• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.581-594
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• 2016

We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.139-148
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• 2019

This study proposed a multiple intervention time series model to predict KTX passenger demand. In order to revise the research of Kim and Kim (Korean Society for Railway, 14, 470-476, 2011) considering only the intervention of the second phase of Gyeong-bu before November of 2011, we adopted multiple intervention seasonal ARIMA models to model the time series data with additional interventions which occurred after November of 2011. Through the data analysis, it was confirmed that the effects of various interventions such as Gyeong-bu and Ho-nam 2 phase, outbreak of MERS and national holidays, which affected the KTX transportation demand, are successfully explained and the prediction accuracy could be quite improved significantly.

• Journal of the Korean earth science society
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• v.23 no.7
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• pp.587-596
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• 2002

A super ensemble model was developed for the seasonal prediction of regional precipitation in Korea using the lag correlated large scale predictors, based on the empirical orthogonal function (EOF) analysis and multiple linear regression model. The predictability of this model was also evaluated by cross-validation. Correlation between the predicted and the observed value obtained from the super ensemble model showed 0.73 in spring, 0.61 in summer, 0.69 in autumn and 0.75 in winter. The predictability of categorical forecasting was also evaluated based on the three classes such as above normal, near normal and below normal that are clearly defined in terms of a priori specified by threshold values. Categorical forecasting by the super ensemble model has a hit rate with a range from 0.42 to 0.74 in seasonal precipitation.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.7 no.3
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• pp.48-55
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• 2004

본 연구는 중 저해상도 위성영상을 이용하여 하천주변 습지를 판별해내는 보다 개선된 기법을 개발해 내는데 그 목적이 있다. 중 저해상도 위성영상의 하나의 화소는 일반적으로 하나의 동질한 물체의 분광반사값을 나타내기보다는 다양한 분광값을 가진 물체들의 대표값으로 나타나게 된다. 특히 본 연구에서는 식생, 수문 및 토양요소의 혼합체인 습지의 판별을 위해서, 하나의 화소가 하나의 물체를 대표함을 전제로 하는 기존의 분석방법 보다는, 혼합화소 (mixed pixel)를 대상지 의 토지 피복을 가장 잘 반영 하는 순수한 화소값(endmember)들로 분해함으로써 보다 정확한 판별 및 분류를 가능케 하고자 하였다. 이를 위하여 일반적으로 극세분광 위성영상의 분석에 활용되는 기법인 분광혼합화소분석(Spectral Mixture Analysis)을 이용하였는데, 습지 각 화소의 식생, 수문 및 토양요소의 흔합정도를 분해한 후, 이들의 분할영상 (fraction images)을 추출해내고 이를 분석에 이용하였다. 팔당상수원보호구역의 소택형 습지를 대상으로 봄 가을의 Landsat 영상에 대한 분석을 수행하였으며, 도출된 결과는 다음과 같다. 첫째, 봄 가을 각각의 영상에 대하여 4개씩 endmember를 선정하였으며, 분할영상과 원자료 각각에 대하여 습지판별을 수행한 결과, 가을영상에 대하여 분할영상을 이용한 방법의 소택 형 습지 판별 정확도가 가장 높은 값을 보여주었다(생산자 정확도 : 83.3%, 사용자 정확도 : 86.5%). 둘째, 소택형 습지로 판별된 지역만을 대상으로 보다 세분화된 분류가 가능한 지 알아보기 위하여 소택형 습지로 판별된 지역의 영상에 대해 ISODATA 무감독분류를 수행한 결과 2개의 클러스터로 대별되었다. 현장조사, 기존 연구의 수심자료 및 식생에 대한 조사를 바탕으로 위의 2개의 클러스터를 조사한 결과, 수문조건에 따른 분류인 아계(subsystem) 단계의 '영구적 침수형 소택형 습지'와 '계절적 침수형 소택형 습지'로 분류할 수 있었다.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.263-274
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• 2013

In this study we evaluated the hydrological applicability of multi-satellite precipitation estimates. Three high-resolution global multi-satellite precipitation products, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), the Global Satellite Mapping of Precipitation (GSMaP), and the Climate Precipitation Center (CPC) Morphing technique (CMORPH), were applied to the Coupled Routing and Excess Storage (CREST) model for the evaluation of their hydrological utility. The CREST model was calibrated from 2002 to 2005 and validated from 2006 to 2009 in the Chungju Dam watershed, including two years of warm-up periods (2002-2003 and 2006-2007). Areal-averaged precipitation time series of the multi-satellite data were compared with those of the ground records. The results indicate that the multi-satellite precipitation can reflect the seasonal variation of precipitation in the Chungju Dam watershed. However, TMPA overestimates the amount of annual and monthly precipitation while GSMaP and CMORPH underestimate the precipitation during the period from 2002 to 2009. These biases of multi-satellite precipitation products induce poor performances in hydrological simulation, although TMPA is better than both of GSMaP and CMORPH. Our results indicate that advanced rainfall algorithms may be required to improve its hydrological applicability in South Korea.

• Journal of the Korean earth science society
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• v.27 no.1
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• pp.83-94
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• 2006

The objective of this paper is to extract and analyze the sediment environment change information in the Sachencheon, Gangneung, Korea that was seriously damaged as a result of typhoon Rusa aftermath early in September, 2002 using multi-temporal remote sensing data. For the extraction of change information, an unsupervised approach based on the automatic determination of thresholding values was applied. As the change detection results, turbidity changes right after typhoon Rusa, the decrease of wetlands, the increase of dry sand and channel width and changes of relative level in the stream due to seasonal variation were observed. Sedimentation in the cultivated areas and restoration works also affected the change near the Sacheoncheon. In addition to the change detection analysis, several environmental thematic maps including microtopographic map, distributions of estimated amount of flood deposits and flood hazard landform classification map were generated by using remote sensing and field survey data. In conclusion, multi-temporal remote sensing data can be effectively used for natural hazard analysis and damage information extraction and specific data processing techniques for high-resolution remote sensing data should also be developed.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1435-1447
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• 2020

The seasonal variations in weather parameters can significantly affect the atmospheric transmission characteristics. Herein, we propose a novel augmented multiple regression algorithm for the accurate estimation of atmospheric transmittance and solar irradiance over highly localized areas. The algorithm employs 1) adaptive atmospheric model selection using measured meteorological data and 2) multiple linear regression computation augmented with the conventional application of MODerate resolution atmospheric TRANsmission (MODTRAN). In this study, the proposed algorithm was employed to estimate the solar irradiance over Taean coastal area using the 2018 clear days' meteorological data of the area, and the results were compared with the measurement data. The difference between the measured and computed solar irradiance significantly improved from 89.27 ± 48.08σ W/㎡ (with standard MODTRAN) to 21.35 ± 16.54σ W/㎡ (with augmented multiple regression algorithm). The novel method proposed herein can be a useful tool for the accurate estimation of solar irradiance and atmospheric transmission characteristics of highly localized areas with various weather conditions; it can also be used to correct remotely sensed atmospheric data of such areas.

• The Korean Journal of Zoology
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• v.31 no.4
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• pp.327-333
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• 1988

계절에 따른 초파리 집단내 역위다형 현상의 변동상을 알기 위하여 전주근교의 과수원에서 1983년 1월부터 11월까지 격월로 여섯번 채집한 집단 표본을 분석하였다. 총 1071마리에서 얻은 15개형의 역위는 모두 편동원체역위(paracentric inversion)였으며, 이 중 7개형은 세계형91위(cosmopolitan inversion)였고, 나머지 8개형은 지역형역위(endemic inversion)였다. 개체당 역위의 평균 보유수는 0.639이고 역위를 보유한 개체의 빈도는 0.465였다. 7개의 세계형역위에 대한 표본집단을 분석한 결과 격월 집단간에서 유의한 차이를 보이는 반면 1월과 1 1월 표본간에서는 그 성황이 오히려 비숫해짐으로서 집단내 총역위빈도는 주기적으로 변동하고 있음이 암시된다. 역위와 환경변수와의 다중상관분석에서는 기온, 습도, 강수량이 역위빈도에 유의한 영향을 미치는 것으로 나타났고 역위 중 In(2L)t가 상기한 환경변수 모두에 대하여 유의한 상관관계를 보인 반면, In(3R)C와 In(3R)P는 어느 것과도 상관성을 보이지 않았다. 염색체내 그리고 염색체간의 연관 또는 조합의 검정결과 연관불평형혈상을 1월, 5월, 그리고 7월 표본의 제3염색체에서, 그리고 비균일조합은 1월과 7월 표본에서 역시 제3염색체에서만 산출 되었다. Seasonal changes of inversion frequencies in Chonju 0. melanogaster populations were studied. A total of 1071 males were collected six times with ho months intewal from January through November in 1983. to analyse diploid sets of chromosomes carried by males, each male was mated to several virgin females homoBvgous for cytologically standard sequence in all chromo-somes. From each mating, more than seven FL larvae were selected in random and tested to find chromosomal aberrations. In the present study, 15 different inversions were found and identified to be paracentric only In both second and third chromosomes; seven were cosmopolitan and the rest eight endemic types. The average frequency of inversions was 0.465 and the mean number of inversions carried by a single male was 0.639. The linkage disequilibria were detected between the leK and right arms of third chromosomes from the samples of January, May and Julv, Whereas nonrandom associations appeared also in the third chromosomes only in January and Julv samples. In multiple regression analysis among frequencies of inversions and environ-mental variables it appeared that mean temperature, relative humidity and total precipitation for a month skipped over 30 days before collected affect to change to frequencies of particular inver-sions. With respect to the behavior of inversions in the present samples, it is suggested, with the Friedman's analysis of variance by ranks of inversion frequency orders, that the frequencies of inversions change cyclically year to year.

• The Korean Journal of Malacology
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• v.15 no.1
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• pp.57-62
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• 1999

Condition indices (Cl) are considered to be useful measurements of the nutritive and health status of bivalves although studies on Cl of bivalves are limited due to the lack of a standard formula for calculating Cl. This study attempts to generate Cl of the mussel, Mytilus coruscus inhabiting along the coastal area of Chuja Island in Cheju using three primary formulas. the formulas used in this study: (1) Cl-vol= [tissue dry weight (g) 1000] /shell cavity volume (ml). (2) Cl-wt= [tissue dry weight (g) 1000] / internal shell cavity capacity (g). (3) Cl-size= tissue wet weight (g)/[shell length (mm)]$\^$3/. Monthly condition indices calculated with the three formulas are compared using ANOVA, Duncan's multiple range test and Pearson correlation coefficient. In Chuja Island M. coruscus collected ranged from 50 to 180 mm in shell length. Monthly ranges of Cl-values were 67.48 to 140.61 (Cl-vol), 74.67 to 118.02 (Cl-wt) and 1.4 10$\^$-5) to 1.6 10$\^$-5/ (Cl-size). Cl-vol values in August were higher than two Cl-values in the other months. Monthly Cl-vol was significantly different from Cl-wt and Cl-size. (p<0.05). The results of this study suggest that volumetric condition index (Cl-vol) used in this study is acceptable as a standard measure to evaluate conditions of M. coruscus.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1579-1590
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• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.