway box structure can be achieved by repairing and reinforcing the damaged part. Therefore, the results of this study are expected to help accurately predict damage in subway box structures, thereby contributing to better maintenance and failure prevention of underground infrastructure.
Engineering components possess discontinuities such as cracks which develop during service while others exist due to structural and material defects. The physical discontinuities in mechanical structures lead to stress concentrations, which can trigger mechanical collapses and catastrophic failures of the structure. Stress field equations of fracture mechanics have been formulated to aid in determination of the critical fracture mechanics parameter namely; the stress intensity factors (SIFs) ahead of cracks. Mathematical formulations for fracture mechanics, however, are known to predict infinite values of stress near the crack tip because of the stress singularity at the crack tip and therefore, provide limited information. Further interpretations and a combination of results from different techniques are frequently required to obtain the complete stress condition. In order to accurately determine mode I stress intensity factors without reverting to supplemental techniques, photoelastic experimental hybrid method (PEHM) is utilized in this research. Hybrid methods synergizing mathematical, numerical and experimental data have been used with great success in experimental stress analysis. Its application in the current fracture mechanics problem shows that photoelastic experimental hybrid method stress intensity factors correspond well with those calculated from theoretical method. In addition, it is noted that varying the geometric condition 2a/W from 0.2 to 0.6 while maintaining constant load had the effect of changing K1/K0 from 1.1 to 1.5. This study synergized mathematical, numerical and experimental methods to determine the stress state at the crack tip under mode I loading. Results from centrally cracked specimens showed that three key variables namely; the applied load, size of the initially present flaw, and the material properties dictate the stress state at the crack tip and interior regions around the vicinity of the crack.
Stress freezing is an important and powerful procedure in 3-dimensional experimental stress analysis using photoelasticity. The application of the stress freezing technique to extract stress components from loaded engineering structures has, however, declined over the years even though its principles are well established. This is attributed to huge costs arising from energy consumption during the process. In addition, significant time is needed to generate the desired information from isoclinic and isochromatic fringes. To overcome the limitations of stress freezing in photoelasticity and transform it into an economical device for stress analysis in an engineering environment, a new stress freezing cycle that lasts 5 h is proposed. The proposed technique is used in several applications of elastomeric seals with different cross-sectional profiles to assess their suitability. It was found that reducing the cycle time can lead to huge energy savings without compromising the quality of the fringes. Moreover, the use of isochromatic only to extract stress components leads to a shorter processing time to achieve desirable information since the process of obtaining isoclinic data is involving. In this paper, results of stress analysis from stress-frozen elastomeric seals with various cross-sections using the new stress freezing cycle are presented.
Few studies have continuously examined the relationship between career decision-making self-efficacy variables and career-related variables in South Korea’s specific cultural context. Accordingly, this study aims to analyse (using Pearson’s correlations and structural equation modelling) the relationships between South Korean college students’ career decision-making self-efficacy, career preparation behaviour, and career decision difficulties. There were positive and negative relationships between career decision-making self-efficacy and career preparation behaviour career decision difficulties, respectively. In addition, we found a positive effect between career preparation behaviour and career decision-making self-efficacy, while career decision difficulties negatively affected career decision-making self-efficacy. Considering the standardised coefficient of the specific direct effect, the effect on career decision-making self-efficacy of career preparation behaviour was larger than that of career decision difficulties. It is recommended that career programmes are developed that help college students to independently set their career goals, actively search for career information, and promote career preparation behaviour while considering their majors. It is also recommended career counselling programmes be designed that can help them establish their self-concept and identity. These findings could provide the necessary basic data for the construction of an effective college career guidance system and inform strategies for improving college students’ career decision-making self-efficacy.
To assess the clinical feasibility of the geriatric nutritional risk index (GNRI) and prognostic nutritional index (PNI) as determinants of survival in patients with stage I to III non-small cell lung cancer (NSCLC). This retrospective study included patients with stage I to III NSCLC from all age groups. Hazard ratios (HRs) for overall survival (OS), cancer-specific survival (CSS), and relapse-free survival (RFS) were calculated using the Cox regression analysis. The concordance index (C-index) of the models was evaluated following the establishment of the prognostic models for survival. The median patient age was 69 years, and 64.6% of the patients were male. In total, 172 (65.4%) patients were classified as having stage I disease, 52 (19.8%) as stage II disease, and 39 (14.8%) as stage III disease. Using multivariate Cox regression analysis, the HRs of GNRI for OS, CSS, and RFS were 0.37 (P = .003), 0.47 (P = .041), and 0.38 (P < .001), respectively. However, the HRs of the PNI for survival outcomes were not statistically significant. Overall, age, sex, tumor-node-metastasis (TNM) stage, pleural invasion (PI), and GNRI were significant determinants of OS and constituted the OS model (concordance index [C-index], 0.824). In addition, age, TNM stage, PI, and GNRI were significant determinants of CSS and constituted the CSS model (C-index, 0.828). Finally, TNM stage, PI, lymphatic invasion, and GNRI were significant determinants of RFS and constituted the RFS model (C-index, 0.783). Our study showed that GNRI, but not PNI, was a predictor of OS, CSS, and RFS in patients with stage I–III NSCLC across all age groups. Excellent discriminant power was observed for OS, CSS, and RFS models.
Purpose: This study investigated the parenting experiences of mothers of young children born moderate-to-late preterm (MLPT) in South Korea. Methods: In this qualitative study, semi-structured focus group interviews were conducted with 10 mothers of MLPT children from infancy to preschool age. The interviews were video-recorded, transcribed verbatim, and analyzed using qualitative content analysis. Results: Four categories resulted from the analysis of parenting experiences of mothers with young MLPT children, as follows: "becoming a mother of an early-born child", "difficulties as the primary caregiver for a high-risk child", "helpful social support, but still a lack of professional support for parenting a high-risk child", and "mothers and children growing together". Conclusion: Mothers of young MLPT children experienced difficulties due to concerns about their child's health, growth and development, and insufficient child-rearing support. Therefore, social support systems should be strengthened and more aggressive nursing strategies should be adopted for mothers of young MLPT children.
This study uses the empirical wavelet transform (EWT) for improving the estimation of soil moisture. We used the bidirectional long short-term memory (BiLSTM), the support vector regression (SVR) and the Gaussian process regression (GPR) for modelling soil moisture using only soil temperature. The performances of the models were evaluated using RMSE, MAE, coefficient of correlation (R) and the Nash-Sutcliffe efficiency (NSE). Soil temperature was decomposed into several multiresolution analysis (MRA) components using the EWT, and the obtained results show that, using only soil temperature as a single input, the BiLSTM was more accurate exhibiting high performances metrics with R and NSE ranging from 0.881 to 0.948, and from 0.775 to 0.899, respectively. However, by using the EWT, high accuracies were obtained, and the GPR was the most accurate model with R and NSE values close to 0.99, showing the largest contribution of the EWT in improving the soil moisture estimation.
In the present chapter, we use the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complete ensemble EMD with adaptive noise (CEEMDAN) for dissolved oxygen (DO) prediction. First, based on water temperature (Tw), DO was modeled using three machines learning models, namely, extreme learning machine (ELM), the ELM optimized Bat algorithm (Bat-ELM) and relevance vector machine (RVM). Second, river Tw was decomposed using EMD, EEMD and CEEMDAN into several intrinsic mode functions (IMF), which were used as input to the ELM, Bat-ELM and RVM. The performances of the models were evaluated using the RMSE, MAE, coefficient of correlation (R) and the Nash-Sutcliffe efficiency (NSE). From the obtained results, the models based on EMD, EEMD and CEEMDAN estimated DO highly more accurate than the single models, with mean RMSE, MAE, R and NSE of 0.835°C, 0.571°C, 0.965 and 0.930 against the values of 2.788°C, 2.232°C, 0.511 and 0.250, respectively
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