• Journal of Korea Port Economic Association
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• v.39 no.1
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• pp.115-129
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• 2023

This study analyzed the risk spillover of BDI on shipping company stock prices through the Copula-CoVaR method based on daily data from January 4, 2010, to October 31, 2022. The main empirical analysis results and policy implications are as follows. First, copula results showed that there was a weak dependence between BDI and shipping company stock prices, and PAN, KOR, and YEN were selected as the most fitting model for dynamic Student-t copula, HMM was selected as the rotated Gumbel copula, and KSS was selected as the best model. Second, in the results of CoVaR, it was confirmed that the upside (downside) CoVaR was significantly different from the upside (downside) VaR in all shipping companies. This means that BDI has a significant risk spillover on shipping companies. In addition, as for the risk spillover, the downside risk is generally lower than the upside risk, so the downside and upside risk spillover were found to be asymmetrical. Therefore, policymakers should strengthen external risk supervision and establish differentiated policies suitable for domestic conditions to prevent systematic risks from BDI shocks. And investors should reflect external risks from BDI fluctuations in their investment decisions and construct optimal investment portfolios to avoid risks. On the other hand, investors propose that the investment portfolio should be adjusted in consideration of the asymmetric characteristics of up and down risks when making investment decisions.

• The Korean Journal of Coaching Psychology
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• v.6 no.2
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• pp.1-26
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• 2022

This study investigated the effect of student-perceived teacher's calling on student's well-being and the mediating effects of student-perceived emotional support and adaptive cognitive emotion regulation strategies. We analyzed data from 494 high school students in South Korea by using structural equation modeling. The results showed that student-perceived emotional support from the teachers mediated the relationship between student-perceived teacher's calling and adaptive cognitive emotion regulation strategies. Also, adaptive cognitive emotion regulation strategies mediated the relationship between student-perceived emotional support and student's well-being. Furthermore, student-perceived emotional support and adaptive cognitive emotion regulation strategies sequentially mediated the indirect positive relationship between student-perceived teacher's calling and student's well-being. Based on these results, implications and recommendations for future research are discussed.

• Journal of Korean Home Economics Education Association
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• v.35 no.2
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• pp.91-110
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• 2023

This study aimed to develop and implement a home economics education program with a travel theme for middle school students and investigate whether it improves their subjective well-being. The ADDIE design model was used to develop and implement the home economics education program titled 'Happiness Travel with You and I'. The program consisted of six sessions focusing on time management, dietary life, and housing life. The 'Happiness Travel with You and I' program was implemented for first-year male and female students at middle school B in Cheongju City. The results revealed that the program reduced negative emotions in adolescents and positively influenced their perception of the value of life. Students rated the program highly in terms of satisfaction with class content, class interest, and recognition of the importance of the class. This study is significant as it is the first attempt to test the effect of a travel-themed home economics education program and suggests the expansion and promotion of travel and leisure education in the field of home economics education.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.167-175
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• 2023

Precipitation data is one of the essential input datasets used in various fields such as wetland management, hydrological simulation, and water resource management. In order to efficiently manage water resources using precipitation data, it is essential to secure as much data as possible by minimizing the missing rate of data. In addition, more efficient hydrological simulation is possible if precipitation data for ungauged areas are secured. However, missing precipitation data have been estimated mainly by statistical equations. The purpose of this study is to propose a new method to restore missing precipitation data using machine learning algorithms that can predict new data based on correlations between data. Moreover, compared to existing statistical methods, the applicability of machine learning techniques for restoring missing precipitation data is evaluated. Representative machine learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were applied. For the performance of classifying the occurrence of precipitation, the RF algorithm has higher accuracy in classifying the occurrence of precipitation than the ANN algorithm. The F1-score and Accuracy values, which are evaluation indicators of the classification model, were calculated as 0.80 and 0.77, while the ANN was calculated as 0.76 and 0.71. In addition, the performance of estimating precipitation also showed higher accuracy in RF than in ANN algorithm. The RMSE of the RF and ANN algorithms was 2.8 mm/day and 2.9 mm/day, and the values were calculated as 0.68 and 0.73.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.573-579
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• 2023

This study compares dose difference between the presence or absence of grid in Chest PA radiography using auto exposure control and compares image quality among presence, absence or virtual grid, and proposes a new clinically useful grid combination for chest radiography. The human body phantom was placed Chest PA position and the dosimeter was placed at T6. The same irradiation conditions and field size were applied. 30 images were obtained in the state in which grid was applied and in the state in which grid was not applied, and an additional 30 images in which the virtual grid was applied to the image without the grid were obtained. Radiation dose was presented to entrance surface dose. The image quality was analyzed by comparing the signal-to-noise and contrast-to-noise ratio. ESD decreased by 48% when the grid was not used, compared to when the grid was used. SNR and CNR increased by 32% and 30% compared to grid use when grid was not used, respectively. In the case of using the virtual grid, it increased by 18% and 16% respectively, compared to the case of using the grid. As a result of this study, it is believed that when using a virtual grid instead of a grid, the quality of the image can be maintained while reducing the patient dose.

• Economic Analysis
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• v.27 no.1
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• pp.67-90
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• 2021

This study found an interesting fact that the nonlinear relationship structure between volatility and trading volume changed before and after the COVID-19 pandemic according to empirical analysis using Bitcoin (BTC) market data that sensitively reflects investors' trading behavior. That is, their relationship appeared positive (+) in a stable market state before COVID-19 pandemic, as in theory based on the information flow paradigm. In a state under severe market stress due to COVID-19 pandemic, however, their dependence structure changed and even negative (-). This can be seen as a consequence of increased market stress caused by COVID-19 pandemics from a behavioral economics perspective, resulting in structural changes in the asset market and a significant impact on the nonlinear dependence of volatility and trading volume (in particular, their dependence at extreme quantiles). Hence, it should be recognized that in addition to information flows, psychological phenomena such as behavioral biases or herd behavior, which are closely related to market stress, can be a key in changing their dependence structure. For empirical analysis, this study performs a test of Ross (2015) for detecting a structural change, and proposes a Copula Regression Quantiles (CRQ) approach that can identify their nonlinear relationship structure and the asymmetric dependence in their distribution tails without the assumption of i.i.d. random variable. In addition, it was confirmed that when the relationship between their extreme values was analyzed by linear models, incorrect results could be derived due to model specification errors.

• Journal of the Korean GEO-environmental Society
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• v.24 no.7
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• pp.5-14
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• 2023

Recently, earthquakes have occurred frequently in worldwide. These earthquakes cause various forms of natural and physical damage. In particular, liquefaction in which the ground shows liquid-like behavior causes great damage to the structure. Accordingly, various liquefaction damage reduction methods are being studied and developed. Therefore, in this study, a method of reducing liquefaction damage in the event of an earthquake applicable to existing structures was studied using the sheet pile method. The 1-G Shaking table test was performed and the ground was constructed with Jumunjin standard sand. A two-story model structure was produced by applying the similitude law, and the input wave applied a sine wave with an acceleration level of 0.6 g and a frequency of 10 Hz. The effect of reducing structure damage according to various embedded depth ratio was analyzed. As a result of the study, the structure settlement when the ground is reinforced by applying the sheet pile method is decreased by about 71% compared to when the ground is not reinforced, and the EDR with minimum settlement is "1". In addition, as the embedded depth ratio is increased, the calculation of the pore water pressure in the ground tends to be delayed due to the sheet pile. Based on these results, the relationship with structural settlement according to the embedded depth ratio is proposed as a relational equation with the graph. The results of this study are expected to be used as basic data in developing sheet pile methods applicable to existing structures in the future.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.17-17
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• 2020

일반적인 연안 해역에서 창조지속시간과 낙조지속시간은 거의 동일한 수준을 보여 조위곡선은 대칭적인 형태를 띠게 된다. 그러나 M4분조나 MS4분조와 같은 천해조가 발달된 해역에서는 그 모체가 되는 M2분조나 S2분조와의 상대적인 위상차에 따라 조위곡선이 비대칭적인 형상을 띠기도 한다. 창조지속시간이 길 경우 낙조시 최대조류속이 크게 되는 낙조우세가 형성되는 반면 낙조지속시간이 길 경우에는 반대로 창조우세가 형성된다. 소류사의 움직임은 유속의 6승에까지 비례하므로 이러한 최대조류속 차이에 따라 창조우세 또는 낙조우세 해역에서 소류사 이동량에는 큰 차이가 발생할 수 있다. 감조하천의 경우 상류로 갈수록 창조우세화하는 것이 일반적인 것으로 알려져 있으며 국내에서는 금강을 비롯한 군산해역이 대표적이다. 창조우세인 군산해역과 반대로 영산강하구 및 목포해역의 경우 낙조우세를 보이고 있는데, 그 근원은 황해에서의 순환특성과 함께 목포해역 인근의 드넓은 조간대에 기인하는 것으로 추정하고 있다. 특히 인접한 무안만에서는 낙조지속시간이 창조지속시간에 비해 2시간 이상 짧아 국내에서 가장 심한 수준의 낙조우세를 보이고 있으며, 영산강하구 낙조우세의 근원으로 평가되고 있다. 한편 조간대가 발달된 해역은 천해조 생성에 따라 낙조우세 경향이 있음과 관련하여 Kang and Moon(2001)은 조간대가 조석파의 전파와 천해조 발생에 미치는 영향성에 대하여 수치실험을 통해 연구한 결과 무안만의 낙조우세는 조간대의 발달에 기인함을 재확인한 바 있다. 따라서 낙조 우세를 수치모형을 통해 재현하기 위해서는 조간대 모의가 필수적임과 동시에 낙조우세를 재현하기 위해서는 천해조 개방경계 설정이 필요하다고 알려져 있다. 또한 이렇게 천해조가 발달된 하구에서 조석비대칭 현상은 퇴적물 이동에 직접적인 영향을 미치게 되는데 Kang et al.(2002)은 소류사 및 부유사 이동에서 이러한 영향성에 대한 연구를 수행한 바 있다. 그 결과 천해조가 발달된 이러한 해역에서 퇴적물 이동과 관련된 수치모의시 M2분조와 같은 천문조 경계조건에 부가하여 M4분조의 천해조까지 포함시킬 것을 제안한 바 있다. 이와 같이 하구역에서는 천해조 발달로 인해 조석비대칭이 두드러지게 발달되어 있는 경우가 많으며, 감조하천 상류로 갈수록 비대칭은 더욱 심화되는 경향이 있다. 따라서 감조하천 하류부에서 조석을 고려한 경계조건을 부여하여 하천의 흐름양상을 수치적으로 모의할 경우 천해조가 발달된 하구역에서는 천해조가 포함된 경계조건은 필수적이라 할 수 있다.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4A
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• pp.507-515
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• 2008

In this study, a method predicting the displacement response of structures from the measured dynamic strain signal is proposed by using mode decomposition technique. Evaluation of bridge stability is normally focused on the bridge completed. However, dynamic loadings including wind and seismic loadings could be exerted to the bridge under construction. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. As previous studies on the prediction of displacement response by using the FBG sensors, the static displacement has been mainly predicted. For predicting the dynamic displacement, it has been known that the measured strain signal includes higher modes and then the predicted dynamic displacement can be inherently contaminated by broad-band noises. To overcome such problem, a mode decomposition technique was used. Mode decomposition technique estimates the displacement response of each mode with mode shape estimated to use POD from strain signal and with the measured strain signal decomposed into mode by EMD. This is a method estimating the total displacement response combined with the each displacement response about the major mode of the structure. In order to examine the mode decomposition technique suggested in this study model experiment was performed.