• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.471-478
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• 2009

The lung diseases classifying as one of the six incurable diseases in modern days are caused mostly by smoking and air pollution. Such causes the lung function damages, and results in malfunction of the exchange of carbon dioxide and oxygen in an alveolus, which the interest is augment with risk diseases of life prolongation. With this in the paper, we proposed a diagnosis method of lung diseases by applying parameters of voice analysis aiming at the getting the voice feature extraction. Firstly, we sampled the voice data from patients and normal persons in the same age and sex, and made two sample groups from them. Also, we conducted an analysis by applying the various parameters of voice analysis through the collected voice data. The relational significance between the patient and normal groups can be evaluated in terms of speech rates and intensity as a part of analized parameters. In conclusion, the patient group has shown slower speech rates and bigger intensity than the normal group. With this, we propose the method of voice feature extraction for lung diseases.

• Journal of Korea Port Economic Association
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• v.36 no.4
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• pp.17-30
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• 2020

In the shipping industry, it is essential to engage in the preemptive prediction of freight rate volatility through market monitoring. Considering that freight rates have already started to fall, the loss of shipping companies will soon be uncontrollable. Therefore, in this study, factors affecting the freight rates of bulk carriers, which have relatively large freight rate volatility as compared to container freight rates, were quantified and analyzed. In doing so, we intended to contribute to future shipping market monitoring. We performed an analysis using a vector error correction model and estimated the influence of six independent variables on the charter rates of bulk carriers by Handy Size, Supramax, Panamax, and Cape Size. The six independent variables included the bulk carrier fleet volume, iron ore traffic volume, ribo interest rate, bunker oil price, and Euro-Dollar exchange rate. The dependent variables were handy size (32,000 DWT) spot charter rates, Supramax 6 T/C average charter rates, Pana Max (75,000 DWT) spot charter, and Cape Size (170,000 DWT) spot charter. The study examined charter rates by size of bulk carriers, which was different from studies on existing specific types of ships or fares in oil tankers and chemical carriers other than bulk carriers. Findings revealed that influencing factors differed for each ship size. The Libo interest rate had a significant effect on all four ship types, and the iron ore traffic volume had a significant effect on three ship types. The Ribo rate showed a negative (-) relationship with Handy Size, Supramax, Panamax, and Cape Size. Iron ore traffic influenced three types of linearity, except for Panamax. The size of shipping companies differed depending on their characteristics. These findings are expected to contribute to the establishment of a management strategy for shipping companies by analyzing the factors influencing changes in the freight rates of charterers, which have a profound effect on the management performance of shipping companies.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• Bulletin of the Korean Chemical Society
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• v.33 no.3
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• pp.1009-1014
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• 2012

The influence of magnetic fields on chemical processes has long been the subject of interest to researchers. For this time numerous investigations show that commonly the effect of a magnetic field on chemical reactions is insignificant with impact less than 10 percent. However, there are some papers that point to the observation of external magnetic field effect on chemical and biochemical systems actually having a significant impact on the reactions. Thus, of great interest is an active search for rather simple but realistic models, that are based on physically explicit assumptions and able to account for a strong effect of low magnetic fields. The present work theoretically deals with two models explaining how an applied weak magnetic field might influence the steady state of a non-equilibrium chemical system. It is assumed that external magnetic field can have effect on the rates of radical reactions occurring in a system. This, in turn, leads to bifurcation of the nonequilibrium stationary state and, thus, to a drastic change in the properties of chemical systems (temperature and reagent concentration).

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.103-113
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• 2019

The effectiveness of monetary policy critically depends upon how well the transmission mechanism functions, so that the desired impact on output and inflation is achieved. The purpose of this paper is to study the transmission mechanism of monetary policy by analyzing the impact on inflation and output during multiple indicator regime (1998-99 to 2014) in an emerging economy-India. The Inflation Targeting Regime is also briefly outlined alongwith the impact on output and inflation. Using quarterly data for the period 1997 to 2017, the paper uses weighted average call money market rate as a proxy for the policy rate and evaluates the strength of the interest rate channel. We use a conventional Structural vector auto regression (SVAR) methodology to evaluate the efficacy and show the impluse response functions. Our results find that changes in the policy rate impact output growth steeply with a lag of about two quarters and the impact on inflation is maximized after three quarters. The study concludes that the monetary policy in India has a significant impact on output and inflation in the short-to-medium-run. After the policy shock, the fall in the output growth rate is of greater magnitude than the fall in inflation.

• Proceedings of the Materials Research Society of Korea Conference
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• 2003.11a
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• pp.26-26
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• 2003

Lithium storage electrodes for rechargeable batteries require mixed electronic-ionic conduction at the particle scale in order to deliver desired energy density and power density characteristics at the device level. Recently, lithium transition metal phosphates of olivine and Nasicon structure type have become of great interest as storage cathodes for rechargeable lithium batteries due to their high energy density, low raw materials cost, environmental friendliness, and safety. However, the transport properties of this family of compounds, and especially the electronic conductivity, have not generally been adequate for practical applications. Recent work in the model olivine LiFePO$_4$, showed that control of cation stoichiometry and aliovalent doping results in electronic conductivity exceeding 10$^{-2}$ S/cm, in contrast to ~10$^{-9}$ S/cm for high purity undoped LiFePO$_4$. The increase in conductivity combined with particle size refinement upon doping allows current rates of >6 A/g to be utilized while retaining a majority of the ion storage capacity. These properties are of much practical interest for high power applications such as hybrid electric vehicles. The defect mechanism controlling electronic conductivity, and understanding of the microscopic mechanism of lithiation and delithiation obtained from combined electrochemical and microanalytical techniques, will be discussed

• The Korean Journal of Financial Management
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• v.13 no.1
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• pp.341-357
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• 1996

Some empirical studies have shown that asset prices respond to announcements of economic news, however, others also have found little evidence. This study assesses how market participants of the S&P 500 Index Futures reacted to the U.S. economic news announcements. For this purpose, using a GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, we use several U.S. news variables, its each surprise component and interest rates. We find that some economic news variables affected significantly on the S&P 500 Index Futures. In other words, we find that weekend variable, lagged volatility, and surprise component of trade deficit increased level of volatility. However, interest rate, M1, unemployment announcements caused the variance of the S&P 500 Index Futures to reduce, and each of the surprise component of M1 and trade deficit increased it. The result suggests that resolution of uncertainty, through economic news announcement, while, in some cases, causes market participants to reduce their forecast of volatility, a large difference between the market's forecast and the realization of the series causes the volatility to increase.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.191-198
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• 2007

Due to the growth of female movement after 1970s' and the resultant interest in female activity changed the type and degree of female crime and thus led to considerable interest and research. As a result, the relative difference between crime rates of sexes has been reduced, but the crime of male and female has been considerably decreased owing to sex role and concept of self-ego, more generally specking, the result of female movement. As countermeasure for the recently increasing frmale crime, this research presents first, enhancement of social education; second, arrangement of relevant institutions; third, activation of the system of female investigator; fourth, the necessity of victimology approach.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.8 s.350
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• pp.77-83
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• 2006

In this paper, we have pesented a reliable data transfer mechanism using Directed Diffusion in WSNs (fireless Sensor Networks). This mechanism involves selecting a route with higher reachability and transferring data along the route chosen, which is based on the end-to-end reliability calculated by the dissemination procedure of Interest packets, while each node of a sensor network maintains the only information on its neighborhood. We performed various experiments changing the link error rates and the number of nodes and discovered that this proposed mechanism improves event-to-sink data transfer reliability in WSNs. We also found that this mechanism spreads traffic load over and reduces energy consumption, which in turn prolongs network lifetime.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.1-25
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• 2009

This paper attempts to estimate the natural rates of output and interest of Korea in a simple DSGE set-up with a few stylized New Keynesian features using Bayesian methods. The major findings of this paper are as follows. First, the estimates of output gaps are less volatile than the measures from conventional approaches, although they exhibit non-negligible variations depending on the model specification. Another key finding is that the hybrid type Phillips curve with a backward-looking component and/or habit formation in consumption may play an important role in characterizing the macroeconomic dynamics of Korea.