• Journal of Multimedia Information System
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• 제9권2호
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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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• 제44권4호
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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.

• 한방재활의학과학회지
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• 제33권3호
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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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• 제10권7호
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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.

• 한방안이비인후피부과학회지
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• 제29권1호
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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.

• 한국전자거래학회지
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• 제26권3호
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• pp.67-80
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• 2021

그래프 표현 학습을 위한 노드 임베딩 기법은 그래프 마이닝에서 양질의 결과를 얻는 데 중요한 역할을 한다. 지금까지 대표적인 노드 임베딩 기법은 동종 그래프를 대상으로 연구되었기에, 간선 별로 고유한 의미를 갖는 지식 그래프를 학습하는 데 어려움이 있었다. 이러한 문제를 해결하고자, 기존 Triple2Vec 기법은 지식 그래프의 노드 쌍과 간선을 하나의 노드로 갖는 트리플 그래프를 학습하여 임베딩 모델을 구축한다. 하지만 Triple2Vec 임베딩 모델은 트리플 노드 간 관련성을 단순한 척도로 산정하기 때문에 성능을 높이는데 한계를 가진다. 이에 본 논문은 Triple2Vec 임베딩 모델을 개선하기 위한 그래프 합성곱 신경망 기반의 특징 추출 기법을 제안한다. 제안 기법은 트리플 그래프의 인접성 벡터(Neighborliness Vector)를 추출하여 트리플 그래프에 대해 노드 별로 이웃한 노드 간 관계성을 학습한다. 본 논문은 DBLP, DBpedia, IMDB 데이터셋을 활용한 카테고리 분류 실험을 통해, 제안 기법을 적용한 임베딩 모델이 기존 Triple2Vec 모델보다 우수함을 입증한다.

• 한국전자통신학회논문지
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• 제12권6호
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• pp.1027-1034
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• 2017

본 논문은 양자게이트의 모든 제어 동작점에서 양자들의 확률진폭, 확률, 평균 기댓값 및 정상상태 단위행렬의 행렬요소 등을 수학적 투사로 측정할 수 있는 새로운 함수 임베딩 방법을 제안하였다. 본 논문의 함수 임베딩 방법은 디랙 기호와 크로네커델타 기호를 사용해 각 제어 동작점에 대한 확률진폭의 직교 정규화조건을 2진 스칼라 연산자에 임베딩 한 것이다. 이와 같은 함수 임베딩 방법은 양자게이트 함수를 단일양자들의 텐서 곱으로 표현하는 유니터리 변환에서 유니터리 게이트의 산술 멱함수 제어에 매우 효과적 수단임을 밝혔다. Ternary 2-qutrit cNOT 게이트에 본 논문이 제안한 함수 임베딩 방법을 적용했을 때의 진화연산과 투사측정 결과를 제시하고, 기존의 방법들과 비교 검토하였다.

• 대한예방한의학회지
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• 제20권3호
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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.

• 한방안이비인후피부과학회지
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• 제28권2호
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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.

• 한국음향학회지
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• 제40권5호
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• pp.496-502
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• 2021

딥러닝 기반의 심층 화자 임베딩 방식은 최근 문장 독립 화자 검증 연구에 널리 사용되고 있으며, 기존의 i-vector 방식에 비해 더 좋은 성능을 보이고 있다. 본 연구에서는 심층 화자 임베딩 방식을 발전시키기 위하여, 화자의 그룹 정보를 도입한 그룹기반 화자 임베딩을 제안한다. 훈련 데이터 내에 존재하는 전체 화자들을 정해진 개수의 그룹으로 비지도 클러스터링 하며, 고정된 길이의 그룹 임베딩 벡터가 각각의 그룹을 대표한다. 그룹 결정 네트워크가 각 그룹에 대응되는 그룹 가중치를 출력하며, 이를 이용한 그룹 임베딩 벡터들의 가중 합을 통해 집합 그룹 임베딩을 추출한다. 최종적으로 집합 그룹 임베딩을 심층 화자 임베딩에 더해주어 그룹기반 화자 임베딩을 생성한다. 이러한 방식을 통해 그룹 정보를 심층 화자 임베딩에 도입함으로써, 화자 임베딩이 나타낼 수 있는 전체 화자의 검색 공간을 줄일 수 있고, 이를 통해 화자 임베딩은 많은 수의 화자를 유연하게 표현할 수 있다. VoxCeleb1 데이터베이스를 이용하여 본 연구에서 제안하는 방식이 기존의 방식을 개선시킨다는 것을 확인하였다.