Evidence integration on health damage for humidifier disinfectant exposure and legal presumption of causation

Mina Ha(Mina Ha),Taehyun Park(Taehyun Park),Jong-Hyun Lee(Jong-Hyun Lee),Younghee Kim(Younghee Kim),Jungyun Lim(Jungyun Lim),Yong-Wook Baek(Yong-Wook Baek),Sol Yu(Sol Yu),Hyen-Mi Chung(Hyen-Mi Chung) 한국역학회 2023 Epidemiology and Health Vol.45 No.-

OBJECTIVES: Inhalation exposure to humidifier disinfectants has resulted to various types of health damages in Korea. To determine the epidemiological correlation necessary for presuming the legal causation, we aimed to develop a method to synthesize the entire evidence. METHODS: Epidemiological and toxicological studies are systematically reviewed. Target health problems are selected by criteria such as frequent complaints of claimants. Relevant epidemiologic studies are reviewed and the risk of bias and confidence level of the total evidence are evaluated. Toxicological literature reviews are conducted on three lines of evidence including hazard information, animal studies, and mechanistic studies, considering the source-to-exposure-to-outcome continuum. The confidence level of the body of evidence is then translated into the toxicological evidence levels for the causality between humidifier disinfectant exposure and health effects. Finally, the levels of epidemiological and toxicological evidence are synthesized. RESULTS: Under the Special Act revised in 2020, if the history of exposure and the disease occurred/worsened after exposure were approved, and the epidemiological correlation between the exposure and disease was verified, the legal causation is presumed unless the company proves the evidence against it. The epidemiological correlation can be verified through epidemiological investigations, health monitoring, cohort investigations and/or toxicological studies. It is not simply as statistical association as understood in judicial precedents, but a general causation established by the evidence as a whole, i.e., through weight-of-the-evidence approach. CONCLUSIONS: The weight-of-the-evidence approach differs from the conclusive single study approach and this systematic evidence integration can be used in presumption of causation.

A serendipity-oriented music recommendation system using artificial neural networks

Taehyun Ha,Sangwon Lee 대한인간공학회 2014 대한인간공학회 학술대회논문집 Vol.2014 No.5

Recently the interests with culture technologies have been heightened and accordingly many studies for recommendation systems considering individuals’ preferences have been done. As a part of this trend, music recommendation systems have been developed in content-based and collaborative filtering methods. Previous studies have emphasized on technical aspects such as the extraction of music items’ features and the development of comparison algorithms for them. However, there are few studies to develop a music recommendation system based on individual users’ cognitive characteristics. To contribute to this issue, the present study proposes a music recommendation method considering serendipity based on individual users’ play records. Serendipity occurs when users meet familiar music items unexpectedly. The serendipity is determined by the comparison between users’ listening records in the past and the recent days. This is to find music items which users have often listen to in the past but do not anymore, and to recommend new music items similar to them. To apply the serendipity in the recommendation process, we utilize artificial neural network models. The artificial neural network models consist of long-term and short-term models, each of which is used to analyze user’s listening behaviors. Music items in a user’s playlist are used to train the models based on the last played times. The models use music items’ features as input values and play counts (the numbers of play times) as output values. Music items’ features are expressed by MFCC(Mel-frequency cepstral coefficients). After the artificial neural network models are trained, each model allocates the predicted play counts for music items in database. The music items’ serendipity degrees are calculated by difference between the predicted play counts in long-term model and short-term model. A high serendipity degree of item means users would satisfy the item with high probability. Finally, the music items in database are appeared in the recommendation list according to the serendipity degrees. The music recommendation method suggested in this study is a new approach based on individual users’ listening habits and preferences. It is expected that this method can improve users’ satisfaction with recommended items.

A Serendipity-Oriented Music Recommendation System Using Artificial Neural Networks

( Taehyun Ha ),( Sangwon Lee ) 한국감성과학회 2014 춘계학술대회 Vol.2014 No.-

Recently the interests with culture technologies have been heightened and accordingly many studies for recommendation systems considering individuals`preferences have been done. As a part of this trend, music recommendation systems have been developed in content-based and collaborative filtering methods. Previous studies have emphasized on technical aspects such as the extraction of music items` features and the development of comparison algorithms for them. However, there are few studies to develop a music recommendation system based on individual users` cognitive characteristics. To contribute to this issue, the present study proposes a music recommendation method considering serendipity based on individual users` play records. Serendipity occurs when users meet familiar music items unexpectedly. The serendipity is determined by the comparison between users`listening records in the past and the recent days. This is to find music items which users have often listen to in the past but do not anymore, and to recommend new music items similar to them. To apply the serendipity in the recommendation process, we utilize artificial neural network models. The artificial neural network models consist of long-term and short-term models, each of which is used to analyze user`s listening behaviors. Music items in a user`s playlist are used to train the models based on the last played times. The models use music items`features as input values and play counts (the numbers of play times) as output values.

Alternating Current(AC) Corrosion Analyzed by Electrochemical Impedance Spectroscopy

Ha, TaeHyun,Kim, DaeKyeong,Bae, JeongHyo,Lee, HyunGoo,Lee, SungJin 한국부식방식학회 2002 Corrosion Science and Technology Vol.31 No.6

So far, many research results on AC corrosion have been reported but each one is not consistent with another. In order to understand the characteristics and factors affecting on AC corrosion, Electrochemical impedance spectroscopy (E.I.S.) was used and changes in kinetics and surface properties was analyzed. Generally, E.I.S. test has been used mainly for the diagnosis of the concrete corrosion and coating material. However, considering the outstanding functions of E.I.S. test, it can be adopted as a good method to study AC corrosion. Electrolyte resistance (R_(sol)), double layer capacitance (C_(dl)) and polarization resistance (R_p) are the basic circuit elements. Using the model which is consist of these basic elements, various results of E.I.S. test can be interpreted. And, through this method the mechanism and characteristics of AC corrosion can be explained.

A study on Emotional Assessment by Interior lighting color of Automobile

TaeHyun Ha,Jinsook Lee,Shin Nam 한국색채학회 2011 한국색채학회 학술대회 Vol.2011 No.10

The interior design was emerged to determine the merchantability of Automobile due to the quality enhancement and diversification of Automobile consumer Needs. Especially, the interior lighting was one of the main factors being obtained the various emotional effects for the image of Automobile. This study thus focused to evaluate the various emotional images that the users felt by the lighting color of Automobile Interior. As this results, the warm color series like as Orange and Red was highly evaluated in the image of 「Luxury·profound」, and the white blue series was highly evaluated in the image of 「Leading-edge」 and 「Gorgeous」. Type B, D and E not integrated with Cluster and Button types highly evaluated in the image of 「Changeable」 In addition, it seemed to be considered Design types followed each material and Button types of Interior skin and factors with the zoning in the further study. Therefore, this study will be presented the basic materials for the study conducted in the future.

Reciprocal nature of social capital in Facebook: an analysis of tagging activity

Ha, Taehyun,Han, Seunghee,Lee, Sangwon,Kim, Jang Hyun Emerald (MCB UP ) 2017 ONLINE INFORMATION REVIEW Vol.41 No.6

Decision Affect Theory에 근거한 사용자 행동 모델 수립과 적용 예시

하태현(Taehyun Ha),이상원(Sangwon Lee) 대한인간공학회 2013 대한인간공학회 학술대회논문집 Vol.2013 No.10

제품의 올바른 설계와 효과적인 사용을 유도하기 위해서는, 제품과 사용자간 상호작용에 근거한 인간행동모델에 대한 연구가 반드시 필요하다. 인간행동모델에 대한 기존 연구는 다양한 분야에서 연구되어 왔으나, 일치된 기준이 마련되지 않아 하나의 관점에서 바라보는데 한계점이 있었다. 또한 제품과 사용자 상호작용 관점에서 다뤄져야 할 행동유도성과, 사용의 결과로 발생되는 감정적 반응에 대해 중심적으로 다루고 있는 연구도 찾아보기 힘든 실정이다. 이에 본 연구에서는 Decision Affect Theory를 바탕으로 행동유도성과 사용자 관심체계, 감정적 반응을 고려한 인간행동모델을 제시하고자 한다. 본 연구에서 우리는 제시 모델을 웹 디자인에 근거한 페이지 간 이동 방법 결정 과정을 예시로 들어 적용하고자 한다. 아울러, 추후 실제적인 조건을 반영한 실험이 진행된다면 모델의 타당성과 정확도를 높일 수 있을 것으로 기대한다.

딥러닝과 LDA 모델링을 통한 AI 분야 장기특허 예측

하태현 ( Taehyun Ha ),이재민 ( Jae-min Lee ),이창환 ( Chang-hoan Lee ),고병열 ( Byoung-youl Coh ) 정보통신정책학회 2021 정보통신정책연구 Vol.28 No.1

본 논문에서는 최근 10년간 등록된 인공지능과 기계학습 분야 특허들을 대상으로 향후 20년간 특허 권리가 유지될 장기특허를 판별, 예측하고 이들의 내용을 LDA 모델링으로 분석하여 인공지능 분야의 기술정책 방향을 제시한다. 딥러닝 모델을 통해 약 16만 건의 미국 특허청 등록 특허의 장기특허 여부를 학습하였으며, 학습된 모델을 3,281개의 인공지능과 기계학습 분야 특허들에 적용하여 장기특허로 예측되는 2,004개의 특허를 판별하였다. 도출된 2004개의 장기특허에 대한 LDA 모델링을 수행하였으며, 장기전략적으로 중요해질 6개의 주요 토픽들을 확인하였다. 또한, 기술통계치와 통계 분석을 통해 인공지능분야 내 장기특허와 단기특허 간 차이점에 대해 알아보았으며, LDA 토픽 모델링을 통해 도출된 결과와 함께, 향후 인공지능 분야에서 고려되어야 할 정책적 함의들에 대해 종합적으로 논의하였다. This study predicts the long-term continuance of patents and analyzes their content based on deep-learning and latent Dirichlet allocation modeling. To predict the long-term continuance of patents, we develop a deep-learning model based on 160 thousand patents submitted to the United States Patent and Trademark Office. The model is applied to 3,281 patents for artificial intelligence of which 2,004 are predicted to remain registered long-term. The long-term patents are analyzed using the latent Dirichlet allocation modeling, and are contrasted with short-term patents. The analysis leads to the discovery of six major topics associated with log-term patents. Several policy implications are drawn.

재생 정보 기반 우연성 지향적 음악 추천에 관한 연구

하태현(Taehyun Ha),이상원(Sangwon Lee) 대한산업공학회 2015 대한산업공학회지 Vol.41 No.2

우연성에 근거한 사용자 중심적 음악 추천 방식 제안

하태현(Taehyun Ha),이상원(Sangwon Lee) 한국HCI학회 2014 한국HCI학회 학술대회 Vol.2014 No.2

문화 기술의 발달과 더불어, 사용자의 요구에 맞는 아이템을 추천해 주기 위한 방법은 다양한 분야에서 연구되고 있다. 특히, 음악에 대한 추천 시스템은 음악이 가진 특징을 다양한 관점에서 분석하고 추천 성능의 정확도를 높이기 위한 방향으로 개발 되어 왔다. 그러나 그러한 시스템이나 방식은 사용자를 이해하고 인지적 특성을 반영하는 데에 있어 여전히 제한적이라고 할 수 있다. 이에 본 연구는 익숙한 것을 우연한 계기로 마주쳤을 때 사용자 선호도가 향상 될 것에 착안하여, 우연성에 근거한 음악 추천 방식을 제안하고자 한다. 이 방식은 사용자의 재생기록 내 음악 아이템들의 “재생기대치”를 계산하고 이에 대한 역수를 우연성 정도로 표현한다. 우연성 정도는 사용자의 음악 아이템들과 데이터베이스 내 음악 아이템들 간 유사도에 근거한 추천 음악 목록 내 특정 아이템의 비중을 정하는 데에 활용된다. 제안하는 방식은 사용자 개인의 재생 기록에 근거하여 우연성을 고려함으로써, 사용자가 추천된 음악 아이템들에 대해 보다 높은 선호도를 가지도록 유도할 것으로 기대된다. With the development of culture technologies, many studies to recommend the items satisfying users’ needs are being done in different domains. Especially, music recommendation systems have been developed in the direction of analyzing the various features of music and improving the recommendation performances. However, those systems still have limitations in understanding users and reflecting their cognitive characteristics. In this sense, our study proposes a music recommendation method based on serendipity, considering that user preferences can be improved when a familiar item is exposed by chance. This method calculates the “play expectations” of items in a user’s playlist, and expresses the serendipity degrees as the reciprocals of those expectation values. The serendipity degrees are utilized in constructing the recommendation list, which is based on the similarities between items of a user’s playlist and of the database. It is expected that the proposed method would induce users to be more satisfied with recommended music items, with the consideration of serendipity based on those users’ play histories.