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Ho-yeon Park(박호연),Kyoung-jae Kim(김경재) 한국컴퓨터정보학회 2023 韓國컴퓨터情報學會論文誌 Vol.28 No.12
본 연구에서는 딥러닝 기법과 정서적 AI를 적용하여 사용자의 감정 상태를 추정하고 이를 추천과정에 반영할 수 있는 추천 시스템에 대한 새로운 연구 프레임워크를 제안한다. 이를 위해 분노, 혐오, 공포, 행복, 슬픔, 놀람, 중립의 7가지 감정을 각각 분류하는 감정분류모델을 구축하고, 이 결과를 추천 과정에 반영할 수 있는 모형을 제안한다. 그러나 일반적인 감정 분류 데이터에서는 각 레이블 간 분포 비율의 차이가 크기 때문에 일반화된 분류 결과를 기대하기 어려울 수 있다. 본 연구에서는 감정 이미지 데이터에서 혐오감 등의 감정 개수가 부족한 경우가 많으므로 데이터 증강을 이용한다. 마지막으로, 이미지 증강을 통해 데이터 기반의 감정 예측 모델을 추천시스템에 반영하는 방법을 제안한다. In this study, we propose a novel research framework for the recommendation system that can estimate the users emotional state and reflect it in the recommendation process by applying deep learning techniques and emotion AI (artificial intelligence). To this end, we build an emotion classification model that classifies each of the seven emotions of angry, disgust, fear, happy, sad, surprise, and neutral, respectively, and propose a model that can reflect this result in the recommendation process. However, in the general emotion classification data, the difference in distribution ratio between each label is large, so it may be difficult to expect generalized classification results. In this study, since the number of emotion data such as disgust in emotion image data is often insufficient, correction is made through augmentation. Lastly, we propose a method to reflect the emotion prediction model based on data through image augmentation in the recommendation systems.
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박호연(Ho-yeon Park),김경재(Kyoung-jae Kim) 한국지능정보시스템학회 2021 지능정보연구 Vol.27 No.2
If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technologys development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the users preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtubes recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, like and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the sellers perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumers perspective. In this paper, to improve the accuracy problem for appropriate recommendation to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This studys predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvan
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Ho Yeon Park(박호연),Donggyu Ahn(안동규),Soon Young Kim(김순영) 한국생명과학회 2021 생명과학회지 Vol.31 No.3
Oryzalin은 미세소관의 형성을 억제하는 dinitroaniline계 제초제이다. Oryzalin은 튜불린에 결합해 식물의 미세소관 배열을 무질서하게 하여 식물 세포의 비등방성 성장을 억제한다. 미세소관과 미세섬유는 세포벽을 구성하고 columella 세포에서 녹말체 침강에 관여하는 세포골격이다. 녹말체는 뿌리 끝에 있는 columella 세포에서 중력을 인지하여 물과 무기염류를 흡수하기 위하여 토양 속으로 자라도록 한다. 식물세포에서 미세소관의 배열은 에틸렌 수준에 따라 조절된다. Oryzalin이 ACC synthase와 ACC oxidase를 활성화시켜 에틸렌 생성을 촉진한다고 알려졌다. 또한 oryzalin은 농도에 의존적으로 뿌리 생장과 굴중성 반응을 억제한다고 보고 되었다. 이 결과에 따라, 본 연구는 Arabidopsis 뿌리에서 이 억제 효과가 에틸렌 생성 억제제인 10<SUP>-4</SUP> M cobalt ions과 10<SUP>-8</SUP> M aminoethoxyvinylglycine (AVG)를 처리하여 회복될 가능성에 초점을 두었다. 뿌리 생장과 굴중성 억제는 cobalt ions과 AVG에 의해 10-20% 회복되었다. 이 결과는 뿌리 생장과 굴중성 반응은 에틸렌의 수준에 따라 조절될 가능성을 제시하였다. Oryzalin is a herbicide that disrupts the arrangement of microtubules by binding to tubulin, thereby blocking the anisotropic growth of plant cells. Microtubules and microfilaments are cytoskeleton components that have been implicated in plant growth through their influence on the formation of cell walls. Microtubules also play roles in the sedimentation of amyloplasts in the root tip columella cells; this sedimentation is related to gravity sensing and results in downward root growth in the soil for absorption of water and minerals. However, the orientation of microtubules changes depending on the level of ethylene in plant cells. A recent study reported that oryzalin stimulated ethylene production via 1-aminocyclopropane-1-carboxylic acid (ACC) synthase and ACC oxidase and caused a concentration-dependent inhibition of root growth and gravitropic responses. The aim of the present study was to investigate the possibility that oryzalin-induced inhibition might be recovered by the application of inhibitors of ethylene production, such as 10<SUP>-4</SUP> M cobalt ions and 10<SUP>-8</SUP> M aminoethoxyvinylglycine (AVG). The inhibition of root growth and gravitropic response was overcome by 10-20% by an 8 hr treatment with cobalt ions or AVG. These results suggest that ethylene levels could regulate root growth and gravitropic responses in Arabidopsis.
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H2SO4 Chemical 공급장치 내 배관 중성화 Flushing 방안 연구
현지태(Ji-Tae Hyeon),박호연(Ho-Yeon Park),류충렬(Choong-Ryul Ryou) 대한전자공학회 2022 대한전자공학회 학술대회 Vol.2022 No.11
본 논문에서는 H2SO4 Chemical의 배관 중성화 Flushing 방안에 대해 새로운 접근으로 비눗물을 이용하여 기존보다 효율적인 중성화 Flushing 방안을 확인하였다. 기존 DIW(De-Ionized Water : 초순수물)를 이용한 Flushing 방안은 중성화 진행 속도가 현저히 늦고, 작업 간 Chemical 접액 및 Fume에 노출되기 쉬운 문제점이 있다. 기존 방안과 비눗물을 이용한 중성화 진행 차이를 비교하고 실제 배관 내의 중성화 정도를 확인하여, H2SO4 Chemical 공급장치 내 배관 중성화 Flushing의 환경안전 측면 개선과 작업 간 업무량 및 Risk를 감소시켜 효율적인 중성화 Flushing 방안 수립에 기여하고자 한다. In this paper, we try a new approach to the neutralization flushing method of H2SO4 Chemical used in the semiconductor manufacturing process. By comparing the difference with DIW by using soapy water instead of DIW (De-Ionized Water: ultrapure water) for neutralizing piping and checking the degree of neutralization in the actual piping, improvement of environmental safety aspects between piping neutralization and flushing in the H2SO4 chemical supply device and It aims to contribute to the establishment of an efficient neutralization flushing plan by reducing the workload and risk between neutralization operations..
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소셜 네트워크 분석과 토픽모델링을 활용한 국내 ESG 연구동향
문건두(Gun-Doo Moon),김경재(Kyoung-Jae Kim),박호연(Ho-Yeon Park) 한국무역연구원 2022 무역연구 Vol.18 No.6
Purpose – The purpose of this study is to derive ESG-related trends covered in academic papers and newspaper articles through social network analysis and topic modeling. Design/Methodology/Approach – We used the data of 249 academic journals and 16,232 press articles for analysis. Findings – The results of this study show trends from various perspectives by analyzing papers and articles separately. Accordingly, it was confirmed that domestic ESG research is gradually expanding from social responsibility to environmental obligations. Research Implications – This study is meaningful in that the overall domestic trends in ESG management, a strategy for sustainable management in this era, were identified separately from academic studies and articles. It is effective in sharing the general knowledge of companies, and through the results, future complementary points for corporate competitiveness can also be discussed. It can also be referred to as basic data to identify and understand domestic ESG research topics, and to develop insights and directions for follow-up studies in sustainable management studies.
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