• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.15-21
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• 2019

Research in detecting fake information gained a lot of interest after the US presidential election in 2016. Information from unknown sources are produced in the shape of news, and its rapid spread is fueled by the interest of public drawn to stimulating and interesting issues. In addition, the wide use of mass communication platforms such as social network services makes this phenomenon worse. Poynter Institute created the International Fact Checking Network (IFCN) to provide guidelines for judging the facts of skilled professionals and releasing "Code of Ethics" for fact check agencies. However, this type of approach is costly because of the large number of experts required to test authenticity of each article. Therefore, research in automated fake news detection technology that can efficiently identify it is gaining more attention. In this paper, we investigate fake news detection systems and researches that are rapidly developing, mainly thanks to recent advances in deep learning technology. In addition, we also organize shared tasks and training corpus that are released in various forms, so that researchers can easily participate in this field, which deserves a lot of research effort.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.6
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• pp.315-324
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• 2022

The experimental data obtained in a wave flume were analyzed using machine learning techniques to establish a model that predicts the input wave height of the wavemaker based on the waves that have experienced wave shoaling and to verify the performance of the established model. For this purpose, artificial neural network (NN), the most representative machine learning technique, and Gaussian process regression (GPR), one of the non-parametric regression analysis methods, were applied respectively. Then, the predictive performance of the two models was compared. The analysis was performed independently for the case of using all the data at once and for the case by classifying the data with a criterion related to the occurrence of wave breaking. When the data were not classified, the error between the input wave height at the wavemaker and the measured value was relatively large for both the NN and GPR models. On the other hand, if the data were divided into non-breaking and breaking conditions, the accuracy of predicting the input wave height was greatly improved. Among the two models, the overall performance of the GPR model was better than that of the NN model.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.21-30
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• 2024

As the self-driving car market continues to grow, the need for charging infrastructure is growing. However, in the case of a wireless charging system, stability issues are being raised because it requires a large amount of power compared with conventional wired charging. SAE J2954 is a standard for building autonomous vehicle wireless charging infrastructure, and the standard defines a communication method between a vehicle and a power transmission system. SAE J2954 recommends using physical media such as Wi-Fi, Bluetooth, and UWB as a wireless charging communication method for autonomous vehicles to enable communication between the vehicle and the charging pad. In particular, UWB is a suitable solution for indoor and outdoor charging environments because it exhibits robust communication capabilities in indoor environments and is not sensitive to interference. In this standard, the process for building a wireless power transmission system is divided into several stages from the start to the completion of charging. In this study, UWB technology is used as a means of fine alignment, a process in the wireless power transmission system. To determine the applicability to an actual autonomous vehicle wireless power transmission system, experiments were conducted based on distance, and the distance information was collected from UWB. To improve the accuracy of the distance data obtained from UWB, we propose a Single Model and Multi Model that apply machine learning and deep learning techniques to the collected data through a three-step preprocessing process.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.53-61
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• 2020

Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.204-207
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• 2009

최근 건강-의료 분야의 학과 신설 및 전공자가 급격하게 늘어남에 따라 해부학 교육의 질적 향성 및 학습자의 학습효과 증진을 도모하기 위해서는 새로운 강의방식의 도입이 필요하다. 따라서 본 연구는 해부학 과목이 전공필수로 포함되어 있는 강원대학교 2개 학과(간호학과, 스포츠과학부) 전공자들을 대상으로 해부학 강의를 위하여 강원대학교 e-러닝 센터와 함께 가상강의 컨텐츠를 개발 과정에 있어 담당교수의 역할을 분석하고, 정규교육과정에서 적용한 후, 학생들의 설문 조사와 가상강의실 운영 성과를 평가하였다. 해부학 가상강의 컨텐츠와 운영에 대하여 학생들의 만족도와 활용가능성, 난이도, 운영의 적절성에서 긍정적인 평가를 받았으며, 가상강의실 게시판 활동을 통하여 학생들이 자율적이고 능동적인 학습활동을 보여 주었다. 그러나 학생들이 개선점으로 지적한 컨텐츠의 질적 향상 및 관련 자료의 보강은 시급히 개선해야 할 부분으로 나타났다. 따라서 본 연구는 건강-의료 분야의 전공자뿐만 아니라 다양한 분야에서 요구하고 있는 강의수요에 비해 턱없이 부족한 해부학 전공 교수진의 교육 부담을 경감하고 해부학교육에 있어 새로운 지평을 열수 있는 가상강의에 대한 연구로서, 향후 해부학 강의에 있어 충분한 활용가치가 있는 효과적인 교수법이 될 것으로 사료된다.

• Journal of Information Management
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• v.38 no.3
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• pp.137-160
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• 2007

The purpose of this study is to examine the meaning and status of the current service of academic libraries in the aspect of its supporting roles for academic courses. The research methods include an examination of model cases from the U.S.A. and Hong Kong and also an electronic questionnaire survey of 32 academic libraries in Korea(67% response rate). With the result of the research analysis, this study aimed to provide optimal administrative plans in e-learning environment.

• Journal of Engineering Education Research
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• v.22 no.2
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• pp.3-15
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• 2019

This study analyzed the evaluations of instructors and experts on flipped learning-based engineering in order to suggest improvement strategies. This study was conducted with 8 engineering courses which participated in the flipped learning course development project of C university from 2017-2018. As a result of the analysis, the instructors and experts pointed out that the pre-learning was not performed and checked effectively. In this regard, the instructors suggested the students' burden of pre-learning, the lack of understanding about flipped learning, and the experts suggested the lack of instructional strategies to facilitate pre-learning. In addition, the instructors and the experts pointed out that the courses were still instructor-centered. The instructors evaluated that they operated the instructor-led course by themselves. In addition, the experts suggested that there was not enough instructional strategies to activate the learner-centered activities. The number of the students and the lecture room environment that were not appropriate for the learner- centered class were the evaluation opinions of both the instructors and the experts. In addition, the professor suggested the lack of understanding and preparation of the flipped learning of the instructors and the learner as the main opinion, and the experts pointed out that the online learning system and classroom was not linked for pre-learning, classroom learning, and the post-learning. Based on these results, suggestions for improvement of flip learning based engineering course were suggested.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.89-103
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• 2022

The purpose of this study is to explore professors' perception of blended learning. For this purpose, the reflective journals written by 56 university professors was analyzed using the keyword network analysis method. The results of this study are as follows: First, as a result of keyword frequency analysis for the blended learning, the keywords showed the highest frequency in the order of (1) 'instructional design', 'student', 'instructional method', 'learning objective' in the area of learning, (2) 'importance', 'instruction', 'feeling', 'student' in the area of feeling, and (3) 'semester', 'plan', 'weekly', and 'instruction' in the area of action plan. Second, the results of analyzing the degree, closeness centrality, and betweenness centrality of network connection are as follows. (1) The keywords 'instruction', 'instructional method', 'instructional design', and 'learning objective' in the area of learning, (2) the keywords 'instruction', 'importance', and 'necessity' in the area of feeling, and (3) 'instruction', 'plan', and 'semester' in the area of action plan showed high values in degree, closeness centrality, and betweenness centrality. Based on the research results, implications for blended learning and professors' perception were discussed.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.462-475
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• 2022

Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.