• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.111-111
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• 2023

최근 기후변화의 영향으로 설계빈도 보다 높은 강우가 발생하고, 하천설계 기준을 초과한 홍수피해가 발생하고 있다. 현재 시행되고 있는 하천관리는 이수 및 치수 목적으로 제방, 보 및 낙차공과 같은 그레이인프라(Grey infrastructure)가 일반적이다. 하지만 그레이인프라를 통한 하천관리 방안은 이산화탄소를 배출하여 기후변화로 인한 극한 기상의 발생을 증가시키고 홍수피해를 가중시키는 등의 악순환이 반복되게 한다. 따라서 그레이인프라에 의한 하천관리 방안은 지속가능한 방안으로 채택할 수 없으므로 최근에 환경적, 사회적 문제를 생태계의 서비스를 통해 해결하고자 하는 자연기반해법(NbS, Nature-based Solutions)의 개념이 주목받고 있다. 이에 본 연구는 합천댐 직하류인 황강을 대상으로 자연기반해법을 활용한 하천관리 방안의 홍수저감 효과를 정량적으로 분석하고자 하였다. 자연기반해법 기술에 포함되는 범람원 굴착(Floodplain excavation)과 제방후퇴(Dyke relocation)를 황강의 홍수위험지역에 적용하였다. HEC-RAS의 부정류 흐름(Unsteady flow) 해석을 통해 하천 홍수위를 산정한 결과, 낙동강 합류점에서 5cm의 홍수위 저감효과를 확인 할 수 있었다. 본 연구의 결과를 통해 하천관리사업의 진행 시 기존의 하천관리 방법이 아닌 자연기반해법을 통한 관리 방안으로 도입할 수 있는 근거로 충분히 활용이 가능할 것으로 기대된다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.9
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• pp.351-360
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• 2018

The LWR (Locally Weighted Regression) model, which is traditionally a lazy learning model, is designed to obtain the solution of the prediction according to the input variable, the query point, and it is a kind of the regression equation in the short interval obtained as a result of the learning that gives a higher weight value closer to the query point. We study on an incremental ensemble learning approach for LWR, a form of lazy learning and memory-based learning. The proposed incremental ensemble learning method of LWR is to sequentially generate and integrate LWR models over time using a genetic algorithm to obtain a solution of a specific query point. The weaknesses of existing LWR models are that multiple LWR models can be generated based on the indicator function and data sample selection, and the quality of the predictions can also vary depending on this model. However, no research has been conducted to solve the problem of selection or combination of multiple LWR models. In this study, after generating the initial LWR model according to the indicator function and the sample data set, we iterate evolution learning process to obtain the proper indicator function and assess the LWR models applied to the other sample data sets to overcome the data set bias. We adopt Eager learning method to generate and store LWR model gradually when data is generated for all sections. In order to obtain a prediction solution at a specific point in time, an LWR model is generated based on newly generated data within a predetermined interval and then combined with existing LWR models in a section using a genetic algorithm. The proposed method shows better results than the method of selecting multiple LWR models using the simple average method. The results of this study are compared with the predicted results using multiple regression analysis by applying the real data such as the amount of traffic per hour in a specific area and hourly sales of a resting place of the highway, etc.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.375-385
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• 2009

For 2007 Korea presidential election, most polls by telephone surveys indicated Lee Myung-Bak led the second runner-up Jung Dong-Young by certain margin. The margin between two candidates can be estimated accurately by averaging individual poll results, provided there exists no systematic bias in telephone surveys. Most Korean telephone surveys via telephone directory are based on quota samples, with the region, the gender and the age-band as quota variables. Thus the surveys may result in certain systematic bias due to unbalanced factors inherent in quota sampling. The aim of this study is to answer the following questions by the analytic methods adopted in Huh et al. (2004): Question 1. Wasn't there systematic bias in estimates of support rates. Question 2. If yes, what was the source of the bias? To answer the questions, we collected eighteen surveys administered during the election campaign period and applied the iterated proportional weighting (the rim weighting) to the last eleven surveys to obtain the balance in five factors - region, gender, age, occupation and education level. We found that the support rate of Lee Myung-Bak was over-estimated consistently by 1.4%P and that of Jung Dong-Young was underestimated by 0.6%P, resulting in the over-estimation of the margin by 2.0%P. By investigating the Lee Myung-Bak bias with logistic regression models, we conclude that it originated from the under-representation of less educated class and/or the over-representation of house wives in telephone samples.

• Journal of Korean Ophthalmic Optics Society
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• v.19 no.1
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• pp.99-104
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• 2014

Purpose: This study was to investigate the functional improvement in peripheral vision following eccentric viewing training. Methods: 14 subjects with normal vision took a part with their right eye, peripheral retinal which is $20^{\circ}$ lateral area from the fovea was examined for contrast sensitivity(CS). Eccentric viewing training was performed for 21days with an hour image viewing and examination was repeated. Results: The critical durations for 0.7 cpd were increased 2.67(467 ms) for pre-eccentric viewing training to 2.79(616 ms) for post-eccentric viewing training (p>0.05). The critical durations for 3.0 cpd were also increased 2.53(341 ms) for pre-eccentric viewing training to 3.04(1102 ms) for post-eccentric viewing training (p>0.05). Conclusions: It is recommended to use higher spatial frequency with higher CS for eccentric viewing training and to train more frequently for a short time. Moreover, the study on Korean standardizing of the visual rehabilitation for low vision based on the etiology is sorely required.

• Asian Journal of Turfgrass Science
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• v.12 no.2
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• pp.113-126
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• 1998

Wear simulator로 답압이 가해진 크리핑 벤트그라스 (Agrostis palustris Huds.) 훼어웨이에서 관수방법·예지물 순환 및 질소시비수준의 여러 가지 잔디관리 요인이 엽조직 및 토양질소 함유량에 미치는 효과를 알아보기 위해 본 연구를 수행하였다. 연구포장은 'Penncross'크리핑 벤트그라스 반디밭으로 1988년에 sharpsburg silty-clay loam (Typic Argiudoll) 토양에 조성하였으며, 실험은 1989년부터 1991년까지 3년간 수행하였다. Split-split-plot 실험디자인을 사용하여 주구에 daily or biweekly irrigation, 세구에 clipping return or removal, 세세구에 low-N (5g), moderate-N (15g), high-N (25g N m-2 yr-1)처리를 난괴법 3 반복으로 배치하였다. 생육기간중 잔디예초는 12mm 예고로 일주일에 4번 실시하였고, 기타 잔디관리는 high maintenance 수준으로 유지되는 한지형 양잔디 훼어웨이 기준으로 실시하였다. 엽조직 및 토양샘플은 1989년 2회, 1990년과 1991년에는 3회씩 채취하여, 네브라스카 주립대 토양식물분석실에서 분석하였다. 답압이 가해진 크리핑 벤트그라스 훼어웨이 잔디에서 엽조직 질소함유량은 여러 가지 잔디관리 방법간 상호작용 효과가 관찰되었다. 1989년 나타난 질소시비수준과 관수방법간의 상호작용에서 daily irrigation 지역의 엽질소 함유량은 질소시비량이 low-N 수준에서 high-N 수준으로 증가함에 따라 3.51%에서 3.94%로 quadratic pattern으로 증가하였다. High-N 처리지역에서 엽질소 함유량은 daily irrigation 관수방법이 biweekly irrigation 관수방법보다 약 4% 정도 더 많은 것으로 나타났다. 엽질소 함유량은 특히 질소시비 수준에 따라 크게 영향을 받는 것으로 나타났다. 1990년 질소시비량이 low-N 수준에서 high-N 수준으로 증가함에 따라 3.50%에서 4.25%로 quadratic pattern으로 증가하였고, 1991년에는 4.20%에서 4.60%까지 linear pattern으로 증가하였다. High-N 처리구의 엽조직 질소함유량은 low-N 처리구와 비교시 1990년에는 21%, 1991년에는 10% 더 많은 것으로 나타났다. 잔디조성후 시간이 경과함에 따라 엽조직의 질소함유량도 증가하였다. Low-N 수준에서 1991년 엽질소 함유량은 1990년에 비해 20% 증가하였으며, high-N 수준에서는 1991년의 엽조직 질소함유량이 1990년 보다 8% 증가한 것으로 나타났다. 따라서, 잔디조성후 경과기간에 따라 연간 시비량을 조절할 필요가 있으며, 특히 새로 조성된 잔디밭과 조성된 지 어느 정도 지난 기존 잔디밭간에 차별화된 관리프로그램이 필요한 것으로 판단되었다. 잔디관리에서 답압이 가중되는 정도에 따라 지역별로 장기간 차별화된 관리 접근을 해야하고, 정기적으로 토양 및 엽분석을 실시해서 시비프로그램에 활용하는 것이 필요하다 하겠다. 본 연구결과 나타난 잔디관리 요인간 상호작용효과는 잔디관리시 여러 가지 관리방법에 따른 효과를 입체적으로 분석해서 해당 골프장 현실에 적합한 통합적인 잔디관리(integrated turfgrass management)의 필요성을 제시한다고 할 수 있겠으며, 또한 답압가중 정도에 따른 잔디관리요인간의 반응효과차이는 향후 무답압 지역에서 실시된 연구결과를 답압을 받고 있는 경기장 및 골프장 등의 잔디밭에 적용할 경우에는 주의깊게 데이터 활용을 해야 되리라고 사료되었다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.15 no.3
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• pp.231-241
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• 2004

This paper drives the exact expression of bit error rate(BER) performance for groupwise iterative-multipath interference cancellation(GWI-MPIC) algorithm for cancelling multipath interference components in a coherent high-speed downlink packet access(HSDPA) system of W-CDMA downlink and the BER performance is evaluated by numerical analysis. The performance of GWI-MPIC is compared to the successive interference cancellation(SIC) algorithm for multipath components. From numerical results, the optimal average BER performance of weighting factor ${\beta}$$\_$h/ for interference cancellation is obtained at ‘${\beta}$$\_$h/=0.8’ and then this weighting factor is hereafter applied to other performance analysis. Numerical results showed that the average BER performance of GWI-MPIC algorithm is rapidly degraded at multipath L=6, but is revealed the good performance than that of SIC algorithm in terms of increasing the number of multipath. This results also indicated that the average BER performance is greatly degraded due to increasing interference power more than multicode K=8. The average BER performance of the proposed algorithm is superior to the performance of SIC algorithm about 3 ㏈ for processing gain PG=128 at multipath L=2 and Average BER=1.0${\times}$10$\^$-5/. And also, the results produced good performance in case of linear monotonic reduction of multipath fading channel gain than that of constant channel gain variation, because multipath fading channel gain which is arrived later is small.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.439-448
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• 1996

Extraction and selection of the informative features play a central role in pattern recognition. This paper describes a modified back-propagation algorithm that performs selection of the informative features and trains a neural network simultaneously. The algorithm is mainly composed of three repetitive steps : training, connection pruning, and input unit elimination. Afer initial training, the connections that have small magnitude are first pruned. Any unit that has a small number of connections to the hidden units is deleted,which is equivalent to excluding the feature corresponding to that unit.If the error increases,the network is retraned,again followed by connection pruning and input unit elimination.As a result,the algorithm selects the most im-portant features in the measurement space without a transformation to another space.Also,the selected features are the most-informative ones for the classification,because feature selection is tightly coupled with the classifi-cation performance.This algorithm helps avoid measurement of redundant or less informative features,which may be expensive.Furthermore,the final network does not include redundant parameters,i.e.,weights and biases,that may cause degradation of classification performance.In applications,the algorithm preserves the most informative features and significantly reduces the dimension of the feature vectors whiout performance degradation.

• Journal of Educational Research in Mathematics
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• v.26 no.1
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• pp.63-77
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• 2016

SENKs (Students who Emigrated from North Korea to South Korea) are exposed to the general problem of Su-Po-Ja(mathematics give-uppers) as well as their own difficulty in learning mathematics. In this study, we conducted the FGI (focus group interview) in order to examine the recognition on mathematics teaching and learning in South Korea with 6 SENKs and 3 teachers who teach the SENKs. As a result, it was found that SENKs' had difficulties in understanding math because of the differences in math terminology used in South and that in North Korea, the unfamiliar problem situation used in math lesson, and the shortage of time for solving math problem. And the teachers reported that they had difficulties in teaching great deal of basic math, SENKs' weak will to learn math, and SENKs' lack of understanding about problem situation because of the inexperience about culture and society in South Korea.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.511-526
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• 2020

Single-cell RNA-sequencing (scRNA-seq) data consists of each cell's RNA expression extracted from large populations of cells. One main purpose of using scRNA-seq data is to identify inter-cellular heterogeneity. However, scRNA-seq data pose statistical challenges when applying traditional clustering methods because they have many missing values and high level of noise due to technical and sampling issues. In this paper, motivated by analyzing scRNA-seq data, we propose a novel spectral-based clustering method by imposing different weights on genes when computing a similarity between cells. Assigning weights on genes and clustering cells are performed simultaneously in the proposed clustering framework. We solve the proposed non-convex optimization using an iterative algorithm. Both real data application and simulation study suggest that the proposed clustering method better identifies underlying clusters compared with existing clustering methods.

• Korean Journal of Construction Engineering and Management
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• v.7 no.6
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• pp.79-89
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• 2006

Current removing process is labor intensive and time consuming, employing a conventional grinding type manual machine. From a social and economic point of views, these kinds of manual tasks bring about social inconvenience and economic loss including traffic jam and high labor costs. The objective of the study was to develop and evaluate a road stripe removal equipment using water jet technology. The following shows the results. First, It was analyzed that the obtainable productivity from the equipment developed in the study is 280% compared to the current equipment. In this study, it was also calculated the Benefit/Cost Ratio and the result showed that the ratio is 3.28, so it is expected that the equipment can produce great benefits for the relevant companies. Second, an analysis was also conducted on the traffic congestion cost, and the equipment could save about \2,550 million per day compared to the conventional equipment. Therefore, it is analyzed that the economic viability of the equipment is sufficient.