• 환경영향평가
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• 제20권4호
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• pp.523-538
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• 2011

This study analyzes the changes in the surface area of each forest cover, based on temperature data analysis and satellite imagery as the basic methods for the impact assessment of climate change on regional units. Furthermore, future changes in the forest cover are predicted using the double exponential smoothing method. The results of the study have shown an overall increase in annual mean temperature in the studied region since 1990, and an especially increased rate in winter and autumn compared to other seasons. The multi-temporal analysis of the changes in the forest cover using satellite images showed a large decrease of coniferous forests, and a continual increase in deciduous forests and mixed forests. Such changes are attributed to the increase in annual mean temperature of the studied regions. The analysis of changes in the surface area of each forest cover using the statistical data displayed similar tendencies as that of the forest cover categorizing results from the satellite images. Accordingly, rapid changes in forest cover following the increase of temperature in the studied regions could be expected. The results of the study of the forest cover surface using the double exponential smoothing method predict a continual decrease in coniferous forests until 2050. On the contrary, deciduous forests and mixed forests are predicted to show continually increasing tendencies. Deciduous forests have been predicted to increase the most in the future. With these results, the data on forest cover can be usefully applied as the main index for climate change. Further qualitative results are expected to be deduced from these data in the future, compared to the analyses of the relationship between tree species of forest and climate factors.

• Investigative Magnetic Resonance Imaging
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• 제20권4호
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• pp.207-214
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• 2016

Purpose: The purpose of this study was to investigate the effect of the number of measurement points on the calculation of transverse relaxation time (T2) with a focus on muscle T2. Materials and Methods: This study assumed that muscle T2 was comprised of a single component. Two phantom types were measured, 1 each for long ("phantom") and short T2 ("polyvinyl alcohol gel"). Right calf muscle T2 measurements were conducted in 9 healthy male volunteers using multiple-spin-echo magnetic resonance imaging. For phantoms and muscle (medial gastrocnemius), 5 regions of interests were selected. All region of interest values were expressed as the mean ${\pm}$ standard deviation. The T2 effective signal-ratio characteristics were used as an index to evaluate the magnetic resonance image quality for the calculation of T2 from T2-weighted images. The T2 accuracy was evaluated to determine the T2 reproducibility and the goodness-of-fit from the probability Q. Results: For the phantom and polyvinyl alcohol gel, the standard deviation of the magnetic resonance image signal at each echo time was narrow and mono-exponential, which caused large variations in the muscle T2 decay curves. The T2 effective signal-ratio change varied with T2, with the greatest decreases apparent for a short T2. There were no significant differences in T2 reproducibility when > 3 measurement points were used. There were no significant differences in goodness-of-fit when > 6 measurement points were used. Although the measurement point evaluations were stable when > 3 measurement points were used, calculation of T2 using 4 measurement points had the highest accuracy according to the goodness-of-fit. Even if the number of measurement points was increased, there was little improvement in the probability Q. Conclusion: Four measurement points gave excellent reproducibility and goodness-of-fit when muscle T2 was considered mono-exponential.

• 패션비즈니스
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• 제15권6호
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• pp.176-203
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• 2011

In today's intensifying global competition, Korean fashion industry is relying on only qualitative data for feasibility study of future projects and developmental plan. This study was conducted in order to support establishment of a scientific and rational management system that reflects market demand. First, fashion market size was limited to the total amount of expenditure for fashion clothing products directly purchased by Koreans for wear during 6 months in spring and summer and 6 months in autumn and winter. Fashion market forecasting model was developed using statistical forecasting method proposed by previous research. Specifically, time series model was selected, which is a verified statistical forecasting method that can predict future demand when data from the past is available. The time series for empirical analysis was fashion market sizes for 8 segmented markets at 22 time points, obtained twice each year by the author from 1998 to 2008. Targets of the demand forecasting model were 21 research models: total of 7 markets (excluding outerwear market which is sensitive to seasonal index), including 6 segmented markets (men's formal wear, women's formal wear, casual wear, sportswear, underwear, and children's wear) and the total market, and these markets were divided in time into the first half, the second half, and the whole year. To develop demand forecasting model, time series of the 21 research targets were used to develop univariate time series models using 9 types of exponential smoothing methods. The forecasting models predicted the demands in most fashion markets to grow, but demand for women's formal wear market was forecasted to decrease. Decrease in demand for women's formal wear market has been pronounced since 2002 when casualization of fashion market intensified, and this trend was analyzed to continue affecting the demand in the future.

• 한국ITS학회 논문지
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• 제22권1호
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• pp.103-114
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• 2023

본 연구에서는 도시부 도로의 다양한 자료를 수집하여 통행속도 변화에 대한 영향을 분석하였고, 이와 같은 빅데이터를 활용하여 GRU 기반의 단기 통행속도 예측 모형을 개발하였다. 그리고 Baseline 모형과 이중지수평활 모형을 비교 모형으로 선정하여 RMSE 지표로 예측 오차를 평가하였다. 모형 평가 결과, Baseline 모형과 이중지수평활 모형의 RMSE는 평균 7.46, 5.94값으로 각각 산출되었다. 그리고 GRU 모형으로 예측한 평균 RMSE는 5.08 값이 산출되었다. 15개 링크별로 편차가 있지만, 대부분의 경우 GRU 모형의 오차가 최소의 값을 나타내었고, 추가적인 산점도 분석 결과도 동일한 결과를 제시하였다. 이러한 결과로부터 도시부 도로의 통행속도 정보 생성 과정에서 GRU 기반의 예측 모형 적용 시 예측 오차를 감소시키고 모형 적용 속도의 개선을 기대할 수 있을 것으로 판단된다.

• 한국산림과학회지
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• 제106권4호
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• pp.457-464
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• 2017

본 연구에서는 소나무 임분밀도관리도를 이용하여 소나무 임분의 건정성을 확보할 수 있는 효율적인 임분밀도 관리 기준을 마련하고 이로부터 실행가능한 임분의 생산목표를 예측하였다. 적정 임분 관리수준은 임내 세장목 비율에 대한 수량비수(Relative yield index: Ry)의 관계를 지수함수로부터 모형 추정을 하였으며, 추정 결과 모형 설명력은 0.424로 나타났다. 임내 세장목 비율은 특정 수량비수에 도달하면 급격히 증가하는 경향이 나타났고, 이 관계식을 근거로 하여 목표하는 적정 Ry 값 0.84를 구하였다. 적정 임분밀도 관리 기준인 Ry 0.84 값의 곡선과 중부지방소나무의 우세목 수고를 예측하여 지위지수별 생산목표를 설정하였다. 중부지방소나무 지위지수 10~16의 범위에서 벌기령을 60년으로 할 때 예측되는 수확본수는 ha당 425~1,311본으로 나타났다. 목표 흉고직경은 지위지수 16이상에서 30 cm 이상 중경재 생산이 가능하며, 지위 14와 12는 20 cm 이상 소경재 생산, 지위 10은 20 cm 미만 소경재 생산이 가능할 것으로 예측되었다.

• 한국컴퓨터정보학회논문지
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• 제25권1호
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• pp.131-140
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• 2020

본 연구는 소비자들의 개인품절반응지수(ISRI) 개발에 대한 연구로, 온라인 쇼핑 시 품절 상황에 대한 소비자들의 인지적 반응, 감정적 반응 및 행동적 반응의 영향을 분석하였다. ISRI는 선행연구인 Kim and Lee(2016/2018)의 인지, 감정 및 행동적 반응을 근거로 하여 개발하였다. 설문 조사는 인터넷 설문 조사 기관에서 2014년 07월에 2주간 진행되었으며 754명의 설문대상자 중 전체 질문에 응답한 회수된 설문지는 총 526부였다. 이 회수된 설문지를 본 연구의 조사에 이용하였다. 자료의 분석은 SPSS 25.0 Package를 이용하였다. 개인풉절반응지수(ISRI)는 정규분포를 형성하였다. 본 연구의 개인품절반응지수(ISRI)는 개인화, 맞춤화가 급속도로 진행되는 4차산업혁명 시대에 소비자의 불편한 쇼핑상황인 품절 시 대안의 마련 근거로써 실무적 의의를 가진다.

• 한국작물학회지
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• 제47권5호
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• pp.390-394
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• 2002

Spikelet number per unit area(SPN) is a major determinant of rice yield. Nitrogen nutrition status and biomass during reproductive stage determine the SPN. To formulate a model for estimating SPN, the 93 field experiment data collected from widely different regions with different japonica varieties in Korea and Japan were analyzed for the upper boundary lines of SPN responses to nitrogen nutrition index(NNI), shoot dry weight and shoot nitrogen content at panicle initiation and heading stage. The boundary lines of SPN showed asymptotic responses to all the above parameters(X) and were well fitted to the exponential function of $f(X)=alphacdot{1-etacdotexp(gamma;cdot;X)}$. Excluding the constant, from the boundary line equation, the values of the equation range from 0 to 1 and represent the indices of parameters expressing the degree of influence on SPN. In addition to those indices, the index of shoot dry weight increase during reproductive stage was calculated by directly dividing the shoot dry weight increase by the maximum value ($800 extrm{g/m}^{-2}$) of dry weight increase as it showed linear relationship with SPN. Four indices selected by forward stepwise regression at the stay level of 0.05 were those for NNI ($I_{NNI}_P$) at panicle initiation, NNI($I_{NNI}_h$) and shoot dry weight($I_{DW}_h$) at heading stage, and dry weight increase($I_{DW}$) between those two stages. The following model was obtained: SPN=48683ㆍ $I_{DWH}$$^{0.482}$ㆍ $I_{NNIp}$$^{0.387}$ㆍ $I_{NNIH}$$^{0.318}$ㆍ $I_{DW}$ $^{0.35}$). This model accounted for about 89% of the variation of spikelet number. In conclusion this model could be used for estimating the spikelet number of japonica rice with some confidence in widely different regions and thus, integrated into a rice growth model as a component model for spikelet number estimation.n.n.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제13권6호
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• pp.397-401
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• 2007

이동객체에 대한 연속 범위 질의(Continuous Range Query)의 응용프로그램이 급속도로 확장되면서 이차원정보를 넘어서 고차원 공간 데이타에 대한 처리를 요구하고 있다. 만약 고차원 데이타에 대한 중첩되어지는 연속 범위 질의의 정보를 기존의 색인으로 구성한다면 객체의 수와 질의의 수가 증가함에 따라 질의처리성능이 저하된다. 본 논문은 이러한 문제점을 해결하기 위하여 PAB(Projected Attribute Bit)-기반의 질의색인방법을 제안한다. 제안하는 기법은 성능향상을 위하여 질의의 정보를 각 속성 축에 투영이라는 작업을 통하여 고차원의 데이타를 1차원 정보들로 변환하고 이러한 정보를 비트단위로 구성하였다. 또한 제안하는 질의색인은 보다 효율적인 질의의 처리를 위하여 점진적인 갱신(Incremental Update)을 지원한다. 다양한 성능평가 및 분석을 통하여 제안하는 방법이 최근에 연구된 CES-기반의 질의색인 기법보다 더 나은 확장성(Scalability)을 가짐을 입증한다.

• Journal of Computing Science and Engineering
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• 제1권2호
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.141-141
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• 2022

Drought is a global phenomenon that affects almost all landscapes and causes major damages. Due to non-linear nature of contributing factors, drought occurrence and its severity is characterized as stochastic in nature. Early warning of impending drought can aid in the development of drought mitigation strategies and measures. Thus, drought forecasting is crucial in the planning and management of water resource systems. The primary objective of this study is to make improvement is existing drought forecasting techniques. Therefore, we proposed an improved version of Seasonal Autoregressive Integrated Moving Average (SARIMA) model (MD-SARIMA) for reliable drought forecasting with three years lead time. In this study, we selected four watersheds of Han River basin in South Korea to validate the performance of MD-SARIMA model. The meteorological data from 8 rain gauge stations were collected for the period 1973-2016 and converted into watershed scale using Thiessen's polygon method. The Standardized Precipitation Index (SPI) was employed to represent the meteorological drought at seasonal (3-month) time scale. The performance of MD-SARIMA model was compared with existing models such as Seasonal Naive Bayes (SNB) model, Exponential Smoothing (ES) model, Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) model, and SARIMA model. The results showed that all the models were able to forecast drought, but the performance of MD-SARIMA was robust then other statistical models with Wilmott Index (WI) = 0.86, Mean Absolute Error (MAE) = 0.66, and Root mean square error (RMSE) = 0.80 for 36 months lead time forecast. The outcomes of this study indicated that the MD-SARIMA model can be utilized for drought forecasting.