• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.521-524
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• 2008

The existing mobile learning service is the method that provides data packets in real time. It has some problems of much data packet cost and transmission speed to provide the mobile learning service for messy capacity by the existing one. To solve the problems, a secondary storage device like USB is added to a mobile phone. In this paper, we suggest to implementation of the mobile learning system of the new method using secondary storage device.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1133-1141
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• 2019

Most of the previous studies for bark recognition have focused on the extraction of LBP-like statistical features. Deep learning approach was not well studied because of the difficulty of acquiring large volume of bark image dataset. To overcome the bark dataset problem, this study utilizes the MobileNet which was trained with the ImageNet dataset. This study proposes two approaches. One is to extract features by the pixel-wise convolution and classify the features with SVM. The other is to tune the weights of the MobileNet by flexibly freezing layers. The experimental results with two public bark datasets, BarkTex and Trunk12, show that the proposed methods are effective in bark recognition. Especially the results of the flexible tunning method outperform state-of-the-art methods. In addition, it can be applied to mobile devices because the MobileNet is compact compared to other deep learning models.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• Educational Technology International
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• v.20 no.2
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• pp.169-194
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• 2019

This study aims to compare and analyze the trends of research on mobile learning conducted in Korea and China to suggest future directions and multifaceted subject areas in its study field. To achieve this purpose, 620 Chinese papers from CNKI (CSSCI and CSCD) database and 205 Korean papers from RISS database (KCI and KCI candidate) published between 2009 and 2018 were selected to be analyzed through applying the frequency analysis and visualized semantic network analysis. The criteria for analysis used in this study are four types: publication years, research subjects, research methods, and keywords. The results of this study are as follows. Firstly, in relation to the year of publication, Korea entered the peak of mobile learning research in 2016 (33 papers), and China reached high publications (94 papers) in 2017. Secondly, with regard to the research subjects, the most frequently studied subjects in Korea and China were targeted to college students, followed by general adult groups. Thirdly, in terms of research methods, quantitative research accounted for a high proportion in Korea, but in China, literature research showed a high frequency. Fourthly, the high frequency keywords appearing in mobile learning research of the two countries were mainly reflected in language learning. Based on the findings, several directions of future research for both countries were suggested.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.863-885
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• 2009

In the era of wireless internet, we are apt to use a mobile phone for learning mathematics, besides the pc computer and the notebook computer. This study was to investigate the effect of M-learning when students were given a wireless mobile phone in terms of their perspectives towards the use of a mobile phone, achievement and attitudes towards mathematics. They were the 3th grader in a high school, who were expected to take Aptitude Test for the entrance of the university level. The most students who took an ubiquitous environment of M-learning showed it as a benefit for learning mathematics and did not spend time at other activities such as listening to music, sending text-message, playing games, etc, but at the M-learning activities. The students who engaged in the M-learning activities were improved a significantly higher score at Aptitude Test than the students who took the make-up courses in the school and also did a significantly higher disposition towards mathematics which was caused by curiosity among 7 components of the mathematical disposition.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.125-136
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• 2021

The development of systems that can generate automatically instructional material is a challenging goal for the e-learning community. These systems pave the way towards large scale e-learning deployment as they produce instruction on-demand for users requesting to learn about any topic, anywhere and anytime. However, realizing such systems is possible with the availability of vast repositories of web information in different formats that can be searched, reused and integrated into information-rich environments for interactive learning. This paradigm of learning relieves instructors from the tedious authoring task, making them focusing more on the design and quality of instruction. This paper presents a mobile learning system (Mole) that supports the generation of instructional material in M-Learning (Mobile Learning) contexts, by reusing and integrating heterogeneous hypermedia web resources. Mole uses open hypermedia repositories to build a Learning Web and to generate learning objects including various hypermedia resources that are adapted to the user context. Learning is delivered through a nice graphical user interface allowing the user to navigate conveniently while building their own learning path. A test case scenario illustrating Mole is presented along with a system evaluation which shows that in 90% of the cases Mole was able to generate learning objects that are related to the user query.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.883-890
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• 2008

It becomes enabled to provide variety of multimedia contents through mobile service with the development of high-speed 3rd generation mobile communication and handsets. MMS (Multimedia Messaging Service) can be displayed in the presentation format which is unified the various multimedia contents such as text, audio, image, video, etc. It is applicable as a new type of ubiquitous learning. In this study we propose to design a mobile learning system by providing profiles which meets the standard of CC/PP and by generating multimedia messages based on SMIL language through the adaptation steps according to the learning environment, the content type, and the device property of learners.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4593-4595
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• 2012

Introduction: Mobile learning enables users to interact with educational resources while in variable locations. Medical students in residency positions need to assimilate considerable knowledge besides their practical training and we therefore aimed to evaluate the impact of using short message service via cell phone as a learning tool in residents of Obstetrics and Gynecology in our hospital. Methods: We sent short messages including data about breast cancer to the cell phones of 25 residents of gynecology and obstetrics and asked them to study a well-designed booklet containing another set of information about the disease in the same period. The rate of learning derived from the two methods was compared by pre- and post-tests and self-satisfaction assessed by a relevant questionnaire at the end of the program. Results: The mobile learning method had a significantly better effect on learning and created more interest in the subject. Conclusion: Learning via receiving SMS can be an effective and appealing method of knowledge acquisition in higher levels of education.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.169-175
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• 2015

The purpose of this study is to investigate the structural relationships among teaching presence, cognitive presence, social presence, service quality, learning flow, and learners' satisfaction in mobile learning. Survey data collected by 255 learners who completed mobile-supported courses offered by an online university in South Korea were analyzed using structural equation modeling. The results suggest that cognitive presence, social presence, and service quality have direct effects on learning flow, and that cognitive presence, service quality, and learning flow have direct effects on learners' satisfaction.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.427-435
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• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.