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나노피브릴화 셀룰로오스와 마이크로피브릴화 셀룰로오스의 무용매 아세틸화
박성수 ( Seongsu Park ),박병대 ( Byung-dae Park ) 한국목재공학회 2020 한국목재공학회 학술발표논문집 Vol.2020 No.1
Nanocellulose has advantages such as hydrophilicity, low density, high strength property and surface area. But, its hydrophilicity become a problem when it contact with polymer matrix in nanocomposites. So, it is necessary to modify hydrophilicity to hydrophobicity through chemical modification. This work focused on the impact of solvent-free acetylation using iodine and sulfuric acids as a catalyst and compared the properties of acetylated cellulose nanofibrils (CNFs) and cellulose microfibrils (CMFs). Various techniques such as attenuated total reflectance infrared (ATR-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and contact angle (CA) measurement were conducted to obtain degree of substitution (DS), crystallinity, thermal stability, and wettability.
박성수(Seongsu Park),이상진(Sangjin Lee) 한국신뢰성학회 2019 신뢰성응용연구 Vol.19 No.2
Purpose: Studies have shown that the factors of up days of equipment, usage, age, cumulative usage, and operational location can influence the failure rate of equipment. The objectives of this study are to identify and prioritize the factors that influence the failure rate of K1A1 tanks under Korean operational circumstances. Methods: This study formulates a multiple regression model to achieve the objectives described above. We collected field operational data of 146 K1A1 tanks by using DELIIS (Defense Logistics Integrated Information System) over a period of three years from April 2009 to March 2012. Results: Yearly usage of equipment is the most significant factor influencing the failure rate of K1A1 tanks. The next most significant factors are cumulative usage and age. Operational location of equipment is not significant factor in this study, as it was in a previous study. Conclusion: The methodology developed in this study may be applied to other equipment to explain their failure rates under diverse operating scenarios.
강재민,박성수,김윤수,감진규,Kang, Jae Min,Park, Seongsu,Kim, Yun Soo,Gahm, Jin Kyu 한국정보통신학회 2021 한국정보통신학회논문지 Vol.25 No.9
홈 트레이닝을 하는 사람들은 전문적인 대면 지도가 없기 때문에 잘못된 자세로 동작을 하여 신체에 무리가 올 수 있다. 본 연구에서는 자세 예측 모델과 다층 퍼셉트론을 이용하여 사용자의 자세를 교정 해주는 "영상 데이터 기반 동작 분류 및 자세 교정 시스템"을 제안한다. 자세 예측 모델로 뼈대 정보를 예측한 후 심층 신경망을 이용하여 어떤 운동 동작인지를 분류한 뒤, 올바른 관절의 각도를 알려주며 교정이 이루어진다. 이 과정에서 동작 분류 모델의 성능을 향상시키기 위해 연속적인 프레임들의 결과를 고려하는 투표 알고리즘을 적용하였다. 다층 퍼셉트론 기반 모델을 자세 분류 모델로 사용했을 때 0.9의 정확도를 가진다. 그리고 투표 알고리즘을 통해 분류 모델의 정확도는 0.93으로 향상된다. There have been recently an increasing number of people working out at home. However, many of them do not have face-to-face guidance from experts, so they cannot effectively correct their wrong pose. This may lead to strain and injury to those doing home training. To tackle this problem, this paper proposes a video data-based pose classification and correction system for home training. The proposed system classifies poses using the multi-layer perceptron and pose estimation model, and corrects poses based on joint angels estimated. A voting algorithm that considers the results of successive frames is applied to improve the performance of the pose classification model. Multi-layer perceptron model for post classification shows the highest accuracy with 0.9. In addition, it is shown that the proposed voting algorithm improves the accuracy to 0.93.