Eunsol Cho

Hanyang University

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Cheol Oh

Hanyang University

Gunwoo Lee

University of Alabama at Birmingham

Yeseo Gu

Hanyang University

Byongho Choe

Jeil Pharmaceutical (South Korea)

Nu Ri Kwon

Jeonbuk Agricultural Research & Extension Services

Hui Rim Park

Hyang-Sik Yoon

Chungbuk National University

Lee Seol Young

Hyun‐Ju Eom

Jeonbuk Agricultural Research & Extension Services

In Jae Kim

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Hanyang University

Korea Advanced Institute of Science and Technology

Jeonbuk Agricultural Research & Extension Services

Korea Transport Institute

Chungbuk National University

Chungnam National University

Seoul Institute

Chung-Ang University

Korea Institute for Advanced Study

Korea Institute of Civil Engineering and Building Technology

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아로니아즙을 첨가한 조청의 품질특성

Hyun-Ju Eom Hui Rim Park Eunsol Cho Nu Ri Kwon Hyang-Sik Yoon

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Quality Characteristics of Jocheong, a Rice Syrup, with Aronia Juice

Journal of the Korean Society of Food Science and Nutrition (2020)

Hyun‐Ju Eom Hui Rim Park Eunsol Cho Nu Ri Kwon Hyang-Sik Yoon

아로니아를 이용하여 고부가가치를 갖는 식품으로 개발하기 위해 전통식품인 조청에 아로니아즙을 첨가하여 항산화 활성이 우수한 조청을 개발하였다. 조청 제조 시, 당화액에 아로니아즙을 비율별로 첨가하여 조청을 제조하였고, 아로니아즙을 첨가하지 않은 조청을 대조구로 하여 품질특성을 조사하였다. 연구결과, 조청의 pH는 아로니아 착즙액 비율이 증가할수록 pH는 낮아졌고, 총산은 아로니아 착즙액이 증가할수록 총산 함량도 유의적으로 증가하였다. 색도 결과, 명도인 L와 황색도인 b값은 아로니아 즙 첨가량이 증가함에 따라 유의적으로 감소하였고, 적색도인 a값은 다른 경향을 보였다. 조청 비점도의 경우 아로니아즙의 함량이 10~50%로 늘어날수록 13~25까지 비점도가 증가하였고 아로니아를 10~20%를 첨가한 조청은 아로니아를 넣지 않은 대조구 보다 약 3배 이상 감소하였다. 가용성 고형물은 모든 원료에서 79~80°Brix를 나타냈으며 환원당의 경우는 샘플 간 경향성이 보이지 않았고 포도당 당량이 높은 샘플은 비점도가 낮게 검출되었다. 생리활성 분석인 총 폴리페놀, 총 탄닌, DPPH와 ABTS의 함량은 농도 의존적으로 아로니아 착즙액 비율이 증가할수록 유의적으로 증가하였다. 따라서 품질분석과 생리활성을 고려했을 때 아로니아 조청 제조 시 당화액에 20%~30%의 아로니아즙을 첨가하는 것이 가장 적절하다고 판단된다.

Brix

ABTS

Fruit juice

10.3746/jkfn.2020.49.10.1115

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Analysis of Intrinsic Factors Leading to Aggressive Driving Behavior to Derive Safety Policy Implications for Bus Drivers

Transportation Research Record Journal of the Transportation Research Board (2022)

Eunsol Cho Su Bin Park Cheol Oh

Significant efforts to develop effective safety policies are essential to prevent bus traffic accidents because city buses accommodate many passengers. An in-depth analysis of causal factors that can be the intrinsic cause of aggressive driving is a first step toward preparing for effective bus safety policies. This study aims to explore traffic safety policy implications and analyze development priorities based on the aforementioned factors including the working environment, health characteristics, human characteristics, living environment, and bus route characteristics. Bus driver survey data obtained by self-reported survey instruments were used to discover factors that affect aggressive driving behavior captured by an in-vehicle data recorder. A binary logistic regression model was established using driver characteristics as independent variables and the level of aggressive driving behavior as the dependent variables. From the model, a total of nine independent variables that have a statistically significant effect on aggressive driving behavior by bus drivers were found. This study then suggested seven bus safety policy implications based on the identified influencing factors. The results of the prioritizing the policy implications show that detailed regulations related to break times for city bus drivers, improvement of company break rooms, and management measures for bus driver mental health and psychological conditions were the top three. The findings of this study would be valuable for the development of effective traffic safety policies that reflect the characteristics of the city bus industry.

Binary logit model

10.1177/03611981221094574

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Thumbnail Image Design to Grab Views in Online Video Platform : Evidence from YouTube

한국경영정보학회 학술대회 (2019)

Eunsol Cho Jiyong Eom Daegon Cho

Thumbnail image is one of the most common forms of video visualization used in online video platforms that let viewers to catch a glimpse of the content before watching the whole video. In recent years, leading online video platforms have adopted the feature of custom thumbnails with which video creators can actively generate or edit images to promote the content. Although prior literatures have revealed visual presentation to significantly influence human attention and purchase decisions, there has been little to no empirical study on whether and which visual features of a video thumbnail are effective in driving more views. In this paper, we conduct an exploratory study on the effect of thumbnail images on video views with a rich data set from the largest video-hosting platform, YouTube. Based on image features extracted from machine-learning models, thumbnail design factors associated with the video views are identified using fixed-effect models.

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Online video

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Riding safety Evaluation of food delivery motor scooters based on Associating Sensor-based riding behavior and road traffic characteristics

Accident Analysis & Prevention (2024)

Yeseo Gu Eunsol Cho Cheol Oh Gunwoo Lee

Seat belt

10.1016/j.aap.2024.107871

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Monitoring Framework for Riding Safety of Delivery Scooters using 100 Naturalistic Riding Study (NRS) Data

Transportation Research Record Journal of the Transportation Research Board (2023)

Eunsol Cho Yeseo Gu Cheol Oh Gunwoo Lee

A traffic safety issue of two-wheeled delivery scooters is emerging because of the rapid increase in demand for food delivery services. In particular, the strict restriction of delivery time leads to aggressive and dangerous riding behavior that causes a high risk of crash occurrence. Systematic traffic safety management is required to effectively prevent crashes of delivery scooters. The objective of this study is to develop a monitoring framework for riding safety that informs when, where, and how serious safety problems occur. High-resolution riding behavior data obtained by an inertial measurement unit sensor installed on delivery scooters, as part of the Korean 100 naturalistic riding study (K-100NRS), were used for developing the methodology. The proposed monitoring framework consists of two components: an unsafe riding event detection algorithm and a method to identify the spatial and temporal identification of riding risks. The ratio of frequency of unsafe events to total riding time for each rider is defined as a monitoring index, which is referred to as the riding risk index in this study. Approximately 95% detection accuracy was achievable by the developed detection algorithm. In addition, the level of riding safety for each rider was evaluated based on the proposed methodology. As an application, a visualization of detected unsafe events was presented for the purpose of riding safety monitoring.

10.1177/03611981231158633

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택시 운수업체의 교통안전수준 영향요인분석 : 운전자의 근무 및 복지 환경을 중심으로

Eunsol Cho ParkJIwon Lee Seol Young Cheol Oh Byongho Choe

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Derivation of riding risk precursors using 100 delivery motor scooter naturalistic riding study

Accident Analysis & Prevention (2023)

Eunsol Cho Yujeong Yun Cheol Oh Gunwoo Lee

Food delivery

10.1016/j.aap.2023.107186

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Evaluation of Road Design Elements Affecting Lateral Driving Safety under High-Speed Driving Conditions

International Journal of Highway Engineering (2021)

Aram Jung Eunsol Cho Jiyong Jang Cheol Oh

10.7855/ijhe.2021.23.5.069

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Prediction of high-risk bus drivers characterized by aggressive driving behavior

Journal of Transportation Safety & Security (2023)

Eunsol Cho Yunjong Kim Seolyoung Lee Cheol Oh

AbstractIdentification of driving behavior is a fundamental to developing effective treatments to address various traffic-related problems. In particular, the driving behavior of city bus drivers is of great interest because the crash severity can become much higher than any other vehicle types due to the larger number of passengers on board. However, there is a lack of effective policy preparation to prevent crashes because of limitations associated with identifying intrinsic factors underlying the cause of traffic crashes based on driving behavior analysis. This study aims to develop a methodology to predict high-risk bus drivers, which can be a baseline in establishing effective bus safety policies. An in-depth questionnaire survey was conducted to collect wellness data to represent intrinsic characteristics used for inputs of the proposed prediction methodology in addition to the aggressive driving behavior data obtained from in-vehicle data recorders. Bus drivers were classified into two groups, normal drivers and risky drivers, based on aggressive driving behavior. The priority of intrinsic factors was determined by a gradient boosting method and further utilized to derive input features of the proposed method. Deep-learning-based neural network models were evaluated to predict risky bus drivers in this study. A model with variables up to 11th priority as inputs was selected as the best model. A classification accuracy of 85% was achievable with the proposed model. The outcome of this study would be valuable in supporting policymaking activities to prevent aggressive driving behavior.Keywords: aggressive driving behaviorartificial neural networkbus driver wellnessgradient boosting methodtraffic safety Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis research was supported by a grant from Transportation and Logistics Research Program funded by Ministry of Land, Infrastructure and Transport of the Korean government (21TLRP-B148683-04).

Boosting

Aggressive driving

10.1080/19439962.2023.2253759

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Citations (1)