폴리케톤/천연섬유 복합재료에 대한 연구

유성환(Sunghwan Ryu),윤정환(Junghwan Yoon),윤주호(Ju-ho Yun),유승을(Seung-eul Yoo) 한국자동차공학회 2017 한국자동차공학회 부문종합 학술대회 Vol.2017 No.5

Automobile interior products are researching and developing automobile parts in three directions of high quality, environment friendly and light weight. As the living standard is improved and passengers stay in the automobile, research on environmentally friendly materials is studied in various aspects In fact. Eco-friendly materials are currently being applied to press forming other than extrusion and injection, and since the natural fiber composite material through injection molding has not been used as an automotive interior product up to now, the test piece is molded through extrusion and injection molding The matrix polymer used in this study was Polyketone (PK or POK) developed by Hyosung. The natural fiber used in this study was Kenaf (lignin removal, lignin The test specimens were prepared by extrusion molding and injection molding using fibers and Sisal (lignin-removing) fibers. The recipe was made before and after the addition of additives (antioxidants, radical formation inhibitors, UV stabilizers) After the optimized mixing ratio was selected, the test specimens were molded for each fiber content, and then the mechanical properties and chemical properties were changed through UTM, FT-IR, DSC, TGA, GCMS and FE-SEM The morphology at the interface between matrix polymer and fiber was analyzed. As a result of the study, it has been studied that the dispersion of physical properties decreases as the additives are added. As the content of fibers increases, the tensile-related properties decrease and flexural-related properties increase. As the content of fibers increases, In order to increase the dispersibility of fiber, it was confirmed by analyzing that fiber is dispersed evenly on the outer surface of specimen when glass bubble is added..

흡연상태에 관한 자가보고 설문의 타당도 평가: 제1기(2009-2011) 국민환경보건기초조사 자료 분석

최욱희,박경화,김현정,류정민,유승도,최경희,김수진,Choi, Wookhee,Park, Kyung Hwa,Kim, Hyun Jeong,Ryu, Jungmin,Yu, Seung Do,Choi, Kyunghee,Kim, Suejin 한국환경보건학회 2014 한국환경보건학회지 Vol.40 No.6

Objectives: The purpose of this study was to assess the validity of self-reported cigarette smoking status and investigate factors associated with the accuracy self-reported and measured urinary cotinine in Korean adults. Methods: We used data from the $1^{st}$ Korean National Environmental Health Survey (2009-2011) among adults aged ${\geq}19$ years (N=6,246). The survey examined self-reported smoking status, and urinary cotinine was regarded as the biomarker of exposure to tobacco smoke. Urinary cotinine was analyzed using a gas chromatography-mass spectrometry (GC/MS) and data analysis was conducted using IBM SPSS version 20.0, which uses the sample weight and calculates variance estimates to adjust for the unequal probability of selection into the survey. Results: We calculated a cut-off point (53.3 ug/L) by using a ROC (Receiver Operating Characteristic) curve. The smoking prevalence was 24.6% based on self-reported data and 28.2% based on urinary cotinine concentrations. When we assessed the agreement between self-reported and urinary cotinine, we found an average agreement of 97.7% among self-reported smokers and 94.5% among self-reported non-smokers. Among self-reported smokers, factors affected the discrepancy were age, household economic status and average number of cigarettes smoked per day. On the other hand, gender, former smoking experience, and exposure to SHS (second hand smoke) were associated with discrepancies among self-reported non-smokers. Conclusion: These results suggest that self-reported data on smoking status provide a valid estimate of actual smoking status. In future research, we will conduct a continuous monitoring study for reliability verification of the data to reduce potential interpretation errors.

비산단지역 환경오염 수준 및 주민의 생체지표 모니터링

김대선,안승철,류정민,유승도,Kim, Dae-Seon,Ahn, Seung Chul,Ryu, Jung Min,Yu, Seung Do 한국환경보건학회 2012 한국환경보건학회지 Vol.38 No.6

Objectives: The main purpose of this study is to produce background data which can be compared with data on vulnerable areas such as industrial complexes in Ulsan, SihwaBanwol, Gwangyang, Yeosu, Pohang, Cheongju and Daesan in Korea. Methods: This study was performed on 1,007 local residents in Gangneung using personal questionnaires and medical check-up. Environmental pollutants including heavy metals in blood and urine were analyzed and the results are as follows. Results: According to the results of medical check-up, 705 subjects were "Normal (A and B)", 232 subjects were "Disease doubtful (R1)" and 70 subjects were "High blood pressure or Diabetes doubtful (R2)". Regarding geometric mean concentration, blood lead was 1.57 ${\mu}g/dL$, urine cadmium was 0.82 ${\mu}g/g-cr$, urine mercury was 0.98 ${\mu}g/g-cr$ and urine arsenic was 15.78 ${\mu}g/g-cr$. In the analysis of 11 kinds of VOCs in blood, vinyl chloride, 1,3-butadiene and dichloroethylene were not detected, while the detection rate of other chemicals was above 70% except chloroform(49.7%) and trichloroethylene(19.0%). In analysis of 16 kinds of PAHs in blood, 10 kinds showed more than 80% in detection rate. Also, detection rate of 4 kinds of PCBs in blood ranged 52 to 78%. Conclusions: Compared with industrial compelxes, the concentration of blood lead was lower, while urine cadmium and mercury levels were similar. Also, urine arsenic ranged at a significant level. Further study is required to find the cause of regional differences in concentrations of environmental pollutants.

비지도학습 기반의 행정부서별 신문기사 자동분류 연구

김현종(Hyun-Jong Kim),유승의(Seung-Eui Ryu),이철호(Chul-Ho Lee),남광우(Kwang Woo Nam) 한국산학기술학회 2020 한국산학기술학회논문지 Vol.21 No.9

행정기관은 정책 대응성을 제고하기 위해 빅데이터 분석에 관심을 기울이고 있다. 빅데이터 중 뉴스 기사는 정책이슈와 정책에 대한 여론을 파악하는데 중요한 자료로 활용될 수 있다. 한편으로 새로운 온라인 매체의 등장으로 뉴스기사의 생산은 급격히 증가하고 있어 문서 자동분류를 통해 기사를 수집할 필요가 있다. 그러나 기존 뉴스 기사의 범주와 키워드 검색방법으로는 특정 행정기관 및 부서별로 업무에 관련된 기사를 자동적으로 수집하는 것에 한계가 있었다. 또한 기존의 지도학습 기반의 분류 기법은 다량의 학습 데이터가 필요한 단점을 가지고 있다. 이에 본 연구에서는 행정부서의 업무특징을 포함한 분류사전을 활용하여 기사의 분류를 효과적으로 처리하기 위한 방법을 제안한다. 이를 위해 행정기관의 업무와 신문기사를 Word2Vec와 토픽모델링 기법으로 부서별 특징을 추출하여 분류사전을 생성하고, 행정 부서별로 신문기사를 자동분류 한 결과 71%정도의 정확도를 얻었다. 본 연구는 행정부서별 신문기사를 자동분류하기 위해 부서별 업무 특징 추출 방법과 비지도학습 기반의 자동분류 방법을 제시하였다는 학문적 · 실무적 기여점이 있다. Administrative agencies today are paying keen attention to big data analysis to improve their policy responsiveness. Of all the big data, news articles can be used to understand public opinion regarding policy and policy issues. The amount of news output has increased rapidly because of the emergence of new online media outlets, which calls for the use of automated bots or automatic document classification tools. There are, however, limits to the automatic collection of news articles related to specific agencies or departments based on the existing news article categories and keyword search queries. Thus, this paper proposes a method to process articles using classification glossaries that take into account each agency"s different work features. To this end, classification glossaries were developed by extracting the work features of different departments using Word2Vec and topic modeling techniques from news articles related to different agencies. As a result, the automatic classification of newspaper articles for each department yielded approximately 71% accuracy. This study is meaningful in making academic and practical contributions because it presents a method of extracting the work features for each department, and it is an unsupervised learning-based automatic classification method for automatically classifying news articles relevant to each agency.

온라인 리뷰 빅데이터 분석을 통한 흰여울문화마을 관광 활성화 방안 연구

이새미 ( Lee Sae-mi ),유승의 ( Ryu Seung-eui ) 한국호텔리조트학회(구 한국호텔리조트카지노산학학회) 2020 호텔리조트연구 Vol.19 No.1

The purpose of this study is to investigate tourists' perceptions by analyzing tourism-related data among social big data, and to derive ideas for using social big data in tourism and promoting the tourism industry. To this end, 1162 tourists' online reviews were collected and analyzed by LDA, a representative topic modeling technique among text mining techniques, and extracted into 7 topics(sightseeing, emotional image 1, purpose of travel, parking dis­comfort, recommendation, architecture, emotional image 2). Topic 7 had the highest weight and consisted of keywords showing the emotional image of tourists. Topic 4 of the seven themes showed tourists' negative feelings about the parking problem. The meaning of this study is as follows. First, by analyzing the comments of tourists, authors suggest ideas for tourism based on the main issues. Second, text mining, which is one of the big data analysis technologies that can quantify reviews, which is unstructured data, is employed to analyze tourist opinions. Third, theoretical and practical implications are suggested based on the analy­sis results of tourist opinions.

비산단 지역주민들의 Skin Prick Test를 통한 알레르기 반응률 조사

김대선(Dae Seon Kim),안승철(Seung Chul Ahn),류정민(Jung Min Ryu),남상훈(Sang Hoon Nam),유승도(Seung Do Yu) 환경독성보건학회 2013 한국독성학회 심포지움 및 학술발표회 Vol.2013 No.5

Bitcoin Price Forecasting Using Neural Decomposition and Deep Learning

Adyan Marendra Ramadhani(마렌드라),Kim Na Rang(김나랑),Lee Tai Hun(이태헌),Ryu Seung Eui(유승의) 한국산업정보학회 2018 한국산업정보학회논문지 Vol.23 No.4

Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.

레이블이 없는 문서 자동분류를 위한 분류체계 생성에 관한 연구

김현종(Hyun-Jong Kim),이강배(Kang-Bae Lee),유승의(Seung-Eui Ryu),홍순구(Soon-Goo Hong) 한국디지털콘텐츠학회 2020 한국디지털콘텐츠학회논문지 Vol.21 No.12

[환경] 업종별 유해성 유기물질의 확인에 대한 연구

박선구(Sun Ku Parkt),김성수(Sung Soo Kim),고오석(O Suk Ko),정성웅(Sung Woong Jung),박준대(Jun Dae Park),유승도(Seung Do Ryu),류재근(Jae Kyun Ryu),조현우(Hyun-Woo Cho) 한국분석과학회 1996 분석과학 Vol.9 No.4

2개 대학병원 응급센터에 내원한 급성 불화수소산 노출 환자에 대한 임상적 경험

한규홍,양중일,조성욱,조용철,유승,이진웅,김승환,유인술,유연호,박정수,Han, Kyu-Hong,Yang, Jung-Il,Jo, Seung-Yook,Cho, Yong-Chul,Ryu, Seung,Lee, Jin-Woong,Kim, Seung-Whan,Yoo, In-Sool,You, Yeon-Ho,Park, Jung-Soo 대한임상독성학회 2009 대한임상독성학회지 Vol.7 No.2

Purpose: We investigated the clinical characteristics and demographics of patients who suffered from hydrofluoric acid chemical injury and the mechanism of damage. Methods: We retrospectively reviewed the medical records of patients who were exposed to hydrofluoric acid from March 2004 to March 2009 and who were seen at the emergency centers in two university teaching hospitals. Results: Forty four patients out of 47 patients suffered from chemical burn, while the injuries of the remaining 3 could not be identified by the medical records. A total of 17 hydrofluoric acid chemical injury patients were enrolled during the study period, and their mean age was $29.6{\pm}7.0$. All the patients were accidentally injured by contact with the material and none of them inhaled or ingested the material. Only 6 patients wore appropriate protective equipments and 5 underwent the water irrigation for more than 10 minutes. The most common exposure area was the hand and forearm (70.5%). Less than 1% of all of the patients had their total body surface (TBS) exposed to hydrofluoric acid (mean=0.35%). The mean time interval from calcium gluconate administration to pain relief was $33.6{\pm}8.8$ hours. Conclusion: When exposed to hydrofluoric acid, it is important to wear protective equipment and undergo water irrigation for more than 10 minutes. Pain and skin damage were observed in all the patients. After treatment, we concluded that administration of calcium gluconate and pain killers was successful in relieving pain, and the prognosis was also positive for the admitted and followed up patients when less than 1% of the TBS was exposed.