• 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 Institute of Convergence Signal Processing
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• v.16 no.3
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• pp.83-89
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• 2015

Connected component labeling (CCL) is a mandatory step in image segmentation where objects are extracted and uniquely labeled. CCL is a computationally expensive operation and thus is often done in parallel processing framework to reduce execution time. Various parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method, modified 8 directional label selection (M8DLS) method, HYBRID1 method, and HYBRID2 method. Soh et al. showed that HYBRID2 outperforms the others and is the best so far. In this paper we propose a new hybrid parallel CCL algorithm termed as HYBRID3 that combines selective four directional label search (S4DLS) with label backtracking (LB). We show that the average percentage speedup of the proposed over M8DLS is around 60% more than that of HYBRID2 over M8DLS for various kinds of images.

• Journal of Digital Contents Society
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• v.3 no.2
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• pp.143-152
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• 2002

In this paper, we present the general interface, label-switched path structure and link bundling in GMPLS to improve the scalability. The LMP protocol is also introduced to efficiently manage the internal link, and the signaling protocol, hierarchical path setup, hi-directional LSP setup and suggested label method are presented. Finally, the techniques of protection and restoration are presented. In specific, various applicable restoration techniques in GMPLS are discussed.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.527-538
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• 2004

In this paper, we present the flow aggregation method and the FLTC(flow lasting time control) algorithm to reduce the number of flows and solve the scalability problem in high speed IP switching networks. The flow aggregation based on the destination address could reduce the total number of flows, improve the label efficiency, and increase the total amount of the switched packets. The FLTC algorithm also eliminates the waste of label by deleting the flow binding efficiently. With the traces of real Internet traffics, we evaluate the performance of these schemes by simulation. The label efficiency, the average number of label used, and the percentage of packets switched and the number of packets switched are used as performance measures for this simulation.

• International Journal of Contents
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• v.7 no.4
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• pp.24-29
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• 2011

In this paper, we propose a method to segment the bottle label in images taken by mobile phones using multi-gradient approaches. In order to segment the label region of interest-object, the saliency map method and Hough Transformation method are first applied to the original images to obtain the candidate region. The saliency map is used to detect the most salient area based on three kinds of features (color, orientation and illumination features). The Hough Transformation is a technique to isolated features of a particular shape within an image. Therefore, we utilize it to find the left and right border of the bottle. Next, we segment the label based on the gradient information obtained from the structure tensor method and edge method. The experimental results have shown that the proposed method is able to accurately segment the labels as the first step of product label recognition system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5546-5559
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• 2019

Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. A variety of methods have been designed for frontal-view AU detection, but few have been able to handle multi-view face images. In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. The network can complete two tasks: AU detection and facial view detection. AU detection is a multi-label problem and facial view detection is a single-label problem. A residual network and multilayer fusion are applied to obtain more representative features. Our method is effective and performs well. The F1 score on FERA 2017 is 13.1% higher than the baseline. The facial view recognition accuracy is 0.991. This shows that our multi-task, multi-label model could achieve good performance on the two tasks.

• Journal of the Korean Society of Food Culture
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• v.17 no.3
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• pp.299-308
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• 2002

The purposes of this study were to examine understanding, attitudes, and behaviors of college students regarding the nutrition labels of food package and the relations among these factors and demographic background such as educational experience with label, major, home place and parents' status. The study was surveyed 471 undergraduate students enrolled in general education classes at local university. Generally, college students could understand nutrition label. Nutrition related class in college didn't influence Nutrition label understanding, use and purchasing behavior. Students whose mothers with higher than college education level and professional work showed strong dependability on nutrition label. Label use, understanding and purchasing behaviors significantly associated with gender of subjects. All the subjects looked at the amount of sodium most frequently among all the nutrients listed on the food package(88.8%). Of all the food labels, the manufacturing date(25.1%) was considered the most important and the refund and exchange(12.9%) was considered the least important information. This paper suggested that nutrition education program for college students needs to be developed in series from elementary school curriculum and to enhance the use of nutrition labels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4665-4683
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• 2019

Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.

• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.852-859
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• 2021

The purpose of this study is to investigate the clothing management behavior and the recognition of care label of college students, as well as the effects of sex and the learning experience of clothing life area on middle and high school students. A survey consisting of 61 questions was conducted on 475 college students(240 males and 235 females) enrolled in a university in Cheongju City, and 450 college students' data were finally analyzed. The results are as follows. First, the level of washing behavior(2.54) was the lowest compared to purchasing behavior(3.13) and storage behavior(3.09). Second, college students were well aware of the attachment of fiber care labels, but 64.7% of the college students did not check the care label. About 30% of them did not know why the care labels were attached, and about 57% did not know whether manufacturers were obligated to attach them. The meaning of precautions for handling in a care label was well inferred from the symbols. Third, there was the positive effect of the learning experience of clothing life area during middle or high school on the college students' clothing management behavior and the level of recognition of a care label. This study is meaningful in confirming the positive effect of clothing life education in adolescence on adult clothing life behavior.

• Journal of KIISE:Information Networking
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• v.29 no.1
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• pp.24-30
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• 2002

Label switching technology is currently emerging as a solution for the rapidly growing of Internet traffic demand. Multiprotocol label switching(MPLS) is one of the standards made by the Internet Engineering Task Force(IETE) intended to enhance speed, scalability, and inter-opearability between label switching technologies. In MPLS, utilization of label space is a very important factor of network performance because labels are basic unit in packet switching. We propose a algorithm to effectively use label space by a multiple disconnect timer at the label switching router. Our algorithm is based on multiple utilization of the connection release timer over the MPLS network with multiple domains. In our algorithm, a relatively linger timeout interval is assigned to the traffic with higher class by the aid of the packet classifier. This reduces delay for making a new connection and also reduces the amount of packets which will be routed to the layer 3. Simulation results shows that reduction of required label number in MPLS network and this indicate our algorithm offers better performance than the existing ones in term of utilization of label space.