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490 results on '"Kang, Minjung"'

3. Stock Index Prediction using Cointegration test and Quantile Loss

Author

Cheong, Jaeyoung, Lee, Heejoon, and Kang, Minjung

Subjects
Quantitative Finance - Statistical Finance, Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning
Abstract

Recent researches on stock prediction using deep learning methods has been actively studied. This is the task to predict the movement of stock prices in the future based on historical trends. The approach to predicting the movement based solely on the pattern of the historical movement of it on charts, not on fundamental values, is called the Technical Analysis, which can be divided into univariate and multivariate methods in the regression task. According to the latter approach, it is important to select different factors well as inputs to enhance the performance of the model. Moreover, its performance can depend on which loss is used to train the model. However, most studies tend to focus on building the structures of models, not on how to select informative factors as inputs to train them. In this paper, we propose a method that can get better performance in terms of returns when selecting informative factors using the cointegration test and learning the model using quantile loss. We compare the two RNN variants with quantile loss with only five factors obtained through the cointegration test among the entire 15 stock index factors collected in the experiment. The Cumulative return and Sharpe ratio were used to evaluate the performance of trained models. Our experimental results show that our proposed method outperforms the other conventional approaches., Comment: 8pages, 2 figures

Published
2021

21. FEM-Based Conductive Heat Transfer Analytical Description of Solidification Rate and Temperature Gradient during Lateral Laser Beam Oscillation Welding of Aluminum Alloy.

Author

Cheon, Jason, Kim, Cheolhee, Kang, Sanghoon, and Kang, Minjung

Subjects
LASER welding, ALUMINUM alloy welding, SOLIDIFICATION, HEAT transfer, FINITE element method, SOLID-liquid interfaces
Abstract

This study investigates the feasibility of utilizing the finite element method (FEM)-based conductive heat transfer (CHT) analysis simulation to determine temperature gradients and solidification rates at the solid–liquid interface during laser beam oscillation welding. By comparing experimental observations with FEM-based CHT analysis, the underlying microstructural evolution and grain formation during welding were examined. FEM-based CHT enables the calculation of temperature gradients (G) and solidification rates (R), offering insights into the formation of equiaxed structures, which are crucial for suppressing hot cracking. Columnar-to-equiaxed structure transition thresholds, such as G/R and G3/R, accurately predict the emergence of fully equiaxed grain structures, validated by electron backscatter diffraction. This research provides valuable insights into temperature gradients and solidification rates in oscillation welding, guiding process design for achieving refined equiaxed structures and minimizing hot cracks. [ABSTRACT FROM AUTHOR]

Published
2024
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26. On the saturation spectrum of odd cycles.

Author

Gould, Ronald J., Kündgen, André, and Kang, Minjung

Abstract

Given a graph H $H$, we say that a graph G $G$ is H $H$‐saturated if H $H$ is not a subgraph of G $G$, but the addition of any new edge to G $G$ creates at least one copy of H $H$. In this paper we determine all pairs (n,m) $(n,m)$ for which there is a C5 ${C}_{5}$‐saturated graph on n $n$ vertices and m $m$ edges. In addition, we determine all but O(nk) $O(nk)$ possible sizes for n $n$‐vertex H $H$‐saturated graphs when H $H$ is an odd cycle C2k+1 ${C}_{2k+1}$ for k≥3 $k\ge 3$. [ABSTRACT FROM AUTHOR]

Published
2024
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36. The effectiveness of Nature-Based Therapy for community psychological distress and well-being during COVID-19: a multi-site trial

Author

Yang, Yeji, primary, Kim, Hyunjin, additional, Kang, Minjung, additional, Baik, Hyunjin, additional, Choi, Yunseok, additional, Jang, Eu-Jean, additional, Chang, Eun-Jin, additional, Yun, Sukyoung, additional, Park, Miok, additional, Park, Eunyeong, additional, Yun, Hojun, additional, Lee, Taek-Joo, additional, Kwon, Yeong-Han, additional, Hong, Kwang-Pyo, additional, Lee, Ai-Ran, additional, Jung, Songhie, additional, Ahn, Tai-Hyeon, additional, Jin, Hye-Young, additional, and Choi, Kee-Hong, additional

Published
2023
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37. Measured sodium excretion is associated with cardiovascular outcomes in non-dialysis CKD patients: results from the KNOW-CKD study

Author

Kang, Seong Cheol, primary, Kang, Minjung, additional, Ryu, Hyunjin, additional, Kim, Seonmi, additional, Kim, Ji Hye, additional, Kang, Eunjeong, additional, Jeong, Yujin, additional, Kim, Jayoun, additional, Kim, Yong-Soo, additional, Kim, Soo Wan, additional, Kim, Yeong Hoon, additional, and Oh, Kook-Hwan, additional

Published
2023
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42. Identification of solidification cracking using multiple sensors and deep learning in laser overlap welded Al 6000 alloy.

Author

Shin, Jeonghun, Kang, Sanghoon, Kim, Cheolhee, Hong, Sukjoon, and Kang, Minjung

Subjects
CONVOLUTIONAL neural networks, DEEP learning, OXYACETYLENE welding & cutting, LASER welding, ARTIFICIAL neural networks, ALUMINUM alloys
Abstract

Solidification cracking, one of the most critical weld defects in laser welding of Al 6000 alloys, occurs at the final stage of solidification owing to shrinkage of the weld metal and deteriorates the joint strength and integrity. The filler metal can control the chemical composition of the weld metal, which mitigates solidification cracking. However, the chemical composition is difficult to control in autogenous laser welding. Temporal and spatial laser beam modulations have been introduced to control solidification cracking in autogenous laser welding because weld morphology is one of the factors that influences the initiation and propagation of solidification cracking. Solidification cracks generate thermal discontinuities and visual flaws on the bead surface. In this study, a high-speed infrared camera and a coaxial charge-coupled device camera with an auxiliary illumination laser (808 nm) were employed to identify solidification cracking during laser welding. Deep learning models, developed using two sensor images of a solidified bead, provided location-wise crack formation information. The multisensor-based convolutional neural network models achieved an impressive accuracy of 99.31% in predicting the crack locations. Thus, applying deep learning models expands the capability of predicting solidification cracking, including previously undetectable internal cracks. [ABSTRACT FROM AUTHOR]

Published
2023
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45. Comparison of cardiovascular event predictability between the 2009 and 2021 Chronic Kidney Disease Epidemiology Collaboration equations in a Korean chronic kidney disease cohort: the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease

Author

Kim, Ji Hye, primary, Kang, Minjung, additional, Kang, Eunjeong, additional, Ryu, Hyunjin, additional, Jeong, Yujin, additional, Kim, Jayoun, additional, Park, Sue K., additional, Jeong, Jong Cheol, additional, Yoo, Tae-Hyun, additional, Kim, Yaeni, additional, Kim, Yong Chul, additional, Han, Seung Seok, additional, Lee, Hajeong, additional, and Oh, Kook-Hwan, additional

Published
2023
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