• 한국컴퓨터정보학회논문지
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• 제28권3호
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• pp.167-175
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• 2023

코로나19 상황에서 대면과 비대면 수업 형태에 따른 대학생의 취득학점과 성적 평점 평균점수가 어떻게 변화되었는지를 연구하게 되었다. 본 연구를 위하여 액세스 데이터베이스를 이용하여 성적데이터를 추출하였다. 연구대상으로 3학기 동안에 152명의 학생들을 대상으로 비대면 수업과 대면 수업에 참여한 학생들의 취득학점 점수와 평점평균값, 중간고사, 기말고사, 과제물 점수, 출석점수를 비교 분석하였다. 분석방법으로는 독립표본 t검정 통계처리를 하였다. 대면반 학생의 취득학점과 평균평점 점수가 더 우수하다는 결론을 얻을 수 있었다. 그 결과 대면반 학생들이 비대면반 학생들의 취득학점 보다 4.39점 높게 나타났고, 평균평점 값이 0.6642점 높게 나타남을 실험 결과를 통하여 알 수 있었다. 비교분석 결과 통계적으로 유의하게 나타났으며, 대면반 수업은 평균 21.22, 비대면반 수업은 16.83점으로 비대면반보다 대면반수업의 학점 취득 점수가 상대적으로 높은 평균 점수를 나타내었다. 결론적으로 대면 학생들의 성적이 비대면 학생의 성적보다 전반적으로 더 높았고, 수업에 참여도가 대면 학생들이 더 높게 나타났음을 확인 할 수 있었다.

• International Journal of Advanced Culture Technology
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• 제10권3호
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• pp.226-236
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• 2022

We compared and analyzed the changes in students' science grades and their effects on science grades by level (upper, middle, and lower) according to non-face-to-face and face-to-face teaching-learning. 66 students from A Middle School in Gyeongsangnam-do were selected for the study. As a result of analyzing the change in science grades according to the teaching-learning type, the average score of science grades by non-face-to-face teaching-learning was lower than the corresponding score of science grades of face-to-face teaching-learning. As a result of comparing the level of understanding of learning content according to the evaluation type (paper-written, study-paper) in non-face-to-face and face-to-face teaching-learning, the average scores of science grades by paper-written and study-paper evaluations in non-face-to-face teaching-learning were significantly low. In addition, as a result of comparing the effect on science grades by level according to the teaching-learning type, the average score of science grades of lower-ranked students in non-face-to-face teaching-learning was relatively low.

• 문화기술의 융합
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• 제10권2호
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• pp.237-244
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• 2024

우리는 수업 형태 (대면, 비대면)에 따라 과학고 학생들의 화학II 및 고급화학 과목의 학업성취도에 미치는 영향을 연구하였다. 연구 대상은 경남 소재 G 과학고등학교 1학년 학생 195명을 대상으로 하였다. 2020년의 비대면 수업과 2021년의 대면 수업에 의한 화학II 및 고급화학 평균 점수를 비교·분석하였다. 수업 형태에 따른 학업성취도를 비교·분석한 결과, 비대면 수업에서 학생들의 화학II 및 고급화학 성적이 높게 나타났다. 수업 형태에 따른 수준별 학업성취도 비교에서, 화학II는 하위권일수록 비대면 수업의 평균 성적이 높게 나타났고, 고급화학은 상위권일수록 비대면 수업의 평균 성적이 높게 나타났다. 또한, 수업 형태의 변화가 화학II의 학업성취도 상위 및 하위 10% 수준에 미치는 영향에서, 상위 10%는 대면 수업에서 높은 성적을 보였고, 하위 10%는 비대면 수업에서 높게 나타났다. 반면, 고급화학에서 상위 10%는 비대면 수업의 평균 성적이 대면 수업의 평균 성적보다 높았고, 하위 10% 수준은 대면 수업의 평균 성적이 비대면 수업의 평균 성적보다 더 높게 나타났다. 이러한 결과를 통해, 과학고 학생들의 교수-학습에서, 교수자는 학업성취 수준에 적합한 교수-학습의 설계 및 수업 처치가 필요한 것으로 나타났다.

• 한국임상보건과학회지
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• 제10권1호
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• pp.1543-1552
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• 2022

Purpose: It is intended to investigate the satisfaction of dental hygiene students with non-face-to-face online classes and use them as basic data for successful lecture design and operation. Methods: The data collected in this study were analyzed using the lBM SPSS Statistics 21 program. The general characteristics of the study subjects were frequency analysis, non-face-to-face online class satisfaction, and test satisfaction were frequency analysis and technical statistics. Through the independent sample T test, a t-test was conducted to find out whether there was an average difference in online class and test satisfaction according to grade. Results: The advantages of non-face-to-face online classes were that repetitive learning was possible (57.7%), the disadvantage was that there was a lack of real-time communication (74.9%), and the most efficient teaching method was a mixed form of online and face-to-face classes (64.9%). The satisfaction level of online classes was 2.69 points for 'self-directed learning habits,' which was the highest compared to the overall average of 2.55 points, and 2.09 points for 'difficulty in interaction between instructors and learners in online classes.'Non-face-to-face test satisfaction was 2.68 points for 'short test time gives fairness to test results,' higher than the overall average of 2.45 points, and 2.07 points for 'no difficulty accessing the test.'In terms of satisfaction with the non-face-to-face test according to the grade, it was found that the third grade showed a more negative attitude than the second grade in terms of sexual fairness (p<0.05). Conclusions: Through the above results, non-face-to-face online classes require various content development and some mixed classes considering the level of students, and instructors' efforts to improve the quality of classes for interaction between instructors and learners are needed.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.211-224
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• 2023

This study surveyed 2nd graders of B high school and 1st graders of A university in Gyeongnam on factors such as behavior control and interaction in non-face-to-face classes, easy or difficult concepts presented in chemistry I and general chemistry textbooks. Based on the results of the survey, the effect of changes in the difficulty level of concepts presented in chemistry I and general chemistry and changes in class types (face-to-face and non-face-to-face) on students' academic achievement by level was compared and analyzed. In the face-to-face class, the average score between the first and second semesters was similar according to the change in the difficulty of the concepts presented in chemistry I and general chemistry. In the non-face-to-face class, the average score of chemistry I in the second semester was quite low, and the average score of general chemistry was rather high. In non-face-to-face classes, the average score of chemistry I in the second semester of low-level students was significantly lowered due to changes in the difficulty of the concept and changes in class types on academic achievement by level. In the case of 10% of students at the lower level, the academic achievement of chemistry I decreased in both the second semester regardless of the changes in the difficulty level of concepts and the changes in class types.

• 한국멀티미디어학회논문지
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• 제24권4호
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• pp.528-537
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• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

• 한국의류학회지
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• 제33권6호
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• pp.938-948
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• 2009

본 연구는 2003년에서 2004년에 실시된 제 5차 한국인 인체치수 조사사업을 통해 확보된 측정사진 중 성인여성 20, 30, 40, 50대 각 150명, 총 600명의 정면과 측면 얼굴사진을 대상으로 얼굴의 연령별 특성을 파악하는데 필요하다고 판단되는 62개의 측정항목과 보다 세부적인 얼굴형태의 분석에 활용될 수 있는 21개의 지수 및 계산항목 총 83개 항목을 본 연구자가 선정한 후 Size Kroea 사업 중 얼굴의 측정 프로그램으로 사용되었던 "Venus face2D"를 이용하여 2차원 간접 측정하였다. 간접 측정기간은 2006년 3월 1일부터 6월 30일까지였다. 연구의 결과는 다음과 같다. 성인여성의 주요 측정항목에 대한 평균 측정치는 얼굴길이 196mm, 상안 62.3mm, 중안 68.9mm, 하안 66.5mm이었고, 이마너비는 125.1mm, 눈살수평너비는 141.2mm, 옆광대점너비 150.8mm 턱아래점너비 124.4mm였다. 이를 바탕으로 우리나라 성인여성 얼굴의 세부항목에 대한 연령집단별 차이를 분석하였으며, 전체 성인여성의 평균 얼굴형과 더불어 각 연령집단별 평균 얼굴형을 제시하였다. 본 연구는 정량화된 수치와 비율을 이용하여 우리나라 성인여성 및 각 연령별 평균 얼굴형을 제시하고, 연령별 얼굴특성을 분석하였다는데 연구의 의의가 있다.

• 사상체질의학회지
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• 제19권2호
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• pp.30-39
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• 2007

1. Objectives The Face is an important standard for the classification of Sasang Constitution. We are developing 3D Automatic Face Recognition Apparatus(3D-AFRA) to analyse the facial characteristics. This apparatus show us 3D image and data of man's face and measure facial figure data. So we should examine the figure restoration error of 3D Automatic Fare Recognition Apparatus(3D-AFRA) in hardware Error Analysis. 2. Methods We scanned Face status by using 3D Automatic Face Recognition Apparatus(3D-AFRA). And also we scanned Face status by using laser scanner(vivid 9i). We compared facial shape data be restored by 3D Automatic Face Recognition Apparatus(3D-AFRA) with facial shape data that be restorated by 3D laser scanner. And we analysed the average error and the maximum error of two data. 3. Results and Conclusions In frontal face, the average error was 0.48mm. and the maximum error was 4.60mm. In whole face, the average error of was 0.99mm. And the maximum error was 6.64mm. In conclusion, We assessed that accuracy of 3D Automatic Face Recognition Apparatus(3D-AFRA) is considerably good.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4080-4097
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• 2020

In recent years, convolutional neural network (CNN) has become the primary method for face detection. But its shortcomings are obvious, such as expensive calculation, heavy model, etc. This makes CNN difficult to use on the mobile devices which have limited computing and storage capabilities. Therefore, the design of lightweight CNN for face detection is becoming more and more important with the popularity of smartphones and mobile Internet. Based on the CPU real-time face detector FaceBoxes, we propose a multi-task lightweight face detector, which has low computing cost and higher detection precision. First, to improve the detection capability, the squeeze and excitation modules are used to extract attention between channels. Then, the textual and semantic information are extracted by shallow networks and deep networks respectively to get rich features. Finally, the landmark detection module is used to improve the detection performance for small faces and provide landmark data for face alignment. Experiments on AFW, FDDB, PASCAL, and WIDER FACE datasets show that our algorithm has achieved significant improvement in the mean average precision. Especially, on the WIDER FACE hard validation set, our algorithm outperforms the mean average precision of FaceBoxes by 7.2%. For VGA-resolution images, the running speed of our algorithm can reach 23FPS on a CPU device.

• Journal of Computing Science and Engineering
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• 제11권1호
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• pp.24-31
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• 2017

Face-based video retrieval has become an active and important branch of intelligent video analysis. Face profiling and matching is a fundamental step and is crucial to the effectiveness of video retrieval. Although many algorithms have been developed for processing static face images, their effectiveness in face-based video retrieval is still unknown, simply because videos have different resolutions, faces vary in scale, and different lighting conditions and angles are used. In this paper, we combined content-based and semantic-based image analysis techniques, and systematically evaluated four mainstream local features to represent face images in the video retrieval task: Harris operators, SIFT and SURF descriptors, and eigenfaces. Results of ten independent runs of 10-fold cross-validation on datasets consisting of TED (Technology Entertainment Design) talk videos showed the effectiveness of our approach, where the SIFT descriptors achieved an average F-score of 0.725 in video retrieval and thus were the most effective, while the SURF descriptors were computed in 0.3 seconds per image on average and were the most efficient in most cases.