• Korean Journal of Ecology and Environment
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• v.46 no.4
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• pp.581-587
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• 2013

The amounts of carbon (C), nitrogen (N) and phosphorus (P) in relation to dry weight (D.W.) were measured in zooplankton from the large four rivers (Han R., Geum R., Yeongsan R. and Seomjin R.) during 2004~2008. The stoichiometry of total zooplankton in four river systems was highly variable. The ranges of average C, N and P-contents were $70{\sim}620mgC\;mg^{-1}$ D.W., $7.1{\sim}85.5{\mu}gN\;mg^{-1}$ D.W. and $2.5{\sim}7.4{\mu}gP\;mg^{-1}$ D.W., respectively. The mean C :N: P atomic ratios reflected large spatial differences. The C : P and N : P ratios of the zooplankton community ranged from 38 to 392 : 1 and from 4 to 65 : 1 in all sampling sites. Self-Organizing Map (SOM) was applied to the survey data, and the study sites were clearly classified into 3 clusters. Clustering was largely affected by the distribution pattern of C, N, P-contents, which is related with characteristics of river systems on the basis of stoichiometry.

• Korean Parent-Child Health Journal
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• v.4 no.1
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• pp.35-55
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• 2001

Recently attention has been focused on the effects of early intervention, or its lack, on both normal and preterm infants. Particularly numerous studies suggest that premature infants are not necessarily understimulated but instead are subjected to inappropriate stimulation. Developmental support and sensory stimulation have become clinical opportunities in which nursing practice can impact on the neurobehavioral outcome of premature infants. Developmental care has been widely accepted and implemented in neonatal intensive care units across the country. Increasingly, attention and concern in caring for low-birth-weight infants and premature infants has led clinicians in the field to explore the effects of a complex of interventions designed to create and maintain a developmentally supportive environment; to provide age-appropriate sensory input; and to protect the infant from inappropriate, excessive and stressful stimulation. The components of developmental care include modifications of the macro-environment to reduce NICU light and sound levels, care clustering, nonnutritive sucking, and containment strategies, such as flexed positioning or swaddling. Sensory stimulation of the premature infants is presented to standardize the modification of a developmental intervention based on physiologic and behavioral cues. The most appropriate type of stimuli are those that are sensitive to infant cues. Evaluation of infant physiological and behavioral responds to specific intervention stimuli may help to identify more appropriate interventions based on infants' cues. A critical question confronting the clinician is that of determining when the evidence supporting a change in practice is sufficient to justify making that change. There are acknowledged limitations in the current studies. Many of the studies examined had small sample sizes; used nonprobability sampling; and used a phase lag design, which introduces the possibility of threats to internal validity and limits the generalizability of the results. Although many issues regarding the effects of developmental interventions remain unresolved, the available research base documents significant benefits of developmental care for LBW infants in consistent outcomes, without significant adverse effects. Particularly, although the individual studies vary somewhat in the definition of specific outcomes measured, instrumentation used, time and method of data collection, and preparaion of the care providers, in all studies, infants receiving the full protocol of individualized developmentally supportive care had improvements in some aspect of four areas of infant functioning: level of respiratory or oxygen support, the establishment of oral feeding; length of hospital stay, and infant behavioral regulation. In summary, based on the available literature, individualized developmental intervention should be incorporated into standard practice in neonatal intensive care. And this implementation needs to be coupled with ongoing research to evaluate the impact of an individualized developmental care programs on the short- and long-tenn health outcomes of LBW infants.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.2 s.36
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• pp.89-94
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• 2006

Forest damage is a worldwide issue and specially, a forest fire involves damage to itself and causes secondary damage such as a flood etc. However, actually, clear analysis on forest fire damage can be hardly conducted due to difficulty in approaching a forest fire and quite a long period of time for analysis. To overcome such difficulty, recently, forest fire damage has been actively investigated with satellite image data, but it is also difficult to obtain satellite image data fitted to the time a forest fire occurred. In addition, it is burdensome to verify accuracy of the obtained image. Therefore, this study was attempted to look into the damaged districts from forest fires by reference to spectroradiometric characteristics of the obtained vegetation with a spectroradiometer as preliminary work to use satellite image data. To begin with, the researcher analyzed the field survey data each measured 3 months and 6 months after occurrence of a forest fire by judging the extent of the damage through visual observation and using a spectroradiometer in order to investigate any potential errors arising out of one-time visual observation. Besides, in this study, groups showing possibilities that trees might be restored to life and wither to death could be classified on the sampling points where forest fire damage is minor.

• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.215-224
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• 2009

A review was given of the role of traditional morphometrics in plant morphological studies using 54 published studies in three major journals and others in Korea, such as Journal of Korean Forestry Society, Korean Journal of Plant Taxonomy, Korean Journal of Breeding, Korean Journal of Apiculture, Journal of Life Science, and Korean Journal of Plant Resources from 1997 to 2008. The two most commonly used techniques of data analysis, cluster analysis (CA) and principal components analysis (PCA) with other statistical tests were discussed. The common problem of PCA is the underlying assumptions of methods, like random sampling and multivariate normal distribution of data. The procedure was intended mainly for continuous data and was not efficient for data which were not well summarized by variances or covariances. Likewise CA was most appropriate for categorical rather than continuous data. Also, the CA produced clusters whether or not natural groupings existed, and the results depended on both the similarity measure chosen and the algorithm used for clustering. An additional problems of the PCA and the CA arised with both qualitative and quantitative data with a limited number of variables and/or too few numbers of samples. Some of these problems may be avoided if a certain number of variables (more than 20 at least) and sufficient samples (40-50 at least) are considered for morphometric analyses, but we do not think that the methods are all mighty tools for data analysts. Instead, we do believe that reasonable applications combined with focus on objectives and limitations of each procedure would be a step forward.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.