• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.5
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• pp.841-849
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• 2001

The pixel information of the object was obtained sequentially and pixels were clustered to a label by the line labeling method. Feature points were determined by finding the slope for edge pixels after selecting the fixed number of edge pixels. The slope was estimated by the least square method to reduce the detection error. Once a matching point was determined by comparing the feature information of the object and the pattern, the parameters for translation, scaling and rotation were obtained by selecting the longer line of the two which passed through the matching point from left and right sides. Finally, modified Hausdorff Distance has been used to identify the similarity between the object and the given pattern. The multi-label method was developed for recognizing the patterns with more than one label, which performs the modified Hausdorff Distance twice. Experiments have been performed to verify the performance of the proposed algorithm and method for simple target image, complex target image, simple pattern, and complex pattern as well as the partially hidden object. It was proved via experiments that the proposed image matching algorithm for recognizing the object had a good performance of matching.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.666-668
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• 1998

In this paper we illustrate the design of a node label data flow machine based on self-timed paradigm. Data flow machines differ from most other parallel architectures, they are based on the concept of the data-driven computation model instead of the program store computation model. Since the data-driven computation model provides the excution of instructions asynchronously, it is natural to implement a data flow machine using self timed circuits.

• Journal of Electrical Engineering and Technology
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• v.6 no.4
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• pp.551-555
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• 2011

The Crab label tag with a loop matching feed and a modified dipole antenna structure was proposed. The antenna impedance is conjugated easily to a radio frequency identification IC chip impedance by a loop matching feed. The reading range of the crab structure tag is 0.9-1.0 m from the upper side of the formula milk can lid. The fabricated label tag size is $44.0{\times}44.0mm^2$. The operating frequency at -3 dB return loss is 861.0-929.0 MHz, and the maximum reading range at the anechoic chamber is 1.5 m.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4092-4107
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• 2015

We propose a background subtraction method for moving cameras based on trajectory classification, image segmentation and label inference. In the trajectory classification process, PCA-based outlier detection strategy is used to remove the outliers in the foreground trajectories. Combining optical flow trajectory with watershed algorithm, we propose a trajectory-controlled watershed segmentation algorithm which effectively improves the edge-preserving performance and prevents the over-smooth problem. Finally, label inference based on Markov Random field is conducted for labeling the unlabeled pixels. Experimental results on the motionseg database demonstrate the promising performance of the proposed approach compared with other competing methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.709-716
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• 2020

In social media, when posting a post, tag information of an image is generally used because the search is mainly performed using a tag. Users want to expose the post to many people by attaching the tag to the post. Also, the user has trouble posting the tag to be tagged along with the post, and posts that have not been tagged are also posted. In this paper, we propose a method to find an image similar to the input image, extract the label attached to the image, find the posts on instagram, where the label exists as a tag, and recommend other tags in the post. In the proposed method, the label is extracted from the image through the model of the convolutional neural network (CNN) deep learning technique, and the instagram is crawled with the extracted label to sort and recommended tags other than the label. We can see that it is easy to post an image using the recommended tag, increase the exposure of the search, and derive high accuracy due to fewer search errors.

• Journal of Korea Trade
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• v.26 no.5
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• pp.106-124
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• 2022

Purpose - The increasing share of Korean private label products (PLPs) in the domestic market helped generate lucrative revenue. In recent years, major South Korean retailers have begun to cast their sights on overseas markets and actively export their PLPs. In China, the proportion of private label fresh food (PLFF) is gradually expanding amid the development of the new retailing model. A profound understanding of the relationship between private label fresh produce and purchase intention may be the answer to helping Chinese retailer private labels expand supply chains in Korea. This study, taking Chinese retailers as an example, examines the impacts of selection factors of private label fresh food and perceived value on purchase intention. Apart from that, the relationship between the selection factors and purchase intention will be analyzed with perceived value as a mediator. Design/methodology - This work aims to empirically analyze the purchase intention of private label fresh food using statistical analysis. In this study, a hypothetical causal model consisting of 6 latent variables and 24 measured variables is developed based on the literature review. To validate the research hypotheses and the research model, SPSS23.0/AMOS23.0 is used to analyze factors such as validity and reliability, as well as structural equation modeling. Findings - The hypothetical model established in this study is of general applicability. In respect to PLFF, perceived value, while significantly influencing purchase intention in combination with four selection factors (perceived quality, perceived price, brand trust, and store image), mediates partially between the first three factors and purchase intention, which rules out the impact and mediating effect of store image on purchase intention. Originality/value - These research results, as helpful insights into the present circumstances of Chinese PLFF in the domestic market, provide useful information and guidance for Korean retailers and service providers to innovate production and service, as well as develop marketing and promotion strategies, so that they can shift private label goods with advantages from domestic demand to export, thus increasing overseas profitability. Further, this work will also contribute to relevant research.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.194-195
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• 2004

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.243-248
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• 2017

This study set the standard for label standardization of foreign sports apparel. Based on the theory of labels, the labels were scanned around the internal T-shirt of sports apparel brands. Based on the plan, 32 young men and women surveyed 32 tentative labels. As a result, The survey revealed that the respondents preferred the information of size, fit, washing symbol mark, and function. Based on these results, the complementary labels were designed for balance, simplicity, coherency, cognitive, and harmonization. The study interviewed 12 men and women in their 20s and 30s. As a result, the subjects indicated that they selected cognitive preference for labels because of the maximum, minimum spacing, and emphasis on information representative.

• Proceedings of the Korea Society of Design Studies Conference
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• 2003.11a
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• pp.104-105
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• 2003

• 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.