• Journal of Korean Library and Information Science Society
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• v.44 no.3
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• pp.125-152
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• 2013

The purpose of this study is to construct a substantive theory that described and explained how pregnant women are seeking information in context of pregnancy. A qualitative design based on the method of grounded theory was utilized. In-depth interviews were conducted with 16 pregnant women, selected by theoretical sampling who receiving antenatal care 3 different university hospitals. The interview data were transcribed and systematically analyzed according to the open coding, axial coding and selective coding. 'Increasing Sense of Self-Control over Pregnancy' was the core category for describing and guiding the process of seeking information during the pregnancy. To construct a theory of 'Increasing Sense of Self-Control over Pregnancy' in this study, eight hypothetical statement were generated from the relationships among the core category and the other categories, thus four patterns were derived as follows: 'Desperately Seeking', 'Actively Seeking', 'Unconcernedly Seeking', 'Resignedly Seeking'. The implications of the findings are discussed in terms of development of LIS theory in Korea, theory development of information behavior, education for LIS, and information service practice. The suggestions for further research are provided.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.102-108
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• 1996

Adaptive resonance theory (ART2) characterized by its built-in mechanism of handling the stability-plasticity switching and by the adaptive learning without forgetting informations learned in the past, is based on an unsupervised template matching. We propose an improved tow-stage learning algorithm for aRT2: the original unsupervised learning followed by a new supervised learning. Each of the output nodes, after the unsupervised learning, is labeled according to the category informations to reinforce the template pattern associated with the target output node belonging to the same category some dominant classes from exhausting a finite number of template patterns in ART2 inefficiently. Experimental results on a set of 2545 FLIR images show that the ART2 trained by the two-stage learning algorithm yields better accuracy than the original ART2, regardless of th esize of the network and the methods of evaluating the accuracy. This improvement shows the effectiveness of the two-stage learning process.

• The Journal of Fisheries Business Administration
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• v.14 no.2
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• pp.1-14
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• 1983

This paper considers a classification of the 88 islands located at Kyong-nam area in Korea, using by examples of 12 components of the islands. By means of principal component analysis 2 principle components were extracted, which explained a total of 73.7% of the variance. Using an eigen variable criterion (λ>1), no further principle components were discussed. Principal component 1 and 2 explained 63.4% and 10.3% of the total variance respectively, The representation of the unrelated factor scores along the first and second principal axes produced a new information with respect to the classification of the islands. Based upon the representation, 88 islands were classified into 6 groups i. e. A, B, C, D, E, and F according to similarity of the components among them in this paper. The "Group F" belongs to a miscellaneous assortment that does not fit into the logical category. category.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.409-410
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• 2015

In the design of any picture book, graphic artists employ the use of color, typeface and illustration to produce attractive book covers. This study is an analysis of selected book covers in the 'Africana Children's Book Awards' (Young Readers category) between 2009 and 2014. The book covers are mainly illustrations originally done in media such as acrylics and oil paintings on canvas. In the article, the author analyses the front covers in terms of hue, Saturation and brightness using a colour map. This work also discusses why certain colours have been used in the design of the covers thus; providing information on color scheme trends for picture books in the Children's Africana Awards Category.

• Journal of Information Technology Services
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• v.4 no.1
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• pp.117-128
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• 2005

When we conduct development of a large-size SI project, it is highly necessary to utilize an appropriate application framework which helps to build a qualified software with high productivity in a short period of time. In this paper, we propose the architecture of a dynamically reconfigurable CBD application integration framework that has been used for developing large-scale e-business applications to achieve high development productivity and maintainability. This Integration framework can easily extend its functionalities, and dynamically change its configuration during run time according to the business category, such as applying interaction patterns among main components in software architecture, rules, policies, and environmental parameters. Dynamic reconfiguration has the feature to make applications be easily customized for changeable requirements. Through our application integration framework, huge sizes of contents can be managed according to the business category as well, by keeping configuration informations and huge volumes of source codes. In order to evaluate out application integration framework in terms of performance criteria, we present experimental results of throughputs from the framework by yielding dynamic configuration without any performance degradation.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.279-284
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• 2017

The visual impact of fonts on lots of documents and design work is significant. Accordingly, the users desire to appropriately use fonts suitable for their intention. However, existing font recommendation programs are difficult to consider what users want. Therefore, we propose a font recommendation system based on user-evaluated font attribute value. The properties of a font are called attributes. In this paper, we propose a font recommendation module that recommends a user 's desired font using the attributes of the font. In addition, we classify each attribute into three types of usage, personality, and shape, suggesting the font that is closest to the desired font, and suggest an optimal font recommendation algorithm. In addition, weights can be set for each use, personality, and shape category to increase the weight of each category, and when a weight is used, a more suitable font can be recommended to the user.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.251-253
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• 2008

Erosion and landslide cause serious damage to forest areas. As a consequence, partial or complete destruction of vegetation occurs, which leads to many cascading problems. In this study, an attempt has been made to identify the forest areas, which are under different risk categories of erosion and landslide, in part of Eastern Ghats of Tamil Nadu. Relevantthematic maps were generated from satellite data, topographical maps, primary and secondary data and weights to each map were assigned appropriately. Weighted overlay analysis was carried out to identify the erosionprone forest areas. The result of erosion and landslide prone model reveals that 4712 ha(17%) of forest area is under high risk category and 15879 ha(58.65%) isunder medium risk category. The results of spatial modeling would be very much useful to the forest officials and conservationist to plan for effective conservation.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.261-262
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• 2006

According to the previous work, various well known methods including maximum a posteriori probability classifier (MAP) and multi layer perceptron networks classifier (MLP) showed competitive results. Since even the small errors often leads to a fatal result, we investigated the method that reduces classification error perfectly by screening out some ambiguous patterns. Those ambiguous patterns can be examined by routine biopsy. We incorporated an ambiguous category in MAP and MLP. Classification results involving 216 spectra gave 100% sensitivity for the case of MLP.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.11-15
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• 2020

In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.375-381
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• 2014

Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods.