• 자원환경지질
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• 제13권1호
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• pp.1-19
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• 1980

본논문(本論文)에서 1926년 2월부터 1943년 5월까지 국내(國內)에서 Wiechert 지진계(地震計)로 관측(觀測)된 국지자료(局地資料)가 제시(提示) 연구되었다. 일본(日本) 기상청(氣象廳)(JMA) 현용(現用) S-P monogram(travel time table)을 기초(基礎)로 하고 주로 Tsuboi의 지진(地震) 규모(規模)(magnitude) 계산식(式)과 진도자료(震度資料)의 보조(補助) 이용(利用)으로 적절한 한계내(限界內)에서 가능한 한(限) 많은 지진요소(地震要素)(parameter)를 산출하였다. 또한 진앙분포(震央分布)와 관련한 지진(地震) 특성(特性)이 인접지질구조(隣接地質構造)와 연관(連關) 논의(論議)되었으며 몇몇의 통계결과(統計結果)가 일본(日本) 구주지역(九州地域)과 비교 분석됨으로서 한국의 지진(地震) 활동(活動)에 관한 합리적(合理的)인 해석(解析)이 내려졌다. 지진(地震) mechanisrn을 규명(規明)하기에는 충분(充分)하지 않지만, 단편적인 자료(資料)들을 superposition method 에 의하여 종합(綜合)한 결과(結果), 일본(日本) 남서부(南西部)(구주(九州)) 지역(地域)의 그것과 대체로 일치(一致)하는 동일서(東一西) 압축(壓縮)의 stress field가 작용(作用)하는 일반적 경향성(傾向性)을 발견(發見)할 수 있었다.

• 대한공간정보학회지
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• 제14권1호
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• pp.13-19
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• 2006

산불의 피해를 조사함에 있어서 현재 상당수의 조사가 육안 관측에 의존하고 있으나, 광범위한 면적에 걸쳐 피해가 확대되고, 발생 직후 접근이 용이하지 않다는 점에서 위성영상 등을 이용한 산불 피해 조사가 이용되고 있다. 따라서, 본 연구에서는 산불 직후의 위성영상을 이용하여 통계적 해석에 의해 산불 피해 정도를 분류하고자 하며, 1차와 2차에 걸쳐 요인분석을 실시하였다. 그 결과 1차 요인 분석에서는 분류하기 어려웠던 산불 발생 지역에 포함되어 있는 하천의 수초, 논두렁 등을 2차 요인분석에 의하여 비교적 정확하게 분류할 수 있었다. 또한, 1차 요인분석으로 분류된 영역들에 대하여 2차 요인분석을 하여 산불 피해가 경미한 지역과 강한 지역을 각각 분류 할 수 있었다. 이를 검증하기 위하여 위성영상에서 얻은 산불 피해 영상과 분광반사계를 이용하여 얻은 실측 자료와는 높은 상관관계를 가지고 있으므로 위성영상자료를 이용하여 산불 피해 정도를 분류하는데 유용하게 이용될 수 있다.

• Journal of the Korean Wood Science and Technology
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• 제48권5호
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• pp.591-607
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• 2020

This study was aimed to investigate the changes of chemical composition of the volatile organic compounds (VOCs) emitted from red pine needles in the process of needle abscission or senescence. The VOCs in intact, senescent, and litter red pine needle samples were analyzed by headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS). And then, multivariate statistical interpretation of the processed data sets was conducted to investigate similarities and dissimilarities of the needle samples. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to investigate the dataset structure and discrimination between samples, respectively. From the data preview, the levels of major components of VOCs from needles were not significantly different between needle samples. By PCA investigation, the data reduction according to classification based on the chlorophyll a / chlorophyll b (Ca/Cb) ratio were found to be ideal for differentiating intact, senescent, and litter needles. The following OPLS-DA taking Ca/Cb ratio as y-variables showed that needle samples were well grouped on score plot and had the significant discriminant compounds, respectively. Several compounds had significantly correlated with Ca/Cb ratio in a bivariate correlation analysis. Notably, the litter needles had a higher content of oxidized compounds than the intact needles. In summary, we found that chemical compositions of VOCs between intact, senescent, and litter needles are different each other and several compounds reflect characteristic of needle.

• 대한두개안면성형외과학회지
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• 제23권4호
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• pp.171-177
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• 2022

Background: The treatment of zygoma complex fractures is of crucial importance in the field of plastic surgery. However, surgical methods to correct zygoma complex fractures, including the number of fixation sites, differ among operators. Although several studies have compared two-point and three-point fixation, no comparative research has yet been conducted on one-point versus two-point fixation using computed tomography scans of surgical results. Therefore, the present study aimed to address this gap in the literature by comparing surgical results between one-point and two-point fixation procedures. Methods: In this study, we randomly selected patients to undergo surgery using one of two surgical methods. We analyzed patients with unilateral zygoma complex fractures unaccompanied by other fractures according to whether they underwent one-point fixation of the zygomaticomaxillary buttress or two-point fixation of the zygomaticomaxillary buttress and the zygomaticofrontal suture. We then made measurements at three points-the zygomaticofrontal suture, inferior orbital wall, and malar height-using 3-month postoperative computed tomography images and performed statistical analyses to compare the results of the two methods. Results: All three measurements (zygomaticofrontal suture, inferior orbital wall, and malar height) showed significant differences (p< 0.05) between one-point and two-point fixation. Highly significant differences were found for the zygomaticofrontal suture and malar height parameters. The difference in the inferior wall measurements was less meaningful, even though it also reached statistical significance. Conclusion: Using three parameters in a statistical analysis of imaging findings, this study demonstrated significant differences in treatment outcomes according to the number of fixations. The results indicate that bone alignment and continuity can be achieved to a greater extent by two-point fixation instead of one-point fixation.

• 대한원격탐사학회지
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• 제13권1호
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• pp.47-56
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• 1997

본 연구소에서는 호소 수질오염의 중요한 관심대상인 영양상태를 평가하기 위해 원격탐 사기법을 적용하였다. 원격탐사기법을 적용하는데 있어서 기존의 회귀식을 이용한 방법과는 달리 분류기법을 사용하여 영양상태를 평가하였다. 부영양화는 조류의 이상증식에 의해 유발되므로, 수 체의 조류농도와 밀접한 항목인 클로로필 a와 투명도를 원격탐사 데이터에 적용하였다. 본 연구 에서 영향상태의 분류는 최대우도법과 최소거리법을 이용하였으며, 다음과 같은 결과를 얻었다. 첫째, 광역수계의 영양상태 평가시 원격탐사 데이터를 적용함에 있어 기초적인 분류기법만을 수 행하여도 70%이상의 정확도를 얻을 수 있었다. 둘째, 분류정확도면에서 최소거리법이 최대우도법에 비하여 양호하게 나타났다. 이것은 샘플이 정규분포를 이루고는 있으나 통계적인 기법을 적용하기에는 샘플수가 너무 적은 것에 기인한 것 으로 차후 통계적 분포에 영향을 받지 않는 인공신경망을 이용한 분류기법의 도입이 요구된다. 셋째, 본 연구결과를 이용하면 수계의 영양상태를 신속하고 주기적이며 가시적인 분석평가를 할 수 있어 호소의 영양상태 진행정도에 따라 적절한 대응책을 수립하는데 기초자료로서 활용할 수 있을 것으로 기대된다.

• 한국인터넷방송통신학회논문지
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• 제10권2호
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• pp.129-134
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• 2010

통계청에 따르면 심혈관 등의 성인병 질환으로 연 600~800명이 사망하는 것으로 나타나고, 고혈압, 동맥경화증, 심장병, 뇌졸중 등은 혈액의 흐름에 장애가 생겨 발생하는 심혈관계질환으로 오늘날 성인병의 주종을 이루고 있는 사망률이 높은 질병으로 구분된다. 또한 사망한 심혈관질환자 중 올바른 응급처치를 했더라면 생존했을 환자가 약 40%를 차지하고 있어 응급상황 발생 시 신속한 대응이 요구된다. 따라서 본 논문에서는 AdaBoost알고리즘의 weak classifier를 결합하여 strong classifier를 생성하는 방법을 통하여 효과적인 분석으로 심전도를 측정할 수 있도록 하고, 심혈관 질환자에게 발생한 응급상황을 빠른 시간 내에 관리 데스크에 전달할 수 있는 시스템을 제안하였다. 이에 따라 심전도 센서를 기반으로 측정한 데이터를 ZigBee통신으로 단말기에 전송하고 응급 상황을 판정하여 관리데스크에 긴급경보와 모니터링을 제공함으로써 신속한 의료서비스 제공이 가능하도록 하였다.

• 산업경영시스템학회지
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• 제40권2호
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• pp.129-136
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• 2017

Among the information technology and automation that have rapidly developed in the manufacturing industries recently, tens of thousands of quality variables are estimated and categorized in database every day. The former existing statistical methods, or variable selection and interpretation by experts, place limits on proper judgment. Accordingly, various data mining methods, including decision tree analysis, have been developed in recent years. Cart and C5.0 are representative algorithms for decision tree analysis, but these algorithms have limits in defining the tolerance of continuous explanatory variables. Also, target variables are restricted by the information that indicates only the quality of the products like the rate of defective products. Therefore it is essential to develop an algorithm that improves upon Cart and C5.0 and allows access to new quality information such as loss cost. In this study, a new algorithm was developed not only to find the major variables which minimize the target variable, loss cost, but also to overcome the limits of Cart and C5.0. The new algorithm is one that defines tolerance of variables systematically by adopting 3 categories of the continuous explanatory variables. The characteristics of larger-the-better was presumed in the environment of programming R to compare the performance among the new algorithm and existing ones, and 10 simulations were performed with 1,000 data sets for each variable. The performance of the new algorithm was verified through a mean test of loss cost. As a result of the verification show, the new algorithm found that the tolerance of continuous explanatory variables lowered loss cost more than existing ones in the larger is better characteristics. In a conclusion, the new algorithm could be used to find the tolerance of continuous explanatory variables to minimize the loss in the process taking into account the loss cost of the products.

• 대한원격탐사학회지
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• 제25권6호
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Journal of the Korean Data and Information Science Society
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• 제25권5호
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• pp.1095-1106
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• 2014

본 연구에서는 악성간암환자의 생존기간에 영향을 주는 인자를 찾기 위하여 반응변수를 악성간암 환자의 생존을 임상변수의 정보와 SNP유전인자를 통합한 자료를 대상으로 이해하기 쉬운 나무구조 생존모형과 심볼릭자료분석을 실시하여 영향을 주는 유의한 인자 뿐 아니라 그 임계치를 구하여 임상적으로 유용한 결과를 찾아 임상에 적용하는 것이 목적이다. 악성간암환자의 임상자료를 계량화하여 통계적 예후진단 모형을 구함으로써 임상변수 간 숨겨진 변수간의 관계를 규명하고 생존기간 군에 따른 예측 분류모형을 구하여 현시적으로 진단후 예후에 영향을 주는 중요 임상변수와 유전체변수 그 임계치를 구하여 임상에서의 치료계획에 중요한 근거를 제시했다. 심볼릭데이터 분석 결과 정상, 만성 간염, 간염, 악성간염 등의 4개 군으로 구성된 1840명의 대상자를 분석 5 유전체의 20개 SNP가 밝혀진 바 있다. 즉 IL10-ht2가 악성간암의 발병에 매위 강한 관련이 있고 TGFB L10P-Prosms가 만성 간염 환자 중 악성간암 발생 위험을 줄여주는 유전체로 밝혀졌다. SNP변수와 질병군의 컴셉트 변수에 따라 상관정도를 원의 반지름 길이로 상대적으로 나타내 줌으로써 가장 판별력 있는 심볼릭변수를 상대적으로 비교할 수 있었다. 임상자료와 유전체자료를 통합하여 심볼릭 나무구조 생존모형을 구하여 생존기간을 군으로 한 나무구조모형을 유의한 변수와 기준치와 함께 구할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.