• Journal of the Korean Society of Environmental Restoration Technology
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• v.14 no.3
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• pp.1-14
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• 2011

In order to determine temporal changes of the urban landscape, interdependence and interaction among geo-spatial objects can be analyzed using GIS analytic methods. In this study, to investigate changes in the landscape structure of the Namyangju area, the size and shape of landscape patches, and the distance between the patches were analyzed with the Spatial Autocorrelation Method. In addition, both global and local spatial autocorrelation analyses were conducted. The results of global Moran's I revealed that both patch size and shape index transformed to a more dispersed pattern over time. Next, the local Moran's I of patch size in all time series determined that almost all patches were of a high-low pattern. Meanwhile, the local Moran's I of the shape index was found to have changed from a high-high pattern to a high-low pattern in time series. Finally, as time passes, the number of hot spot patches about size and shape index had been decreased according to the results of hot spot analysis. These changes appeared around the development projects in the study area. From the results of this study, degradation of landscape patches in Namyangju were ascertained and their specific areas were delineated. Such results can be used as useful data in selecting areas for conservation and for preparing plans and strategies in environmental restoration.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1727-1737
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• 2018

In this paper, we propose a system to detect spatial structures in land cover maps and to detect time-series spatial structure changes. At first, the proposed system detects patches in a certain area at different times and calculates their measures to analyse spatial structure patterns of the area. Then the system conducts patch mapping among the detected time-series patches and decides 6 types of patch changes such as keeping, creating, disappearing, splitting, merging, and changing in a mixed way. Also, the system stores the patch-based spatial structure patterns of time-series land cover maps in binary form to extract changes. This demonstrated that the proposed change detection system can be used as a basis for planning the reconstruction of the urban spatial structure by measuring the degree of urban sprawl.

• Safety and Health at Work
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• v.12 no.3
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• pp.296-303
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• 2021

Background: Rapid population aging in developed countries has resulted in the working-age population increasingly being tasked with the provision of informal care. Methods: An educational intervention was delivered to 21 carer-employees employed at a Canadian University. Work role function, job security, schedule control, work-family conflict, familywork conflict, and supervisor and coworker support were measured as part of an aggregated workplace experience score. This score was used to measure changes pre/post intervention and at a follow-up period approximately 12 months post intervention. Three random intercept models were created via linear mixed modeling to illustrate changes in participants' workplace experience across time. Results: All three models reported statistically significant random and fixed effects intercepts, with a positive coefficient of change. Conclusion: This suggests that the intervention demonstrated an improvement of the workplace experience score for participants over time, with the association particularly strong immediately after intervention.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.268-273
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• 1998

This study applies an on-line system, which employes an adaptive reconstruction technique to monitor and forecast ocean surface changes. The system adaptively generates an appropriate synthetic time series with recovering missing measurements for sequential images. The reconstruction method incorporates temporal variation according to physical properties of targets and anisotropic spatial optical properties into image processing techniques. This adaptive approach allows successive refinement of the structure of objects that are barely detectable in the observed series. The system sequentially collects the estimated results from the adaptive reconstruction and then statistically analyzes them to monitor and forecast the change in surface characteristics.

• Proceedings of the CALSEC Conference
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• 2005.11a
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• pp.147-153
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• 2005

When a customer wants to buy an item at the Internet shopping mall, one of the difficulties is to decide when to buy the item because its price changes over time. If the shopping mall can be able to recommend appropriate buying points, it will be greatly helpful for the customer. Therefore, in this presentation, we propose a method to recommend buying points based on the time series analysis using a database that contains past prices data of items. The procedure to provide buying points for an item is as follows. First, we search past time series patterns from the database using normalized similarity, which are similar to the current time series pattern of the item. Second, we analyze the retrieved past patterns and predict the future price pattern of the item. Third, using the future price pattern, we recommend when to buy the item.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.333-340
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• 2017

Since realtime search words are centered on the fact that the search growth rate of an issue is rapidly increasing in a short period of time, it is not possible to express an issue that maintains interest for a certain period of time. In order to overcome these limitations, this paper evaluates the daily and hourly persistence of the realtime words that belong to the top 10 for a certain period of time and extracts the search word that are constantly interested. Then, we present the method of using the time series analysis and the neural network to know how the interest of the upper search word changes, and show the result of forecasting the near future change through the actual example derived through the method. It can be seen that forecasting through time series analysis by date and artificial neural networks learning by time shows good results.

• The Journal of Bigdata
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• v.7 no.2
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• pp.217-224
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• 2022

Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Therefore, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'A'. According to the simulation results, it was confirmed that Bidirectional-LSTM(Bi-LSTM) compared to LSTM(Long Short-Term Memory) takes more simulation time about more than 50%, but the prediction accuracy of non-periodic time series data such as clothing product sales data is the same.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.46 no.2
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• pp.186-194
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• 2013

We compiled and analyzed long-term time-series data collected in Korea to evaluate changes in oceanographic conditions and marine ecosystems near Jeju Island ($33^{\circ}00^{\prime}-34^{\circ}00^{\prime}\;N$, $125^{\circ}30^{\prime}-127^{\circ}30^{\prime}\;E$) from 1981 to 2010. Environmental data included depth-specific time series of temperature and salinity that have been measured bimonthly since 1961 in water columns at 175 fixed stations along 22 oceanographic lines in Korean waters by the National Fisheries Research & Development Institute, and time series of estimated volume transport of the Tsushima Warm Current (TWC) and Korea Strait Bottom Cold Water (KSBCW) for the period from 1961 to 2008. We analyzed the species composition in terms of biomass of fish species caught by Korean fishing vessels in the waters near Jeju Island (1981-2010). Data were summarized and related to environmental changes using canonical correspondence analysis (CCA). The CCA detected major shifts in fish community structure between 1982 and 1983 and between 1990 and 1992; the dominant species were a filefish during 1981-1992 and chub mackerel from 1992 to 2007. CCA suggested that water temperature and salinity in the mixed layer and the volume transport of the TWC and the KSBCW were significantly related to the long-term changes in the fish community in the waters off Jeju Island. Fish community shifts seemed to be related to the well-established 1989 regime shift in the North Pacific. Further studies are required to elucidate the mechanisms driving climate change effects on the thermal windows and habitat ranges of commercial species to develop fisheries management plans based on reliable projections of long-term changes in the oceanographic conditions in waters off Jeju Island.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.183-190
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• 2021

In this paper, we implement a system for a fund recommendation based on the investment propensity and for a future fund price prediction. The investment propensity is classified by scoring user responses to series of questions. The proposed system recommends the funds with a suitable risk rating to the investment propensity of the user. The future fund prices are predicted by Prophet model which is one of the machine learning methods for time series data prediction. Prophet model predicts future fund prices by learning the parameters related to trend changes. The prediction by Prophet model is simple and fast because the temporal dependency for predicting the time-series data can be removed. We implement web pages for the fund recommendation and for the future fund price prediction.

• Journal of Korean Academy of Nursing
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• v.49 no.2
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• pp.149-160
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• 2019

Purpose: The purpose of this study was to examine the effects of oral cryotherapy on oral mucositis, reactive oxygen series, inflammatory cytokines, and oral comfort in patients undergoing chemotherapy for gynecologic cancers. Methods: Participants were randomly assigned to the experimental group (n=25, receiving oral cryotherapy during chemotherapy) and the control group (n=25, receiving the usual care consisting of 0.9% normal saline gargles three times before meals). Oral mucositis was assessed using the oral assessment guide, while oral comfort was assessed using the oral perception guide. Reactive oxygen series was measured as total oxidant stress, and the level of two inflammatory markers, interleukin-6 (IL-6) and tumor necrosis factor-alpha ($TNF-{\alpha}$), were examined. The data were analyzed using t-test, chi-square test, Fisher's exact test, Mann-Whitney U-test, and repeated measures analysis of variance. Results: There was a significant difference in the oral mucositis score, reactive oxygen series score, $TNF-{\alpha}$ level, and oral comfort score between the two groups, and there were significant changes over time and in the group-by-time interactions. There was a significant difference in the IL-6 score between the two groups, but there were no significant changes over time or in the group-by-time interactions. Conclusion: The study results revealed that oral cryotherapy was more effective than the usual care regime of normal saline gargles for reducing oral mucositis, reactive oxygen series, and inflammatory cytokines and for improving oral comfort in gynecologic cancer patients undergoing chemotherapy.