• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.592-599
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• 2007

The advancement of wireless technology improves the electronic learning by combining with the mobile function, and promotes the expanded transition to the mobile learning. Basically, the mobile learning provides the usefulness in terms of tile and space to provide learners with the access to the educational contents. However, the small display device and limited memory space of mobile device is limiting the access to the learning contents simply to the text-based transmission. This paper designed and implemented the multimedia mobile learning system that reduces the size of parser by define into MSMIL composed only of needed tag to multimedia contents production in the mobile devices by using the SMIL that supports the multimedia object synchronization reduces the data of multimedia learning data and enhances the transmission efficiency by applying the macro method in creating the contents of learning. The results of implementation indicates that it simplifies the designing language, makes the language learning easy, and saves the CPU resources for the parsing by reducing the size of parser.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.199-209
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• 2022

Typical anomaly detection algorithms are trained by using prior data. Thus the batch learning based algorithms cause inevitable performance degradation when characteristics of newly incoming normal data change over time. We propose an online anomaly detection algorithm which can consider the gradual characteristic changes of incoming normal data. The proposed algorithm based on one-class classification model includes both offline and online learning procedures. In offline learning procedure, the algorithm learns the prior data to be close to centroid of the latent space and then updates the centroid of the latent space incrementally by new incoming data. In the online learning, the algorithm continues learning by using the updated centroid. Through experiments using public underwater acoustic data, the proposed online anomaly detection algorithm takes only approximately 2 % additional learning time for the incremental centroid update and learning. Nevertheless, the proposed algorithm shows 19.10 % improvement in Area Under the receiver operating characteristic Curve (AUC) performance compared to the offline learning model when new incoming normal data comes.

• Journal of the Korean Institute of Educational Facilities
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• v.28 no.4
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• pp.11-17
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• 2021

This study is aimed at presenting an efficient management plan of user participatory design in a situation where the School Space Innovation Project is in progress. 2 schools that were the targets of the Incheon School Space Innovation Project in 2019 were selected for this, and features such as the physical environment of that classroom, classroom usage plan, and the stepwise outcome of the user participatory design workshop were contemplated. Especially the workshop outcome was compared and analyzed quantitatively, focusing on the actual master plan and zoning plan, in order to identify the feature that opinions of various users are reflected on the actual plan. As a result, the following conclusion could be reached. Firstly, it was confirmed that the expression about the user preferential space influences the classroom usage plan of that classroom. Vague expressions about the whole space held a large majority of the objects for the linguistic expression of the preferential space. The expression mode as limited as the expression of the actions that users want to carry out in the space. On the other hand, when the usage purpose of the classroom was definite, it was confirmed that the demand for furniture·facility is relatively high. Secondly, according to the analysis of zoning for each function, it seems that the stereotype, which is arranged on the basis of the chalkboard at the front of existing classrooms, was applied in the case of the learning zone. However, in cases of other functions, a tendency was identified that the user carries out an image description that reflects the physical features of the space. Sufficient preparation will need to precede for the efficient management of the user participatory design workshop and the acceptance of various opinions. It seems that especially the classroom usage plan, number of workshops, consultation of each step, and the education about the space expression mode affect the master plan.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.51.3-51.3
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• 2019

Solar magnetograms are important for studying solar activity and predicting space weather disturbances1. Farside magnetograms can be constructed from local helioseismology without any farside data2-4, but their quality is lower than that of typical frontside magnetograms. Here we generate farside solar magnetograms from STEREO/Extreme UltraViolet Imager (EUVI) $304-{\AA}$ images using a deep learning model based on conditional generative adversarial networks (cGANs). We train the model using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) $304-{\AA}$ images and SDO/Helioseismic and Magnetic Imager (HMI) magnetograms taken from 2011 to 2017 except for September and October each year. We evaluate the model by comparing pairs of SDO/HMI magnetograms and cGAN-generated magnetograms in September and October. Our method successfully generates frontside solar magnetograms from SDO/AIA $304-{\AA}$ images and these are similar to those of the SDO/HMI, with Hale-patterned active regions being well replicated. Thus we can monitor the temporal evolution of magnetic fields from the farside to the frontside of the Sun using SDO/HMI and farside magnetograms generated by our model when farside extreme-ultraviolet data are available. This study presents an application of image-to-image translation based on cGANs to scientific data.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• Journal of the Korean association of regional geographers
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• v.4 no.2
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• pp.201-217
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• 1998

This paper examined the strategy of experiential learning on Web-based cyberspace for constructive education of social studies. The results as follows : The first, constructivism has brought the paradigm shift in traditional principles of teaching and learning, constructivism is not a theory about teaching, it is a theory about knowledge and learning, learning is understood as a self-regulated process of resolving inner cognitive conflicts that often become apparent through experience, collaborative discourse, and reflection. It is proper for constructive education of social studies to carry out from cognitive constructivism to socio-cultural constructivism, from socio-cultural constructivism to cognitive constructivism and co-constructivism, considering the aim or objectives of social studies education. The second, Web-based Instruction(WBI) can provide learners for constructive environments which can be proper for teaching and learning. WBI was suggested as the best medium for constructive education of social studies in the information age. WBI must design teaching and learning so that may not be teacher-centered, if teacher-centered, it is not constructivism. The third, Web-based cyberspace is the proper mediated experience fields for experiential learning to effectively study regions or space because of overcoming distance fractions through the time-space convergence, it actualize the constructive education of social studies in the space age.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.35-40
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• 2019

Traditional Malware Detection is susceptible for detecting malware which is modified by polymorphism or obfuscation technology. By learning patterns that are embedded in malware code, machine learning algorithms can detect similar behaviors and replace the current detection methods. Data must collected continuously in order to learn malicious code patterns that change over time. However, the process of storing and processing a large amount of malware files is accompanied by high space and time complexity. In this paper, an HDFS-based distributed processing system is designed to reduce space complexity and accelerate feature extraction time. Using a distributed processing system, we extract two API features based on filtering basis, 2-gram feature and APICFG feature and the generalization performance of ensemble learning models is compared. In experiments, the time complexity of the feature extraction was improved about 3.75 times faster than the processing time of a single computer, and the space complexity was about 5 times more efficient. The 2-gram feature was the best when comparing the classification performance by feature, but the learning time was long due to high dimensionality.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.125-134
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• 2022

This paper implements an AR manual for a vehicle that can be used even in the vehicle interior space where it is difficult to apply the augmentation method of AR content, which is mainly used, and applies a deep learning model to improve the augmentation matching between real space and virtual objects. Through deep learning, the logo of the steering wheel is recognized regardless of the position, angle, and inclination, and 3D interior space coordinates are generated based on this, and the virtual button is precisely augmented on the actual vehicle parts. Based on the same learning model, the function to recognize the main warning light symbols of the vehicle is also implemented to increase the functionality and usability as an AR manual for vehicles.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.1
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• pp.223-242
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• 2020

Innovation in university library spaces is challenging to effectively support the education of the university's future learning and innovation capabilities, including creativity, critical thinking, communication and collaboration. The purpose of this study is to investigate the perception and need of library space from the perspective of users and librarians, and to suggest the direction of space innovation through this. For this study, we designed each questionnaire for users and librarians, and collected responses from 363 users and 186 librarians in the university library to analyze their needs and perceptions about their library space. The librarian's need for the space was analyzed by the size of the library and the demographic factors of the librarian. The user's need was analyzed by the user's attributes. In addition, we analyzed the differences between librarians and users for the need for space and space services. The results of this paper may be useful for reference when planning a new library or building a space based on user.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1517-1520
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• 2003

Generally, the environment we want to apply classifier system to is composed of several state spaces. So in this paper, we propose the layered classifier system having multifarious rule bases. From sensor's inputs, the lower layer of the layered classifier system learns strategies for each environmental state space. The higher layer learns how to allot each rule base of the strategy for environmental state space properly. To evaluate the proposed architecture of classifier system, we designed virtual environment having multifarious state spaces and from the analysis of the experimental results, we affirm that layered classifier system could find better strategies during a little time than other established classifier system's findings.