• The Journal of Korean Association of Computer Education
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• v.12 no.3
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• pp.53-62
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• 2009

As Information communication technology develops, requirements for new educational media and new contents gets bigger and bigger. Especially PDA is required for educational media on m-learning environment. We design and implement intra-educational contents sequencing model and educational contents sequencing model between educational contents for PDA as educational supplementary media for m-learning environment, and LMS supporting PDA. And we balanced the work load of PDA and LMS and constructed practical service platform for using PDA as educational supplementary device.

• Journal of Construction Engineering and Project Management
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• v.7 no.3
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• pp.26-38
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• 2017

The architecture, engineering, and construction (AEC) industry is yet to formulate a holistic strategy to realign the evolving technological infrastructures with organisational ambitions and adaptive knowledge of the workforce. This study attempts to create an understanding of the underlying processes adopted by technology-enhanced construction organisations to disseminate and maintain knowledge within the workforce in order to keep pace with the evolving construction technologies. The study adopted expansive learning and adaptive expertise constructs to help better explain workplace learning support structures for organisational effectiveness in a turbulent situation. The two theories were tailored to empirically evaluate three case study construction organisations that have embarked on technology-enabled organisational changes. The study concluded on the creation of a facilitating workplace learning environment to enable the workforce to adapt into and resolve any inherent contradictions and cognitive ambiguities of the changing organisational conditions. This could ensure that novel and conflicting features of the emerging technologies can be adapted across the myriad multi-functional project activities in order to expand the frontiers of the technological capabilities to address the eminent issues confronting the AEC sector.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.81-88
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• 2006

This study was about design and development of the 'Mobile Learning Evaluation Announced System'(MLEAS), which could operate the e-learning and send the result of that to the parents. From this, teachers and parents can communicate with each other dynamically. Especially, this system does not simply present results of students 'learning but provide the extra learning which can make up for students' wrong questions in the base of the evaluation standard. This paper suggests this total learning solution so that parents can perceive the state of their children's learning under the mobile learning environment.

• Journal of Computing Science and Engineering
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• v.4 no.1
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• pp.80-95
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• 2010

In order to truly integrate e-Learning system into regular curriculum at a university, mobile access to Learning Management Systems has to be enabled. Mobile devices have the potential to be integrated into the classroom, because they contain unique characteristics such as portability, social interactivity, context sensitivity, connectivity and individuality. Adoption of Learning Management Systems by students is still on the low rate, mostly because of poor usability of existing e-Learning systems. Our initial research has confirmed this hypothesis. Usability issue is rising to the higher level on the mobile platform, because of the mobile devices' limited screen size, input interfaces and bandwidth, and also because of the context of use. Our second hypothesis was that it is wrong to consider a mobile device as a surrogate for desktop or laptop personal computer (PC). By just adopting the existing Learning Management System on mobile devices with adaptive technologies such as Google proxy, we do not acquire the satisfactory results. Usability can prove to be even lower compared to desktop application. One possible solution to the problem could be development of rich client applications for today's mobile devices that would raise the usability to a higher level. We developed a PocketPC prototype application by using user-centered design principles, which we presented as a third alternative in usability research conducted among university students. Results gathered in such a way have confirmed that development of e-Learning system, in order to be widely accepted by students, needs to have the user(student) in the center of development process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.12
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• pp.5564-5569
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• 2011

In this paper we introduced Augmented Reality (AR) on Android platform for ubiquitous learning (u-learning). Android is breaking new ground for mobile computing and open technologies. Android is versatile as it is not limited only to mobile phones, but it can be installed on various devices. Android gives developers the opportunity to leverage their development skills, while also building an exciting and active community. Augmented Reality (AR) is going to be the future of most apps; all it takes is a decent processor, a camera, a compass and a GPS, all of which are becoming increasingly common on smart phones. Through AR we can have educational tools that provide individuals with total flexibility to receive, send, and review training and detailed product information through an increasingly ubiquitous web-enabled communication device. In this paper, we proposed Augmented Reality for Species Identification using Android Smartphone with augmented reality in species determination. This study is appropriate in the field of Biology. This is useful in outdoor experimental activities of the students. Like for example while they are visiting the zoo, botanical garden and etc.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.28 no.3
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• pp.174-182
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• 2017

Objectives: This study was conducted in order to develop a computerized test to measure the level of mathematic achievement and related cognitive functions in children and adolescents in South Korea. Methods: The computerized Comprehensive Learning Test-Mathematic (CLT-M) consists of the whole number computation test, enumeration of dot group test, number line estimation test, numeral comparing test (magnitude/distance), rapid automatized naming test, digit span test, and working memory test. To obtain the necessary data and to investigate the reliability and validity of this test, 399 children and adolescents from kindergarten to middle school were recruited. Results: The internal consistency reliability of the CLT-M was high (Cronbach's alpha=0.76). Four factors explained 66.4% of the cumulative variances. In addition, the data for all of the CLT-M subtests were obtained. Conclusion: The computerized CLT-M can be used as a reliable and valid tool to evaluate the level of mathematical achievement and associated cognitive functions in Korean children and adolescents. This test can also be helpful to detect mathematical learning disabilities, including specific learning disorder with impairment in mathematics, in Korea.

• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.737-739
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• 2015

To accommodate today's higher education student, fewer textbooks are printed and more are becoming digital. Keeping with the modern era, hybrid versions of textbooks have all end-of-chapter assessment content moved to digital learning systems such as MindTap$^{TM}$ by Cengage $Learning^{(R)}$. In this work, we introduce new pedagogical strategies to combat academic e-cheating, specifically cheating on assessments given in online astronomy courses. The strategies we present in this work are employed in Horizons: Exploring the Universe, Hybrid, 13th Edition, and Universe, Hybrid, 8th Edition, by Seeds, Backman, and Montgomery.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.341-342
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• 2011

• 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.