• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.29 no.1
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• pp.103-112
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• 2016

Objective : Thread embedding acupuncture has become popular as a minimally invasive treatment for facial wrinkles and laxity. However, there is little published clinical practice guidelines about disinfection, sterilization and aseptic technique for thread embedding acupuncture. This study is to introducing a specific guidelines about disinfection, sterilization and aseptic technique for thread embedding acupuncture.Method : We reviewed internal regulations and guidelines about hospital infection, and Traditional Korean medicine doctors, nurses, and director of central supply room discussed in depth and established a regulation of disinfection, sterilization and aseptic technique for thread embedding acupuncture.Result : The regulation of disinfection, sterilization and aseptic technique for thread embedding acupuncture consisted of ① management of supplies, ② guidelines of disinfection, sterilization, and reuse, ③ aseptic technique for thread embedding acupuncture.Conclusion : Microbial management is an essential element of medical care and quality. Traditional Korean medicine doctors will care for disinfection, sterilization, and this should not neglect to comply with the procedures and guidelines in the medical field as well as to understand the aseptic techniques.

• Journal of Technologic Dentistry
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• v.35 no.4
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• pp.281-286
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• 2013

Purpose: Surface modification is important techniques in modern dental and orthopedic implants. This study was performed to try embedding of bioactive materials in porous Ti implants. Methods: Porous Ti implant samples were fabricated by sintering of spherical Ti powders in a high vacuum furnace. It's diameter and height were 4mm and 20mm. Embedding process was used to suction and vacuum chamber. Loading properties of porous Ti implants were evaluated by scanning electron microscope(SEM), confocal laser scanning microscope(CLSM), and UV-Vis-NIR spectrophotometer. Results: Internal pore structure was formed fully open pore. Average pore size and porosity were $10.253{\mu}m$ and 17.506%. Conclusion: Porous Ti implant was fabricated successfully by sintering method. Particles are necking strongly each other and others portions were vacancy. This porous structure can be embedded to bioactive materials. Therefore bioactive materials will be able to embedding to porous Ti implants. Bioactive materials embedding in the porous Ti implant will induced new bone faster.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2892-2913
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• 2016

Network virtualization promises to play a dominant role in shaping the future Internet by overcoming the Internet ossification problem. However, due to the injecting of additional virtualization layers into the network architecture, several new security risks are introduced by the network virtualization. Although traditional protection mechanisms can help in virtualized environment, they are not guaranteed to be successful and may incur high security overheads. By performing the virtual network (VN) embedding in a security-aware way, the risks exposed to both the virtual and substrate networks can be minimized, and the additional techniques adopted to enhance the security of the networks can be reduced. Unfortunately, existing embedding algorithms largely ignore the widespread security risks, making their applicability in a realistic environment rather doubtful. In this paper, we attempt to address the security risks by integrating the security factors into the VN embedding. We first abstract the security requirements and the protection mechanisms as numerical concept of security demands and security levels, and the corresponding security constraints are introduced into the VN embedding. Based on the abstraction, we develop three security-risky modes to model various levels of risky conditions in the virtualized environment, aiming at enabling a more flexible VN embedding. Then, we present a mixed integer linear programming formulation for the VN embedding problem in different security-risky modes. Moreover, we design three heuristic embedding algorithms to solve this problem, which are all based on the same proposed node-ranking approach to quantify the embedding potential of each substrate node and adopt the k-shortest path algorithm to map virtual links. Simulation results demonstrate the effectiveness and efficiency of our algorithms.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1391-1402
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• 2021

This paper focuses use context-sensitive spelling error correction using generative adversarial network. Generative adversarial network[1] are attracting attention as they solve data generation problems that have been a challenge in the field of deep learning. In this paper, sentences are generated using word embedding information and reflected in word distribution representation. We experiment with DCGAN[2] used for the stability of learning in the existing image processing and D2GAN[3] with double discriminator. In this paper, we experimented with how the composition of generative adversarial networks and the change of learning corpus influence the context-sensitive spelling error correction In the experiment, we correction the generated word embedding information and compare the performance with the actual word embedding information.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.67-80
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• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.

• Journal of the Korea Fashion and Costume Design Association
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• v.7 no.3
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• pp.7-15
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• 2005

Culture industry is appearing as an important sector of economy. Many kinds of culture industry like movie, music, drama, animation and game are creating enormous wealth all over the world. Fashion is a kind of culture industry too and even sometimes treated as art. Korean fashion is not treated as real culture but still as a part of textile industry. Internationally Korean fashion has not yet much to show, and despite of it's potential it does not attract much interest from other countries. In this paper properties and effects of mulberry handmade paper clothes were investigated with five clothes made of it. In making handmade mulberry paper clothes various techniques could be applied and these techniques could bring new effects. Because mulberry handmade paper does not have little flexibility than ordinary texture, much efforts should be put to the detail works. Handmade mulberry paper clothes have enormous potential as art, because various approach could be applied.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.61-70
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• 2021

This study aimed to analyze the research trends of the abstract data of ergonomic studies registered in MEDLINE, a medical bibliographic database, using word embedding. Medical-related ergonomic studies mainly focus on work-related musculoskeletal disorders, and there are no studies on the analysis of words as data using natural language processing techniques, such as word embedding. In this study, the abstract data of ergonomic studies were extracted with a program written with selenium and BeutifulSoup modules using python. The word embedding of the abstract data was performed using the word2vec model, after which the data found in the abstract were vectorized. The vectorized data were visualized in two dimensions using t-Distributed Stochastic Neighbor Embedding (t-SNE). The word "ergonomics" and ten of the most frequently used words in the abstract were selected as keywords. The results revealed that the most frequently used words in the abstract of ergonomics studies include "use", "work", and "task". In addition, the t-SNE technique revealed that words, such as "workplace", "design", and "engineering," exhibited the highest relevance to ergonomics. The keywords observed in the abstract of ergonomic studies using t-SNE were classified into four groups. Ergonomics studies registered with MEDLINE have investigated the risk factors associated with workers performing an operation or task using tools, and in this study, ergonomics studies were identified by the relationship between keywords using word embedding. The results of this study will provide useful and diverse insights on future research direction on ergonomic studies.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.399-403
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• 2022

Natural texts are analyzed to obtain their intended meaning to be classified depending on the problem under study. One way to represent words is by generating vectors of real values to encode the meaning; this is called word embedding. Similarities between word representations are measured to identify text class. Word embeddings can be created using word2vec technique. However, recently fastText was implemented to provide better results when it is used with classifiers. In this paper, we will study the performance of well-known classifiers when using both techniques for word embedding with Arabic dataset. We applied them to real data collected from Wikipedia, and we found that both word2vec and fastText had similar accuracy with all used classifiers.