• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.341-350
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• 2006

The sand table training is one of the most effective training method in military operations which can accomplish missions such as simulation and rehearsal without limitations related to time, space, money and so on. Previous sand table training has many problems like that the sand table cannot represent real field condition because of its physical properties. So, it is hard to be preserved and impossible to include much of information into them. In this paper, we make an approach based on Augmented Reality(AR) to solve these problems and propose an efficient military training briefing tool with virtual sand table environment described as actual battle field Virtual Tactical Map(VTM) can realize a virtual military training with simple action like moving marker or tangible interface by hand. Real-time state information of VTM gives us more organic intelligence for entire situation. Tangible AR interface provides users with a contents authoring tool that is natural, intuitive and easy to deal with as interaction between user in real world and system that augmented real world with virtual object. VTM is a newly designed military training briefing tools. A military training content can be reproduced and it is possible that user uses this content later. Thus, it shows us potential possibilities of AR applications on military leaning field.

• Journal of Computing Science and Engineering
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• v.6 no.1
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• pp.40-50
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• 2012

Increase in the number of older people due to demographic changes poses great challenges to the social healthcare systems both in the Western and as well as in the Eastern countries. Support for older people by formal care givers leads to enormous temporal and personal efforts. Therefore, one of the most important goals is to increase the efficiency and effectiveness of today's care. This can be achieved by the use of assistive technologies. These technologies are able to increase the safety of patients or to reduce the time needed for tasks that do not relate to direct interaction between the care giver and the patient. Motivated by this goal, this contribution focuses on applications of acoustic technologies to support users and care givers in ambient assisted living (AAL) scenarios. Acoustic sensors are small, unobtrusive and can be added to already existing care or living environments easily. The information gathered by the acoustic sensors can be analyzed to calculate the position of the user by localization and the context by detection and classification of acoustic events in the captured acoustic signal. By doing this, possibly dangerous situations like falls, screams or an increased amount of coughs can be detected and appropriate actions can be initialized by an intelligent autonomous system for the acoustic monitoring of older persons. The proposed system is able to reduce the false alarm rate compared to other existing and commercially available approaches that basically rely only on the acoustic level. This is due to the fact that it explicitly distinguishes between the various acoustic events and provides information on the type of emergency that has taken place. Furthermore, the position of the acoustic event can be determined as contextual information by the system that uses only the acoustic signal. By this, the position of the user is known even if she or he does not wear a localization device such as a radio-frequency identification (RFID) tag.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.80-91
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• 2019

This study investigated the factors affecting the satisfaction and the continuous behavior intention of university students on MOOC. Adopting a questionnaire, the study collected data from 177 students enrolled in K-MOOC classes as an extracurriculum in the university in metropolitan area. Results indicated the following. First, the university students' satisfaction and continuous use intention on K-MOOC are higher during the semester than in vacation. Second, the perceived ease of use, usefulness, and task-technology fit influence the users'satisfaction on K-MOOC. Meanwhile, the perceived ease of use, task-technology, and user satisfaction likewise influence the users'continuous behavior intention. Hence, the study proposes that the subject matter on K-MOOC and the amount of educational content be made diversified, and the period of K-MOOC be made similar with that of the regular semester. The platform should also be stabilized, and the adequacy in mobile environment be improved. To further activate K-MOOC utilization in the university, the depth of contents and interaction between professors and students also need to be considered.

• Science of Emotion and Sensibility
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• v.17 no.2
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• pp.91-100
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• 2014

This study was to examine how visual angle of matrix corresponding to the physical properties of P300 speller and eye gaze corresponding to the user's personal characteristics influence on the accuracy of P300. Visual angle of the matrix was operated as the distance between the user and the matrix and three groups were composed: 60 cm group, 100 cm groups, and 150 cm group. Eye gaze methods was consisted three conditions. Head moving condition was putting eye gaze using head, pupil moving condition was moving pupil with the head fixed, while the eye fixed condition is to fix the eye gaze at the center of the matrix. The results showed that there was significant difference in the accuracy of P300 speller according to the eye gaze method. The accuracy of the head moving condition was higher than the accuracy of pupil moving conditions, accuracy of pupil moving conditions was higher than the accuracy of the eye fixed conditions. However, the effect of visual angle of matrix and interaction effect were not significant. When P300 amplitude of target character was measured depending on how you stare at the target character, P300 amplitude of the head moving condition was greater than P300 amplitude of the pupil moving condition. There was no significant difference in the error distribution in head moving condition and pupil moving condition, while there was a significant difference between two eye gaze conditions and fixed gaze condition. The error was located at the neighboring characters of the target character in head moving condition and pupil moving condition, while the error was relatively distributed widely in fixed eye condition, error was occurred with high rate in characters far away from the center of matrix.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.685-695
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• 2008

This paper presents a method to segment the object of interest from a set of multi-view images with minimal user interaction. Specifically, after the user segments an initial image, we first estimate the transformations between foreground and background of the segmented image and the neighboring image, respectively. From these transformations, we obtain regions in the neighboring image that respectively correspond to the foreground and the background of the segmented image. We are then able to segment the neighboring image based on these regions, and iterate this process to segment the whole image set. Transformation of foregrounds are estimated by feature-based registration with free-form deformation, while transformation of backgrounds are estimated by homography constrained to affine transformation. Here, both are based on correspondence point pairs. Segmentation is done by estimating pixel color distributions and defining a shape prior based on the obtained foreground and background regions and applying them to a Markov random field (MRF) energy minimization framework for image segmentation. Experimental results demonstrate the effectiveness of the proposed method.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.3-16
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• 2018

The emergence of the fourth industrial revolution is rapidly changing the conventional society and the industry, eroding the boundaries among the technology, culture, and finance. In the mobility industry, as the engineering-based industry converges with the information technology, the mobile space is changing from mobility or safety-centric space into space where the passengers can consume infotainment or contents services. The contents evaluation of the future mobility is conducted in terms of usability or technology acceptance aspect, but according to the trend analysis, the mobility industries, such as vehicle OEMs, it is necessary to evaluate the emotional or sensibility factors for the development of their future mobile space design. Herein, this research study evaluates which sensibility factor should be evaluated in priority to develop the contents interaction in the future mobile space. Thus, using Patrick Jordan's Four Pleasure Model, the priority evaluation has been conducted among 116 Korean drivers. As a result of the statistical analysis and AHP (Analytic Hierarchy Process), it has been found that first, it is necessary to evaluate psychological, ideological, social and physical sensibility in the respective order, and second, it is necessary to evaluate based on the contents user type.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.227-236
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• 2007

Ubiquitous systems have grown explosively over the few years. Nowadays users' needs for high qualify service lead a various type of user terminals. One of various type of user interface, various types of effective human computer interface methods have been developed. In many researches, researchers have focused on using brain-wave interface, that is to say, BCI. Nowadays, researches which are related to BCI are under way to find out effective methods. But, most researches which are related to BCI are not centralized and not systematic. These problems brought about ineffective results of researches. In most researches related in HCI, that is to say - pattern recognition, the most important foundation of the research is to build correct and sufficient DB. But there is no effective and reliable standard research conditions when researchers are gathering brain-wave in BCI. Subjects as well as researchers do not know effective methods for gathering DB. Researchers do not know how to instruct subjects and subjects also do not know how to follow researchers' instruction. To solve these kinds of problems, we propose effective brain-wave DB building system using the five senses stimulation. Researcher instructs the subject to use the five senses. Subjects imagine the instructed senses. It is also possible for researchers to distinguish whether brain-wave is right or not. In real time, researches verify gathered brain-wane data using spectrogram. To verify effectiveness of our proposed system, we analyze the spectrogram of gathered brain-wave DB and pattern. On the basis of spectrogram and pattern analysis, we propose an effective brain-wave DB building method using the five senses stimulation.

• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.179-184
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• 2011

Current mobile devices have surpassed its boundaries as a more communication tool to a smart device which provides additional features. These features have supported the smart life of its users, but have reached its limit from low-performance processors and short-battery time. These issues can be resolved b implementing higher performing hardware, but they come with a burden of high cost. This paper introduces a new way of managing computing resources in a mobile device by enhancing the quality of human-computer interaction. The real-speed felt by users are mainly influenced by the time it takes form a user's input to the device to display the completed result on the screen. Since the size of the screen for mobile devices are small, if the processor only fetch data to be used for displaying on screen, the time can be significantly reduced. MVC+Prefetch Controller pattern accomplished this goal by using the minimum amount of data from DB to fetch display and still manages to support high-speed data transfer to achieve seamless display. This idea has been realized by practice using Samsung mobile phone S8500, which demonstrated the superior performance on user's perspective.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.119-137
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• 2022

Recent recommendation system studies apply various deep learning models to represent user and item interactions better. One of the noteworthy studies is ONCF(Outer product-based Neural Collaborative Filtering) which builds a two-dimensional interaction map via outer product and employs CNN (Convolutional Neural Networks) to learn high-order correlations from the map. However, ONCF has limitations in recommendation performance due to the problems with CNN and the absence of side information. ONCF using CNN has an inductive bias problem that causes poor performances for data with a distribution that does not appear in the training data. This paper proposes to employ a Vision Transformer (ViT) instead of the vanilla CNN used in ONCF. The reason is that ViT showed better results than state-of-the-art CNN in many image classification cases. In addition, we propose a new architecture to reflect side information that ONCF did not consider. Unlike previous studies that reflect side information in a neural network using simple input combination methods, this study uses an independent auxiliary classifier to reflect side information more effectively in the recommender system. ONCF used a single latent vector for user and item, but in this study, a channel is constructed using multiple vectors to enable the model to learn more diverse expressions and to obtain an ensemble effect. The experiments showed our deep learning model improved performance in recommendation compared to ONCF.