• 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.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.531-542
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• 2021

Open-pit mines require constant monitoring as they can cause surface changes and environmental disturbances. In open-pit mines, there is little vegetation at the mining site and can be monitored using InSAR (Interferometric Synthetic Aperture Radar) coherence imageries. In this study, activities occurring in mine were analyzed by applying the recently developed InSAR coherence-based NDAI (Normalized Difference Activity Index). The March 5 Youth Mine is a North Korean mine whose development has been expanded since 2008. NDAI analysis was performed with InSAR coherence imageries obtained using Sentinel-1 SAR images taken at 12-day intervals in the March 5 Youth Mine. First, the area where the elevation decreased by about 75.24 m and increased by about 9.85 m over the 14 years from 2000 was defined as the mining site and the tailings piles. Then, the NDAI images were used for time series analysis at various time intervals. Over the entire period (2017-2019), average mining activity was relatively active at the center of the mining area. In order to find out more detailed changes in the surface activity of the mine, the time interval was reduced and the activity was observed over a 1-year period. In 2017, we analyzed changes in mining operations before and after artificial earthquakes based on seismic data and NDAI images. After the large-scale blasting that occurred on 30 April 2017, activity was detected west of the mining area. It is estimated that the size of the mining area was enlarged by two blasts on 30 September 2017. The time-averaged NDAI images used to perform detailed time-series analysis were generated over a period of 1 year and 4 months, and then composited into RGB images. Annual analysis of activity confirmed an active region in the northeast of the mining area in 2018 and found the characteristic activity of the expansion of tailings piles in 2019. Time series analysis using NDAI was able to detect random surface changes in open-pit mines that are difficult to identify with optical images. Especially in areas where in situ data is not available, remote sensing can effectively perform mining activity analysis.

• Korean Journal of Occupational Health Nursing
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• v.4
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• pp.5-29
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• 1995

Occupational health services in Korea have been operated as dual types : one is operated by occupational health care manager and the other is health care agency without their own personnel. The performance of occupational health service should be different due to the variety of characteristics of health care manager and workplace, qualification of health care manager. This study is to analyze performance of occupational health care services with a particular consideration of job performance shape and efficiency, based on comparing those two types of health care management to show on the basic data for the settlement of more qualitative health care management system at workplace. For this study, total 391 places in Seoul and Inchon city area ; 154 places (39.4%) managed by designated health care manager and 237 places (60.6%) by the agency with their commission are selected as research samples. Tools for data collection are questionnares that have been investigated during the period of 20 September 1993-20 December 1993. Those data are compared with percentiles, mean, standard deviation and B/C ratio using SPSS PC program. Conclusions observed from the tests and each comparison could be summerized as follows : 1. Occupational health care have been accomplished at workplaces with designated people than with agencies people, and coverage rate of the occupational health care services has differences, due to management types. The reason of these results is due to visit only one or two times monthly by the agencies, while their own health care manager obsess, at the workplaces all the times. 2. Most of the expense for environmental control of all health care services expenditures shows that there is almost no fundamental improvement because more expenses are needed for procuring personal protective equipment and measuring work environment instead of environmental improvement. 3. It is investigated how much the cost of occupational health care services needs per worker, and calculated how much the cost needs per service hour per worker. The results from this show that the cost of occupational health services at workplaces with their own managers used less than the cost of health care agencies, eventually the former gives better services with less cost than the latter. 4. Benefit/Cost ratio is also produced by total benefit/total cost. The result from the above way reads 4.57 as a whole, while their own manager having workplaces reads 4.82 and the agencies do l.56. Even if their own manager performing workplaces spent more cost, this system produces more benefit than the agencies management. 5. The B/C ratio for medical organization such as local clinic, health care center and pharmacy shows more than or equal to at the workplaces controlled by the agencies. It is inferred that benefit would be much less than the cost used, with so being inefficient. 6. It is assumed that the efficiency ratio of health education is equal to reduction rate of workers medical organization visit. Estimated reduction rate 5%, 10%, 15%, show that the efficiency ratio of health education have an effect on producing benefits. It is estimated that more benefit can be produced if more qualitative education will be provided for enhancing health care efficiency. 7. Results of this study cannot be generalized because there are large scale of deviation in case of workplaces with less than 300 full time workers, but B/C ratio reads 2.69 as a whole and 3.25 at workplaces with their own health care manager are higher than 1.63 at the workplaces manged by the agencies. Finally, all the benefit concerning health care services could not be quantified, measured and shown on the value of money. This is a reason that a considerable part of benefits are so underestimated. This is also thought that measurement tools should be developed for measuring benefits of health care services with a comprehensive quantification. in the future. It is also expected that efficiency of occupational health care services should be investigated using cost-effectiveness analysis.

• Korean Journal of Agricultural Science
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• v.27 no.1
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• pp.39-47
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• 2000

A large scale field test of prefabricated vertical drains is performed to analyze the effect of parameters of the very soft clay at a test site. Compression index and the coefficient of horizontal consolidation obtained by back-analysis from the settlement data were compared with those obtained by means of laboratory tests. The Hyperbolic, Asaoka's and The Curve fitting methods are used to estimate final settlements and coefficients of consolidation. 1. Final settlement predicted with the Hyperbolic method was the largest, and the settlements predicted with the Asaoka's and the Curve fitting methods were nearly the same range, and it was concluded that smear effect has to be considered on design in the case that spacing of drains is small 2. The relationships of the measured consolidation ratio (Urn) and the designed consolidation ratio($U_t$) were showed as $U_m$ = (1.13~1.17)$U_t$, $U_m$ = (1.07~1.20)$U_t$, $U_m$ = (1.13~1.17)$U_t$ on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. The relations on the Asaoka's and the Curve fitting methods were nearly the same range. 3. The relationships of the field compression index($C_{cfield}$) and virgin compression index($V_{cclab}$) were showed as $C_{cfield}$ = (1.26~1.45)$V_{cclab}$, $C_{cfield}$ = (1.08~1.15) $V_{cclab}$, $C_{cfield}$ = (1.04~1.21)$V_{cclab}$, on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. 4. The ratio ($C_h/C_v$) of the coefficient of vertical consolidation and the coefficient of horizontal consolidation that is obtained by back-analysis from the settlement data was $C_h$=(0.7~0.9)$C_v$, $C_h$=(0.9~1.5)$C_v$, $C_h$=(2.4~3.0)$C_v$ on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. 5. It was concluded that the exact consolidation coefficient must be determined after the final settlement is predicted again when the consolidation is finished, because the field consolidation coefficient is decreased as the time allowed to be alone is increased.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.198-221
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• 2021

After large-scale reforestation in the 1960s and 1970s, forests in Korea have gradually been aging. Net ecosystem CO₂ exchange of old-growth forests is theoretically near zero; however, it can be a CO₂ sink or source depending on the intervention of disturbance or management. In this study, we report the CO₂ budget dynamics of the Gwangneung deciduous old-growth forest (GDK) in Korea and examined the following two questions: (1) is the preserved GDK indeed CO₂ neutral as theoretically known? and (2) can we explain the dynamics of CO₂ budget by the common mechanisms reported in the literature? To answer, we analyzed the 15-year long CO₂ flux data measured by eddy covariance technique along with other biometeorological data at the KoFlux GDK site from 2006 to 2020. The results showed that (1) GDK switched back-and-forth between sink and source of CO₂ but averaged to be a week CO₂ source (and turning to a moderate CO₂ source for the recent five years) and (2) the interannual variability of solar radiation, growing season length, and leaf area index showed a positive correlation with that of gross primary production (GPP) (R²=0.32~0.45); whereas the interannual variability of both air and surface temperature was not significantly correlated with that of ecosystem respiration (RE). Furthermore, the machine learning-based model trained using the dataset of early monitoring period (first 10 years) failed to reproduce the observed interannual variations of GPP and RE for the recent five years. Biomass data analysis suggests that carbon emissions from coarse woody debris may have contributed partly to the conversion to a moderate CO₂ source. To properly understand and interpret the long-term CO₂ budget dynamics of GDK, new framework of analysis and modeling based on complex systems science is needed. Also, it is important to maintain the flux monitoring and data quality along with the monitoring of coarse woody debris and disturbances.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.45-63
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• 2019

The number of domestic construction company is expanding every year while the construction workers' exposure to disaster risk is increasing due to technological advancements and popularity of high-rise buildings. In particular, the industry faces greater fatalities and severe large scale accidents because of construction industry characteristics including influx of foreign workers with different language and culture, large number of aged workers, outsourcing, high place work, heavy machine construction. The construction industry is labor-intensive, which is to be completed under given timeline and consists of unique working environment with a lot of night shifts. In addition, when a fixed construction budget is not secured, there is less investment in safety management resulting in poor risk management at the construction site. Taking account that the construction industry has higher accident risk rate and fatality rate, risky and unique working environment, and various labor pool from foreign to aged workers, preemptive safety management through risk factor identification is a mandatory requirement for the construction industry and site. The study analyzes about 8,500 cases of construction accidents that occurred over the past 10 years and identified risk factor by construction industry sector to secure a systematic insight for risk management. Based on interrelation analysis between accident types, work types, original cause materials and assailing materials, there is correlation between each analysis factor and work industry. Especially for work types, there is great correlation between work tasks and industry type. For reinforced concrete and earthwork are among the most frequent types of accidents, and they are not only high in frequency of accidents, but also have a high risk in categories of occurrence.

• Journal of Korea Port Economic Association
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• v.26 no.4
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• pp.188-206
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• 2010

Data Envelopment Analysis(DEA) is an operations research-based method for measuring the performance efficiency of decision units that are characterized by multiple inputs and outputs. DEA has been applied successfully as a performance evaluation tool in many fields. However, it has not been extensively applied in the shipbuilding industry. This paper applied the input-oriented DEA model, and Malmquist indices to the 7 shipbuilding firms to measure the efficiency and productivity changes during the period of 2004 to 2009. The Malmquist indices will be decomposed into three components such as pure efficiency change, scale efficiency change, and technical change. The empirical results show the following findings. First, the DEA findings indicate that main source of inefficiency is scale rather than pure technical. Second, the Malmquist indices show that an overall decrease in productivity.

• The Journal of Engineering Geology
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• v.13 no.2
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• pp.207-225
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• 2003

According to the data analysis of the regional fracture systems in southern Korea, the fracture orientations show three dominant sets : NNE, NW and WNW. A NNE set is the most abundant and includes most of the largest fractures. The highest fracture density is shown in the Taebaegsan mineralized area corresponding to Ogchon nonmetamorphic belt and the lowest one in the southwestern area of southern Korea. In addition, the density is higher in nonmetamorphic sedimentary rocks such as Choseon Supergroup. Pyeongan Supergroup, Daedong Supergroup and Kyeongsang Supergroup than in Precambrian basements and Jurassic granites. The regional fractures in southern Korea can be classified into four orders designated $F_1,{\;}F_2,{\;}F_3{\;}and{\;}F_4${\;}and{\;}F_4$ on the basis of their trace length. It is quite significant that fractures of each order are self-similar with respect to orientation and the combined fracture length distribution indicates a power-law distribution with an exponent of -2.04. As fractures were analyzed based on the tectonic provinces, Gyeonggj Massif and Kyeongsang Basin have all orders of fractures from $F_1$ to $F_4$. Most of the large scale faults may be ascribed to the products of slip accumulation through multiple deformation. Others besides $F_1$ fractures are thought to be evenly distributed through the whole area of southern Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3740-3747
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• 2009

To protect the citizens' health of Gyeongju and to secure basic data for the assessment of health and environmental risk, distribution characteristics of meteorological elements were investigated and numerical simulation of wind field using RAMS model was carried out. In addition, measurement and analysis of air pollutants, forecasting the behavior air pollutants using ISC-AEROMOD view, and health and environmental risk-influenced zones were defined through managing air polluting materials to prevent health damage and property damage. According to the survey results of air pollution in Gyeongju and surroundings, average annual concentration of air pollutants in Gyeongju was slightly lower than that in Pohang and Ulsan areas, but concentration of particulate matters and nitrogen dioxide at Gyeongju Station Square and Yonggang Crossing were sometimes higher than that in Pohang and Ulsan areas. Results of the modeling of moving and diffusion of air pollutants that affect citizens' health showed that parts of the 1st through 4th industrial complexes together with POSCO were included in particulate matters and sulfur dioxide influenced areas in Pohang Steel Complex area, and that Haedo-dong, Sangdae-dong, Jecheol-dong and Jangheung-dong in Pohangnam-gu represented locally worsened air quality due to a quantity of air pollutant emission from dense steel industries and large scale industrial facilities.