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Youngjun Yun,Ajeong Choi,Suk Gyu Hahm,Jong Won Chung,Yong Uk Lee,Ji Young Jung,Joo-Young Kim,Jeong-Il Park,Sangyoon Lee,Jaewon Jang IEEE 2017 IEEE electron device letters Vol.38 No.5
<P>In this letter, we demonstrated a high performance organic thin-film transistor using thermally evaporated amorphous phase MoO<SUB>x</SUB> as a hole injection layer between metal electrodes and organic semiconductor. The fabricated organic thin-film transistors showed the field-effect mobility of 7 cm<SUP>2</SUP>/Vs in linear and saturation regimes and an ON/OFF current ratio of 10<SUP>7</SUP>. The MoO<SUB>x</SUB> hole injection layers significantly reduced the injection barrier from metal electrode, resulting in the improvement of ohmic contact properties of a synthesized thiophene-rich heteroacene, dibenzothiopheno [6,5-b:6',5'-f] thieno [3,2-b] thiophene p-type organic semiconductor, as compared with those with single metals. Furthermore, high performance organic thin-film transistors can be successfully realized with Al electrode, which is not suitable for p-type organic semiconductors due to its low work function by introducing a 75-nm-thick MoO<SUB>x</SUB> hole injection layer.</P>
정영준(Youngjun Jung),황현선(Hyunsun Hwang),이창기(Changki Lee) 한국정보과학회 2020 정보과학회논문지 Vol.47 No.9
언어 생성(language generation) 작업에서는 Sequence-to-Sequence 모델을 이용하여 자연어를 생성하는 딥러닝 기반의 모델이 활발히 연구되고 있으며, 기존에 문서에서 핵심 문장만 추출(extractive)하는 방식을 사용하였던 문서 요약 분야에서도 생성(abstractive) 요약 연구가 진행되고 있다. 최근에는 BERT와 MASS 같은 대용량 단일 언어 데이터 기반 사전학습(pre-training) 모델을 이용하여 미세조정(fine-tuning)하는 전이 학습(transfer learning) 방법이 자연어 처리 분야에서 주로 연구되고 있다. 본 논문에서는 MASS 모델을 이용하여 한국어 언어 생성을 위한 사전학습을 수행한 후 이를 한국어 문서 요약에 적용하였다. 실험 결과, MASS 모델을 이용한 한국어 문서 요약 모델이 기존 모델들보다 높은 성능을 보였고, 추가로 MASS 모델에 상대 위치 표현 방법을 적용하여 문서 요약 모델의 성능을 개선하였다. In the language generation task, deep learning-based models that generate natural languages using a Sequence-to-Sequence model are actively being studied. In the field of text summarization, wherein the method of extracting only the core sentences from the text is used, an abstract summarization study is underway. Recently, a transfer learning method of fine-tuning using pre-training model based on large amount of monolingual data such as BERT and MASS has been mainly studied in the field of natural language processing. In this paper, after pre-training for the Korean language generation using MASS, it was applied to the summarization of the Korean text. As a result of the experiment, the Korean text summarization model using MASS was higher performance than the existing models. Additionally, the performance of the text summarization model was improved by applying the relative position representation method to MASS.
전기점화 엔진에서 합성가스 첨가량에 따른 외부공기와 EGR에 의한 희박연소특성 연구
윤영준(Youngjun Yun),최영(Young Choi),강건용(Kernyong Kang) 한국자동차공학회 2007 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
The combustion system with diluted air-fuel mixtures has high-efficiency and exhausts low amount of harmful emissions compared to normal combustion system which uses stoichiometric mixtures. The purpose of this study is, before the upcoming research about reducing harmful emission, to investigate combustion stability, lean misfire limit through COVIMEP and thermal efficiency by the combustion system using ambient air and EGR dilution. In order to expand lean misfire limit, synthetic gas(syngas) which is in general prepared from reforming gasoline was utilized. The major components of syngas are H₂, CO and N₂ gases. The percentage of syngas addition was changed from 0 to 30% in energy fraction, air excess ratio(λ) was changed from 1 to 1.8 and EGR rate was varied up to 30%. As a result, with the increase of syngas addition, combustion stability was improved. And lean misfire limit was expanded to A=1.8, EGR rate=28% when syngas fraction is 30%. Thermal efficiency was improved in high dilution area(λ=1.6, EGR=23%), the case using ambient air dilution was effective in efficiency rise.
김영준 ( Youngjun Kim ),윤종근 ( Jonggeun Yun ),허재혁 ( Jaehyuk Hur ),신재호 ( Jaeho Shin ),강우철 ( Woochul Kang ) 한국정보처리학회 2017 한국정보처리학회 학술대회논문집 Vol.24 No.2
우리나라에 장애인 인구의 10% 정도인 약 25만 명의 사람들이 살아가고 있다[3]. 그러한 분들을 위한 여러 복지와 편의시설이 만들어지고 있지만 아직 도로를 안전하게 다니기에는 미흡한 부분이 많다. 시각장애인들이 좀 더 안전하게 생활을 할 수 있도록 하는 보조 장치를 제안한다. 사용자가 필요한 순간의 모습을 촬영한 뒤 딥 러닝으로 축적된 학습데이터를 이용하여 그 장면을 분석한다. 그 결과를 하나의 문장으로 표현하여 이어폰을 통해 사용자에게 서비스를 제공한다. 지원된 음성 서비스를 통해 시각장애인들이 걸어가는 길에 어떠한 장애물이 있는지 알려주어 위험한 상황에 놓이지 않고 안전하게 길을 걸어 다닐 수 있도록 보조해준다.
Three-Dimensional Optical Encryption of Quick Response Code
Kim, Youngjun,Yun, Hui,Cho, Myungjin The Korea Institute of Information and Commucation 2018 Journal of information and communication convergen Vol.16 No.3
In this paper, we present a three-dimensional (3D) optical encryption technique for quick response (QR) code using computational synthesized integral imaging, computational volumetric reconstruction, and double random phase encryption. Two-dimensional (2D) QR code has many advantages, such as enormous storage capacity and high reading speed. However, it does not protect primary information. Therefore, we present 3D optical encryption of QR code using double random phase encryption (DRPE) and an integral imaging technique for security enhancement. We divide 2D QR code into four parts with different depths. Then, 2D elemental images for each part of 2D QR code are generated by computer synthesized integral imaging. Generated 2D elemental images are encrypted using DRPE, and our method increases the level of security. To validate our method, we report simulations of 3D optical encryption of QR code. In addition, we calculated the peak side-lobe ratio (PSR) for performance evaluation.