• 한국어교육
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• 제28권4호
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• pp.337-366
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• 2017

이 연구는 미국 대학에서 제2외국어로 한국어를 배우는 영어권 학습자의 부사형 조사 '-에'의 다양한 의미론적 뜻의 습득을 고찰한다. 이 연구는 초급반, 중급반, 고급반의 45명 참가자들을 대상으로, 한국어 학습 첫 학기 교실에서 이 조사가 가르쳐 졌을 때 각 단계의 학습자들이 어떻게 그 의미를 해석하며 실제로 어떻게 사용하는가에 대한 수행능력을 중심으로 이루어졌다. 이 연구 결과는 다양한 의미론적 뜻에 대해 서로 다른 발달 과정을 보여주고 있다. 통계 분석 결과에 따르면 초급반과 중급반에서는 이 연구 과제 '-에'의 의미 중 시간과 목표, 정적인 위치적 의미의 습득이 접촉의 의미나, 개별의 의미보다 좀 더 쉽게 습득 된다는 것을 보여주고 있다. 반면에 고급반에서는 개별의 의미를 제외하고는 모든 의미론적인 의미가 거의 목표점까지 도달하였다. 이 연구는 의미론적 복합성과 다른 언어권 간의 영향과 같은 요인과 함께, 제2 언어 빈도수, 언어학적 입력, 습득 방식과 같은 다양한 요인이 '-에'의 습득에 영향을 미친다는 것을 제시하고 있다.

• 대한공업교육학회지
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• 제35권2호
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• pp.124-139
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• 2010

이 연구는 공대생을 대상으로 공학에 대한 태도 변인과 두뇌 우성에 영향을 미치는 관련 변인과의 관계를 연구하는데 그 목적이 있다. 이 연구의 결과는 다음과 같다. 첫째, 공대 학생틀의 공학에 대한 태도는 인지적 요소(3.73), 정의적 요소(3.05), 행동적 요소(2.86)순으로 나타났으며 현실적 맥락에서 공학적인 인지적 능력 배양뿐만 아니라 실험 실습과 같은 행동적 요소를 강화할 수 있는 교수-학습 전략이 필요한 것으로 분석된다. 둘째, 공대 학생들의 두뇌 우성 사고(A유형:분석자, B유형:관리자, C유형:협동자, D유형:합성가)에 따른 공학태도는 유의미한 차이가 나타나지 않았지만, 분석적 사고의 특성을 갖는 A유형의 학생들이 학업 성취도가 높은 것으로 나타났다. 이러한 연구의 결과를 바탕으로 교수-학습적인 측면에서 학생을의 사고 유형에 따른 교수-학습 전략의 변화가 필요하며, 특히, 개별 학습자의 약한 우성적 특성과 사고유형을 개발하기 위하여 교수자의 교수-학습 전략과 실천이 중요하다는 교수자들의 인식변화가 선행되어야 할 것이다.

• 영어어문교육
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• 제13권2호
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• pp.91-108
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• 2007

This study investigates learners' perception on their engagement in rating activities in the EFL essay-writing context. The current study aims to address the answers to the following research questions: 1) What attitude do students show about their participation in the rating tasks? and 2) which of three aspects (e.g. the degree of rating experience, the exposure to English composition instruction and learning, and proficiency level) significantly influences learners' rating activities? 104 EFL learners participated in the rater training session. After participants finished rater training session, they rated three sample essays and peer essays using the given scoring guide. Based on the analysis of survey responses that students made, students showed positive attitude toward their engagement in rating tasks. For research question 2, only L2 writing proficiency seriously affected students' perception on the rating tasks. Advanced level of subjects did not feel stressed by a grade of peers as low level of subjects did. They were also critical about the benefits of self- and peer-assessment, suggesting that a peer's feedback on their own essay was not so useful and that a self-rating does not fully help learners identify their writing proficiency.

• 한국축산식품학회지
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• 제40권2호
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• pp.183-196
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• 2020

The protective effect of pig skin gelatin water extracts (PSW) and the low molecular weight hydrolysates of PSW generated via enzymatic hydrolysis with Flavourzyme® 1000L (LPSW) against scopolamine-induced impairment of cognitive function in mice was determined. Seventy male ICR mice weighing 20-25 g were randomly assigned to seven groups: Control (CON); scopolamine (SCO, 1 mg/kg B.W., intraperitoneally (i.p.); tetrahydroaminoacridine 10 [THA 10, tacrine; 10 mg/kg B.W. per oral (p.o.) with SCO (i.p.)]; PSW 10 (10 mg/kg B.W. (p.o.) with SCO (i.p.); PSW 40 (40 mg/kg B.W. (p.o.) with SCO (i.p.); LPSW 100 (100 mg/kg B.W. (p.o.) with SCO (i.p.); LPSW 400 (400 mg/kg B.W. (p.o.) with SCO (i.p.). All treatment groups, except CON, received scopolamine on the day of the experiment. The oxygen radical absorbance capacity of LPSW 400 at 1 mg/mL was 154.14 μM Trolox equivalent. Administration of PSW and LPSW for 15 weeks did not significantly affect on physical performance of mice. LPSW 400 significantly increased spontaneous alternation, reaching the level observed for THA and CON. The latency time of animals receiving LPSW 400 was higher than that of mice treated with SCO alone in the passive avoidance test, whereas it was shorter in the water maze test. LPSW 400 increased acetylcholine (ACh) content and decreased ACh esterase activity (p<0.05). LPSW 100 and LPSW 400 reduced monoamine oxidase-B activity. These results indicated that LPSW at 400 mg/kg B.W. is a potentially strong antioxidant and contains novel components for the functional food industry.

• 농촌계획
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• 제15권2호
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• pp.111-120
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• 2009

The purpose of this study was to analyze the lifestyles of tourists visiting rural experience tourist destinations and the influential relations of the attributes to affect rural experience tourism. The research findings show that the lifestyles of tourists had significant impacts on their preference for rural experience programs(0.2502/3.0l2). Second, their lifestyles had also significant impacts on the need for rural experience tourist destinations(5.039/3.363). Third, their preference for rural experience programs had significant influences on their intentions for revisits(0.386/3.l60). Fourth, their preference for rural experience programs had significant influences on their intentions for word of mouth(1.448/8.073). Fifth, their need for rural experience tourist destinations had significant impacts on their intentions for revisits(1.940/5.594). And finally, their need for rural experience tourist destinations had no significant influences on their intentions for word of mouth(-1.0611-1.421). According to the analysis results of the regression coefficient of the measuring model, enjoying leisure(1.130/6.775) and pursuing health(1.110/9.001) were large influential factors in lifestyle; pursuing learning(1.47317.946) was the biggest influential factor in preference for rural experience programs; and a natural environment(1.220/8.990) was the biggest influential factor in the need for rural experience tourist destinations.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• 무역학회지
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• 제45권3호
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• pp.55-69
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• 2020

This study examines the research trends and knowledge structure of international trade studies using topic modeling method, which is one of the main methodologies of text mining. We collected and analyzed English abstracts of 1,868 papers of three Korean major journals in the area of international trade from 2003 to 2019. We used the Latent Dirichlet Allocation(LDA), an unsupervised machine learning algorithm to extract the latent topics from the large quantity of research abstracts. 20 topics are identified without any prior human judgement. The topics reveal topographical maps of research in international trade and are representative and meaningful in the sense that most of them correspond to previously established sub-topics in trade studies. Then we conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. We discovered 2 hot topics(internationalization capacity and performance of export companies, economic effect of trade) and 2 cold topics(exchange rate and current account, trade finance). Trade studies are characterized as a interdisciplinary study of three agendas(i.e. international economy, International Business, trade practice), and 20 topics identified can be grouped into these 3 agendas. From the estimated results of the study, we find that the Korean government's active pursuit of FTA and consequent necessity of capacity building in Korean export firms lie behind the popularity of topic selection by the Korean researchers in the area of int'l trade.

• International Journal of Contents
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• 제16권4호
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• pp.50-67
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• 2020

This study investigated whether writing weekly listening logs could influence college English learners' metacognitive awareness and listening proficiency. In addition, the Metacognitive Awareness Listening Questionnaire (MALQ) was applied to examine the learners' knowledge of their listening process. It is process-oriented research conducted by analyzing the MALQ and students' listening logs as to how their metacognitive awareness and listening proficiency have changed during the semester. Eighty-nine students who took an English listening practice course at a university participated in this study. The research findings are as follows. First, it turned out that there was a significant relationship between EFL university students' listening comprehension and some subscales of metacognitive awareness. Second, the students had an opportunity to reflect on learning through regular listening activities, and weekly listening logs, which included important information about listening process and practice. Third, as the students' listening proficiency increased at the end of the semester, it was found that introducing listening logs along with classroom lessons helped the students improve their listening ability. Finally, the high proficiency group students used multiple strategies simultaneously, regardless of the type of listening strategies, while the low proficiency group students used one or two limited listening strategies. However, the low proficiency group students may have had trouble expressing their ideas in English or recognizing the listening strategies they used, not because they did not use a lot of listening strategies. Therefore, teachers should regularly check if students are following their instructions and help them use appropriate strategies for better understanding.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제68차 하계학술대회논문집 31권2호
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• pp.313-315
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• 2023

본 논문에서는 ¹⁸F-FDG PET과 CT에서 추출한 영상인자를 이용하여 비소세포폐암의 전이를 예측하는 머신러닝 모델을 생성하였다. ¹⁸F-FDG는 종양의 포도당 대사 시 사용되며 이를 추적하여 환자의 암 세포를 진단하는데 사용되는 의료영상 기법 중 하나이다. PET과 CT 영상에서 추출한 이미지 특징은 종양의 생물학적 특성을 반영하며 해당 ROI로부터 계산되어 정량화된 값이다. 본 연구에서는 환자의 의료영상으로부터 image texture 프절 전이 예측에 있어 유의한 인자인지를 확인하기 위하여 AUC를 계산하고 단변량 분석을 진행하였다. PET과 CT에서 각각 4개(GLRLM_GLNU, SHAPE_Compacity only for 3D ROI, SHAPE_Volume_vx, SHAPE_Volume_mL)와 2개(NGLDM_Busyness, TLG_ml)의 image texture feature를 모델의 생성에 사용하였다. 생성된 각 모델의 성능을 평가하기 위해 accuracy와 AUC를 계산하였으며 그 결과 random forest(RF) 모델의 예측 정확도가 가장 높았다. 추출된 PET과 CT image texture feature를 함께 사용하여 모델을 훈련하였을 때가 각각 따로 사용하였을 때 보다 예측 성능이 개선됨을 확인하였다. 추출된 영상인자가 림프절 전이를 나타내는 바이오마커로서의 가능성을 확인할 수 있었으며 이러한 연구 결과를 바탕으로 개인별 의료 영상을 기반으로 한 비소세포폐암의 치료 전략을 수립할 수 있을 것이라 기대된다.