공립유치원 교사 임용후보자 선정 경쟁시험 교직논술 내용분석: 네트워크 분석을 중심으로

유선영 ( Yu¸ Sunyoung ),박선혜 ( Park¸ Seonhye ),조해연 ( Cho¸ Heayoun ) 미래유아교육학회 2021 미래유아교육학회지 Vol.28 No.4

본 연구는 네트워크 분석을 활용하여 공립유치원 교사 임용후보자 선정 경쟁시험 교직논술의 특성 및 경향성을 분석하는 데 목적이 있다. 이를 위해 2001학년도부터 2021학년도까지 임용시험에 출제된 교직논술 문항을 분석대상으로 선정하였으며, 워드클라우드, 연결망 시각화, 빈도, 연결정도중앙성, 사이중앙성을 제시하고 주요 핵심 단어를 분석하였다. 연구결과 첫째, 1기(2001-2008학년도)에는 교육, 2기(2009-2012학년도)와 3기(2013-2021학년도)에는 교사가 가장 중요한 단어로 나타났다. 1기에서는 교육, 학교, 학습과 관련된 주제에서 2기와 3기에서는 교사에 관련된 주제가 주로 출제되는 것으로 경향이 변화하였다. 교직논술 시험 문항이 교과학습지도와 관련성이 높으며, 이론과 지식을 복합적으로 통합한 형태로 출제되고 있음을 확인할 수 있었다. 둘째, 2001학년도부터 2021학년도까지 임용시험에서 등장 순위가 가장 높은 핵심 키워드 5개는 교사, 유아, 부모, 교육, 유치원이었다. 각각의 단어들은 매 시기별로 반복해 등장하지만, 연결된 단어들을 살펴보면 중점을 둔 방향이 서로 상이하다. ‘교육’ 중심 네트워크는 1기에서 가장 강조되었다가 상대적으로 점차 등장 빈도가 줄어들었고 3기에는 인성이 새롭게 등장하였다. ‘교사’는 2ㆍ3기로 갈수록 빈번하게 출제되는 경향으로 변화하였고, 3기에서는 교사를 중심으로 부모, 유치원, 유아, 교육, 상담 등 단어가 제시되는 등 교사 전문성 향상 관련 단어들이 나타나 교육 현장에서 경험할 상황이 출제되었음을 알 수 있었다. 이러한 결과를 통해 교직논술 시험이 현장을 토대로 고차원적이고 창의적 사고를 평가하는 방향으로 개선이 필요하다는 시사점을 얻었다. This study aimed to analyze the characteristics and trends of the essay type kindergarten teacher appointment examination questions using network analysis. For this purpose, this study used the essay type examination questions that were carried out from the year of 2001 to 2021. The text network analysis was used for investigate the key words, work clouds, frequency, degree centrality, and between centrality. The results were as follows. First, the main key word in the first term(from the year of 2001 to 2008) is ‘education’. The main key words in the second term(from the year of 2009 to 2012) and third term(from the year of 2013-2021) are ‘teacher’. The topics in the first term was related to education, school, and learning and most of the essay questions in the second and third term were related to teacher. Second, the top frequency key words from the year of 2001 to 2021 were teacher, children, parents, education, and kindergarten. The ‘education’ centered network was most emphasized in the first term, but the frequency of its appearance gradually decreased, and the ‘personality’ newly appeared in the third term. The ‘teacher’ appeared frequently in the second and third term, and the words related to improving teacher professionalism were presented in the third term. Through these results, it was suggested that the essay questions need to improve in the direction of evaluating higher and creative thinking based on the education field.

Causal Analyses of Statin to Prevent Liver Disease Progression: A Nationwide Study Using Superlearning Targeted Maximum Likelihood Estimation

Sunyoung Cho(Sunyoung Cho),Heejo Koo(Heejo Koo),Beom Kyung Kim(Beom Kyung Kim),Euna Han(Euna Han) 대한약학회 2024 약학회지 Vol.68 No.1

Many studies have shown that statins reduce the risk of progression to liver cirrhosis (LC) and hepatocellular carcinoma (HCC) among at-risk populations. However, causality has not been proved. This study examined whether statins could prevent LC and HCC in patients with progressive and worsening chronic liver disease, using a robust methodology for causality. Between 2002 and 2013, 52,145 patients with chronic liver diseases were identified from the National Health Insurance Service database in South Korea. The inverse probability weighting (IPW) and superlearning targeted maximum likelihood estimation (TMLE) were used to assess the causality of statin use on the risk of LC and HCC, adjusting for sex, age, comorbidities, and co-medications. Multivariable superlearning TMLE revealed that statin use was associated with reduction in the incidence risk of LC (Marginal odds ratio (MOR) 0.59, 95% confidence interval [CI] 0.50-0.65) and HCC (MOR 0.59, 95% CI 0.50-0.67). Such a protective effect was more evident with atorvastatin and lipophilic statin. This population-based observational study indicated the benefit of statin use, particularly atorvastatin and lipophilic statin, for causally reducing the risk of LC and HCC.

The Effect of Airborne Particulate Matter on Skin Barrier Function in Seoul Analysed As Real World Data

( Sunyoung Cho ),( Nayoung Kim ),( Wonjin Seo ),( Taeryoung Lee ),( Sekyoo Jeong ),( Hyunjung Kim ) 한국피부장벽학회 2018 한국피부장벽학회지 Vol.20 No.2

The skin is the outermost barrier that directly and continuously contacts environment. In order to develop preventive strategies against skin damage, inflammation, and skin aging by the airborne particulate matter (PM) in Seoul Metropolitan area, we have been investigating the deleterious effects of PM on skin barrier, and its underlying mechanisms. Previous studies reported that PM can penetrate the barrier-damaged skin area and induce inflammatory responses in keratinocytes. Once reaching the dermal layer, PM can also inhibit collagen synthesis in dermal fibroblast. However, there are few reports about the direct effects of PM on skin barrier function. In this study, using a newly developed IoT-based at-home device measuring trans-epidermal water loss (TEWL) and stratum corneum hydration (SCH), we investigated the potential correlation between PM and skin barrier function in daily based measurements. Total 26 participants (13 healthy volunteers and 13 atopic dermatitis-diagnosed volunteers) were enrolled for the study and participants were administrated to measure the TEWL and SCH at least once a day for 5 months. During the same period, daily PM concentration, UV irradiation strength, ambient temperature and relative humidity data were also collected and analysed against participants-generated clinical data. As results, while skin barrier function, expressed by TEWL, in healthy volunteers was not affected by PM, impairment of skin barrier by PM was observed in atopic dermatitis patients. These results suggest that PM can aggravate skin barrier function in predisposed skin, such as atopic dermatitis. Since TEWL data can also provide the information about the basal skin barrier condition, daily based TEWL measurement can be used for not only identifying more susceptible groups for PM induced skin damages, but also evaluating the efficacy of various preventive strategies, including cosmetics.

Recognizing Human-Object Interactions via Target Localization

Sunyoung Cho,Jihun Park,Young Sook Shin,Sang-ho Lee 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10

The recognition of human-object interactions is a challenging problem due to the variety of object appearance, body poses, occlusions and the scene layout. The difficulty is particularly pronounced in actions interacting with small and partially occluded objects. Indeed, it is difficult to identify those objects by general object detectors, which makes it hard for accurate recognition of human-object interactions. In order to deal with this challenge, we propose a target prediction model that aims to identify regions relevant to the human-object interactions. Our model predicts the precise target location relating to the specific action by formulating it to a fully convolutional network that enables fine-grained localization. We jointly learn the appearance and location of the target by exploiting the target-specific segmentation information. We show that our target prediction model outperforms state-of-the-art methods in identifying small and occluded objects, and its result can be used to improve the recognition of human-object interactions.

Adversarial Domain Adaptation for Noisy Speech Emotion Recognition

Sunyoung Cho,Soosung Yoon,Hyunseung Song 제어로봇시스템학회 2022 제어로봇시스템학회 국제학술대회 논문집 Vol.2022 No.11

Speech Emotion Recognition (SER) has achieved many great results with deep learning techniques. However, noise discrepancy is still a challenging task due to the distribution shift between training and test data. In this paper, we present a novel approach based on unsupervised domain adaptation method to alleviate the distribution shift problem for noisy SER. Specifically, we apply an adversarial domain adaptation with bridge mechanism to model an intermediate domain for knowledge transfer. We construct a bridge layer by exploiting speech denoising approach to extract the domain-specific noise representation. Experimental results show that our method provides average improvements of 2.29% and 3.88% in weighted and unweighted accuracies over the baseline for SER with various noise settings.

High-Mobility Pyrene-Based Semiconductor for Organic Thin-Film Transistors

Cho, Hyunduck,Lee, Sunyoung,Cho, Nam Sung,Jabbour, Ghassan E.,Kwak, Jeonghun,Hwang, Do-Hoon,Lee, Changhee American Chemical Society 2013 ACS APPLIED MATERIALS & INTERFACES Vol.5 No.9

<P>Numerous conjugated oligoacenes and polythiophenes are being heavily studied in the search for high-mobility organic semiconductors. Although many researchers have designed fused aromatic compounds as organic semiconductors for organic thin-film transistors (OTFTs), pyrene-based organic semiconductors with high mobilities and on–off current ratios have not yet been reported. Here, we introduce a new pyrene-based p-type organic semiconductor showing liquid crystal behavior. The thin film characteristics of this material are investigated by varying the substrate temperature during the deposition and the gate dielectric condition using the surface modification with a self-assembled monolayer, and systematically studied in correlation with the performances of transistor devices with this compound. OTFT fabricated under the optimum deposition conditions of this compound, namely, 1,6-bis(5′-octyl-2,2′-bithiophen-5-yl)pyrene (BOBTP) shows a high-performance transistor behavior with a field-effect mobility of 2.1 cm<SUP>2</SUP> V<SUP>–1</SUP> s<SUP>–1</SUP> and an on–off current ratio of 7.6 × 10<SUP>6</SUP> and enhanced long-term stability compared to the pentacene thin-film transistor.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/aamick/2013/aamick.2013.5.issue-9/am4005368/production/images/medium/am-2013-005368_0007.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/am4005368'>ACS Electronic Supporting Info</A></P>

An Outbreak Associated with Sapovirus GI.3 in an Elementary School in Gyeonggi-do, Korea

Cho Seung-Rye,Yun Su Jung,Chae Su-Jin,Jung Sunyoung,Kim Jong Hwa,Yong Kum Chan,Cho Eul Ho,Choi Wooyoung,Lee Deog-Yong 대한의학회 2020 Journal of Korean medical science Vol.35 No.34

On October 4, 2018, an outbreak of gastroenteritis associated with sapovirus occurred among elementary school students in Gyeonggi-do, Korea. Epidemiologic studies were conducted in a retrospective cohort approach. Using self-administered questionnaires, we collected information on symptoms and food items consumed. Of the 999 subjects, 17 developed patients that met the case definition. The main symptom was vomiting (100%), and the symptomatic age was 6-12 years. Positive samples were identified by conventional reverse transcription polymerase chain reaction for sequencing. They were classified into genotype GI.3 by phylogenetic analysis. This is the first report of an outbreak associated with sapovirus GI.3 in Korea.

Learning Drone-control Actions in Surveillance Videos

Sunyoung Cho,Dae Hoe Kim,Yong Woon Park 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10

We address the problem of recognizing drone-control actions in surveillance videos. The goal is to identify malicious actions involving drone-controller interactions to understand ’who is flying and controlling a drone to attack buildings’ from surveillance videos. The challenge is to learn the difference between actions with similar appearance as the human can perform control-actions with various objects (e.g., drone-controller vs. phone). To deal with this challenge, we propose a new model to learn human-object interaction actions with object context derived by human pose. We incorporates two networks for capturing both human action and its relevant object using Convolutional Neural Networks architecture. We validate our model on our new action dataset which includes drone-control actions, and show that our model outperforms other models.

Feature Refinement via Canonical Correlation Analysis for Multimodal Emotion Recognition

Sunyoung Cho,Kyungchul Wee,Tae Yoon Chun 제어로봇시스템학회 2023 제어로봇시스템학회 국제학술대회 논문집 Vol.2023 No.10

합성 데이터를 통한 부분 가려짐에 강인한 군용 차량 검출

조선영(Sunyoung Cho) 한국정보과학회 2021 정보과학회 컴퓨팅의 실제 논문지 Vol.27 No.11

최근 심층 신경망 기반 객체 검출 기술의 발전에도 불구하고 부분적으로 가려진 객체를 검출하는 것은 여전히 어려운 문제이다. 객체의 외관 및 형태에 대한 제한적인 정보로 인해 가려짐이 있는 객체에 대한 정확한 바운딩 박스를 찾거나 클래스를 구별하는 것이 어렵기 때문이다. 본 논문에서는 가려짐을 갖는 데이터를 합성하여 생성하고, 이를 이용한 모델 학습을 통해 부분 가려짐이 있는 객체의 검출 성능을 향상시키는 방법을 제안한다. 다양한 가려짐 상황을 고려하기 위해 다양한 가려짐 레벨 및 종류에 따라 합성 데이터를 생성한다. 제안하는 방법의 성능을 평가하기 위해 실제 군용 차량에 대한 데이터셋을 수집하였고, 이에 대한 합성 데이터를 생성하여 모델 학습에 활용하였다. 다양한 실험을 통해 합성 데이터를 이용하여 학습한 모델이 부분 가려짐을 갖는 객체 검출 성능을 향상시킴을 보였다. Although advances in object detection are based on deep neural networks, detecting partially occluded objects remains a difficult task. Localizing or classifying objects under partial occlusion is difficult due to limited information about the appearances and shapes of the objects. This paper generates synthetically occluded data and presents a method to improve object detection under partial occlusion by synthetic data. We generated synthetic data with various levels and types of occlusion to consider various occlusion situations. To evaluate our method, we collect a military vehicle dataset and exploit the synthetically occluded data generated by our method for model learning. We show that our model trained with synthetic data improves object detection under partial occlusion through various experiments.