• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.101-109
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• 2018

In order to predict the future needs of the aircraft repair parts, each military group develops and applies various techniques to their characteristics. However, the aircraft and the equipped weapon systems are becoming increasingly advanced, and there is a problem in improving the hit rate by applying the existing demand prediction technique due to the change of the aircraft condition according to the long term operation of the aircraft. In this study, we propose a new prediction model based on the conventional time-series analysis technique to improve the prediction accuracy of aircraft repair parts by using machine learning model. And we show the most effective predictive method by demonstrating the change of hit rate based on actual data.

• Analytical Science and Technology
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• v.8 no.4
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• pp.499-503
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• 1995

In order to study how and why the stabilities of lanthanoid(III) complexes in solutions vary across the series, the formation constants of the adducts of tris(2-thenoyltrifluoroacetonato)lanthanoids(III) with seven carboxylic acids in chloroform have been determined by solvent extraction technique at 298K. The formation constants with carboxylic acids generally decrease with increasing the atomic number, but in the middle of the series, they change only slightly. Such trends have been interpreted as related to a change of the coordination number in the middle of the series. It has been attempted to determine the number of water molecules coordinated to the adducts as well as $Eu(TTA)_3$ in chloroform by measuring the fluorescence life time of europium(III), to ensure the assignment of the coordination number.

• Water for future
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• v.15 no.3
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• pp.23-36
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• 1982

The stochastic variations and structures of time series data on water quality were examined by employing the techniques of autocorrelation function, variance spectrum, Fourier series, autoregressive model and ARIMA model. These time series included hourly and daily observation on DO, turbidity, conductivity pH and water temperature. The measurement was made by automatic recording instrument at Noryangjin and Dook-do located in the downstream part of Han River during 1975 and 1976. Hourly water quality time series varied with the dominant 24-hour periodicity, and the 12-hour periodicity was also observed. An important factor affecting 24-hour periodic variation of DO is believed to be photosynthesis by algae. These phenomena might be attributable to periodic discharges of municipal sewage. Noryangjin site showed the more distinct 12-hour periodicity than Dook-do site did, and tidal effect might be responsible for the difference. The water quality, as measured by DO and turbidity, was better in the afternoon compared with the quality in the morning. This change can be explained by the periodic variation of DO, temperature and the amount of municipal wewage discharge. It was also observed that the water temperature at Noryangjin was higher than the temperature at Dook-do. This difference might have been caused by the pollutants that were added to the section between two sites. The correlation coefficients between some of the variables were fairly high. For example, the coefficient was -0.88 between DO and water temperature, 0.75 between turbidity and river flow, and 0.957 between water temperature and air temperature. The lag time of heat transfer from the air to the water was estimated as 24 days. The first order auto-regressive model was appropriate for explaning standardized hourly DO time series. The ARIMA model of (1, 0, 0) type provided relatively satisfactory results for daily DO time series after the removal of significant harmonic value.

• Knowledge Management Research
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• v.22 no.2
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.363-370
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• 2023

Due to the spread of COVID-19, the use of public transportation such as urban railroads has changed significantly since the beginning of 2020. Therefore, in this study, daily time series data for each urban railway station were collected for three years before COVID-19 and after the spread of COVID-19, and the similarity of time series analysis was evaluated through DTW (Dynamic Time Warping) distance method to derive regression centers for each cluster, and the effect of various external events such as COVID-19 on changes in the number of users was diagnosed as a time series impact detection function. In addition, the characteristics of use by cluster of urban railway stations were analyzed, and the change in passenger volume due to external shocks was identified. The purpose was to review measures for the maintenance and recovery of usage in the event of re-proliferation of COVID-19.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.212-217
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• 2020

The purpose of this study was to analyze the volatility and properties of a time series for tangerine prices in Jeju using the GARCH model of Bollerslev(1986). First, it was found that the time series for the rate of change in tangerine prices had a thicker tail rather than a normal distribution. At a significance level of 1%, the Jarque-Bera statistic led to a rejection of the null hypothesis that the distribution of the time series for the rate of change in tangerine prices is normally distributed. Second, the correlation between the time series was high based on the Ljung-Box Q statistic, which was statistically verified through the ARCH-LM test. Third, the results of the GARCH(1,1) model estimation showed statistically significant results at a significance level of 1%, except for the constant of the mean equation. The persistence parameter value of the variance equation was estimated to be close to 1, which means that there is a high possibility that a similar level of volatility will be present in the future. Finally, it is expected that the results of this study can be used as basic data to optimize the government's tangerine supply and demand control policy.

• Ocean and Polar Research
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• v.43 no.4
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• pp.229-243
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• 2021

Quality control (QC) to process observed time series has become more critical as the types and amount of observed data have increased along with the development of ocean observing sensors and communication technology. International ocean observing institutions have developed and operated automatic QC procedures for these observed time series. In this study, the performance of automated QC procedures proposed by U.S. IOOS (Integrated Ocean Observing System), NDBC (National Data Buy Center), and OOI (Ocean Observatory Initiative) were evaluated for observed time-series particularly from the Yellow and East China Seas by taking advantage of a confusion matrix. We focused on detecting additive outliers (AO) and temporary change outliers (TCO) based on ocean temperature observation from the Ieodo Ocean Research Station (I-ORS) in 2013. Our results present that the IOOS variability check procedure tends to classify normal data as AO or TCO. The NDBC variability check tracks outliers well but also tends to classify a lot of normal data as abnormal, particularly in the case of rapidly fluctuating time-series. The OOI procedure seems to detect the AO and TCO most effectively and the rate of classifying normal data as abnormal is also the lowest among the international checks. However, all three checks need additional scrutiny because they often fail to classify outliers when intermittent observations are performed or as a result of systematic errors, as well as tending to classify normal data as outliers in the case where there is abrupt change in the observed data due to a sensor being located within a sharp boundary between two water masses, which is a common feature in shallow water observations. Therefore, this study underlines the necessity of developing a new QC algorithm for time-series occurring in a shallow sea.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.2
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• pp.240-250
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• 1998

This study is to have attempted to analyze the characteristics of the change in forest landscape pattern of Seoul for 18 years by grasping it through satellite image data on the forest area in Seoul where a rapid change according urbanization and industrialization is going on. On the basis of Landsat MSS data- satellite image data, this writer analyzed the change in the number and size of patch and the mean edge length of each forest land, and the index of patch shape by each year from a landscape -ecological point of view. The results are as follows; First, in the pattern change of the forest patch of Seoul, the highest patch fragmentation area is the forest of the Yangchon-gu district where is decreasing it forest area by 654ha, 511ha, 495ha, 402ha each year from its total size of 742ha in 1979. Second, the change tendency shows that the average forest size decreased at 552.58ha in 1983, 435.03ha in 1988, 396.23ha in 1992, and 379.96ha in 1996. And analysis showed that even in the number of patches, the forest fragmentation phenomenon was presenting by the increase of development disturbance. Third, the mean edge by year was longest at 23,385m in 1979, but it is decreasing continuously. This shows the regular and artificial uniformity of forest landscape by disturbance-effect increase of the built-up development and shows low portion against edge effect by the time-series change like 1979>1983>198>1992>1996. Finally, in the analysis of a shape index indicated by ratio of size and edge, total averages were 2.56, 2.33, 2.17, 2.14, 2.14 each year, so that it is considered that the disturbance and ecological health status against forest landscape can be grasped according to being examined as 1979>1983>1988>1992, 1996 by the time-series change of the landscape.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1133-1148
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• 2019

Tree growth and vitality in forest shows seasonal changes. So, in order to detect forest damage accurately, we have to use satellite images before and after damages taken at the same season. However, temporal resolution of high or medium resolution images is very low,so it is not easy to acquire satellite images of the same seasons. Therefore, in this study, we estimated spectral information of the same DOY using time-series Landsat images and used the estimates as reference values to assess forest damages. The study site is Hwasun, Jeollanam-do, where forest damage occurred due to hail and drought in 2017. Time-series vegetation index (NDVI, EVI, NDMI) maps were produced using all Landsat 8 images taken in the past 3 years. Daily normal vegetation index maps were produced through cloud removal and data interpolation processes. We analyzed the difference of daily normal vegetation index value before damage event and vegetation index value after event at the same DOY, and applied the criteria of forest damage. Finally, forest damage map based on daily normal vegetation index was produced. Forest damage map based on Landsat images could detect better subtle changes of vegetation vitality than the existing map based on UAV images. In the extreme damage areas, forest damage map based on NDMI using the SWIR band showed similar results to the existing forest damage map. The daily normal vegetation index map can used to detect forest damage more rapidly and accurately.