TiZrN 코팅의 레이저 침탄에서 탄소 포텐셜에 따른 침입 거동

이병현,김태우,홍은표,김성훈,이희수,Lee, Byunghyun,Kim, Taewoo,Hong, Eunpyo,Kim, Seonghoon,Lee, Heesoo 한국결정성장학회 2021 한국결정성장학회지 Vol.31 No.6

Laser-carburized TiZrN 코팅의 침탄 공정에서 탄소 페이스트 두께에 따른 탄소의 침투 깊이 및 압축잔류응력 변화를 탄소 포텐셜 측면으로 비교·고찰하였다. 스크린 프린팅과 스핀 코팅 방법을 이용하여 각각 1.1 mm와 0.4 mm의 두께로 탄소 페이스트를 도포하고, 동일한 레이저 조사 조건에서 레이저 침탄을 실시하였다. 탄소 페이스트가 두꺼워질수록 침탄된 TiZrN 시료의 회절 패턴이 더 저각으로 이동하였으며, 고용체 강화 및 격자 왜곡의 심화를 나타내었다. TEM을 이용한 미세구조 분석에서도 두꺼운 페이스트로부터 침탄된 TiZrN 내 결정질 결함이 증가하고 높은 탄소 농도를 보였으며, 이는 페이스트 두께가 두꺼워질수록 탄소 포텐셜도 높아짐을 의미하였다. XPS depth profile 분석에서도 두꺼운 페이스트를 통해 침탄된 TiZrN 시료에서 높은 탄소 농도 및 탄화물 형성을 보이면서, 탄소 페이스트 두께 조절에 의해 침탄에서 표면 탄소농도와 탄소 포텐셜 증가가 일어남을 나타내었다. 아울러, 탄소 농도의 증가는 표면의 압축잔류응력 증가(3.67 GPa에서 4.58 GPa로)에 기여하였음을 확인하였다. Penetration depth and compressive residual stress of laser-carburized TiZrN coating by thickness of carbon paste were investigated in terms of carbon potential. The carbon paste was covered with a thickness of 1.1 mm using screen printing, and applied to a thickness of 0.4 mm using spin coating, and laser carburization was performed under the same conditions. As the thickness of carbon paste increased, the diffraction pattern of the laser-carburized TiZrN coating shifted to a lower angle, indicating solid solution strengthening and lattice distortion. For microstructure analysis using TEM, the defects and carbon concentration of the laser-carburized TiZrN coating increased as the carbon paste was thicker. It indicated that the variation of the carbon potential corresponds to the change in the paste thickness. In XPS depth profile analysis, high concentration of carbon and formation of carbide were observed in laser-carburized TiZrN coating with thick carbon paste. It revealed that the carbon concentration on the surface and carbon potential were changed by the thickness control of carbon paste. The compressive residual stress increased from 3.67 GPa to 4.58 GPa by the variation of carbon concentration.

낙동강수계 인공습지의 영양염 저류 효과

이병현(Byunghyun Lee),정인용(Inyong Jeong),김태석(Tae Suk Kim),문보라(Bora Moon),이석모(Suk Mo Lee) 한국생태공학회 2022 한국생태공학회지 Vol.9 No.1

As projects for maintenance and restoration of the river basin ecosystem are implemented, a riverine ecobelt creation project is being promoted to purchase and restore land in the Nakdong River basin. This study analyzed the effects of nutrients retention at two artificial wetlands and one pot seedling restoration area created by the Nakdong River basin's riverine ecobelt creation project. By type of retention, the amount of nutrient stored by plants was the highest, and the amount of nutrient stored by land plants was higher than that of aquatic plants. The total amount of retention was the largest in september in all study sites. As a result of evaluating the nutrients retention effect of each study site, the reduction rate of pot seedling restoration area was the highest. And the result showed that pot seedling restoration area reduces nutrients by 10.19% of nitrogen, 58.89% of phosphorus out of the total nutrients loading, through the only 5.95% area of the total basin area. Considering the Nakdong River basin's riverine ecobelt creation project in terms of the nutrients retention effect, it is more appropriate for that purpose to promote planting-type creation projects such as pot seedling restoration.

Studies on the Chemical Treatment for Vernalization of Chinese Milk Vetch

Byunghyun Lee(이병현),Jinki Jo(조진기),Hangkyun Park(박항균) 한국양봉학회 1989 韓國養蜂學會誌 Vol.4 No.2

An integrated development methodology of low noise accessory drive system in internal combustion engines

박기춘,공진형,이병현,Park, Keychun,Kong, Jinhyung,Lee, Byunghyun The Acoustical Society of Korea 2016 韓國音響學會誌 Vol.35 No.3

자동차의 저소음 보기류 구동 시스템을 개발하는 체계적인 방법론이 전산해석과 리그 실험을 통해 제시되었다. 벨트 구동 소음 예측의 두 가지 난제는 1) 벨트와 풀리 접촉면에서의 스틱-슬립 비선형성과 2) 벨트 구동 시스템과 파워트레인 회전진동계와의 연성이다. 본 연구에서는 최근 개발된 해석 방법을 이용하여 벨트 구동시스템과 엔진 회전진동계를 통합한 해석 모델을 구축하였고, 다양한 파워트레인 운전 조건에서 정합성을 확보하였다. 통합 모델을 이용하여 스틱-슬립 소음이 발생하는 벨트 시스템을 개선할 수 있음을 확인하였다. 또한 새로운 방법론을 통해 신엔진 개념설계에서 NVH (Noise, Vibration and Harshness), 기능, 연비 등을 고려한 개념 설계안을 제시하였다. A systematic development process for the low noise FEAD (Front End Accessory Drive) system is presented by combining CAE (Computer Aided Engineering) and the experimental rig test. In the estimation of the belt drive noise, two main difficulties arise from the high non-linearity due to the stick-slip contacts on the interfaces of the belt and pulleys, and the interaction of the belt drive system with the powertrain rotational parts. In this work, a recently developed analysis method of the belt drive has been employed considering powertrain rotational dynamics. As results, it shows good correlation with the vehicle tests in various operational modes. The established model has been employed to validate the new design improving the stick-slip noise of the problematic FEAD system. Furthermore, the best proposal of FEAD system in terms of functionality [NVH (Noise, Vibration and Harshness), fuel economy, cost. etc.] has been suggested in the concept design stage of new engine through this presented methodology.

E-커머스 사용자의 평점과 리뷰 유용성이 상품 추천 시스템의 성능 향상에 미치는 영향 분석

LIU FAN,이병현(Byunghyun Lee),최일영(Ilyoung Choi),정재호(Jaeho Jeong),김재경(Jaekyeong Kim) 한국지능정보시스템학회 2022 지능정보연구 Vol.28 No.1

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the users past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

사용자의 정성적 선호도와 정량적 선호도를 고려하는 추천 시스템 성능 향상에 관한 연구

이승우(Seungwoo Lee),강경모(Kyungmo Kang),이병현(Byunghyun Lee),이청용(Qinglong Li),김재경(Jaekyeong Kim) 한국경영과학회 2022 經營 科學 Vol.39 No.1

With the recent rapid development of ICT (Information and Communication Technology) and mobile devices, most users receive various types of information. Thus, users would face information overload issues, which takes much time to select products and services they need or prefer. Therefore, a personalized recommender system has become a practical methodology to address such issues. Existing studies mainly utilized quantitative preferences (e.g., star ratings, click). However, such methodology has limitations in that quantitative information can not fully reflect the user"s preference. Therefore, we proposed a novel recommender system methodology that utilized quantitative and qualitative preferences information. To evaluate the performance of the proposed methodology we collected the real-world dataset that contains 771,824 reviews, 648,210 users, and 470 hotels on Tripadvisor.com. The performance of the proposed methodology using quantitative and qualitative preferences information showed better performance than quantitative preferences.

CNN 기반 리뷰 유용성 점수 예측을 통한 개인화 추천 서비스 성능 향상에 관한 연구

이청용(Qinglong Li),이병현(Byunghyun Lee),이흠철(Xinzhe Li),김재경(Jae Kyeong Kim) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.3

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users purchasing decisions. Accordingly, the users information search cost can reduce which can positively affect the companys sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

지속 가능한 비즈니스와 기술 혁신을 위한 스마트 컨설팅 접근법: 국내 건설 업종과 IT 업종 사례 분석

신병규(Byunggyu Shin),이병현(Byunghyun Lee),김기휘(Gihwi Kim),권우철(Woocheul Kwon),김재경(Jaekyeong Kim) 한국경영과학회 2023 한국경영과학회지 Vol.48 No.2

LCOE 저감을 위한 대규모 해상풍력 단지의 설치 공법, 장비 및 발주방안에 대한 연구

정태균(Jung, Taegyun),이병현(Lee, Byunghyun),박현수(Park, Hyeonsoo),박근우(Park, Guenwoo) 대한토목학회 2023 대한토목학회 학술대회 Vol.2023 No.10

MBTI 성격유형을 반영한 심층 신경망 기반 직무 추천 서비스

장예화(Yihua Zhang),이병현(Byunghyun Lee),정재호(Jaeho Jeong),김재경(Jaekyeong Kim) 한국인터넷전자상거래학회 2021 인터넷전자상거래연구 Vol.21 No.4

Recently, as the value of employment information services through which job seekers can obtain information has increased due to difficulties in employment, job portal sites provide job postings and job recommendations suitable for job seekers. Job portal sites that provide these services mainly recommend jobs to job seekers based on the competencies required for the job and do not consider the job seeker"s personality type. However, in several previous studies, it was confirmed that the personality type of job seekers had a significant influence on job satisfaction and performance. In other words, it can be said that personality type acts as an essential factor in job-seekers job selection. Therefore, in this study, a job recommendation methodology reflecting MBTI personality type was proposed to improve the service system provided by existing employment portal sites. For this purpose, we used resume data that includes information about job seekers" educational background, major, age, gender, self-introduction, and the company and job for which they are applying. A personality dictionary was constructed for each MBTI personality type. The MBTI personality type of each job seeker was derived by applying this to the self-introduction text of the resume data. And using the DNN model, a job recommendation system that reflects the personality type of job seekers was built. In addition, as a result of confirming the effectiveness of the methodology proposed in this study through the evaluation index, it was confirmed that the recommendation accuracy was higher when the MBTI personality type was included than when it was not included.