• Journal of Korean Elementary Science Education
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• v.28 no.1
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• pp.24-34
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• 2009

The purpose of this study is to examine the effects of background knowledge on problem solving. To achieve this aim, I proposed the model which shows problem solving process centering around background knowledge, conducted the lessons concerning the concept 'weightlessness' on pre-service elementary teachers, and then classified the pre-service elementary teachers into several groups by the difference of the results presented in the process of solving the problems on weightlessness. And I examined qualitatively the effects of background knowledge on problem solving through the interview with 11 volunteers. On the cause of the failing the problem solving, the failure of acquiring or activating the background knowledge related to the learning concept was most frequently, secondly the use of the background knowledge unrelated to the learning concept, and thirdly the failure of understanding the teaming concept. To acquire or activate the background knowledge related to the teaming concept was more difficult than to understand the new teaming concept, and the cases that use the background knowledge unrelated to the learning concept failed to solve problem. The result of interview, all interviewee understood the learning concept correctly, but all of them who fail to acquire or activate the background knowledge related to the learning concept, or use the background knowledge unrelated to the learning concept, could not solve the problem.

• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.149-156
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• 2019

The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.52-61
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• 2006

For the detection of moving objects, background subtraction methods are widely used. An adaptive Gaussian mixture model combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, probabilistic learning approach does not work well in high traffic regions. In this paper, we Propose a reliable learning method of complex and dynamic backgrounds in high traffic regions.

• The Journal of Korean Association of Computer Education
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• v.5 no.2
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• pp.29-37
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• 2002

The purpose of this study is to examine the effects of the level of background knowledge and the metacognition supporting tool (MST) on the learning activities and outcomes in a web problem based learning environment from the theoretical perspectives of PBL (problem-based-learning) in the Web and metacognition. Results suggest that the level of learners' background knowledge could play a slight role in problem oriented learning activities. This was because learning tasks were characterized by ill-structured problems in Web-based problem learning and this sort of learning might have been a somewhat newer experience for the selected students. Providing MST for learners' PBL activities, however, was highly beneficial for learners who practice PBL in Web based learning environments. In addition, for PBL outcomes, variables like problem solution reporting documents. the use of MST had a more positive effect on the lower level learners' background knowledge than the higher level learners'.

• Journal of Korea Multimedia Society
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• v.18 no.2
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• pp.138-148
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• 2015

Object detection methods based on background learning are widely used in video surveillance. However, when a car runs with headlights on, these methods are likely to detect the car region and the area illuminated by the headlights as one connected change region. This paper describes a method of separating the car region from the area illuminated by the headlights. First, we detect change regions with a background learning method, and extract blobs, connected components in the detected change region. If a blob is larger than the maximum object size, we extract candidate object regions from the blob by clustering the intensity histogram of the frame difference between the mean of background images and an input image. Finally, we compute the similarity between the mean of background images and the input image within each candidate region and select a candidate region with weak similarity as an object region.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1946-1963
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• 2014

Most background subtraction methods focus on dynamic and complex scenes without considering robustness against noise. This paper proposes a background subtraction algorithm based on dictionary learning and sparse coding for handling low light conditions. The proposed method formulates background modeling as the linear and sparse combination of atoms in the dictionary. The background subtraction is considered as the difference between sparse representations of the current frame and the background model. Assuming that the projection of the noise over the dictionary is irregular and random guarantees the adaptability of the approach in large noisy scenes. Experimental results divided in simulated large noise and realistic low light conditions show the promising robustness of the proposed approach compared with other competing methods.

• Journal of Korean Academy of Nursing Administration
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• v.12 no.1
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• pp.140-150
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• 2006

Purpose: The purpose of this study was to determine learning styles and preferred learning methods of clinical nurses. Method: Data were collected from 735 nurses at one university hospital in Seoul. Learning style inventory, a self-report questionnaire was completed by the subjects. Result: Learning styles of nurses were accommodator 35.9%, diverger 30.4%, converger 18.2%, assimilator 15.5%. Learning styles varied significantly with clinical practice area and academic background. Furthermore, RO(reflective observation) learning mode varied significantly according to the clinical practice area. AC(abstractive conceptualization) learning mode varied significantly with job position. AC and AE(active experimentation) learning modes varied significantly according to the academic background and preferred learning method. Preferred learning methods were lecture 24.8%, clinical practice 23.1%, self-directed learning 21.5%, audiovisual education 16.7%, and group discussion 13.9%. Preferred learning methods varied significantly with learning styles and career. Lecture was preferred in diverger and self-directed learning was preferred in assimilator. Clinical practice was preferred in accommodator and converger. Conclusions: This study suggested that clinical education should be applied to nurses after examining learning styles and preferred learning methods. In conclusion, to identify the nurses' learning styles could be helpful for developing the effective educational skill.

• The Mathematical Education
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• v.42 no.4
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• pp.453-463
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• 2003

The main purpose of this paper is to develope elementary mathematics teaching-learning programs for pre-service elementary teachers. The elementary mathematics education program developed in this work is divided into two parts: One is the theory, the other is the practice. The theory deals with the foundations of mathematics, the objectives of mathematics education, the history of mathematics education in Korea, the psychology of mathematics learning, the theories of mathematics teaching and learning, and the methods of assessment. With respect to the practice, this study examines the background knowledge and activities of numbers and their operation, geometry, measurement, statistics and probability, pattern and function.

• Journal of East Asia Management
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• v.4 no.2
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• pp.39-62
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• 2023

Online and offline mixed teaching mode has become an important way to promote the connotative development of higher education. Under the background that offline teaching has become mature, in order to further promote the development of online education, and promote the implementation of the mixed teaching mode, to mix and to provide basis for the construction of the mixed teaching mode, this study takes the online learning effect as the evaluation basis, adopts the online questionnaire survey to conduct statistical analysis of the online learning behavior of 2213 college students, and discusses the differentiation phenomenon of online learning groups from the micro, meso and macro perspectives. It is found that there are significant differences in the online learning effect of college students in terms of the type of learning platform, whether the school implements the online offline mixed teaching mode, education background, grade (bachelor's degree), and region. Colleges and universities should strengthen the promotion of online and offline mixed teaching mode; The online learning platform should improve the platform function and strengthen the functional differentiation design of learning resources for students. Education departments pay attention to the learning effect of online learners in different regions, and bridge the gap in regional education.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.38-43
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• 2021

As the number of augmented reality games using augmented reality technology increases, the demands of users are also increasing. Game technologies used in augmented reality games are mainly games using MARKER, MARKERLESS, GPS, etc. Games using this technology can augment the background and other objects. To solve this problem, we want to help develop augmented reality games by analyzing objects in the background, which is an important element of augmented reality. To analyze the background in the augmented reality game, the background object was analyzed by applying a deep learning model using TensorFlow Lite in the UNITY engine. Using this result, we obtained the result that augmented objects can be placed in the game according to the types of objects analyzed in the background. By utilizing this research, it will be possible to develop advanced augmented reality games by augmenting objects that fit the background.