• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.33-41
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• 2019

In this paper, we propose a method to learn the redesigned class with background and object for semantic segmentation of indoor scene image. Semantic image segmentation is a technique that divides meaningful parts of an image, such as walls and beds, into pixels. Previous work of semantic image segmentation has proposed methods of learning various object classes of images through neural networks, and it has been pointed out that there is insufficient accuracy compared to long learning time. However, in the problem of separating objects and backgrounds, there is no need to learn various object classes. So we concentrate on separating objects and backgrounds, and propose method to learn after class simplification. The accuracy of the proposed learning method is about 5 ~ 12% higher than the existing methods. In addition, the learning time is reduced by about 14 ~ 60 minutes when the class is configured differently In the same environment, and it shows that it is possible to efficiently learn about the problem of separating the object and the background.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.449-456
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• 2012

The college students who have relatively weak academic background feel more difficult in learning programming language grammars and programming skills in introductory course. At the end of semester, most of students had the negative attitude to programming and only a few students could write the programs for the given problems because they spent most of time to learn grammars instead of learning problem solving skills and logics. In this study, we propose to use Scratch in introductory programming course to help students to understand grammars and problem solving skills. It's necessary to educate first-time programmers how to solve the problems before they learn grammars of the programming language in their first programming language course. This paper shows that Scratch allows students not only to learn problem solving skills in programming but also to motivate students themselves in the class.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.3
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• pp.67-84
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• 2004

Information literacy is usually described as the ability to locate, manage and use information effectively for a range of purposes. It Is acknowledged the most people are likely to change careers throughout their lives. Therefore, people must learn Information literacy. Those who are information literate must be able to recognize when information is needed and have the ability to locate, evaluate, use effectively the needed information, ultimately have how to team. Since a basic objective of education ts for student to learn how to learn, Information literacy become increasingly more important for all students. This paper presents an overview of definitions, standards and models of information literacy and information literate. Based on these, we suggested learning method and curriculum of information literacy for the lower grades of elementary school.

• 대한공업교육학회지
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• v.30 no.1
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• pp.84-95
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• 2005

With recent advancement in informational sciences, educational systems are undergoing a revolutionary facelift on how students attain their education. These developments have diminished the traditional method of teaching and learning and have improved the overall quality of the educational environment. They have also ignited the birth of cyber-education in which students have the flexibility and responsibility on what and when to study, thus providing a greater freedom to control one's education. Cyber-educational environment not only requires physical hardware but also an organizational structural component and most importantly, a software component that drives and gives the student management power to tailor their studies based upon their educational requirements. Due to the fact that individuals learn and develop at different rates, one of the important features of a cyber-educational program will be its ability to adjust and customized to individual needs and requirements. In this thesis, a novel software which encompasses these ideas was designed and developed. This program allows the individual more flexibility and management of courses based on their ability and needs. Because the software customizes to one's ability and it allows the student to advance at a more comfortable pace, it boosts the student's confidence and desire to learn. In the future, new and improved programs similar to the one developed here will further enhance the cyber-educational environment and will undoubtedly improve the overall quality of student's education.

• Journal of Engineering Education Research
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• v.21 no.4
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• pp.10-19
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• 2018

This study introduces an engineering design class which is experimental in a sense that it is planned and implemented with three key concepts such as learner-centered education, living lab and community based learning. With the class run in being connected with one regional community in Seoul, it focuses on its educational effects acquired through the living lab-based approach. And this research investigates the student's experiences of when, what and how they learn in a learner-centered class. It shows that, rather than taking professor's one dimensional lectures in classroom, the students learn actively when they face with the problem in the field. Students have come to carry out engineering design from the perspective of stakeholders, not from the supplier or producer's perspective in the process of meeting with the problem in reality. Team based collaborative activities are crucial in the entire design process. More importantly, students' design products have been transformed into more useful and meaningful ones as stakeholders of the local community have participated into the students' works. However, we need to recognize that there are some important issues that need to be solved institutionally and systematically in order for such educations to spread. This study suggests several educational arrangements for those issues.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.701-706
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• 2019

The advent of the AI(: Artificial Intelligence) has applied to many industrial and general applications have havingact on our lives these days. Various types of machine learning methods are supported in this field. The supervised learning method of the machine learning has features and targets as an input in the learning process. There are many supervised learning methods as well and their performance varies depends on the characteristics and states of the big data type as an input data. Therefore, in this paper, in order to compare the performance of the various supervised learning method with a specific big data set, the supervised learning methods supported in the Tensorflow and the Sckit-Learn are simulated and analyzed in the Jupyter Notebook environment with python.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4326-4344
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• 2021

The SinGAN is one of generative adversarial networks that can be trained on a single nature image. It has poor ability to learn more global features from nature image, and losses much local detail information when it generates arbitrary size image sample. To solve the problem, a non-linear function is firstly proposed to control downsampling ratio that is ratio between the size of current image and the size of next downsampled image, to increase the ratio with increase of the number of downsampling. This makes the low-resolution images obtained by downsampling have higher proportion in all downsampled images. The low-resolution images usually contain much global information. Therefore, it can help the model to learn more global feature information from downsampled images. Secondly, the attention mechanism is introduced to the generative network to increase the weight of effective image information. This can make the network learn more local details. Besides, in order to make the output image more natural, the TVLoss function is introduced to the loss function of SinGAN, to reduce the difference between adjacent pixels and smear phenomenon for the output image. A large number of experimental results show that our proposed model has better performance than other methods in generating random samples with fixed size and arbitrary size, image harmonization and editing.

• The monthly packaging world
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• s.167
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• pp.82-88
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• 2007

• Proceedings of the Korean Nutrition Society Conference
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• 2004.05a
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• pp.30-30
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• 2004