• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.75-77
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• 1998

Perhaps one of the most versatile approaches to learning in practical domains lies in case based reasoning. To date, however, most case based reasoning systems have tended to focus on relatively simple domains. The current study involves the development of a decision support system for a complex production process with a limited database. This paper presents a set of critical issues underlying CBR, then explores their consequences for a complex domain. Finally, the performance of the system is examined for resolving various types of quality control problems.

• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.81-90
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• 2020

The use of machine learning technologies such as deep and reinforcement learning has proliferated in various domains with the advancement of deep neural network studies. To make the learning successful, both big data acquisition and fast processing are required. However, for some physical world applications such as autonomous drone flight, it is difficult to achieve efficient learning because learning with a premature A.I. is dangerous, cost-ineffective, and time-consuming. To solve these problems, simulation-based approaches can be considered. In this study, we analyze recent trends in drone simulation technologies and compare their features. Subsequently, we introduce Octopus, which is a highly precise and scalable drone simulator being developed by ETRI.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1228-1248
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• 2020

Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.

• Journal of Educational Research in Mathematics
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• v.8 no.2
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• pp.425-437
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• 1998

The difference between "open" mathematics education and education of "open mathematics" arises from the difference of tearcher's understanding on the meaning of "teaching and learning mathematics" in the paper. Discusses the agreements and the worries of the researchers, the teachers, the students in korea, about open educationism, firstly, Three practical cases in mathematics lesson in korea are reviewed and analyzed in the respect of learning principles, secondly. Thirdly, the paper examines how to be modified two main learning principles, individualised learning and self-regulation of learning by teachers in the process of instruction. Finally, open mathematics advocated by Fisher(1984) and closed mathematics are compared especially in the probability unit. It concludes that the open approaches in mathematics lessons in korea need to improve with respect to teacher's attitude for didactic contents or mathematical knowledge. It is argued that teacher's open or flexible understanding of mathematical knowledge is no less important than that of their pupils.ant than that of their pupils.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.215-223
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• 2021

3D data can be categorized into two parts : Euclidean data and non-Euclidean data. In general, 3D data exists in the form of non-Euclidean data. Due to irregularities in non-Euclidean data such as mesh and point cloud, early 3D deep learning studies transformed these data into regular forms of Euclidean data to utilize them. This approach, however, cannot use memory efficiently and causes loses of essential information on objects. Thus, various approaches that can directly apply deep learning architecture to non-Euclidean 3D data have emerged. In this survey, we introduce various deep learning methods for mesh and point cloud data. After analyzing the operating principles of these methods designed for irregular data, we compare the performance of existing methods for shape classification and segmentation tasks.

• Journal of The Korean Association For Science Education
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• v.27 no.6
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• pp.519-528
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• 2007

Although science continues to be a key subject in the education of the majority of young people throughout the world, it is becoming increasingly clear that school science is failing to win the hearts and minds of many of today's younger generation. Researchers have begun to look at ways in which the learning that takes place in museums, science centres and other informal settings can add value to science learning in schools. Four case studies are used to illustrate the potential afforded by informal contexts to research aspects of science learning. The case studies involve: the European Union PENCIL (Permanent European Resource Centre for Informal Learning) project (a network of 14 museums and science centres working with schools to enhance learning in maths and science); a large natural history museum in England; the Tate Modernart gallery in London, and the Outdoor Classroom Action Research Project which involved researchers working in school grounds, field centres and farms. The range of research questions that were asked are examined as are the methodological approaches taken and the methods used to collect and analyse data. Lessons learned from the studies about research in the informal contexts are discussed critically.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.41 no.3
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• pp.149-155
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• 2015

Surgical approaches to the condylar fracture include intraoral, preauricular, submandibular, and retromandibular approaches. Each approach has its own advantages and disadvantages. When a patient needs esthetic results and an intraoral approach is not feasible, the transmasseteric antero-parotid facelift approach is considered. This approach permits direct exposure and allow the surgeon to fixate the fractured unit tangentially. Tangential fixation is critical to osteosynthesis. Disadvantages of the transmasseteric antero-parotid facelift approach include damage to the facial nerve and a longer operation time. However, after the initial learning curve, facial nerve damage can be avoided and operation time may decrease. We report three cases of subcondylar fractures that were treated with a transmasseteric antero-parotid facelift approach. Among these, two cases had trivial complications that were easily overcome. Instead of dissecting through the parotid gland parenchyma, the transmasseteric antero-parotid facelift approach uses transmasseteric dissection and reduces facial nerve damage more than the retromandibular transparotid approach. The esthetic result is superior to that of other approaches.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.205-208
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• 2008

Contents application from contents development of web technical base and with the operation different environment information of the educational resources integration the importance and necessity of the management central chain e-Learning system will be able to operate are raising its head with base. Is the actual condition which develops the development process where but, the education application currently is not standardized in base. Approaches with an educational domain from the present paper consequently, and defines MDA(Model Driven Architecture) coats e-Learning System. Also uses a studying contents standard metadata and about the contents storage space analyzes and plans the core property which uses MDA automatic tools leads and under developing boil e-Learning System will be able to provide the contents which does in actual professor own necessity.