• Journal of Multimedia Information System
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• v.9 no.2
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• pp.93-102
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• 2022

Dependency parsing is a decision problem of the syntactic relation between words in a sentence. Recently, deep learning models are used for dependency parsing based on the word representations in a continuous vector space. However, it causes a mislabeled tagging problem for the proper nouns that rarely appear in the training corpus because it is difficult to express out-of-vocabulary (OOV) words in a continuous vector space. To solve the OOV problem in dependency parsing, we explored the proper noun embedding method according to the embedding unit. Before representing words in a continuous vector space, we replace the proper nouns with a special token and train them for the contextual features by using the multi-layer bidirectional LSTM. Two models of the syllable-based and morpheme-based unit are proposed for proper noun embedding and the performance of the dependency parsing is more improved in the ensemble model than each syllable and morpheme embedding model. The experimental results showed that our ensemble model improved 1.69%p in UAS and 2.17%p in LAS than the same arc-eager approach-based Malt parser.

• ETRI Journal
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• v.44 no.4
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• pp.599-612
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• 2022

This study focuses on improving a word embedding model to enhance the performance of downstream tasks, such as those of dialog systems. To improve traditional word embedding models, such as skip-gram, it is critical to refine the word features and expand the context model. In this paper, we approach the word model from the perspective of subword embedding and attempt to extend the context model by integrating various sentence models. Our proposed sentence model is a subword-based skip-thought model that integrates self-attention and relative position encoding techniques. We also propose a clustering-based dialog model for downstream task verification and evaluate its relationship with the sentence-model-based subword embedding technique. The proposed subword embedding method produces better results than previous methods in evaluating word and sentence similarity. In addition, the downstream task verification, a clustering-based dialog system, demonstrates an improvement of up to 4.86% over the results of FastText in previous research.

• Journal of Korean Medicine Rehabilitation
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• v.33 no.3
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• pp.129-134
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• 2023

Objectives The purpose of this study was to review the studies of catgut embedding related to obesity treatment. Methods We searched the papers with key words of obesity and catgut embedding via searching Research Information Sharing Service, DBpia, Koreanstudies Information Service System, Oriental Medicine Advanced Searching Integrated System, Scopus, PubMed. Additional data including study design, study topics, characteristics of participants and treatment, outcomes was extracted from full text of each study. Results There were nine studies about the catgut embedding related to obesity treatment. Five articles were conducted in China, two articles were conducted in Mexico, and two articles was published in Korea. Analysis of seven experimental studies and two observational studies were conducted to describe each research subject, method, and research results. Conclusions More interest and further research will be needed on catgut embedding related to obesity treatment in the Korean medicine to achieve clinical application and to develop treatment protocols for the obesity disease.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3353-3370
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• 2016

The way of combination of Product Perfect Codes (PPCs) is based on the theory of short codes constructing long codes. PPCs have larger embedding rate than Hamming codes by expending embedding columns in a coding block, and they have been proven to enhance the performance of the F₅ steganographic method. In this paper, the proposed modified product codes called MPCs are introduced as an efficient way to embed more data than PPCs by increasing 2^r₂-1-r₂ embedding columns. Unlike PPC, the generation of the check matrix H in MPC is random, and it is different from PPC. In addition a simple solving way of the linear algebraic equations is applied to figure out the problem of expending embedding columns or compensating cases. Furthermore, the MPCs over ℤ₄ have been proposed to further enhance not only the performance but also the computation speed which reaches O(n₁+σ). Finally, the proposed ℤ₄-MPC intends to maximize the embedding rate with maintaining less distortion , and the performance surpasses the existing improved product perfect codes. The performance of large embedding rate should have the significance in the high-capacity of covert communication.

• 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.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1027-1034
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• 2017

In this paper, we propose a new function embedding method that can measure mathematical projections of probability amplitude, probability, average expectation and matrix elements of stationary-state unit matrix at all control operation points of quantum gates. The function embedding method in this paper is to embed orthogonal normalization condition of probability amplitude for each control operating point into a binary scalar operator by using Dirac symbol and Kronecker delta symbol. Such a function embedding method is a very effective means of controlling the arithmetic power function of a unitary gate in a unitary transformation which expresses a quantum gate function as a tensor product of a single quantum. We present the results of evolutionary operation and projective measurement when we apply the proposed function embedding method to the ternary 2-qutrit cNOT gate and compare it with the existing methods.

• Journal of Society of Preventive Korean Medicine
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• v.20 no.3
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• pp.93-113
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• 2016

Objectives : The focus of the review was laid on differences in research pertaining to thread embedding acupuncture between domestic and foreign journals. Methods : We collected 53 Korean articles on thread embedding acupuncture study from 4 Korean article searching sites, and 80 foreign articles from Pubmed. We analyzed the number of the theses according to the publication year, study method, journal, and subject. Results : A total number of 134 thread embedding acupuncture articles were categorized as 100 clinical trials, 14 experimental papers, and 19 literature reviews. Of the collected clinical trials, 55 were case studies, 6 were CCTs and 39 were RCTs. The domestic clinical trials were comprised of 36 case studies, 1 CCT, and 2 RCTs, and foreign clinical trials were comprised of 19 case studies, 5 CCTs, and 37 RCTs. Although only 12 of the 39 domestic clinical trials exclusively treated thread embedding acupuncture to the experimental group, 38 out of 61 foreign clinical trials undertook thread embedding acupuncture as the sole treatment. While the 2 domestic RCTs research had no significant evidence that the experimental group was different from the control group, the experimental group demonstrated better responses than the control group in 31 of the 37 foreign RCT studies. Conclusions : Studies on thread embedding acupuncture are more intensively studied in the foreign field in comparison to the domestic field. Referring to the results from the foreign thread embedding acupuncture studies, domestic use of thread embedding acupuncture should be expanded. Also, more refined research needs to be conducted in the domestic field in order for the Koran medicine to lead the thread embedding acupuncture. This study is limited in that the literature search in the foreign journals were restricted.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.28 no.2
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• pp.45-53
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• 2015

Objective : This study was performed to investigate the effect of Needle-Embedding Therapy on peripheral facial paralysis. Method : We investigated 60 cases of patients with peripheral facial paralysis, and devided patients into two groups : We treated one group by complex korean medical treatment with Needle-Embedding therapy, and did the other group by complex korean medical treatment without Needle-Embedding therapy. Yanagihara grading system at baseline and final were used for evaluating the effect of the treatment. Results : 1. In Needle-Embedding therapy group and non Needle-Embedding therapy group, compared with baseline, at final, Y score was significantly increased.2. At final, there was significant difference in improvement between Needle-Embedding therapy group and non Needle-Embedding therapy group. Conclusions : Needle-Embedding therapy seem to be effective to improve symptoms of peripheral facial paralysis. Further studies will be needed to identify the beneficial of Needle-Embedding therapy on peripheral facial paralysis.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.496-502
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• 2021

Recently, deep speaker embedding approach has been widely used in text-independent speaker verification, which shows better performance than the traditional i-vector approach. In this work, to improve the deep speaker embedding approach, we propose a novel method called group-based speaker embedding which incorporates group information. We cluster all speakers of the training data into a predefined number of groups in an unsupervised manner, so that a fixed-length group embedding represents the corresponding group. A Group Decision Network (GDN) produces a group weight, and an aggregated group embedding is generated from the weighted sum of the group embeddings and the group weights. Finally, we generate a group-based embedding by adding the aggregated group embedding to the deep speaker embedding. In this way, a speaker embedding can reduce the search space of the speaker identity by incorporating group information, and thereby can flexibly represent a significant number of speakers. We conducted experiments using the VoxCeleb1 database to show that our proposed approach can improve the previous approaches.