• 로봇학회논문지
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• 제14권2호
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• pp.139-149
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• 2019

In this paper, we present auto-annotation tool and synthetic dataset using 3D CAD model for deep learning based object detection. To be used as training data for deep learning methods, class, segmentation, bounding-box, contour, and pose annotations of the object are needed. We propose an automated annotation tool and synthetic image generation. Our resulting synthetic dataset reflects occlusion between objects and applicable for both underwater and in-air environments. To verify our synthetic dataset, we use MASK R-CNN as a state-of-the-art method among object detection model using deep learning. For experiment, we make the experimental environment reflecting the actual underwater environment. We show that object detection model trained via our dataset show significantly accurate results and robustness for the underwater environment. Lastly, we verify that our synthetic dataset is suitable for deep learning model for the underwater environments.

• 한국건설관리학회논문집
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• 제24권5호
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• pp.73-82
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• 2023

Optimization of Construction Site Layout Planning (CSLP) heavily relies on workers' travel paths. However, traditional path generation approaches predominantly focus on the shortest path, often neglecting critical variables such as individual wayfinding tendencies, the spatial arrangement of site objects, and potential hazards. These oversights can lead to compromised path simulations, resulting in less reliable site layout plans. While Deep Reinforcement Learning (DRL) has been proposed as a potential alternative to address these issues, it has shown limitations. Despite presenting more realistic travel paths by considering these variables, DRL often struggles with efficiency in complex environments, leading to extended learning times and potential failures. To overcome these challenges, this study introduces a refined model that enhances spatial navigation capabilities and learning performance by integrating workers' visibility into the reward functions. The proposed model demonstrated a 12.47% increase in the pathfinding success rate and notable improvements in the other two performance measures compared to the existing DRL framework. The adoption of this model could greatly enhance the reliability of the results, ultimately improving site operational efficiency and safety management such as by reducing site congestion and accidents. Future research could expand this study by simulating travel paths in dynamic, multi-agent environments that represent different stages of construction.

• 국제물리치료학회지
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• 제12권1호
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• pp.2261-2266
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• 2021

Background: Deep learning related research works on website medical images have been actively conducted in the field of health care, however, articles related to the musculoskeletal system have been introduced insufficiently, deep learning-based studies on classifying orthopedic manual therapy images would also just be entered. Objectives: To create a deep learning model that categorizes cervical mobilization images and establish a web application to find out its clinical utility. Design: Research and development. Methods: Three types of cervical mobilization images (central posteroanterior (CPA) mobilization, unilateral posteroanterior (UPA) mobilization, and anteroposterior (AP) mobilization) were obtained using functions of 'Download All Images' and a web crawler. Unnecessary images were filtered from 'Auslogics Duplicate File Finder' to obtain the final 144 data (CPA=62, UPA=46, AP=36). Training classified into 3 classes was conducted in Teachable Machine. The next procedures, the trained model source was uploaded to the web application cloud integrated development environment (https://ide.goorm.io/) and the frame was built. The trained model was tested in three environments: Teachable Machine File Upload (TMFU), Teachable Machine Webcam (TMW), and Web Service webcam (WSW). Results: In three environments (TMFU, TMW, WSW), the accuracy of CPA mobilization images was 81-96%. The accuracy of the UPA mobilization image was 43~94%, and the accuracy deviation was greater than that of CPA. The accuracy of the AP mobilization image was 65-75%, and the deviation was not large compared to the other groups. In the three environments, the average accuracy of CPA was 92%, and the accuracy of UPA and AP was similar up to 70%. Conclusion: This study suggests that training of images of orthopedic manual therapy using machine learning open software is possible, and that web applications made using this training model can be used clinically.

• 디지털산업정보학회논문지
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• 제16권2호
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• pp.27-33
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• 2020

Backward compatibility is one of the key issues for radio equipment supporting IEEE 802.11, the typical wireless local area networks (WLANs) communication protocol. For a successful packet decoding with the backward compatibility, the frame format detection is a core precondition. This paper presents a novel frame format detection method based on a deep learning procedure for WLANs affiliated with IEEE 802.11. Considering that the detection performance of conventional methods is degraded mainly due to the poor performances in the symbol synchronization and/or channel estimation in low signal-to-noise-ratio environments, we propose a novel detection method based on convolutional neural network (CNN) that replaces the entire conventional detection procedures. The proposed deep learning network provides a robust detection directly from the receive data. Through extensive computer simulations performed in the multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), the proposed method exhibits superb improvement in the frame format detection compared to the conventional method.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.16-24
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• 2024

With an increase in the relevance of next-generation integrated networking environments, the need to effectively utilize advanced networking techniques also increases. Specifically, integrating Software-Defined Networking (SDN) with Multi-access Edge Computing (MEC) is critical for enhancing network flexibility and addressing challenges such as security vulnerabilities and complex network management. SDN enhances operational flexibility by separating the control and data planes, introducing management complexities. This paper proposes a reinforcement learning-based network path optimization strategy within SDN environments to maximize performance, minimize latency, and optimize resource usage in MEC settings. The proposed Enhanced Proximal Policy Optimization (PPO)-based scheme effectively selects optimal routing paths in dynamic conditions, reducing average delay times to about 60 ms and lowering energy consumption. As the proposed method outperforms conventional schemes, it poses significant practical applications.

• 아동학회지
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• 제25권4호
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• pp.163-175
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• 2004

In this study, 63 university seniors majoring Child Studies were in an 8-week Web Based Instruction (WBI) program. Student characteristics of learning motivation, self-regulatory learning strategy, and learning style (Kolb, 1985) were the independent variables. Learning effects as dependent variables were measured by paper test and work assessment. Spearman's $\rho$ was calculated and tests of rank order difference were used for the data analysis. Results showed that learning motivation and self-regulatory learning strategy had meaningful positive relations with learning effects on the paper test score. Learning effects showed differences by learning style. These findings indicated that the learner's characteristics should be considered in the design and development of more effective WBI environments.

• International Journal of Internet, Broadcasting and Communication
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• 제5권1호
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• pp.9-13
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• 2013

This paper presents the concepts of ubiquitous computing technology to construct a ubiquitous learning environment that enables learning to take place anywhere at any time. This ubiquitous learning environment is described as an environment that supports students' learning using digital media in geographically distributed environments. The u-learning model is a web-based e-learning system that could enable learners to acquire knowledge and skills through interaction between them and the ubiquitous learning environment. Students are allowed to be in an environment of their interest. The communication between devices and the embedded computers in the environment allows learner to learn while they are moving, hence, attaching them to their learning environment.

• Educational Technology International
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• 제11권1호
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• pp.171-192
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• 2010

This study was to investigate the elaborated project based learning model for scientifically gifted in the context of R & E project learning. It is important for the scientifically gifted to provide the appropriate learning environments instead of general learning model for the gifted. Although R & E project learning model is effective, the model has the limitations of managing the course for the scientifically gifted. To improve R & E learning model, the elaborated project based learning model was suggested with integration of both project based learning model and goal based scenario. The elaborated project-based learning model was comprised with 'basic learning process', 'elaboration through inquiry', and 'presentation and reflection'. To measure the satisfaction, eighty scientifically gifted students participated in the class. The result shows that learners were satisfied with the elaborated project-based learning up to 90%, and teachers were satisfied with this model up to 77%.

• 한국IT서비스학회지
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• 제14권1호
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• pp.115-128
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• 2015

As the life expectancy of human beings gets longer and our society changes into highly competitive arena, the implementation of online adult learning is growing, and therefore the learners in self-regulated scaffolding learning environments is becoming an important topic. This study is to investigate the main effects of scaffolding and self-regulation and the interaction effect on discussion participation and comprehension in online learning environments. To do this, ninety-nine adults taking online learning courses with the open university in Korea were investigated. Adult learners were divided into one of the four groups (no scaffolding, conceptual, strategic, and conceptual and strategic scaffoldings). Regarding self-regulation, learners were divided into two groups (low and high self-regulated) based on the mean score of subjective report of self-regulated learning. The results are as follows : First, 'strategic scaffolding' is more effective than 'conceptual scaffolding' in discussion participation (F=2.772, p < .05) and comprehension test (F=7.156, p < .05). Second, high self-regulated learners more actively participate than low self-regulated learners in discussion (F=6.230, p < .05), and achieve higher scores (F=4.863, p < .05). Third, there is no interaction effect between scaffolding strategies and the level of self-regulation. The theoretical and practical implications of these findings are discussed.

• 기독교교육논총
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• 제63권
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• pp.255-281
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• 2020

코로나 19로 인해 온라인 학습으로 대표되는 학습 방법의 변화가 보편화 되고 있는 지금 본 연구는 온라인 학습자의 자기주도학습능력, 교수자-학습자 상호작용 및 학습자-학습자 상호작용, 수업만족도의 관계를 구조적으로 탐색하고자 하였다. 연구결과 고등학생과 대학생 집단 모두 온라인 학습자의 자기주도학습능력은 학습자-학습자 상호작용을 증가시켰으며, 또한 높아진 학습자-학습자 상호작용은 수업만족도를 증가시켰다. 변인 간의 잠재평균비교분석을 통해 대학생과 고등학생 집단 간 변인들의 통계적 유의미한 평균 차이를 확인할 수 없었지만, 다집단 분석을 통해 고등학생의 경우 자기주도학습능력이 수업만족도와 교수자-학습자 상호작용에 직접적인 영향을 주지 않았고, 대학생의 경우 모두 유의미한 영향을 주었음을 확인할 수 있었다. 온라인 학습의 수업만족도 향상을 위한 자기주도학습능력과 학습자-학습자 상호작용의 중요성을 바탕으로 본 연구의 시사점 및 후속 연구를 위한 제안점을 논의하였다.