• Korean Journal of Fisheries and Aquatic Sciences
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• 제18권2호
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• pp.109-118
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• 1985

This experiment was carried out to evaluate the water quality in the lower part of the Nagdong river in Korea. Three hundred and sixty water samples were collected from the 15 stations from December 1981 to November 1982 by tide(see Fig.1). Water temperature, pH, chloride ion, salinity, total coliform, fecal coliform, viable cell count and the composition of coliform were observed to evaluate the water quality. The variations of water temperature was ranged from $2.0^{\circ}C\;to\;29.5^{\circ}C$ and as mean value from $15.8^{\circ}C\;to\;18.9^{\circ}C$. The range of pH was 6.00-8.88 and 7.20-7.96 as mean value. The concentration of chloride ion from St. 1 to 5 was higher as 17.51-771 mg/l in flood tide than 13.12-264.58 mg/l in ebb tide. Specially, water quality at St.1 (Samrangjin) which located about 46 km far from Hadan was also influenced by tide. Salinities of water in flood tide were a litte higher ($11.05{\sim}31.08\%0$) than those of in ebb tide ($7.80{\sim}29.28\%0$). Total coliform MPN's ranged from 3.6/100 m/l to 460,000/100ml. The geometric mean value of the upper area (included St. $1{\sim}3$) was $259{\sim}538/100ml$, that of the middle area (included St. $4{\sim}6$) was $1,097{\sim}39,544/100ml$ for it leveled heavy contamination. Specially, in the ebb tide St. 10 was influenced by St. 6 and 7. In the upper area, the geometric mean value of fecal coliform MPN's was $109{\sim}199/100ml$ but in the area in cluded St. 5, 6 and 7 were heavily contaminated by domestic sewage, waste water from the factories area and bird's excrement. Composition of coliform was $17\%$ Escherichia coli group, $33\%$ Citrobacter freundii group, $28\%$ Enterobacter aerogenes group and $21\%$ others. Plate count of samples was varied from <30 to $3.9{\times}10^4/ml$ during the study period.

• Journal of the Korean Geographical Society
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• 제42권4호
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• pp.582-600
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• 2007

In a long history of geography, a variety of regional concepts have been suggested to represent the particular situations in each period. Today, post-modem situations, characterized by the development of capitalism and globalization, demand a new variety of regional concepts. The regional characteristics, such as social relations, institutional systems, ideologies and symbolism, are now perceived basically on the level of subjectivity. Currently, it is the most urgent task to integrate many conflicting opinions among a variety of subjects into the one that would seek a voluntary consent from the majority of regional residents. In this paper, it is suggested that the concept of identity is the most efficient in examining and explaining the post-modem trend of a region: variability, subjectivity, mobility, changeability, Finally, it is suggested that a special attention should be paid to the role of institutions, that is institutionalization, in the construction of regional identity, to understand and interpret the cultural-historical aspect of a regional change.

• Journal of Broadcast Engineering
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• 제8권4호
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• pp.325-338
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• 2003

Video adaptation in response to dynamic resource conditions and user preferences is required as a key technology to enable universal multimedia access (UMA) through heterogeneous networks by a multitude of devices In a seamless way. Although many adaptation techniques exist, selections of appropriate adaptations among multiple choices that would satisfy given constraints are often ad hoc. To provide a systematic solution, we present a general conceptual framework to model video entity, adaptation, resource, utility, and relations among them. It allows for formulation of various adaptation problems as resource-constrained utility maximization. We apply the framework to a practical case of dynamic bit rate adaptation of MPEG-4 video streams by employing combination of frame dropping and DCT coefficient dropping. Furthermore, we present a descriptor, which has been accepted as a part of MPEG-21 Digital Item Adaptation (DIA), for supporting terminal and network quality of service (QoS) in an interoperable manner. Experiments are presented to demonstrate the feasibility of the presented framework using the descriptor.

• Journal of Korean Society of Transportation
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• 제21권4호
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• pp.113-132
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• 2003

Using nonradial data envelopment analysis(DEA) under assumptions of strong disposability and variable returns scale, this paper annually estimates productive. technical and allocative efficiencies of three publicly-owned rail transit properties which are different in terms of organizational type: Seoul Subway Corporation(SSC, local public corporation), the Seoul Metropolitan Electrified Railways sector (SMESRS) of Korea National Railroad(the national railway operator controlled by the Ministry of Construction and Transportation(MOCT)), and Busan Urban Transit Authority (BUTA, the national authority controlled by MOCT). Using the estimation results of Tobit regression analysis. the paper next computes their true productive, true technical and true allocative efficiencies, which reflect only the impacts of internal factors such as production activity by removing the impacts of external factors such as an organizational type and a track utilization rate. And the paper also computes an organizational efficiency and annually gross efficiencies for each property. The paper then conceptualized that the property produces a single output(car-kilometers) using four inputs(labor, electricity, car & maintenance and track) and uses unbalanced panel data consisted of annual observations on SSC, SMESRS and BUTA. The results obtained from DEA show that, on an average, SSC is the most efficient property on the productive and allocative sides, while SMESRS is the most technically-efficient one. On the other hand. BUTA is the most efficient one on the truly-productive and allocative sides, while SMESRS on the truly-technical side. Another important result is that the differences in true efficiency estimates among the three properties are considerably smaller than those in efficiency estimates. Besides. the most cost-efficient organizational type appears to be a local public corporation represented by SSC, which is also the most grossly-efficient property. These results suggest that a measure to sort out the impacts of external factors on the efficiency of rail transit properties is required to assess fairly it, and that a measure to restructure (establish) an existing(a new) rail transit property into a local public corporation(or authority) is required to improve its cost efficiency.

• Korean Journal of Plant Resources
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• 제36권5호
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• pp.517-526
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• 2023

The harvest time of the late-ripening 'Fuji' apple (Malus × domestica) is variable, depending on the coloration of the fruit skin. Ethephon, a plant growth regulator, promotes the ethylene production and induces physiological responses associated with fruit maturation in climacteric fruit crops, such as apples. This study aimed to investigate the effect of ethephon treatment on fruit characteristics after fruit enlargement, with the objective of proposing an economical and stable harvest control method for 'Fuji'/M.26 apples. Fruit characteristics were assessed at 10-days intervals following the application of 100 mg/L ethephon and mixture of 100 mg/L ethephon and 0.5% CaCl₂ at 145 days after full bloom (DAFB). Starch contents of ethephon-treated (ET) and ethephon with CaCl₂-treated (EC) apples began to decrease from 155 DAFB, and the starch contents of ET and EC at 10 days before harvest were similar to those of control at harvest time. Red coloration of fruit skin in EC was lower compared to ET but higher than control. The average fruit firmness was low in ET, while the control and EC exhibited similar levels of firmness. Fruit sugar acid ratios did not show significant differences between treatments. However, the titratable acidity of EC was significantly lower than that of the control at 10 days before harvest. Moreover, fruit sugar acid ratio of ET and EC at 10 days before harvest in 2021 was similar to their sugar acid ratio at harvest time. Therefore, it was thought that fruit maturation and skin coloration could be accelerated by 10 days from the harvest time through the combined treatment of 100 mg/L ethephon and 0.5% CaCl₂ at the end of fruit enlargement in 'Fuji'/M.26.

• Journal of Intelligence and Information Systems
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• 제24권4호
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.