• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1C
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• pp.63-74
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• 2008

Site response analyses were performed based on equivalent linear technique using the local geologic and dynamic site characteristics, which include soil profiles, shear wave velocity profiles and depth to bedrock for 125 sites collected in Korean Peninsula. From the results of site response analyses, 2-parameters site classification system based on the combination of mean shear wave velocity of soil and depth to bedrock was newly recommended for regions of shallow bedrock depth in Korea. First, as the borders of bedrock depth (H) for site classification were determined as 10m and 20m, the soil sites were divided into 3 classes as $H_1$, $H_2$ and $H_3$ sites. And then, the 3 site classes were subdivided into 7 classes based on the mean shear wave velocity of soil ($V_{s,soil}$). The feasibility of new site classification system was verified and the representative site coefficients ($F_a$ and $F_v$) and design response spectrum were suggested by analyzing uniform trend and dispersion of site coefficients for each site class. The suggested site coefficients and the regression curves present the nonlinear characteristics of soils according to the change of rock outcrop acceleration with uniform trend effectively. From the comparison between the mean values of response spectrum which was acquired from the site response analysis and the suggested design response spectrum, there was a little difference in some of site classes and it was verified to adjust the integration interval to make it more suitable for the site condition in Korea.

• Journal of the Korean Geotechnical Society
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• v.21 no.6
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• pp.101-115
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• 2005

For the site characterization at two inland areas, Gyeongju and Hongsung, which represent geomorphic and geologic characteristics of inland region in Korea, in-situ seismic tests containing borehole drilling investigations and resonant column tests were peformed and site-specific seismic response analyses were conducted using equivalent linear as well as nonlinear scheme. The soil deposits in Korea were shallower and stiffer than those in western US, from which the site coefficients and site classification system in Korea were derived. Most sites were categorized as site classes C and D based on the mean shear wave velocity $(V_s)$ of the upper 30 m $(V_s30)$, ranging between 250 and 650 m/s. According to the acceleration response spectra determined from the site response analyses, the site coefficients specified in the current Korean seismic design guide underestimate the ground motion in the short-period band and overestimate the ground motion in mid-period band. These differences can be explained by the differences in the bedrock depth and the soil stiffness profile between Korea and western US. The site coefficients, $F_a$ for short-period and $F_v$ for mid-period, were re-evaluated and the site classification system, in which sites C and D were subdivided according to $V_s20,\;V_s15,\;and\;V_s10$ together with the existing $V_s30$ was introduced accounting for the local geologic conditions at inland region of the Korean peninsula. The proposed site classification system in this paper is still rudimentary and requires modification.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.201-214
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• 2020

This study aimed to early detect damaged trees by pine wilt disease using the vegetation indices of UAV images. The location data of 193 pine wilt disease trees were constructed through field surveys and vegetation index analyses of NDVI, GNDVI, NDRE and SAVI were performed using multi-spectral UAV images at the same time. K-Means algorithm was adopted to classify damaged trees and confusion matrix was used to compare and analyze the classification accuracy. The results of the study are summarized as follows. First, the overall accuracy of the classification was analyzed in order of NDVI (88.04%, Kappa coefficient 0.76) > GNDVI (86.01%, Kappa coefficient 0.72) > NDRE (77.35%, Kappa coefficient 0.55) > SAVI (76.84%, Kappa coefficient 0.54) and showed the highest accuracy of NDVI. Second, K-Means unsupervised classification method using NDVI or GNDVI is possible to some extent to find out the damaged trees. In particular, this technique is to help early detection of damaged trees due to its intensive operation, low user intervention and relatively simple analysis process. In the future, it is expected that the utilization of time series images or the application of deep learning techniques will increase the accuracy of classification.

• Journal of the Korean Geotechnical Society
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• v.28 no.3
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• pp.25-34
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• 2012

In order to verify that the recently proposed two-parameters site classification system and the corresponding site coefficients are suitable for the local geological conditions in Korea, a comparison was conducted with current Korean seismic code, Eurocode-8, NYC DOT seismic code. The design spectrum of the current Korean seismic code is significantly amplified in the long-period range, whereas the other response spectra, including the proposed two-parameters approach, are significantly amplified in the short-period range, which is a typical geological condition in Korea. In addition, based on the results of site response analyses in the specific $10km{\times}10km$ area of Gyeongju, spatial distributions of site coefficients from site-specific seismic response analyses were compared with the proposed site coefficients, as well as those specified in the current Korean seismic code. The site coefficients ($F_a$ and $F_v$) from the current Korean seismic codes show significantly high spatial error distributions compared with those specified by the two-parameters site classification system. Therefore, the proposed system is suitable for regions of shallow bedrock including the Korean peninsula.

• The KIPS Transactions:PartB
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• v.8B no.4
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• pp.335-342
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• 2001

본 논문은 보로노이 공간분류를 활용하여 원격탐사 영상인식을 위한 다층 신경망 분류기를제안한다. 제안된 다층 신경망 분류기는 보로노이 다각형 영역으로 클래스를 구분하며, 초평면 방정식의 계수를 오류 역전과 학습 초기의 연결 강도, 임계치 그리고 은닉층의 노드 수로 결정한다. 제안된 방법은 오류역전과 학습 알고리즘에서 임의로 정해주던 초기 정보를 사전 분석에 의해 공학적으로 결정함으로써 느린 수렴 속도와 학습실패 등의 단점을 피할 수 있는 장점이 있다. 보로노이 다이어그램에 대한 경계선의 초평면 방정식은 훈련집합의 클래스별 평균값을 구하여 Mathematica 패키지로 계산하였다. 제안된 다층 신경망에 의한 영상분류기의 인식능력을 평가하기 위하여 원격탐사 영상인식에서 자주 활용되는 최소거리 분류 방법과 최대우도 분류 방법으로 처리해서 비교한 결과, 최소거리 분류 방법은 실험화상에 대해 81.4%, 최대우도 부류기에 의한 분류는 87.8%, 제안한 방법은 92.2% 정확성을 가진 분류결과를 나타냈다.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.111-120
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• 2017

In this paper, we propose novel feature extraction algorithm for classification of seabed sediment. In previous researches, acoustic reflection coefficient has been used to classify seabed sediments, which is constant in terms of frequency. However, attenuation of seabed sediment is a function of frequency and is highly influenced by sediment types in general. Hence, we developed a feature vector by using attenuation variation with respect to frequency. The attenuation variation is obtained by using reflected signal from the second sediment layer, which is generated by broadband chirp. The proposed feature vector has advantage in number of dimensions to classify the seabed sediment over the classical scalar feature (reflection coefficient). To compare the proposed feature with the classical scalar feature, dimension of proposed feature vector is reduced by using linear discriminant analysis (LDA). Synthesised acoustic amplitudes reflected by seabed sediments are generated by using Biot model and the performance of proposed feature is evaluated by using Fisher scoring and classification accuracy computed by maximum likelihood decision (MLD). As a result, the proposed feature shows higher discrimination performance and more robustness against measurement errors than that of classical feature.

• Korean Journal of Ichthyology
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• v.17 no.2
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• pp.91-97
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• 2005

A taxonomic review of the genus Leiognathus was made based on specimens collected from the coast of Korea from 1997 to 2004. The genus Leiognathus in Korea includes 6 species: Leiognathus elongatus, L. fasciatus, L. lineolatus, L. nuchalis, L. rivulatus, and L. bindus. Leiognathus bindus is reported for the first time in Korea. It is characterized by having bright orange spots between the 2nd and 5th spines of the dorsal fin. Detailed morphological measurements and counts and a key to species of Leiognathus from Korea are provided.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.101-111
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• 1999

This paper presents concrete pattern recognition method to identify the strength of concrete by evidence accumulation with multiple parameters based on artificial intelligence techniques. At first, variance(VAR), zero-crossing(ZCR), mean frequency(MEANF), and autoregressive model coefficient(ARC) and linear cepstrum coefficient(LCC) are extracted as feature parameters from ultrasonic signal of concrete. Pattern recognition is carried out through the evidence accumulation procedure using distance measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results(92% successful pattern recognition rate) are presented to support the feasibility of the suggested approach for concrete pattern recognition.

• Korean Journal of Ichthyology
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• v.12 no.1
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• pp.1-13
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• 2000

The taxonomic review of the family Triglidae was made based on the specimens collected from the south-western coasts of Korea from 1986 to 1999. The family Triglidae from Korea was classified into 10 species belonging to 3 genera: Pterygotrigla hemisticta, Chelidonichthys kumu, Lepidotrigla, japonica. L. guentheri, L. alata, L. kishinouyei, L. kanagashira, L. microptera, L. hime and L. abyssalis. Of them, L. kanagashira was reported for the first time in South Sea of Korea. A key to species of the family Triglidae from Korea was provided, with description of their morphological characteristics and their distribution.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.131-144
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• 2002

도로의 그룹핑(Grouping)이란 도고 계획, 설계, 관리, 조사 계획 및 정비 방침 등을 세우기 위해 유사한 성격의 도로 구간을 군집화하는 방법이다. 기존에 일반적으로 적용되고 있는 도로 그룹핑 방법은 그룹 수를 미리 지정함으써 분석가의 주관적 판단이 개입되었고, 그룹핑 변수 선정에 대한 근거가 부족하였다. 이에 본 연구에서는 기존에 일반적으로 적용되고 있는 도로 그룹핑 방법을 개선하여 새로운 방법론을 제시하였다. 또한 새로 제시된 방법론의 검증을 위해 도로 교통량 통계연보에서 제공하고 있는 일반국도의 2000년 294개 상시조사 지전의 교통량 자료를 이용하여 분석하였다. 연구 결과 기존의 월, 요일 변동계수만을 적용한 그룹핑 방법보다는 기타 교통지표(AADT, $\Sigma$K1000(K값의 상위 1000번 순위까지의 누적 값), 중차량 비율, 주야율)를 동시에 적용할 때 좀 더 효율적이면서 세부적으로 분류됨을 알 수 있었다. 또한 기타 교통지표론 적당한 그룹핑 결과로는 5그룹의 국도 기능 분류가 가능함을 알 수 있었다. 그 결과 기존의 소재지역과 기능에 따른 국토의 구분을 지방 산업도로 그룹, 지역 간선도로 그룹, 대도시 주변형 도로 그룹, 중소도시 주변형 및 관광도로 그룹, 관광도로 그룹으로 분류할 수 있었다. 본 연구에서의 도로 그룹핑 결과에 각 지역특성을 추가하여 분석한다면 도로의 계획, 선계, 관리 등에 매우 유용한 자료로 활용되리라 예상한다. 또한 본 연구의 결과를 이용하면 좀 더 효율적으로 설계시간계수 선정, 전역 조사 지점의 AADT추정, 상시 교통량 조사 자료의 누락 데이터 보정 및 교통량 조사의 스케줄링에 많이 활용할 수 있을 것으로 기대된다.