• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.6
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• pp.89-96
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• 2021

In this study, a deep-learning image analysis model was established and validated for AI-based monitoring of the tidal flat ecosystem for marine protected creatures Ocypode stimpsoni and their habitat. The data in the study was constructed using an unmanned aerial vehicle, and the U-net model was applied for the deep learning model. The accuracy of deep learning model learning results was about 0.76 and about 0.8 each for the Ocypode stimpsoni and their burrow whose accuracy was higher. Analyzing the distribution of crabs and burrows by putting orthomosaic images of the entire study area to the learned deep learning model, it was confirmed that 1,943 Ocypode stimpsoni and 2,807 burrow were distributed in the study area. Through this study, the possibility of using the deep learning image analysis technology for monitoring the tidal ecosystem was confirmed. And it is expected that it can be used in the tidal ecosystem monitoring field by expanding the monitoring sites and target species in the future.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.871-884
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• 2021

The purpose of this study was to determine ways to increase efficiency in constructing and verifying artificial intelligence learning data on land cover using aerial and satellite images, and in applying the data to AI learning algorithms. To this end, multi-resolution datasets of 0.51 m and 10 m each for 8 categories of land cover were constructed using high-resolution aerial images and satellite images obtained from Sentinel-2 satellites. Furthermore, fine data (a total of 17,000 pieces) and coarse data (a total of 33,000 pieces) were simultaneously constructed to achieve the following two goals: precise detection of land cover changes and the establishment of large-scale learning datasets. To secure the accuracy of the learning data, the verification was performed in three steps, which included data refining, annotation, and sampling. The learning data that wasfinally verified was applied to the semantic segmentation algorithms U-Net and DeeplabV3+, and the results were analyzed. Based on the analysis, the average accuracy for land cover based on aerial imagery was 77.8% for U-Net and 76.3% for Deeplab V3+, while for land cover based on satellite imagery it was 91.4% for U-Net and 85.8% for Deeplab V3+. The artificial intelligence learning datasets on land cover constructed using high-resolution aerial and satellite images in this study can be used as reference data to help classify land cover and identify relevant changes. Therefore, it is expected that this study's findings can be used in the future in various fields of artificial intelligence studying land cover in constructing an artificial intelligence learning dataset on land cover of the whole of Korea.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.513-521
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• 2014

The purpose of this research is to develope and implement a convergent educational contents based on theoretical background of integrated education using agent based modeling in the ubiquitous learning environment. The structure of this contents consists of three modules that were designed by trans-disciplinary concept and situated learning theory. These three modules are: convergent problem presenting module, resource of knowledge module and learning of agent based modeling and IT tools module. After the satisfaction survey of the implemented content, out of 5 total value, the average value was 3.86 for effectiveness, 4.13 for convenience and 3.86 for design. The result of the survey shows that the users are generally satisfied. By using this u-learning contents, learners can experience and learn how to solve the convergent problem by utilizing IT tools without any limitation of device, time and space. At the same time, the proposal of structural design of contents can be a good guideline to the researchers to develop the convergent educational contents in the future.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2007.05a
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• pp.70-73
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• 2007

Wireless technologies and mobile devices are getting more and more powerful. This cause in the learning research area more get research about the mobile learning situation has been done by many ambitious researchers because of not only its low cost, portability, and communication, but pedagogical reason such as authentic learning and socio-constructivism. This paper describes the overview of computer supported ubiquitous learning environment. This paper also shows what this new design of the learning environment is, how to design it, and some related researches.

• 한국IT서비스학회:학술대회논문집
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• 2007.05a
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• pp.391-395
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• 2007

다가오는 유비쿼터스시대를 맞이하여 IT가 급속히 확산되고 있다. 그중에서도 컴퓨터환경은 계속발전되어 초등학생의 93.3%, 중, 고등, 대학생의 97.3%가 컴퓨터를 사용하고 있다. 이 논문은 5년동안 산학연구프로젝트로 진행된 u-learning 시스템 개발을 토대로 일부적용한 결과를 기술하였다. 일부 보완점이 필요하지만 긍정적인 결과가 나왔다. 그러나 컴퓨터의 보급이나 인터넷 및 IT 같은 정보기술이 곧 u-learning 교육발전과 직결되지는 않는다. 교육정보화는 전통적인 시각을 뛰어넘어 유비쿼터스시대의 새로운 단계로 발전해야만 한다. 이런 가운데 유비쿼터스 컴퓨팅과 네트워크 패러다임은 미래교육 시스템이 나아가야 할 새로운 방향을 제시한다.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.407-409
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• 1998

This paper deals with temporal-difference learning that is a method for approximating long-term future cost as a function of current state in knowlege-poor environment, a function approximator is used to approximate the mapping from state to future cost, a linear function approximator is limited because mapping from state to future cost has a nonlinear characteristic, so a nonlinear function approximator is used to approximate the mapping from state to future cost in this paper, and that TD learning using a nonlinear function approximator is stable is proved.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.609-620
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• 2010

In order to provide a tailored education for learners within the ubiquitous environment, it is critical to undertake an analysis of the learning activities of learners. For this purpose, SCORM (Sharable Contents Object Reference Model), IMS LD (Instructional Management System Learning Design) and other standards provide learning design support functions, such as, progress checks. However, in order to apply these types of standards, contents packaging is required, and due to the complicated standard dimensions, the facilitation level is lower than the work volume when developing the contents and this requires additional work when revision becomes necessary. In addition, since the learning results are managed by the server there is the problem of the OS being unable to save data when the network is cut off. In this study, a system is realized to manage the actions of learners through the event interception of a web-browser by using event hooking. Through this technique, all HTMLbased contents can be facilitated again without additional work and saving and analysis of learning results are available to improve the problems following the application of standards. Furthermore, the ubiquitous learning environment can be supported by tracking down learning results when the network is cut off.

• Journal of Drive and Control
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• v.19 no.4
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• pp.104-109
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• 2022

For autonomous driving research using AI, datasets collected from road environments play an important role. In other countries, various datasets such as CityScapes, A2D2, and BDD have already been released, but datasets suitable for the domestic road environment still need to be provided. This paper analyzed and verified the dataset reflecting the Korean driving environment. In order to verify the training dataset, the class imbalance was confirmed by comparing the number of pixels and instances of the dataset. A similar A2D2 dataset was trained with the same deep learning model, ConvNeXt, to compare and verify the constructed dataset. IoU was compared for the same class between two datasets with ConvNeXt and mIoU was compared. In this paper, it was confirmed that the collected dataset reflecting the driving environment of Korea is suitable for learning.

• Journal of Korean Society on Water Environment
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• v.37 no.4
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• pp.275-285
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• 2021

Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

• Annual Conference of KIPS
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• 2009.04a
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• pp.1002-1005
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• 2009

정보 통신 기술의 발달은 전산화, 정보화, 지식 정보화 과정을 거쳐 현재 차세대 패더다임인 유비쿼터스화 단계에 이르렀다. 시간과 장소에 구애받지 않고 언제 어디서나 창의적이고 효율적인 학습자 중심의 교육환경을 제공할 수 있는 u-러닝 기술은 7차 교육과정 수행과 함께 필수적인 요소로 부각되고 있다. 최근 교육과정이 u-러닝 환경으로의 변화에 따라 교수 학습 체계 역시 변화가 예상된다. 그리고 ICT를 활용한 교수 학습 모형과 교과별 콘텐츠에 대한 개발이 활발한 이유도 현재 u-러닝이 적극 추진되면서 교육환경에 대한 새로운 요구와 필요성이 증대되고 있기 때문이다. 따라서 교육환경인 u-러닝 시대에 맞추어 교과목에 대한 교수-학습 모형 연구가 이루어 져야 할 것이다. 본 연구에서는 선행 연구된 학습모형을 비교, 분석하여 유비쿼터스와 u-러닝에 대한 특성과 기능, 유비쿼터스 컴퓨팅 기술에 대해 고찰한다. 그리고 기존 컴퓨터 교과 분석을 통하여 컴퓨터교과의 중요성과 교육방법 영역, 컴퓨터교과 교수-학습 모형에 대한 연구를 통해 u-러닝 환경에서의 컴퓨터 교육을 위한 프로젝트 기반 교수-학습 모형을 설계하고 구현한다.