• Journal of The Korean Association For Science Education
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• v.26 no.3
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• pp.342-355
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• 2006

The purpose of this study was to develop a classification scheme for laboratory instruction, which could occupy a central and distinctive role in science education. For this study, literature on laboratory instruction types were analyzed. Utilizing several of these theoretical frameworks, a Classification Scheme for Laboratory Instruction (CSLI), which clearly represents various features of laboratory instruction, was created. The developed CSLI consisted of two descriptors: one is the procedure for laboratory instruction, and the other is a way of approach. The procedure is either designed by the students or provided for them from an external source. A dichotomy also exists for the approach taken toward the activity: deductive or inductive. Validity was established for the CSLI. In addition, laboratory instruction according to CSLI was divided into four types: verification, discovery, exploratory, and investigation. Although this study demonstrated only limited features of laboratory instruction due to the absence of a field test, it serves as a model for more comprehensive studies.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.4
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• pp.611-617
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• 2004

Pixel classification is one of basic image processing issues. The general characteristics of the pixels belonging to various classes are discussed and the radical principles of pixel classification are given. At the same time. a pixel classification scheme based on image direction measure is proposed. As a typical application instance of pixel classification, an adaptive multi-level median filter is presented. An image can be classified into two types of areas by using the direction information measure, that is. smooth area and edge area. Single direction multi-level median filter is used in smooth area. and multi-direction multi-level median filter is taken in the other type of area. What's more. an adaptive mechanism is proposed to adjust the type of the filters and the size of filter window. As a result. we get a better trade-off between preserving details and noise filtering.

• Journal of Korean Library and Information Science Society
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• v.31 no.4
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• pp.111-138
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• 2000

The lxnpose of this study is to pmvide the basic data for developing rational classification scheme of intemet resources in the fields of the information & telecommunications technology. The coverage of this study is, kt, to dehe the concept of informtion & telecommunications, and also to investigate the division of information & telecommunications technology through the literature, seumd, to analyze the using library classification schemes for internet resources, and thud, to review classi6cation system of the directory search engines. In this study, I w new

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.1-8
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• 2017

Naive Bayes nearest neighbor (NBNN) is a simple image classifier based on identifying nearest neighbors. NBNN uses original image descriptors (e.g., SIFTs) without vector quantization for preserving the discriminative power of descriptors and has a powerful generalization characteristic. However, it has a distinct disadvantage. Its memory requirement can be prohibitively high while processing a large amount of data. To deal with this problem, we apply a spherical hashing binary code embedding technique, to compactly encode data without significantly losing classification accuracy. We also propose using an inverted index to identify nearest neighbors among binarized image descriptors. To demonstrate the benefits of our method, we apply our method to two existing NBNN techniques with an image dataset. By using 64 bit length, we are able to reduce memory 16 times with higher runtime performance and no significant loss of classification accuracy. This result is achieved by our compact encoding scheme for image descriptors without losing much information from original image descriptors.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.1
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• pp.117-141
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• 2017

The thesaurus can control the headings in a classification scheme and can serve as an index of the classification scheme itself. In records management, the thesaurus controls functional terms and expands the access point of search to complement the functional classification scheme. In recent years, ISO 25964, the international standard for thesaurus construction, has been revised because of changes in the information environment and the development of thesaurus construction and utilization systems. Part 1 of ISO 15489, the international standard for overall records management, was also amended in 2016. In addition, the Integrated Public Sector Vocabulary (IPSV) in the UK, EuroVoc in Europe, and Functions of New Zealand (FONZ) in New Zealand have been effectively building and linking thesauri to reflect recent trends. In this study, we propose a thesaurus construction guideline for systematic record management in terms of related standards and cases, and suggest an improvement plan for the thesaurus construction guideline in Korea.

• Computers and Concrete
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• v.4 no.3
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• pp.243-257
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• 2007

A novel method for recognition, characterization, and quantification of deterioration in bridge components and laboratory concrete samples is presented in this paper. The proposed scheme is based on grey level co-occurrence matrix texture analysis using Haar's discrete wavelet transform on concrete imagery. Each image is described by a subset of band-filtered images containing wavelet coefficients, and then reconstructed images are employed in characterizing the texture, using grey level co-occurrence matrices, of the different types and degrees of damage: map-cracking, spalling and steel corrosion. A comparative study was conducted to evaluate the efficiency of the supervised maximum likelihood and unsupervised K-means classification techniques, in order to classify and quantify the deterioration and its extent. Experimental results show both methods are relatively effective in characterizing and quantifying damage; however, the supervised technique produced more accurate results, with overall classification accuracies ranging from 76.8% to 79.1%.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1557-1563
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• 2015

Multiclass classification is typically performed using the voting scheme method based on combining binary classifications. In this paper we propose multiclass classification method for large data, which can be regarded as the revised one-vs-all method. The multiclass classification is performed by using the hat matrix of least squares support vector machine (LS-SVM) ensemble, which is obtained by aggregating individual LS-SVM trained on each subset of whole large data. The cross validation function is defined to select the optimal values of hyperparameters which affect the performance of multiclass LS-SVM proposed. We obtain the generalized cross validation function to reduce computational burden of cross validation function. Experimental results are then presented which indicate the performance of the proposed method.

• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.151-171
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• 2012

In applying RFID/USN technology to various industries, it is needed to solve the problem caused by the system differences. Accordingly, this study introduces the common standard platform concept, and suggests the standard data scheme which provides the uniform perspective of classifying supply chain data and of using vocabularies. We selected several industry areas applicable for the platform, which are pharmaceutical, cosmetics, food and liquor industry. We collect and organize terminologies used in the supply chain of each industry, and then classify them according to the defined data attributes. The standardized vocabularies are suggested based on the contextured scheme of data classification. This study could provide more convenient way of communication between business partners, system developers and users of the platform.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.2
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• pp.143-146
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• 2017

In this paper, a novel approach is proposed to improve the performance of speech/music classification using the recurrent neural network (RNN) in the enhanced voice services (EVS) of 3GPP for hearing aids. Feature vectors applied to the RNN are selected from the relevant parameters of the EVS for efficient speech/music classification. The performance of the proposed algorithm is evaluated under various conditions and large speech/music data. The proposed algorithm yields better results compared with the conventional scheme implemented in the EVS.

• Journal of Korean Library and Information Science Society
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• v.38 no.3
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• pp.161-181
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• 2007

This study examined the changes, development and characteristics of the cartographic materials field in LCC as a suggestion of an ideal classification scheme which is necessary for classily the cartographic materials in libraries. The main purpose this study is to suggest how to apply the examination results to the related subject field in KDC focused on the integration and subdivision of area table and developing subject subdivision table.