• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• Korean Journal of Ecology and Environment
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• v.56 no.2
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• pp.140-150
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• 2023

The Hangang River^* is necessary to manage the water environment of severe pollution due to the high density of residential areas, parks, and agriculture and the large population concentrated there. Benthic macroinvertebrates, such as chironomids larvae, are bioindicator species that reflect environmental changes and are crucial for water quality monitoring. In this study, we investigated morphological characteristics and molecular analysis of the chironomids larvae inhabiting the Hangang River area for water environment surveys. For this research, 20 rivers, lakes, and urban area in the Hangang River basin were selected. Chironomids larvae were collected from July to September 2022, and their appearance and characteristics were identified through morphological identification. In addition, phylogenetic analysis was performed based on the mtCOI gene sequences of the collected chironomids larvae, and identification at the genus level was confirmed. As a result, 32 species and 18 genera of 3 subfamilies of Chironomidae larvae were identified, and Stictochironomus sp. dominated most sites(6 sites). The morphological characteristics of the identified chironomids larvae, such as the mentum, ventromental plate, and antenna, were organized into table and pictorial keys, and a Bayesian inference molecular phylogeny was presented. These results provide basic morphological information for genus-level identification and can be used as fundamental information for water quality management.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.107-119
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• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• 한국노년학
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• v.37 no.1
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• pp.173-200
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• 2017

This study is designed to verify the actual effect on the prevention of the elderly suicide of the integrated crisis intervention service which has been widely provided across all local communities in Gyeonggi-province focusing on the integrated crisis intervention model developed for the prevention of elderly suicide. The integrated crisis intervention model for the local communities and its manual were developed for the prevention of elderly suicide by integrating the crisis intervention theory which contains local community's integrated system approach and the stress vulnerability theory. For the analysis of the effect, the geriatric depression and suicidal ideation scale was adopted and the data was collected as follows; The data was collected from 258 people in the first preliminary test. Then, it was collected from the secondary test of 184 people after the integrated crisis intervention service was performed for 6 months. The third collection of data was made from 124 people after 2 or 3 years later using the backward tracing method. As for the analysis, the researcher used the R Statistics computing to conduct the test equating, and the vertical scaling between measuring points. Then, the researcher conducted descriptive statistics analysis and univariate analysis of variance, and performed multi-level modeling analysis using Bayesian estimation. As a result of the study, it was found out that the integrated crisis intervention model which has been developed for the elderly suicide prevention has a statistically significant effect on the reduction of elderly suicide in terms of elderly depression and suicide ideation in the follow-up measurement after the implementation of crisis intervention rather than in the first preliminary scores. The integrated crisis intervention model for the prevention of elderly suicide was found to be effective to the extent of 0.56 for the reduction of depression and 0.39 for the reduction of suicidal ideation. However, it was found out in the backward tracing test conducted 2-3 years after the first crisis intervention that the improved values returned to its original state, thus showing that the effect of the intervention is not maintained for long. Multilevel analysis was conducted to find out the factors such as the service type(professional counseling, medication, peer counseling), characteristics of the client (sex, age), the characteristics of the counselor(age, career, major) and the interaction between the characteristics of the counselor and intervention which affect depression and suicidal ideation. It was found that only medication can significantly reduce suicidal ideation and that if the counselor's major is counseling, it significantly further reduces suicidal ideation by interacting with professional counseling. Furthermore, as the characteristics of the suicide prevention experts are found to regulate the intervention effect on elderly suicide prevention in applying integrated crisis intervention model, the primary consideration should be given to the counseling ability of these experts.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.607-614
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• 2016

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of $polynomials{\times}3$ types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.417-423
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• 2017

To provide a ecological restoration strategy considering genetic diversity of Abies koreana in Mt. Jiri, the genetic diversity and the genetic differentiation among sub-populations such as Banyabong, Byeoksoryeong, and Cheonwangbong were investigated. The average number of alleles (A) was 7.8, the average number of effective alleles ($A_e$) was 4.9, observed heterozygosity ($H_o$) was 0.578, and expected heterozygosity ($H_e$) was 0.672, respectively. The level of genetic diversity within sub-populations ($H_e=0.672$) was lower than those of both population ($H_e=0.778$) and species ($H_e=0.759$) level. However, the level of genetic diversity was high compared those of Genus Abies. Genetic differentiation was 0.014 from F-statistics ($F_{ST}$) and was 0.004 from AMOVA analysis (${\Phi}_{ST}$). There was no almost genetic differentiation among sub-populations in Mt. Jiri from bayesian clustering. Therefore, If the seeds are sampled sufficiently by selecting the parameters from three sub-populations, it is possible that we could obtain genetically appropriate materials for ecological restoration.

• Information Systems Review
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• v.10 no.2
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• pp.137-158
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• 2008

Rapid development of various information technologies creates new opportunities in online and offline markets. In this changing market environment, customers have various demands on new products and services. Therefore, their power and influence on the markets grow stronger each year. Companies have paid great attention to customer relationship management. Especially, personalized product recommendation systems, which recommend products and services based on customer's private information or purchasing behaviors in stores, is an important asset to most companies. CRM is one of the important business processes where reliable information is mined from customer database. Data mining techniques such as artificial intelligence are popular tools used to extract useful information and knowledge from these customer databases. In this research, we propose a recommendation system that predicts customer's purchase intention. Then, customer's purchasing intention of specific product is predicted by using data mining techniques using receipt data set. The performance of this suggested method is compared with that of other data mining technologies.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.83-97
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• 2000

The traffic management schemes through traffic signal control and information provision could be effective when the link-level data and trip-level data were used simultaneously in analysis Procedures. But, because the trip-level data. such as origin, destination and departure time, can not be obtained through the existing surveillance systems directly. It is needed to estimate it using the link-level data which can be obtained easily. Therefore the objective of this study is to develop the model to estimate O-D demand using only the link flows in highway network as a real time. The methodological approaches in this study are kalman filer, least-square method and normalized least-square method. The kalman filter is developed in the basis of the bayesian update. The normalized least-square method is developed in the basis of the least-square method and the natural constraint equation. These three models were experimented using two kinds of simulated data. The one has two abrupt changing Patterns in traffic flow rates The other is a 24 hours data that has three Peak times in a day Among these models, kalman filer has Produced more accurate and adaptive results than others. Therefore it is seemed that this model could be used in traffic demand management. control, travel time forecasting and dynamic assignment, and so forth.

• Journal of Acupuncture Research
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• v.23 no.6
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• pp.239-254
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• 2006

The 3rd Japan-Korea Workshop on Acupuncture and EBM was held at Kanazawa on June $16^{th}$. From Korea team, 4 papers were presented. Dr. Hahn introduced a new approach of data analysis on series of n-of-1 trials using the Bayesian statistics. It offered important information for the future n-of-1 trials. Dr. Park clearly demonstrated the significance of various sham devices proposed and stressed the importance of research questions when we choose the control intervention in RCT. Dr. Lee reported the results of survey in Korean Medical Doctors (KMD) for their point selection and techniques to the distal and local points. Dr. Kim presented the results of face to face survey on the KMD with 28 items for acupuncture treatment on the knee OA. Finally, a draft of protocol was introduced by Dr. Kim. The title was "multi-center, a randomized, single blinded, two arms, parallel-group study to compare the effectiveness and safety of 'individualized acupuncture' and 'standardized minimal acupuncture' in Korean and Japanese patients with knee osteoarthritis (Phase IV)". From Japan team, 7 speakers presented their comments and proposals on the protocol. Dr. Takahashi introduced several issues regarding n-of-1 trials and pointed out the importance of obtaining generalizability from n-of-1 trials. Dr. Shichidou pointed the importance of research design, selection of outcome measures and reduction of biases. Dr. Itoh presented the results of point selection for the knee OA based on the literature survey. Dr. Sumiya introduced several differences between KMD and Japanese acupuncturists based on the questionnaire used in KMD survey. Dr. Furuya demonstrated a result of press tack needle and its sham device on shoulder stiffness. Dr. Yamashita introduced the results of literature survey regarding adverse events occurred by acupuncture on knee OA. Dr.Tsukayama stressed the importance of responsibility of Institutional Review Board (IRB) for the conduction of clinical trials. After several issues were discussed, the need of continued meeting for final protocol development was agreed, then the workshop was closed.

• Atmosphere
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• v.16 no.1
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• pp.19-31
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• 2006

The purpose of this study is to develop the statistical model to predict sea level pressure of typhoon period in south coast of the Korean Peninsula. Seven typhoons, which struck south coast of the Korean Peninsula, are selected for this study, and the data for analysis include the central pressure and location of typhoon, and sea level pressure and location of 19 observing site. Models employed in this study are the first order regression, the second order regression and the neural network. The dependent variable of each model is a 3-hr interval sea level pressure at each station. The cause variables are the central pressure of typhoon, distance between typhoon center and observing site, and sea level pressure of 3 hrs before, whereas the indicative variable reveals whether it is before or after typhoon passing. The data are classified into two groups - one is the full data obtained during typhoon period and the other is the data that sea level pressure is less than 1000 hPa. The stepwise selection method is used in the regression model while the node number is selected in the neural network by the Schwarz's Bayesian Criterion. The performance of each model is compared in terms of the root-mean square error. It turns out that the neural network shows better performance than other models, and the case using the full data produces similar or better results than the case using the other data.