http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
분리막 공정과 LNG 냉열 및 심냉 증류를 이용한 전자급 고순도 이산화탄소의 분리
고영수(YOUNGSOO KO),장경룡(KYUNGRYONG JANG),김정훈(JUNGHOON KIM),조영주(YOUNGJOO JO),조정호(JUNGHO CHO) 한국수소및신에너지학회 2024 한국수소 및 신에너지학회논문집 Vol.35 No.1
In this paper, a new technology to obtain electronic grade, highly pure carbon dioxide by using membrane and liquefied natural gas (LNG) cold heat assisted cryogenic distillation has been proposed. PRO/II with PROVISION release 2023.1 from AVEVA company was used, and Peng-Robinson equation of the state model with Twu’s alpha function to predict pure component vapor pressure versus temperature more accurately was selected for the modeling of the membrane and cryogenic distillation process. Advantage of using membrane separation instead of selecting absorber-stripper configuration for the concentration of carbon dioxide was the reduction of carbon dioxide capture cost.
딥러닝 자동 분류 모델을 위한 공황장애 소셜미디어 코퍼스 구축 및 분석
이수빈,김성덕,이주희,고영수,송민,Lee, Soobin,Kim, Seongdeok,Lee, Juhee,Ko, Youngsoo,Song, Min 한국정보관리학회 2021 정보관리학회지 Vol.38 No.2
This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media. 본 연구는 공황장애 말뭉치 구축과 분석을 통해 공황장애의 특성을 살펴보고 공황장애 경향 문헌을 분류할 수 있는 딥러닝 자동 분류 모델을 만들고자 하였다. 이를 위해 소셜미디어에서 수집한 공황장애 관련 문헌 5,884개를 정신 질환 진단 매뉴얼 기준으로 직접 주석 처리하여 공황장애 경향 문헌과 비 경향 문헌으로 분류하였다. 이 중 공황장애 경향 문헌에 나타난 어휘적 특성 및 어휘의 관계성을 분석하기 위해 TF-IDF값을 산출하고 단어 동시출현 분석을 실시하였다. 공황장애의 특성 및 증상 간의 관련성을 분석하기 위해 증상 빈도수와 주석 처리된 증상 번호 간의 동시출현 빈도수를 산출하였다. 또한, 구축한 말뭉치를 활용하여 딥러닝 자동 분류 모델 학습 및 성능 평가를 하였다. 이를 위하여 최신 딥러닝 언어 모델 BERT 중 세 가지 모델을 활용하였고 이 중 KcBERT가 가장 우수한 성능을 보였다. 본 연구는 공황장애 관련 증상을 겪는 사람들의 조기 진단 및 치료를 돕고 소셜미디어 말뭉치를 활용한 정신 질환 연구의 영역을 확장하고자 시도한 점에서 의의가 있다.
오성보(Seongbo Oh),김세호(Seho Kim),김호찬(Hochan Kim),부창진(Changjin Boo),안재현(Jaehyun Ahan),고성민(Seoungmin Ko),고영수(Youngsoo Ko) 대한전기학회 2006 대한전기학회 학술대회 논문집 Vol.2006 No.7
Recent earth systems not only make earth resistance decreased by installing earth electrode but also are demanded by earth construction for the protection of human life and equipments through total investigation about circumstances. Layer constructions in Jeju island consist of multi-layer of scoria, rocks and shale except clay layers on the surface, which needs the construction of the coring earth electrode suitable in the condition of the area. For this reason, we've used the coring earth electrode. But the coring earth electrode is expected to slow down the performance of this equipment according to the progress of time changing the effects. It is also applied for the stability of earth system construction and management after the construction work analyzing the condition of the earth system. Therefore, this is actually focused on the analysis on measuring the earth resistance and the soil resistivity that cover the range where the remarkable contrast can be expected to be seen in the layer structures.