• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.25-50
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• 2021

General-purpose classification scheme encompasses all subject areas, While the whole classification scheme is constructed by library studies experts, structure and preparation of each specific subject area's classification should be referenced to that specific subject. In order for the whole system to be practical and useful classification scheme, not just a simple collection of each subject area's scheme, it is necessary to set the rule for properly distributing the amount of classification items, and the collections assigned to these items. The rule to set the distribution of items based on the amount of document collections is called 'literary warrant'. This study examines actual status of assignment of each classification items to information resources, as a result of application of Korean Decimal Classification, and then suggests a way to improve these practices.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.45-51
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• 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.

• Journal of Korean Society of Forest Science
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• v.81 no.3
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• pp.214-223
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• 1992

To investigate the structure of the plant community of Mt. Jookyup area in Kwangnung forest, thirty-seven plots were set up by the clumped sampling method. The classification by TWINSPAN and two kinds of multivariate ordination(RA, DCA) were applied to the study area in order to classify them into several groups based on woody plants and environmental variables. The classification have been successfully overlayed on an ordination of the same data using DCA. The plots can be classified into five groups by TWINSPAN and DCA. The successional trends of tree species by both techniques seem to be expected two ways in the canopy layer. The first is from Pinus densiflora to Carpinus laxiflora and the second is from Pinus densiflora through Quercus mongolica to Carpinus laxiflora. In the understory layer, it was expected that Rhododendron mucronulatum ${\rightarrow}$Lindera obtusiloba, Symplocos chinensis for. pilosa, Viburunum erasum, Styrax obassia${\rightarrow}$Euonymus sachalinensis, Sorbus alnifolia. As the result of the analysis for the relationship between the stand scores of DCA and environmental variables, they had a tendency to increase significantly from the P. densiflora community to Quercus spp. community that was soil pH, total nitrogen, available phosphate and exchangeable potassium, sodium, calcium and magnesium.

• Journal of Biomedical Engineering Research
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• v.39 no.5
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• pp.213-219
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• 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.

• Journal of Korean Society of Forest Science
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• v.79 no.2
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• pp.173-186
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• 1990

To investigate the structure of the plant community of Soribong area in Kwangnung forest, forty-six plots were set up by the clumped sampling method. The classification by TWINSPAN and four kinds of multivariate ordination(PO, PCA, RA, DCA) were applied to the study area in order to classify them into several groups based on woody plants and environmental variables. The classification had been successfully overlayed on an ordination of the same data using DCA. The plots can be classified into four groups by TWINSPAN and DCA. The successional trends of tree species by both techniques seem to be from Pinus densiflora through Quercus mongolica, Q. serrata, Q. aliena, Carpinus laxiflora, Sorbus alnifolia to C. cordata, Fraxinus rhynchophylla, Cornus controversa in the canopy layer, and from Rhododendron mucronulatum, Rhus triohocarpa, Lespeoleza cyrtobotrya, Weigela subsessilis through Corylus sieboldiana, Lindera obtusiloba to Slaphylea bumalda, Callicarpa japonica, Lonicera maackii in the understory layer. As a result of the analysis for the relationship between the stand scores of DCA and environmental variables, they had a tendancy to increase significantly from the P. densiflora community to C. cordata community that was soil pH and the amount of humus, total nitrogen and exchangeable cations.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1326-1328
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• 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.

• Journal of KIBIM
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• v.10 no.2
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• pp.1-11
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• 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.

• Korea Trade Review
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• v.44 no.5
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• pp.127-142
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• 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.

• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.21-33
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• 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.