• Smart Structures and Systems
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• v.31 no.1
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• pp.57-68
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• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.

• Structural Engineering and Mechanics
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• v.90 no.1
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• pp.27-40
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• 2024

The prediction of VIV amplitude is essential for the design and fatigue life estimation of steel tubes in tubular transmission towers. Limited to costly and time-consuming traditional experimental and computational fluid dynamics (CFD) methods, a machine learning (ML)-based method is proposed to efficiently predict the VIV amplitude of steel tubes in transmission towers. Firstly, by introducing the first-order mode shape to the two-dimensional CFD method, a simplified response analysis method (SRAM) is presented to calculate the VIV amplitude of steel tubes in transmission towers, which enables to build a dataset for training ML models. Then, by taking mass ratio M^*, damping ratio ξ, and reduced velocity U^* as the input variables, a Kriging-based prediction method (KPM) is further proposed to estimate the VIV amplitude of steel tubes in transmission towers by combining the SRAM with the Kriging-based ML model. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by using three full-scale steel tubes with C-shaped, Cross-shaped, and Flange-plate joints, respectively. The results show that the SRAM can reasonably calculate the VIV amplitude, in which the relative errors of VIV maximum amplitude in three examples are less than 6%. Meanwhile, the KPM can well predict the VIV amplitude of steel tubes in transmission towers within the studied range of M^*, ξ and U^*. Particularly, the KPM presents an excellent capability in estimating the VIV maximum amplitude by using the reduced damping parameter S_G.

• Advances in aircraft and spacecraft science
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• v.5 no.2
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• pp.225-237
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• 2018

The difficulties of satellite vibration testing are due to the commonly expressed qualification requirements being incompatible with the limited performance of the entire controlled system (satellite + interface + shaker + controller). Two features cause the problem: firstly, the main satellite modes (i.e., the first structural mode and the high and low tank modes) are very weakly damped; secondly, the controller is just too basic to achieve the expected performance in such cases. The combination of these two issues results in oscillations around the notching levels and high amplitude beating immediately after the mode. The beating overshoots are a major risk source because they can result in the test being aborted if the qualification upper limit is exceeded. Although the abort is, in itself, a safety measure protecting the tested satellite, it increases the risk of structural fatigue, firstly because the abort threshold has been already reached, and secondly, because the test must restart at the same close-resonance frequency and remain there until the qualification level is reached and the sweep frequency can continue. The beat minimum relates only to small successive frequency ranges in which the qualification level is not reached. Although they are less problematic because they do not cause an inadvertent test shutdown, such situations inevitably result in waiver requests from the client. A controlled-system analysis indicates an operating principle that cannot provide sufficient stability: the drive calculation (which controls the process) simply multiplies the frequency reference (usually called cola) and a function of the following setpoint, the ratio between the amplitude already reached and the previous setpoint, and the compression factor. This function value changes at each cola interval, but it never takes into account the sensor signal phase. Because of these limitations, we firstly examined whether it was possible to empirically determine, using a series of tests with a very simple dummy, a controller setting process that significantly improves the results. As the attempt failed, we have performed simulations seeking an optimum adjustment by finding the Least Mean Square of the difference between the reference and response signal. The simulations showed a significant improvement during the notch beat and a small reduction in the beat amplitude. However, the small improvement in this process was not useful because it highlighted the need to change the reference at each cola interval, sometimes with instructions almost twice the qualification level. Another uncertainty regarding the consequences of such an approach involves the impact of differences between the estimated model (used in the simulation) and the actual system. As limitations in the current controller were identified in different approaches, we considered the feasibility of a new controller that takes into account an estimated single-input multi-output (SIMO) model. Its parameters were estimated from a very low-level throughput. Against this backdrop, we analyzed the feasibility of an LQG control in cancelling beating, and this article highlights the relevance of such an approach.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.45-56
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• 2007

As function and scale of the transit center get larger, the efficient guidance system in the transit center is essential for transit users in order to find their efficient routes. Although there are several studies concerning optimal path for the road, but insufficient studies are executed about optimal path inside the building. Thus, this study is to develop the algorithm about optimal path for car owner from the basement parking lot to user's destination in the transfer station. Based on Dijkstra algorithm which calculate horizontal distance, several factors such as fatigue, freshness, preference, and required time in using moving devices are objectively computed through rank-sum and arithmetic-sum method. Moreover, optimal public transportation is provided for transferrer in the transfer station by Neuro-Fuzzy model which is reflected on people's tendency about public transportation mode choice. Lastly, some scenarios demonstrate the efficiency of optimal path algorithm for pedestrian in this study. As a result of verification the case through the model developed in this study is 75 % more effective in the scenario reflected on different vertical distance, and $24.5\;{\sim}\;107.7\;%$ more effective in the scenario considering different horizontal distance, respectively.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.3-20
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• 2019

With advancements in technology of virtual reality, it is used for various purposes in many fields such as medical care and healthcare, but as the same time there are also increasing reports of nausea, eye fatigue, dizziness, and headache from users. These symptoms of motion sickness are referred to as cybersickness, and various researches are under way to solve the cybersickness problem because it can cause inconvenience to the user and cause adverse effects such as discomfort or stress. However, there is no official standard for the causes and solutions of cybersickness at present. This is also related to the absence of tools to quantitatively measure the cybersickness. In order to overcome these limitations, this study proposed quantitative and objective cybersickness evaluation method. We measured 128-channel EEG waves from ten participants experiencing visually stimulated virtual reality. We calculated the relative power of delta and alpha in 11 regions (left, middle, right frontal, parietal, occipital and left, right temporal lobe). Multiple regression models were obtained in a stepwise manner with the motion sickness susceptibility questionnaire (MSSQ) scores indicating the susceptibility of the subject to the motion sickness. A multiple regression model with the highest under the area ROC curve (AUC) was derived. In the multiple regression model derived from this study, it was possible to distinguish cybersickness by accuracy of 95.1% with 11 explanatory variables (P_D.MF, P_D.LP, P_D.MP, P_D.RP, P_D.MO, P_A.LF, P_A.MF, P_A.RF, P_A.LP, P_A.RP, P_A.MO). In summary, in this study, objective response to cybersickness was confirmed through 128 channels of EEG. The analysis results showed that there was a clearly distinguished reaction at a specific part of the brain. Using the results and analytical methods of this study, it is expected that it will be useful for the future studies related to the cybersickness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.336-343
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• 2017

In this study, we analyze the relationship between long-term care workers' emotional labor, professional quality of life, and service quality. The subjects of this study were 211 long-term care workers working in long-term care facilities. Self-administered questionnaires were used. In the research model, emotional labor(surface behavior, internal behavior) was verified using independent variables, with professional quality of life (compassion satisfaction, compassion fatigue) as the parameters and service quality as the dependent variables. The results of this study are as follows. First, the fit of the model was good. Second, the direct effects of emotional labor on service quality were not significant. However, there was a positive correlation between internal behavior and service quality, and a negative correlation between surface behavior and service quality. Third, professional quality of life(empathy satisfaction) was the most influential variable in terms of service quality. The results of this study demonstrate the necessity to payclinical and academic attention to long-term care workers' emotional labor and quality of life.

• Journal of the Korean Society of Food Science and Nutrition
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• v.32 no.7
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• pp.1076-1081
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• 2003

The effects of dietary supplementation of JR-22, formula containing distilled extracts of traditional herbs on maximal exercise performance and endurance were evaluated in mouse and rat model. In acute forced swimming test with 4%∼8% of the body weight attached to the tail, it was shown that dietary JR-22 supplementation increased endurance in exercise performance. There was no change of blood lactic acid, ammonia, inorganic phosphorous ion and creatine kinase activity, however ATP concentration in muscle was increased by JR-22 supplementation. Also, insulin-like growth factor-l (IGF-1) concentration in blood was significantly increased by JR-22 supplementation. In addition, the oxidative damage induced by exercise was reduced by JR-22 supplementation. In these results, we suggested that JR-22 supplementation enhanced maximal endurance exercise performance by the mechanism of increasing ATP and IGF-1 concentration and reducing oxidative damage.

• International Journal of Highway Engineering
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• v.8 no.3 s.29
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• pp.49-61
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• 2006

This study suggests long-life asphalt pavement method which can save maintenance cost by increasing the design and performance period of pavements. The high modulus asphalt binder developed and then various physical tests are performed. Laboratory performance tests and accelerated pavement test are conducted for the high modulus and conventional mixtures. The test results show that dynamic modulus values of high modulus mixtures are higher than those of the conventional mixtures, The high modulus mixtures yield better fatigue, rutting and moisture damage performance than conventional mixtures. Structural analysis is performed and a database is built up for long life asphalt pavement design. Pavement response model is developed through a multiple regression analysis program, SPSS using the database. A design software for the long life pavements is developed based on the pavement response model and laboratory and field performance tests results. In addition, optimum pavement sections and materials are suggested. The suggested AC thickness of long life asphalt pavement is 29cm. A Life cycle cost analysis(LCCA) is conducted to check the economical efficiency of the long life pavement section. The LCCA result shows that initial construction costs of long life and conventional pavements are almost equal, but long life pavement is more profitable in terms of the LCCA.

• Journal of Ocean Engineering and Technology
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• v.33 no.4
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• pp.350-357
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• 2019

The development of marine technology is expected to increase the demand for marine plants because of increasing oil prices. Therefore, there is also expected to be an increase in the demand for cylindrical structures such as URF (umbilical, riser, flowline) structures and spars, which are used operating in various seas. However, a cylindrical structure experiences vortex induced motion (VIM) in a current. In particular, for risers and umbilicals, it is important to identify the characteristics of the VIM because interference between structures can occur. In addition, various studies have been conducted to reduce VIM because it is the cause of fatigue damage to structures. The helical strake, which was developed for VIM reduction, has an excellent VIM reduction performance, but is difficult to install on structures and has a negative effect on heave motion. Therefore, the purpose of this study was to supplement the shortcomings of the helical strake and develop a high-performance reduction device. In the reduction device developed in this study, a string is placed around the structure inside the flow, causing vibration. The vibration of this string causes a small turbulence in the flow field, reducing the VIM effect on the structure. Finally, in this study, the 2-DOF motion characteristics of models without a suppression device, models with a helical strake, and models with a string were investigated, and their reduction performances were compared through model tests.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.375-388
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• 2021

This paper aims to help prevent the spread of COVID-19 by analyzing confirmed cases of COVID-19 in Daejeon. A high volume of visitors, downtown areas, and psychological fatigue with prolonged social distancing were considered as risk factors associated with the spread of COVID-19. We considered the weekly confirmed cases in each administrative district as a response variable. Explanatory variables were the number of passengers getting off at a bus station in each administrative district and the elapsed time since the Korean government had imposed distancing in daily life. We employed a mixed-effects zero-inflated Poisson regression model because the number of cases was repeatedly measured with excess zero-count data. We conducted k-means clustering to identify three groups of administrative districts having different characteristics in terms of the number of bars, the population size, and the distance to the closest college. Considering that the number of confirmed cases might vary depending on districts' characteristics, the clustering information was incorporated as a categorical explanatory variable. We found that Covid-19 was more prevalent as population size increased and a district is downtown. As the number of passengers getting off at a downtown district increased, the confirmed cases significantly increased.