• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.545-550
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• 2022

In Deep Learning method, it is well known that it requires large amount of data to train the deep neural network. And it also requires the labeling of each data to fully train the neural network, which means that experts should spend lots of time to provide the labeling. To alleviate the problem of time-consuming labeling process, some methods have been suggested such as weak-supervised method, one-shot learning, self-supervised, suggestive learning, and so on. In this manuscript, those methods are analyzed and its possible future direction of the research is suggested.

• Information Systems Review
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• v.25 no.1
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• pp.47-73
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• 2023

Management decision-making based on artificial intelligence(AI) plays an important role in helping decision-makers. Business decision-making centered on AI is evaluated as a driving force for corporate growth. AI-based on accurate analysis techniques could support decision-makers in making high-quality decisions. This study proposes an effective decision-making method with the application of multi-label feature selection. In this regard, We present a CFS-BR (Correlation-based Feature Selection based on Binary Relevance approach) that reduces data sets in high-dimensional space. As a result of analyzing sample data and empirical data, CFS-BR can support efficient decision-making by selecting the best combination of meaningful attributes based on the Best-First algorithm. In addition, compared to the previous multi-label feature selection method, CFS-BR is useful for increasing the effectiveness of decision-making, as its accuracy is higher.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.25-34
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• 2015

Recently, in distribution market, demand for electronic shelf label system is increasing gradually to provide the accurate price immediately and detailed product information to consumers and reduce operation costs. Most of electronic shelf label system companies develop the full-graphic display device to display a wide variety of product information as well as the exact price. Our system had introduced Go-Back-N retransmission method in the early. However, we encountered performance problems that it delayed updating of the electronic shelf label system and exhausted the battery life time. Proposed adaptive image retransmission technique based on the selective scheme is that tags of electronic shelf label system recognize idle time of transmission cycle and require partial image retransmission to sever by itself. As a result, it can acquire much more opportunities of partial image retransmission within the same period and increase reception rate of full image for each tags. The experimental result shows that adaptive image retransmission technique's reception rate of full image for each tags is approximately 4% higher than existing previous works. And total battery life time increases 30 hours because tag reduce wake-up time as it receive only lost data instead of whole data.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.29-41
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• 2021

Recently, there are increasing attempts to utilize deep learning methodology in the fashion industry. Accordingly, research dealing with various fashion-related problems have been proposed, and superior performances have been achieved. However, the studies for fashion style classification have not reflected the characteristics of the fashion style that one outfit can include multiple styles simultaneously. Therefore, we aim to solve the multi-label classification problem by utilizing the dependencies between the styles. A multi-label recognition model based on a graph convolution network is applied to detect and explore fashion styles' dependencies. Furthermore, we accelerate model training and improve the model's performance through transfer learning. The proposed model was verified by a dataset collected from social network services and outperformed baselines.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.915-926
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• 2020

Outlier detection methods help one to identify unusual instances in data that may correspond to erroneous, exceptional, or surprising events or behaviors. This work studies conditional outlier detection, a special instance of the outlier detection problem, in the context of incorrect data label identification. Unlike conventional (unconditional) outlier detection methods that seek abnormalities across all data attributes, conditional outlier detection assumes data are given in pairs of input (condition) and output (response or label). Accordingly, the goal of conditional outlier detection is to identify incorrect or unusual output assignments considering their input as condition. As a solution to conditional outlier detection, this paper proposes the ratio-based outlier scoring (ROS) approach and its variant. The propose solutions work by adopting conventional outlier scores and are able to apply them to identify conditional outliers in data. Experiments on synthetic and real-world image datasets are conducted to demonstrate the benefits and advantages of the proposed approaches.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.169-177
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• 2011

As demand for efficiency in handling dynamic XML data grows, new dynamic XML labeling schemes have been researched. The key idea of the dynamic XML labeling scheme is to find ancestor-descendent-sibling relationships and to minimize memory space to store total label, response time and range of relabeling incurred by update operations. The prime number labeling scheme is a representative scheme which supports dynamic XML documents. It determines the ancestor-descendant relationships between two elements by a simple divisibility test of labels. When a new element is inserted into the XML data using this scheme, it does not change the label values of existing nodes. However, since each prime number must be used exclusively, labels can become significantly large. Therefore, in this paper, we introduce a novel technique to effectively reduce the problem of label overflow. The suggested idea is based on tree decomposition. When label overflow occurs, the full tree is divided into several sub-trees, and nodes in each sub-tree are separately labeled. Through experiments, we show the effectiveness of our scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.54-57
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• 2007

Much researches have studied for seamless mobility service. Those focused on minimizing the delay time due to the handover. In this paper, we suggest seamless mobility service with the network-based preemptive operations. With these operations, if it's found that the MT(Mobile Terminal)'s handover using L2-trigger event, old access network buffers the delivering data. Therefore this can decrease the data drop rates. And also, this can deal with the ping-pong's phenomenon of MT. At the end of MT's movement, these operations can provide seamless mobility service sending buffered data after checking the MT's movement. This mechanism uses MPLS-LSP(MultiProtocol Label Switching-Label Switched Path) in core network for fast process.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.233-238
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• 2020

Most deep learning (DL) approaches for bridge damage localization based on a structural health monitoring system commonly use supervised learning-based DL models. The supervised learning-based DL model requires the response data obtained from sensors on the bridge and also the label which indicates the damaged state of the bridge. However, it is impractical to accurately obtain the label data in fields, thus, the supervised learning-based DL model has a limitation in that it is not easily applicable in practice. On the other hand, an unsupervised learning-based DL model has the merit of being able to train without label data. Considering this advantage, this study aims to propose and theoretically validate a damage localization approach for bridges using a variational autoencoder, a representative unsupervised learning-based DL network: as a result, this study indicated the feasibility of VAE for damage localization.

• Journal of the Korean Society of Clothing and Textiles
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• v.23 no.2
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• pp.307-313
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• 1999

This study investigated material the type(sewn-in stamped-on etc) of the label and its placement(location on the product) on children's clothing in order to survey consumer complaints to suggest the improvement. The data were collected from label-producing companies by surveying children's clothing displayed at department store as well as by questionnaire to 205 consumers who were mothers of preschool children. The results were as follows : 1. 100% polyester was the most used raw material for brand labels and nylon was for care labels. 2. Most brand labels were one piece labels and located inside the back of neck line by sewn-in either on the top on each sides or on all four sides, Care labels were usually sewn-in on the inside of left-side seam line. The texture of care label was softer than that of brand label and two pieces of care labels were widely used, 3. 67.3% of consumers complained of its stiffness while 36.1% of consumers complained of rough surface and edge 85.4% of consumers complained of an itch caused by brand labels and claimed to detach labels. For care labels 36.6% expressed displeasure of stiffness of labels while 39% complained of annoyance due to too many pieces of labels. 4. Major suggestions from the consumers were change of raw materials and relocation of brand labels. For the care labels changes of material form and type of labels were suggested and one piece of label and smaller size were preferable.

• Journal of the Korea Convergence Society
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• v.11 no.10
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• pp.45-51
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• 2020

Recently, many games provide data related to the users' game play, and there have been a few studies that predict opponent move by combining machine learning methods. This study predicts opponent move using match data of a real-time strategy game named ClashRoyale and a multi-label classification based on machine learning. In the initial experiment, binary card properties, binary card coordinates, and normalized time information are input, and card type and card coordinates are predicted using random forest and multi-layer perceptron. Subsequently, experiments were conducted sequentially using the next three data preprocessing methods. First, some property information of the input data were transformed. Next, input data were converted to nested form considering the consecutive card input system. Finally, input data were predicted by dividing into the early and the latter according to the normalized time information. As a result, the best preprocessing step was shown about 2.6% improvement in card type and about 1.8% improvement in card coordinates when nested data divided into the early.