• 한국문헌정보학회지
• /
• 제55권2호
• /
• pp.25-50
• /
• 2021

범용의 문헌분류체계는 모든 주제분야를 포괄한다. 전체적인 분류체계는 문헌정보학 전문가가 구성하더라도, 개별 주제영역의 분류항목 구성과 전개는 해당 주제영역의 그것을 참고하는 것이 효율적이다. 전체 주제를 포괄하는 문헌분류체계가 각 주제분야에서 개발한 분류체계의 단순한 모음이 아닌 실용적인 분류체계가 되기 위해서는 각 항목에 배정되는 문헌량의 다과를 반영한 항목 설정과 세분이 필요하다. 분류항목의 설정에 있어 문헌량의 다과에서 항목 설정의 타당성과 근거를 찾는 것을 문헌적 근거(literary warrant)라 부른다. 본고에서는 한국십진분류법(KDC)에 전개된 각각의 분류항목에 어느 정도의 정보자원이 배정되고 있는지를 실증적으로 확인하고 이를 기반으로 향후 개정방안을 제시하고자 한다.

• 대한의용생체공학회:의공학회지
• /
• 제43권1호
• /
• pp.45-51
• /
• 2022

The purpose of this study is to improve the classification accuracy compared to the existing InceptionV3 model by proposing a new model modified with the fully connected hierarchical structure of InceptionV3, which showed excellent performance in medical image classification. The data used for model training were trained after data augmentation on a total of 1026 chest X-ray images of patients diagnosed with normal heart and Cardiomegaly at Kyungpook National University Hospital. As a result of the experiment, the learning classification accuracy and loss of the InceptionV3 model were 99.57% and 1.42, and the accuracy and loss of the proposed model were 99.81% and 0.92. As a result of the classification performance evaluation for precision, recall, and F1 score of Inception V3, the precision of the normal heart was 78%, the recall rate was 100%, and the F1 score was 88. The classification accuracy for Cardiomegaly was 100%, the recall rate was 78%, and the F1 score was 88. On the other hand, in the case of the proposed model, the accuracy for a normal heart was 100%, the recall rate was 92%, and the F1 score was 96. The classification accuracy for Cardiomegaly was 95%, the recall rate was 100%, and the F1 score was 97. If the chest X-ray image for normal heart and Cardiomegaly can be classified using the model proposed based on the study results, better classification will be possible and the reliability of classification performance will gradually increase.

• 한국산림과학회지
• /
• 제81권3호
• /
• pp.214-223
• /
• 1992

경기도(京畿道) 광릉(光陵) 죽엽산(竹葉山) 삼림(森林)의 식물군집구조분석(植物群集構造分析)을 위하여 37개소에 조사지(調査地)(1개 조사지당 $10m{\times}10m$ 방형구(方形區) 5개씩을 설치)를 설정하고 식생조사(植生調査)를 실시하여 얻어진 자료에 대해 TWINSPAN에 의한 classification과 2종류의 ordination(RA, DCA) 방법을 적용하였다. Classification에 의하면 소나무 군집, 소나무-서어나무 군집, 신갈나무-서어나무, 신갈나무-서어나무-갈참나무 군집, 혼효림(混淆林) 군집의 5개 집단으로 조사지가 분리되었다. Ordination에 의한 조사지 분석에서는 DCA가 RA보다 효과적이었으며 5개 집단으로 분리되었다. 종에 대한 두 기법분석에 의해 추정된 천이과정(遷移過程)은 교목상층(喬木上層)에서 소나무${\rightarrow}$서어나무와 소나무${\rightarrow}$신갈나무${\rightarrow}$서어나무의 2가지로 추정되었으며, 교목하층(喬木下層) 및 관목층(灌木層)은 진달래${\rightarrow}$생강나무, 노린재나무, 덜꿩나무, 쪽동백나무${\rightarrow}$회나무, 팥배나무 순이었다. 환경인자(環境因子)의 ordination분석에 의하면 소나무 군집에서 신갈나무, 참나무류 군집으로 갈수록 토양산도, 전질소함량, 치환성 칼륨, 칼슘, 나트륨, 마그네슘 등의 토양성질(土壤性質)들이 양호하여졌다. 소나무와 서어나무는 신갈나무, 졸참나무, 갈참나무와 상이(相異)한 niche에 존재하였다.

• 대한의용생체공학회:의공학회지
• /
• 제39권5호
• /
• pp.213-219
• /
• 2018

Stomach cancer is the most diagnosed cancer in Korea. When gastric cancer is detected early, the 5-year survival rate is as high as 90%. Gastroscopy is a very useful method for early diagnosis. But the false negative rate of gastric cancer in the gastroscopy was 4.6~25.8% due to the subjective judgment of the physician. Recently, the image classification performance of the image recognition field has been advanced by the convolutional neural network. Convolutional neural networks perform well when diverse and sufficient amounts of data are supported. However, medical data is not easy to access and it is difficult to gather enough high-quality data that includes expert annotations. So This paper evaluates the efficacy of transfer learning in gastroscopy classification and diagnosis. We obtained 787 endoscopic images of gastric endoscopy at Gil Medical Center, Gachon University. The number of normal images was 200, and the number of abnormal images was 587. The image size was reconstructed and normalized. In the case of the ResNet50 structure, the classification accuracy before and after applying the transfer learning was improved from 0.9 to 0.947, and the AUC was also improved from 0.94 to 0.98. In the case of the InceptionV3 structure, the classification accuracy before and after applying the transfer learning was improved from 0.862 to 0.924, and the AUC was also improved from 0.89 to 0.97. In the case of the VGG16 structure, the classification accuracy before and after applying the transfer learning was improved from 0.87 to 0.938, and the AUC was also improved from 0.89 to 0.98. The difference in the performance of the CNN model before and after transfer learning was statistically significant when confirmed by T-test (p < 0.05). As a result, transfer learning is judged to be an effective method of medical data that is difficult to collect good quality data.

• 한국산림과학회지
• /
• 제79권2호
• /
• pp.173-186
• /
• 1990

광릉(光陵) 소리봉(蘇利峯)지역 삼림의 식물군집구조분석(植物群集構造分析)을 위하여 46개소에 조사지(1개조사지당 $10{\times}10m$ 방형구(方形區) 5개씩 설치)를 설정하고 식생조사를 실시하여 얻은 자료에 대하여 TWINSPAN에 의한 classification방법과 4종류의 ordination(PO, PCA, RA, DCA) 방법을 적용하였다. Classification에 의하여 갈참나무-서어나무군집, 서어나무-갈참나무-소나무군집, 갈참나무-서어나무-까치박달나무군집, 까치박달-갈참나무군집 등 4개의 group으로 조사지가 분리되었다. Ordination에 의한 조사지분석에서는 DCA가 가장 효과적이었으며 4개의 group으로 분리되었다. 종에 대한 두 기법분석에 의해 추정된 천이과정(遷移過程)은 교목상층(喬木上層)에서 소나무${\rightarrow}$신갈나무, 졸참나무, 갈참나무, 서어나무, 팥배나무${\rightarrow}$까치박달나무, 물푸레나무, 층층나무의 순이었고, 교목하층(喬木下層) 및 관목층(灌木層)은 진달래, 개옻나무, 참싸리, 병꽃나무${\rightarrow}$참개암나무, 생강나무${\rightarrow}$고추나무, 작살나무, 괴불나무의 순이었다. 환경인자(環境因子)의 ordinationqnstjr에 의하면 소나무군집에서 까치박달나무군집으로 갈수록 토양산도, 유기물함량, 전질소함량, 치환성양이온함량등의 토양성질들이 양호하여졌다. 서어나무, 갈참나무, 신갈나무등은 까치박달나무, 층층나무와 상이한 niche에 존재함이 나타났다.

• Journal of Electrical Engineering and Technology
• /
• 제13권6호
• /
• pp.2511-2520
• /
• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.1326-1328
• /
• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

• 한국BIM학회 논문집
• /
• 제10권2호
• /
• pp.1-11
• /
• 2020

BIM(Building Information Modeling) is a salient technology for influential innovation in the construction industry. The patent network analysis is useful for suggesting the direction of technology development and exploring the research and development field. Therefore, the purpose of this study is to analyze the BIM technology structure and core technologies according to the convergence of BIM technology and market expansion. In this study, social network analysis was conducted by establishing a co-classification IPC network for the United States BIM patent. In particular, the characteristics of the major technical areas in the BIM technology network were identified through centrality analysis. G06F017/00, digital computing or data processing method, is a core technology field in the BIM network. Arrangements, apparatus or systems for transmission of digital information, H04L029/00 is an influential technology across the network. B25J009/00 for program controlled manipulators is an intermediary technology field and G06T019/00, manipulating 3D models or images for computer graphics, is an important field for technological development competitiveness.

• 무역학회지
• /
• 제44권5호
• /
• pp.127-142
• /
• 2019

As Korea's steel industry exports 38 percent of its total steel production, the future management environment of the steel industry will change depending on the outcome of the FTA negotiations. The overall industrial structure of the domestic steel industry depends on the rules of origin, which are directly linked to the effect of concessionary tariffs. Therefore, negotiations on rules of origin are as important as tariff liberalization for Korea's steel industry. Korea's cold-rolled and plated companies are expected to be negatively affected as the country of origin standards of steel products have not considered the steel production processes in Korea. In future FTA talks, the country of origin rules should be agreed on a change of tariff classification basis. This result would secure a stable export market through increased predictability of steelmakers and reduce the risk of increased costs of oil and intangible products. In addition, the government should consider the structure of domestic supply and demand so that it does not impose constraints on the change of tariff classification. Finally, participants in the negotiations should consider the opinions of the domestic steel industry.

• 품질경영학회지
• /
• 제50권1호
• /
• pp.21-33
• /
• 2022

Purpose: As most defense technologies are classified as confidential, the corresponding International Patent Classifications (IPCs) require special attention. Consequently, the list of defense-related IPCs has been managed by the government. This paper aims to evaluate the defense-related IPCs and propose a methodology to revalidate and improve the IPC classification scheme. Methods: The patents in military technology and their corresponding IPCs during 2009~2020 were utilized in this paper. Prior to the analysis, patents are divided into private and public sectors. Social network analysis was used to analyze the convergence structure and central defense technology, and association rule mining analysis was used to analyze the convergence pattern. Results: While the public sector was highly cohesive, the private sector was characterized by easy convergence between technologies. In addition, narrow convergence was observed in the public sector, and wide convergence was observed in the private sector. As a result of analyzing the core technologies of defense technology, defense-related IPC candidates were identified. Conclusion: This paper presents a comprehensive perspective on the structure of convergence of defense technology and the pattern of convergence. It is also significant because it proposed a method for revising defense-related IPCs. The results of this study are expected to be used as guidelines for preparing amendments to the government's defense-related IPC.