• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.1-9
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• 2016

In this paper, we propose a method of measuring bone mineral density in a peripheral-type clinical X-ray CT using a phantom, and we propose a method of classifying osteoporosis using bone mineral density and bone structure parameters together. It segments the trabecular bone region and cortical bone region for the six sections of the phantom and calculates the average HU value of the segmented regions. By using these values, it derives an expression converting HU value to bone mineral density. It segments trabecular bone of 1 cm region in the end part of distal radius and extracts the bone mineral density and structural parameters for the trabecular bone region. We extracted bone mineral density and structural parameters for the 18 subjects each of normal and osteoporotic group. We carried out classification experiments using three classification methods; SAD, SVM, ANN. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, likelihood ratio of the classification was improved in the order of ANN, SVM and SAD. Also, The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, likelihood ratio of the classification was improved when we use the bone mineral density and structural parameters together.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.79-83
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• 2021

This paper proposes a novel image classification method based on few-shot learning, which is mainly used to solve model overfitting and non-convergence in image classification tasks of small datasets and improve the accuracy of classification. This method uses model structure optimization to extend the basic convolutional neural network (CNN) model and extracts more image features by adding convolutional layers, thereby improving the classification accuracy. We incorporated certain measures to improve the performance of the model. First, we used general methods such as setting a lower learning rate and shuffling to promote the rapid convergence of the model. Second, we used the data expansion technology to preprocess small datasets to increase the number of training data sets and suppress over-fitting. We applied the model to 10 monkey species and achieved outstanding performances. Experiments indicated that our proposed method achieved an accuracy of 87.92%, which is 26.1% higher than that of the traditional CNN method and 1.1% higher than that of the deep convolutional neural network ResNet50.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.2
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• pp.55-65
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• 2008

This study was surveyed to analyze vegetation structure of mountainous wetlands in Chung-ju city and around area from September to November, 2006. 6 sites of total 15 potential mountainous wetlands were discriminated throughout field survey. By classification system of mountainous wetlands presented in manual of forest wetlands research, types of wetlands were classified into slant and a flat. Many sites were covered with land plants as Pueraria thunbergiana and so on. To understand vegetation structure of mountainous wetlands, Height, DBH (diameter at breast height), DI (Dominance Index), Sociability and Constancy were surveyed and Based on this result, a projection chart was drawn. As results, Salix koreensis in tree layer and Persicaria thunbergii and Impatiens textori in herb layer were surveyed as broadly distributed species. This study is mainly focused on vegetation condition of mountainous wetlands. But, it will be needed studying on classification system of mountainous wetland type and functional assessment for conservation or management of wetlands.

• Korean Journal of Environment and Ecology
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• v.8 no.2
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• pp.107-120
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• 1995

To investigate the forest structure of Chuwang valley of Chuwangsan National Park, thirty plots were set up and surveyed. Importance values, DBH class distribution, species diversity indices, DCA Ordination, CCA ordiantion and TWINSPAN classification were used for vegetational structure analysis. Pinus densiflora Quercus serata, Q. mongolica, Q. variabilis were appeared to be dominant species in thirty plots. According to the analysis of classification by TWINSPAN, the thirty plots divided four groups. Groups were Q. mongolica-P. densiflora-Carpinus laxiflora community(I), P. densiflora-Q. variabilis community(II), Q. serrata-Q. variabilis community(III), broad-leaved mixed community(IV) Species diversity(H') of investigated area was calculated 1.17~l.32. The successional trend was seemed to be from P. densiflora to Q. spp. in the canopy layer.

• Journal of Environmental Policy
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• v.3 no.2
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• pp.113-137
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• 2004

The objective of this study is to introduce the definitions and classification methods of wastes in international agreements and legislations, examine the concept of wastes and their classification systems in Korea, and finally analyze and compare the concept of wastes in different countries for finding better solutions and suggestions. The study summarizes the concept of wastes as introduced in the Basel Convention, OECD, EU, US, and UK. First, each of the member countries adapt to the same concepts of wastes as defined in their international agreements; second, the intention of the wastes holder and the conditions of the wastes are considered at the same time when defining the concepts. Upon close examination of the classification of wastes systems as introduced in the Basel Convention, OECD, EU, US, and UK, the wastes are classified into toxic and non-toxic wastes according to the existence of poisonous substances. Therefore, it is classified as a toxic waste when any toxic substance on its list is included in the waste, while others are considered as a non-toxic waste if they don't contain poisonous substances. Secondly, in the UK, the matter of toxic or non-toxic wastes are classified, not according to the existence of the poisonous substances, but based on the generation of sources. In Korea, the concepts of wastes are divided into the two categories - a concept as defined in actual legislations and a concept in its translation. The Korean classification of the wastes include Wastes Management Act, amended in 1995, which stipulates that toxic substances should be managed in a special way as the designated wastes. It appears that the Act utilizes the classification method that classifies the wastes according to the existence of poisonous substance. Korea's concepts of wastes should be changed after recognition of the concepts in international agreement (Basel Convention, EU) and other foreign laws(US, UK) that consider subjective and objective standards at the same time when they define the concepts. Also, the development of technology in recycling and reuse of the wastes can remove the current absolute notion of the wastes so that it also should not be passed over. Also, because a classification structure of wastes has a close relationship with a disposal structure, its classification system should be fixed gradually to come up with the development of wastes disposal technology and its policy.

• Journal of the Korean Society for information Management
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• v.18 no.3
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• pp.285-309
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• 2001

The industry information grows faster than any other information resources in the Internet age. Unfortunately, however, there is no consensus on the standard of the classification among the information providers of the industry fields. This may a problematic issue not only in building a continuous and systematic development of the industry information, but also in the use of the information among the users. This study aims to propose a well-structured and/or an efficient classification scheme for the industry information to help the users with easy to retrieve the Internet resources. To do this, we analyzed the subject classification scheme of the domestic industry information on the web sites, which is largely adopted the \"Korean Standard for the Industry Classification\". In addition, we suggested the principle of the subject classification and their hierarchial structure derived from the analysis of the knowledge and document classification scheme. As a result, it was suggested an optimized industry classification scheme based on the analysis of the validity test of classification item measured by the quantitative analysis of the industry information, which it currently accessible through the Internet. Internet.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.35-46
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• 2011

In this paper, we proposed the method of emotion recognition using staged classification model and Fisher's linear discriminant. By organizing the staged classification model, the proposed method improves the classification rate on the Fisher's feature space with high complexity. The staged classification model is achieved by the successive combining of binary classification model which has simple structure and high performance. On each stage, it forms Fisher's linear discriminant according to the two groups which contain each emotion class, and generates the binary classification model by using Adaboost method on the Fisher's space. Whole learning process is repeatedly performed until all the separations of emotion classes are finished. In experimental results, the proposed method provides about 72% classification rate on 8 classes of emotion and about 93% classification rate on specific 3 classes of emotion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• Journal of The Korean Association of Information Education
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• v.21 no.3
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• pp.297-307
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• 2017

The purpose of this study is to propose algorithm classification system for algorithm education for Elementary and Secondary Students. We defines the components of the algorithm and expresses the algorithm classification system by the analysis synthesis method. The contents of the study are as follows. First, we conducted a theoretical search on the classification purpose and classification. Second, the contents and limitations of the classification system for the proposed algorithm contents were examined. In addition, we examined the contents and selection criteria of algorithms used in algorithm education research. Third, the algorithm components were redefined using the core idea and crosscutting concept proposed by the NRC. And the crosscutting concept of algorithm is subdivided into algorithm data structure and algorithm design strategy, and its contents are presented using analytic synthesis classification scheme. Finally, the validity of the proposed contents was verified by the review of the expert group. It is expected that the study on the algorithm classification system will provide many implications for the contents selection and training method in the algorithm education.