• Journal of Korea Multimedia Society
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• v.9 no.2
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• pp.139-150
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• 2006

A schematic map is a special purpose map which is generated to recognize it's objects easily and conveniently via simplifying and highlighting logical geometric information of a map. To manufacture the schematic map with road, label and icon, we must generate simplified route map and replace many geometric objects. Performing a give task, however, there are an amount of overlap areas between geometric objects whenever we process the replacement of geometry objects. Therefore we need replacing geometric objects without overlap. But this work requires much computational resources, because of the high complexity of the original geometry map. We propose the schematic map generation system whose map consists of icons and label. The proposed system has following steps: 1) eliminating kinks that are least relevant to the shape of polygonal curve using DCE(Discrete Curve Evolution) method. 2) making an evenly distributed route using CVT(Centroidal Voronoi Tessellation) and Grid snapping method. Therefore we can keep the structural information of the route map from CVT method. 3) replacing an icon and label information with collision avoidance algorithm. As a result, we can replace the vertices with a uniform distance and guarantee the available spaces for the replacement of icons and labels. We can also minimize the overlap between icons and labels and obtain more schematized map.

• Korean journal of food and cookery science
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• v.17 no.4
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• pp.405-411
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• 2001

This research was conducted to evaluate consumers' understanding and preference for shelf life and ingredient listings in food label by survey using a questionnaire. The questionnaires were collected from female adult consumers residing in Seoul, Kyonggi, Choongnam and Chungbuk areas. Most of the consumers showed good understanding for shelf life and ingredient listings in food label. They had no particular preference for the types of shelf life labelling. They preferred listing all ingredients completely with amounts, but differentiated from food additives. They wanted to get more information about shelf life and ingredient listings in food label that is mainly associated with food safety They showed more preference for labelling the shelf life in the information panel with a storage condition. Most of them chose the ingredient list format in which each ingredient was written in one row in bold characters with percentage labelling.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.91-99
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• 2019

Existing node-to-node based optimal path searching is built on the assumption that all destination nodes can be arrived at from an origin node. However, the recent appearance of the adaptive path search algorithm has meant that the optimal path solution cannot be derived in node-to-node path search. In order to reflect transportation data at the links in real-time, the necessity of the node-to-link (or link-to-node; NL) problem is being recognized. This research assumes existence of a network with link-label and non-additive path costs as a solution to the node-to-link optimal path problem. At the intersections in which the link-label has a turn penalty, the network retains its shape. Non-additive path cost requires that M-similar paths be enumerated so that the ideal path can be ascertained. In this, the research proposes direction deletion and turn restriction so that regulation of the loop in the link-label entry-link-based network transformation method will ensure that an optimal solution is derived up until the final link. Using this method on a case study shows that the proposed method derives the optimal solution through learning. The research concludes by bringing to light the necessity of verification in large-scale networks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1291-1300
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• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.

• The Korea Genome Conference
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• 2006.09a
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• pp.41-41
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• 2006

• The monthly packaging world
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• s.94
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• pp.111-113
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• 2001

• The monthly packaging world
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• s.94
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• pp.102-103
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• 2001

• The monthly packaging world
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• s.94
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• pp.98-101
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• 2001

• Bulletin of Korea Environmental Preservation Association
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• v.18 no.1
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• pp.28-31
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• 1996

• The monthly packaging world
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• s.241
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• pp.98-108
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• 2013