• Journal of the Korean Applied Science and Technology
• /
• v.35 no.1
• /
• pp.173-185
• /
• 2018

Effects of three different types of dispersions and flow improving additives composed with fatty acid esters, fatty acid metal salts and amide compound on the vulcanization and the mechanical properties properties of rubber compounds of EPDM and carbon black as fillers. were investigated using Mooney viscometer, moving die rheometer, hardness tester, and universal test machine. The aging characteristics of vulcanized EPDM compounds were also investigated. The Mooney viscosity measured at $125^{\circ}C$ showed a tendency to decrease in the order of amide type> metal salt type > ester type additive. Scorch time showed little or no difference with the addition of ester or metal salt type additives, but the amide type additive shortened a scorch time more than one minute. Rheological measurement data obtained at $160^{\circ}C$ showed that the vulcanization time was faster for metal salt type and amide type additive systems. Delta torque values of EPDM compound increased with metal salt type and amide type additives, but slightly decreased with ester type additive. The tensile strength of the EPDM compound was greatly improved when an ester type additive was added, but the amide type or metal salt type additive had no significant effect. The elongation was significantly improved for metal salt type additive, while the rest were not significantly affected. The tear strength of the EPDM compounds increased with the addition of all kinds of additives, and it increased remarkably in the case of metal salt type additive. Hardness of the EPDM compounds was nearly same value regardless of additive types. The thermal aging of the EPDM blend at $100^{\circ}C$ for 24 h showed little change in the case of metal salt type or amide type additive, but the elongation tends to decrease by 10-20% for all EPDM compounds containing additives.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.19 no.6
• /
• pp.658-665
• /
• 2013

In this study, we measured particulate matter(PM) which emerged as the hot issue from the International Maritime Organization(IMO) and the exhaust emission using HANBADA, the training ship of Korea Maritime University. In particular, the PM was obtained with TEM grid. PM structure was observed by electron microscopy. And exhaust gases such as NOx, $CO_2$, and CO were measured using the combustion gas analyzer(PG-250A, HORIBA). The results of this study are as follows. 1) When the ship departed from the port, the maximum difference in PM emissions were up to 30 % due to the Bunker Change. 2) Under the steady navigation, emission of PM was $1.34mg/m^3$ when Bunker-A is changing L.R.F.O(3 %). And, at the fixed L.R.F.O (3 %), emission of PM was $1.19mg/m^3$. When the main engine RPM increased up to 20 % with fixed L.R.F.O(3 %), emission of PM was $1.40mg/m^3$. When we changed to low quality oil(L.R.F.O(3 %)), CO concentration from main engine increased about 16 %. On the other hand, when the main engine RPM is rising up to 20 %, CO concentration is increased more than 152 percent. These results imply that the changes of RPM is a dominant factor in exhaust emission although fuel oil type is an important factor. 3) The diameter of PM obtained with TEM grid is about $4{\sim}10{\mu}m$ and its structure shows porous aggregate.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.12
• /
• pp.51-57
• /
• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.15 no.3
• /
• pp.159-174
• /
• 2020

This study is an empirical study analyzing the effects of positive psychological capital on job burnout. In addition, positive psychological capital played a role in organizational citizenship behavior, and tried to verify the role of organizational citizenship behavior as a black box, or parameter, between job burnout. And then, the sub-factors of organizational citizenship behavior were divided into two: individual-oriented organizational citizenship behavior and organization-oriented organizational citizenship behavior. To this end, a questionnaire survey was conducted for members of small and medium-sized enterprises to compare and analyze the relationship between variables. Positive psychological capital is increasing interest in that it can reduce the job burnout of members and embrace the propensity of young generations represented by millennials because it can improve the effectiveness by developing positive mental states and strengths of the organization. There is a need for research as a keyword. As a result of this study, first, it was found that positive psychological capital of SME(small and medium-sized enterprises) members had a positive effect on organizational citizenship behavior. Second, positive psychological capital was found to have a significant negative effect on job burnout. Third, it was a verification of how positive psychological capital and organizational citizenship behavior affect job burnout. In the relationship between positive psychological capital and job burnout, organization-oriented organizational citizenship behavior was found to play a mediating role. However, it was found that individual-oriented organizational citizenship behaviors among the organizational citizenship behaviors are not valid. In this study, positive psychological capital and job burnout, which have been mainly studied in service workers' emotional workers(crew, nurses, counselors, etc.), nursery teachers, and social workers, were applied to SME members by using the parameters of organizational citizenship behavior. You can put that implication on things. The positive psychological capital and organizational citizenship behavior can be further enhanced through SME members' love for the company, improvement of consideration among employees and resulting organizational commitment and work performance. It could also provide momentum for sustainable management for small and medium-sized enterprises that are relatively short of capital and resources.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1107-1118
• /
• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Popular Narrative
• /
• v.25 no.1
• /
• pp.165-191
• /
• 2019

The purpose of this article is to elucidate why the motif of time travel is repeated in the science fiction narrative by examining the functions of this motif in the SF movie series of Star Trek in its narrative and non-narrative aspects. Star Trek IV: The Voyage Home (1986) aims to attract the audience's interest in the story through the use of plausible time travel in the form of the slingshot effect which causes the spacecraft to fly at very fast speeds around an astronomical object. The movie also touches upon the predestination paradox that arises from a change of history in which it describes a formula of transparent aluminum that did not exist at the time. The film also serves as an evocation of the ideology of ecology by including humpback whales in the central narrative and responding to the real issue of the whale protection movement of the times. Star Track VIII: First Contact (1996) intends to interest the audience in the narrative with the warp drive, a virtual device that enables travel at speeds faster than that of light and a signature visual of Star Trek, at the time of its birth through time travel. The film emphasizes the continuation of peaceful efforts by warning the destruction of humanity that nuclear war can bring. It tackles with the view of pacifism and idealism by stressing the importance of cooperation between countries in the real world by making the audience anticipate the creation of the United Federation of Planets through encounters with the extraterrestrial. Star Trek: The Beginning (2009) improves interest through the idea of time travel to the past, this time using a black hole and the parallel universe created thereby. The parallel universe functions as a reboot, allowing a new story to be created on an alternate timeline while maintaining the original storyline. In addition, this film repeats the themes pacifism and idealism shown in the 1996 film through the confrontation between Spock (and the Starfleet) and Nero, the destruction of the Vulcan and the Romulus, and the cooperation of humans and Vulcans. Eventually, time travel in three Star Trek films has the function of maximizing the audience's interest in the story and allowing it to develop freely as a narrative tool. It also functions as an ideal solution for commenting on current problems in the non-narrative aspect. The significance of this paper is to stress the possibility that the motif of time travel in SF narrative will evolve as it continues to repeat in different forms as mentioned above.

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