• Journal of Korea Foundry Society
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• v.43 no.6
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• pp.271-278
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• 2023

In the present study, A356 melt degassing experiments were conducted under various impeller rotation speed and inert gas flow rate conditions to determine changes in the melt temperature, composition and density during a degassing treatment. The melt temperature was found to decrease gradually as the degassing time increased, but a clear correlation between the impeller rotation speed or inert gas flow rate and the melt heat loss could not be confirmed. Regardless of the impeller rotation speed or inert gas flow rate, the Mg and Ti contents in the A356 melt scarcely changed, even after degassing for more than 10 minutes, while Sr contents decreased at the maximum degassing rate of 70 ppm. From a quantitative analysis of the degassing rate under each experimental condition based on the hydrogen concentration in the melt derived from the melt density and the degassing model equation, the inert gas flow rate was found to affect the degassing rate rather than the impeller rotation speed under the degassing operation condition employed in the present study.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.178-185
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• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• Korean journal of food and cookery science
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• v.13 no.4
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• pp.462-471
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• 1997

Effect of citron on Dongchimi (watery radish kimchi) fermentation was investigated by sensory evaluation and the measurement of non-volatile organic acids, soluble pectin, and the texture during fermentation up to 36 days. Dongchimi with various levels of citron (0, 1, 2, 4, 6％) was fermented at 10$^{\circ}C$. In sensory evaluation, citron-added Dongchimi showed the higher scores in most characteristics than Dongchimi without citron in which Dongchimi with 2％ citron was the most preferable. The non-volatile organic acids of Dongchimi were identified as lactic acid, oxalic acid, succinic acid, malic acid, and citric acid. There were significant changes in the contents of lactic acid, succinic acid, malic acid, and citric acid during fermentation. Generally, the content of hydrochloric acid-soluble pectin (HSP) of Dongchimi occupied the higher ratio in the total soluble pectin content. Generally, the content of hot water-soluble pectin (HWSP) of Dongchimi decreased and that of sodium hexametaphosphate-soluble pectin (NaSP) increased during fermentation. The hardness of radish in Dongchimi showed the highest score on 23$\^$rd/ day and decreased thereafter.

• Journal of the Mineralogical Society of Korea
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• v.26 no.3
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• pp.161-174
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• 2013

The geology of the weondong deposit area consists mainly of Cambro-Ordovician and Carboniferous-Triassic formations, and intruded quartz porphyry and dyke. The skarn mineralized zone in the weondong deposit is the most prospective region for the useful W-mineral deposits. To determine the skarn-mineralization age, U-Pb SHRIMP and K-Ar age dating methods were employed. The U-Pb zircon ages of quartz porphyry intrusion (WD-A) and feldspar porphyry dyke (WD-B) are 79.37 Ma and 50.64 Ma. The K-Ar ages of coarse-grained crystalline phlogopite (WD-1), massive phlogopite (WDR-1), phlogopite coexisted with skarn minerals (WD-M), and vein type illite (WD-2) were determined as $49.1{\pm}1.1$ Ma, $49.2{\pm}1.2$ Ma, $49.9{\pm}3.6$ Ma, and $48.3{\pm}1.1$ Ma, respectively. And the ages of the high uranium zircon of hydrothermally altered quartz porphyry (WD-C) range from 59.7 to 38.7 Ma, which dependson zircon's textures affected by hydrothermal fluids. It is regarded as the effect of some hydrothermal events, which may precipitate and overgrow the high-U zircons, and happen the zircon's metamictization and dissolution-reprecipitation reactions. Based on the K-Ar age datings for the skarn minerals and field evidences, we suggest that the timing of W-skarn mineralization in weondong deposit may be about 50 Ma. However, for the accurate timing of skarn mineralization in this area, the additional researches about the sequence of superposition at the skarn minerals and geological relationship between skarn deposits and dyke should be needed in the future.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.603-611
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• 2020

This study collected and analyzed transportation card data in order to better understand the operation and usage of city buses in Ulsan Metropolitan City in Korea. The analysis used quantitative and qualitative indicators according to the characteristics of the data, and also the categories were classified as general status, operational status, and satisfaction. The existing city bus survey method has limitations in terms of survey scale and in the survey process itself, which incurs various types of errors as well as requiring a lot of time and money to conduct. In particular, the bus means indicators calculated using transportation card data were analyzed to compensate for the shortcomings of the existing operational status survey methods that rely entirely on site surveys. The city bus index calculated by using the transportation card data involves quantitative operation status data related to the user, and this results in the advantage of being able to conduct a complete survey without any data loss in the data collection process. We took the transportation card data from the entire city bus network of Ulsan Metropolitan City on Wednesday April 3, 2019. The data included information about passenger numbers/types, bus types, bus stops, branches, bus operators, transfer information, and so on. From the data analysis, it was found that a total of 234,477 people used the city bus on the one day, of whom 88.6% were adults and 11.4% were students. In addition, the stop with the most passengers boarding and alighting was Industrial Tower (10,861 people), A total of 20,909 passengers got on and off during the peak evening period of 5 PM to 7 PM, and 13,903 passengers got on and off the No. 401 bus route. In addition, the top 26 routes in terms of the highest number of passengers occupied 50% of the total passengers, and the top five bus companies carried more than 70% of passengers, while 62.46% of the total routes carried less than 500 passengers per day. Overall, it can be said that this study has great significance in that it confirmed the possibility of replacing the existing survey method by analyzing city bus use by using transportation card data for Ulsan Metropolitan City. However, due to limitations in the collection of available data, analysis was performed only on one matched data, attempts to analyze time series data were not made, and the scope of analysis was limited because of not considering a methodology for efficiently analyzing large amounts of real-time data.

• Journal of the Microelectronics and Packaging Society
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• v.28 no.1
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• pp.39-46
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• 2021

The quantitative measurement of interfacial adhesion energy (Gc) of multilayer thin films for Cu interconnects was investigated using a double cantilever beam (DCB) and 4-point bending (4-PB) test. In the case of a sample with Ta diffusion barrier applied, all Gc values measured by the DCB and 4-PB tests were higher than 5 J/㎡, which is the minimum criterion for Cu/low-k integration without delamination. However, in the case of the Ta/Cu sample, measured Gc value of the DCB test was lower than 5 J/㎡. All Gc values measured by the 4-PB test were higher than those of the DCB test. Measured Gc values increase with increasing phase angle, that is, 4-PB test higher than DCB test due to increasing plastic energy dissipation and roughness-related shielding effects, which matches well interfacial fracture mechanics theory. As a result of the 4-PB test, Ta/Cu and Cu/Ta interfaces measured Gc values were higher than 5 J/㎡, suggesting that Ta is considered to be applicable as a diffusion barrier and a capping layer for Cu interconnects. The 4-PB test method is recommended for quantitative adhesion energy measurement of the Cu interconnect interface because the thermal stress due to the difference in coefficient of thermal expansion and the delamination due to chemical mechanical polishing have a large effect of the mixing mode including shear stress.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Korean Society of Forest Science
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• v.10 no.1
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• pp.29-39
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• 1970

Forest fires often destory forests that have taken years to grow in a few minutes. Forest fire therefore, is an important problem in forest management and have caused heavy losses to the nations economy. In order to resolve this problem many investigations have been made in many countries. However, ecological studies on the forest after accidental fire have not yet been made in Korea. In order to conduct such a study, a burned area on Mt. Samak which is located at Dukduwon-ri, Seo-myon, Chunsung-gun, Kangwon-do, was chosen as experimental plot in 1967. The remaining seeds were collected from the burned area, and investigations on their germination rate and their productivity were made comparing to those of the seeds of undemaged area, and following results were obtained. 1. The number of seed collected from the control plots were 740 while it was 537 from the test plots (Table 3, 4). It was considered that this difference between burned and unburned area was mainly due to the fact that some of the seeds had been burnt by the fire, and the unfavorable environmental conditions in the burned area was also considered to be a reason. In the germination rate in the control plots showed 28.1% while it was 3.2% in the test plots. This difference was considered to be due to complete loss of viability of the seed by burning and high heat. 2. In the test plots, sixteen seeds of the Alnus japonica were collected and six of these seeds germinated (index number 100) which was the highest germination rate among the species of collected seeds. From these results, it was considered that a high temperature (above $150^{\circ}C$) caused reduction of the germination rate (Quadrat. 1.2). Seeds of Carex lanceolata var. Nana, were appeared much more in the higher plots than in the lower plots and it seemed to be due to the fact that the forest floor plants were much more abundant in the lower plots than in the higher plots which is covered with shrubbery. And some small seeds midght be able to avoid the effect of fire being burried in the soil or under the gravel. 3. With Pinus densiflora, 43 seeds were collected, and 11 of these germinated in the control plots. However in the test plots, 11 seeds were collected and no seed germinated. This shows that the Pinus densiflora was the weakest in resisting to heat among the observed species in this study. 4. Without exception the germination rate showed a higher index in the herbs than in the woody plants and it is believed that the herbs produced more seed than the wood plants because of the abundance of herbs colony.