• Educational Technology International
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• v.12 no.1
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• pp.67-94
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• 2011

The major concern of e-learning environment design is to create and improve artifacts that support human learning. To facilitate effective and efficient learning, e-learning environment designers focused on the contemporary information technologies. Web 2.0 services, which empower users and allow the inter-transforming interactions between users and information technologies, have been increasingly changing the way that people learn. By adapting these Web 2.0 technologies in learning environment, educational technology can facilitate learners' abilities to personalize learning environment. The main purpose of this study is to conceptualize comprehensively constructs for understanding the inter-transforming relationships between learner and learning environment and mutable learning environments' impact on the process through which learners learn and strive to shape their learning environment. As results, this study confirms conceptualization of four constructs by incorporating aspects of design that occur in e-learning environments with Web 2.0 technologies. First, learner-designer refers to active and intentional designer who is tailoring an e-learning environment in the changing context of use. Second, learner's secondary design refers to learner's design based on the primary designs by design experts. Third, transactional interaction refers to learner's inter-changeable, inter-transformative, co-evolutionary interaction with technological environment. Fourth, trans-active learning environment refers to mutable learning environment enacted by users.

• International Journal of Contents
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• v.12 no.4
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• pp.8-16
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• 2016

This research proposes the composition logic of an Active Learning Environment (ALE), to enable discovery by learning through experience, whilst increasing knowledge about modern architectural heritage. Linking information to the historical heritage using Information and Communication Technology (ICT) helps to overcome the limits of previous learning methods, by providing rich learning resources on site. Existing field trips of cultural heritages are created to impart limited experience content from web resources, or receive content at a specific place through humanities Geographic Information System (GIS). Therefore, on the basis of the blended space theory, an augmented space experience method for overcoming these shortages was composed. An ALE space framework is proposed to enable discovery through learning in an expanded space. The operation of ALE space is needed to create full coordination, such as a Content Management System (CMS). It involves a relation network to provide knowledge to the rule engine of the CMS. The application is represented with the Deoksugung Palace Seokjojeon hall example, by describing a user experience scenario.

• Knowledge Management Research
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• v.9 no.4
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• pp.17-33
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• 2008

This study proposes University 2.0 as a model case of open, flexible, and distributed learning environment for higher education based on theoretical foundations and perspectives. As web 2.0 technologies emerge into the field of education, ways of generating and disseminating information and knowledge have been drastically changed. Professors are no longer the only source of knowledge. Students using internet often become prosumers of knowledge who search and access information through the web as well as publish their own knowledge using the web. A concept and framework of University 2.0 is introduced for implementing the new interactive learning paradigm with an open, flexible and distributed learning environment for higher education. University 2.0 incorporates online and offline learning environments with various educational media. Furthermore, it employs various learning strategies and integrates formal and informal learning through learning communities. Both instructors and students in University 2.0 environment are expected to be active knowledge generators as well as creative designers of their own learning and teaching.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.295-303
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• 2022

This study compares two cases in which flipped learning is applied in the active learning classroom (ALC) and fixed classroom of advanced engineering education. To this end, the difference in pre-learning, academic achievement, and class satisfaction between ALC and fixed classroom flipped learning were compared. The results revealed that students in ALC flipped learning watched more video lectures for pre-learning than those in the fixed classroom flipped learning and achieved higher scores on final tests, though they obtained lower points on midterm exam. In addition, examination of class satisfaction with questions about class factors, instructor factors, and overall satisfaction revealed that ALC flipped learning showed higher satisfaction in all factors than the fixed classroom flipped learning. This case study suggests that the ALC environment, a learning space built to facilitate learner-centered activities, is more effective for flipped learning that requires active interaction in the classroom.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.165-171
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• 2017

It is difficult to recognize Human's facial expression in the real-world. For these reason, when database and test data have similar condition, we can accomplish high accuracy. Solving these problem, we need to many facial expression data. In this paper, we propose the algorithm for gathering many facial expression data within various environment and gaining high accuracy quickly. This algorithm is training initial model with the ASSL (Active Semi-Supervised Learning) using deep learning network, thereafter gathering unlabeled facial expression data and repeating this process. Through using the ASSL, we gain proper data and high accuracy with less labor force.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3283-3292
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• 2022

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.122-129
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• 2021

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1083-1090
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• 2003

Web-based learning is currently an active area of research and a considerable number of studies have been conducted on its application in the learning environment. However, in spite of many advances in the research and development of the educational contents, questions about how the environment affects learning remains largely unanswered. In this article, we propose a Web-based learning environment to improve the educational effect. The goal of this article is not to provide a complete system to support Web-based learning but rather to describe some meaningful strategies and fundamental design concepts that utilize information technologies to support teaching and learning.