• Journal of the Korean Home Economics Association
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• v.45 no.2
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• pp.91-103
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• 2007

The purpose of this study is to investigate the types and functions of brand labels on clothing. We surveyed the materials and manufacturing methods for brand labels by visiting the label stores and label manufacturers. 200 pieces of children's wear were surveyed. The label attributes that were studied were: the number of labels, the location of the labels, the attachment system for the labels, the color of the labels, the materials used to make the labels, manufacturing methods, and the size of the labels. From this investigation a brand label was classified into a main label and a point label. The main results were: 1. Materials such as fabrics, nonwovens, leather, suede, rubber, PVC, silicone, and metals are used for brand labels. The manufacturing methods for brand labels are weaving, printing, high frequency, heating, and molding. 2. More than 54% of clothes have more than two brand labels attached. This percentage exceeds the attaching of only one brand label in rate. An inside brand label is located at a certain place. This inside label uses only fabric material reflecting inherent brand color and design. The outside brand label is located at several places with consideration of the clothes design. This label uses various materials, colors, and characters matching with the clothes. As for the size, an inside label is mainly medium in size, whereas an outside label is small. 3. A brand label is classified into a main label (first label) and a point label (second label), which are defined as follows. A main label indicates the brand name and is located inside at a certain place using an inherent brand design and a fabric material. A point label is an additional label to express brand image and is located outside at various places for decoration using various characters and design and materials.

• The Journal of Information Technology
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• v.5 no.4
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• pp.155-164
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• 2002

MPLS is a integrated technology by using routing function and label swapping in the network layer. Based on the previous forwarding equivalence classes, it adds the fixed length label in ingress of the MPLS domain. For the routing, without the packet header information, it uses label for the forwarding decisions. In this paper, traffic engineering requirements in the MPLS internet will be setup. The traffic engineering function have to be performed previously with the network topology. In addition to, we presents the IP network topology and main function with MPLS signaling protocol.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1851-1854
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• 2002

The Internet with conventional routing scheme cannot meet user demands driven from drastic growth in the Internet user and various service and traffic type. MPLS(Multi Protocol Label Switching) was introduced to the Internet fur solution to resolve this problem. MPLS is a paradigm to integrate higher layer’s software routing functions including layer-3 routing with layer-2 switching. But, the exponential growth of Internet traffic brings out of label space. One scalable solution to cope with this problem is to introduce flow merge technique, i. e. a group of flows is forwarded using the same label. Specially, IETF(Internet Engineering Task Force) recommends that ATM based MPLS system may include VC merge function, so it is scalable to increase of internet traffic. We implemented the MPLS LER system that includes the look-up and forwarding function in incoming path and VC merging function and limited traffic management function in outgoing path. This paper describes the implementation of the LER’s outgoing parts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1749-1756
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• 2014

Knowledge about protein subcellular localization provides important information about protein function. This paper improves a label power-set multi-label classification for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. Among multi-label classification methods, label power-set method can effectively model the correlation between subcellular locations of proteins performing certain biological function. With constrained optimization, this paper calculates combination weights which are used in the linear combination representation of a multi-label by other multi-labels. Using these weights, the prediction probabilities of multi-labels are combined to give final prediction results. Experimental results on human protein dataset show that the proposed method achieves higher performance than other prediction methods for protein subcellular localization. This shows that the proposed method can successfully enrich the prediction probability of multi-labels by exploiting the overlapping information between multi-labels.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.1-7
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• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Journal of the East Asian Society of Dietary Life
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• v.6 no.3
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• pp.335-344
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• 1996

This study was to investigate consumer recognition on food label of food package. The results of this study were as follows: 1. when shopping for food, the items considered the most were taste of family, food safety, nutrition and price, respectively. 2. 95.5% of respondents confirmed of the food label of food package when shopping for food. The items confirmed the most on food label were expiration date, manufacture date, manufacturer, food additives and nutrition, respectively. 3. 85.3% of respondents did not satisfy on the food label of food label of food package. 43.6% of respondents demanded food additives more detailed. 28.2% of respondents demanded nutrition information more detailed. 28.2% of respondents demanded food function more detailed. 4. The food which respondents satisfied on food label most were snack '||'&'||' cookies, nuddle, spices, can '||'&'||' bottled food, instant food, processed meat foo, frozen food and imported food, respectively. 5. The group with the most hphrases falling in the top rank was nutrition/calories. The phrases in the nutrition/calories group scored in the top rank were 3 "positive" nutritional characteristics(addition of vitamins, addition of DHA, high dietary fiber) and 5 "nagative" nutritional characteristics(no sugar, low sugar, low calories, low salt and low cholesterol). The group with the most phrases falling in the third rank was ingredient. The phrases in the ingredient scored in the third rank were add of food additives. 6. 55.5% of respondents did not know Recommended Daily Allowance(RDA) information and 61.9% of respondents did not understand the nutrition declaration(content) of food package but 65.7% of resspondents understood the nutrition claim of food package. From these result, respondents were more affected by nutrition claim than by nutrition declaration on food package when shopping for food.ood.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.594-596
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• 2021

Future healthcare systems will heavily rely on ill-labeled data due to scarcity of the experts who are trained enough to label the data. Considering the contamination of the dataset, it is not desirable to make the neural network being overconfident to the dataset, but rather giving them some margins for the prediction is preferable. In this paper, we propose a novel epoch-wise decaying label-smoothing function to alleviate the model over-confidency, and it outperforms the neural network trained with conventional cross entropy by 6.0%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.1-14
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• 2024

The most optimal route-search algorithm thus far has introduced a method of applying node labels and link labels. Node labels consider two nodes simultaneously in the optimal route-search process, while link labels consider two links simultaneously. This study proposes a turn-label-based optimal route-search technique that considers two turns simultaneously in the process. Turn-label-based optimal route search guarantees the optimal solution of dynamic programming based on Bellman's principle as it considers a two-turn search process. Turn-label-based optimal route search can accommodate the advantages of applying link labels because the concept of approaching the limit of link labels is applied equally. Therefore, it is possible to reflect rational cyclic traffic where nodes allow multiple visits without expanding the network, while links do not allow visits. In particular, it reflects the additional cost structure that appears in two consecutive turns, making it possible to express the structure of the travel-cost function more flexibly. A case study was conducted on the metropolitan urban railway network consisting of transportation card terminal readers, aiming to examine the scalability of the research by introducing parameters that reflect psychological resistance in travel with continuous pedestrian transfers into turn label optimal path search. Simulation results showed that it is possible to avoid conservative transfers even if the travel time and distance increase as the psychological resistance value for continuous turns increases, confirming the need to reflect the cost structure of turn labels. Nevertheless, further research is needed to secure diversity in the travel-cost functions of road and public-transportation networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.992-999
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• 2014

This paper presents an extensive experimental comparison of a variety of multi-label learning methods for the accurate prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular locations. We compared several methods from three categories of multi-label classification algorithms: algorithm adaptation, problem transformation, and meta learning. Experimental results are analyzed using 12 multi-label evaluation measures to assess the behavior of the methods from a variety of view-points. We also use a new summarization measure to find the best performing method. Experimental results show that the best performing methods are power-set method pruning a infrequently occurring subsets of labels and classifier chains modeling relevant labels with an additional feature. futhermore, ensembles of many classifiers of these methods enhance the performance further. The recommendation from this study is that the correlation of subcellular locations is an effective clue for classification, this is because the subcellular locations of proteins performing certain biological function are not independent but correlated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2562-2570
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• 2014

One of the important hints for inferring the function of unknown proteins is the knowledge about protein subcellular localization. Recently, there are considerable researches on the prediction of subcellular localization of proteins which simultaneously exist at multiple subcellular localization. In this paper, label power-set classification is improved for the accurate prediction of multiple subcellular localization. The predicted multi-labels from the label power-set classifier are combined with their prediction probability to give the final result. To find the accurate probability estimates of multi-classes, this paper employs pair-wise comparison and error-correcting output codes frameworks. Prediction experiments on protein subcellular localization show significant performance improvement.