• Journal of Korean Society of Archives and Records Management
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• v.23 no.4
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• pp.73-89
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• 2023

This study aims to analyze the frequency of keywords used in Korean abstracts, which are unstructured text data in the domestic record management research field, using text mining techniques to identify domestic record management research trends through distance analysis between keywords. To this end, 1,157 keywords of 77,578 journals were visualized by extracting 1,157 articles from 7 journal types (28 types) searched by major category (complex study) and middle category (literature informatics) from the institutional statistics (registered site, candidate site) of the Korean Citation Index (KCI). Analysis of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Scattertext using Word2vec was performed. As a result of the analysis, first, it was confirmed that keywords such as "record management" (889 times), "analysis" (888 times), "archive" (742 times), "record" (562 times), and "utilization" (449 times) were treated as significant topics by researchers. Second, Word2vec analysis generated vector representations between keywords, and similarity distances were investigated and visualized using t-SNE and Scattertext. In the visualization results, the research area for record management was divided into two groups, with keywords such as "archiving," "national record management," "standardization," "official documents," and "record management systems" occurring frequently in the first group (past). On the other hand, keywords such as "community," "data," "record information service," "online," and "digital archives" in the second group (current) were garnering substantial focus.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.825-838
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• 2014

The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.

• Korea Trade Review
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• v.41 no.5
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• pp.113-138
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• 2016

This study analyzed the impact when Korea expands Chinese Renminbi(RMB) as invoicing currency on the trade to China using Monte-Carlo simulation. Primarily, we analyzed the impact on the balance of Korean Won(KRW) converted from RMB in a case that simulated exchange rate(Korean won to Chinese Renminbi) and realized historically identical probability distribution but in different stochastic process. In addition, we developed the simulation of the case where the volatility of RMB to KRW exchange rate abnormally expanded. The major results found in this study are as follows. First, in the case where RMB exchange rate simulated in identical probability distribution but in the different stochastic process, no matter how much RMB was utilized as invoicing currency, expansion of the RMB exchange rate and exchange rate volatility operated as positive mechanism to increase the KRW converted balance. Secondly, while the expansion of US dollar exchange rate volatility positively influences the balance on average, it caused a polarization of balance, which makes under-average-balance lower and over-average-balance higher. On the contrary, the expansion of RMB exchange rate volatility even shows a similar mechanism but the impact is more moderate than USD exchange rate volatility. Thirdly, as RMB exchange rate volatility expanded, the balance of translated invoicing currency (RMB) declined, whilst the negative impact of RMB exchange rate volatility on balance of translated invoicing currency(RMB) showed diminishing effect. Lastly, the influence of RMB's exchange rate volatility through RMB usage ratio trends similar to bull spread strategy, which is a combination of call option with put option. Therefore, since RMB usage in invoicing currency could spawn a hedging effect, corporations might utilize RMB as a strategic device for maximizing profits.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.55-63
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• 2013

This study investigates the trends and uncertainty of the impacts of climate change on paddy rice production in the Geumho river basin. The Long Ashton Research Station stochastic Weather Generator (LARS-WG) was used to derive future climate data for the Geumho river basin from 15 General Circulation models (GCMs) for 3 Special Report on Emissions Scenarios (SRES) (A2, A1B and B1) included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. The Food and Agricultural Organization (FAO) AquaCrop, a water-driven crop model, was statistically calibrated for the 1982 to 2010 climate. The index of agreement (IoA), prediction efficiency ($R^2$), percent bias (PBIAS), root mean square error (RMSE) and a visual technique were used to evaluate the adjusted AquaCrop simulated yield values. The adjusted simulated yields showed RMSE, NSE, IoA and PBIAS of 0.40, 0.26, 0.76 and 0.59 respectively. The 5, 9 and 15 year central moving averages showed $R^2$ of 0.78, 0.90 and 0.96 respectively after adjustment. AquaCrop was run for the 2020s (2011-2030), 2050s (2046-2065) and 2090s (2080-2099). Climate change projections for Geumho river basin generally indicate a hotter and wetter future climate with maximum increase in the annual temperature of $4.5^{\circ}C$ in the 2090s A1B, as well as maximum increase in the rainfall of 45 % in the 2090s A2. The means (and ranges) of paddy rice yields are projected to increase by 21 % (17-25 %), 34 % (27-42 %) and 43 % (31-54 %) for the 2020s, 2050s and 2090s, respectively. The A1B shows the largest rice yield uncertainty in all time slices with standard deviation of 0.148, 0.189 and $0.173t{\cdot}ha^{-1}$ for the 2020s, 2050s and 2090s, respectively.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.559-569
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• 2016

A long-run relationship of stock, monetary, realty markets, and business conditions has been suggested to exist due to internal and external shocks. This study investigates whether such a relationship really exists and then performs statistical tests to discern features of the long-run adjustment processes from short-run discrepancies because it is difficult to find studies that examine the market relationship. The comovement relationship of the whole market does not appear to hold for the entire study period; however, it is found to exist for the period before the financial crisis. Estimated error correction models show consistently declining equilibrium errors each period that suggests a recovering process of the long-run equilibrium from short-run secessions.

• Journal of Ecology and Environment
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• v.44 no.4
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• pp.241-255
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• 2020

Background: The stem exclusion stage is a stage of forest development that is important for understanding the subsequent understory reinitiation stage and maturation stage during which horizontal heterogeneity is formed. Over the past 11 years (2009-2019), we observed a deciduous broad-leaved forest in the Albongbunji Basin in Ulleungdo, South Korea in its stem exclusion stage, where Fagus engleriana (Engler's beech) is the dominant species, thereby analyzing the changes in the structure (density and size distributions), function (biomass and species richness), and demographics. Results: The mean stem density data presented a bell-shaped curve with initially increasing, peaking, and subsequently decreasing trends in stem density over time, and the mean biomass data showed a sigmoidal pattern indicating that the rate of biomass accumulation slowed over time. Changes in the density and biomass of Fagus engleriana showed a similar trend to the changes in density and biomass at the community level, which is indicative of the strong influence of this species on the changing patterns of forest structure and function. Around 2015, a shift between recruitment and mortality rates was observed. Deterministic processes were the predominant cause of tree mortality in our study; however, soil deposition that began in 2017 in some of the quadrats resulted in an increase in the contribution of stochastic processes (15% in 2019) to tree mortality. The development of horizontal heterogeneity was observed in forest gaps. Conclusions: Our observations showed a dramatic shift between the recruitment and mortality rates in the stem exclusion stage, and that disturbance increases the uncertainty in forest development increases. The minor changes in species composition are likely linked to regional species pool and the limited role of the life-history strategy of species such as shade tolerance and habitat affinity. Our midterm records of ecological succession exhibited detailed demographic dynamics and contributed to the improvement of an ecological perspective in the stem exclusion stage.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.499-505
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• 2015

Trend Impact Analysis(: TIA) is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. An adaptive neuro-fuzzy inference system is a kind of artificial neural network that integrates both neural networks and fuzzy logic principles, It is considered to be a universal estimator. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Adaptive Neuro-Fuzzy Inference System(: ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes. The trigger attributes can be calculated by a stochastic dynamic model; then different scenarios are generated using Monte-Carlo simulation. To compare the proposed method, a simple simulation is provided concerning the impact of river basin drought on the annual flow of water into a lake.