• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.23-28
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• 2021

Augmented reality is a technique for combining virtual images into real life by showing information of virtual 3D objects on top of a real-world environment (Azuma et al., 2001). This study is an augmented reality-based educational content delivery device that receives user input that selects either a preset object or a photographed object for augmented reality-based training; It includes a three-dimensional design generation unit that generates a stereoscopic model of the augmented reality environment from an object, a three-dimensional view of the scene, a disassembly process of the developing road from a three-dimensional model, and a content control unit provided by the user terminal by generating educational content including a three-dimensional model, a scene chart, a scene, a decomposition process, and a coupling process to build a coupling process from the scene to the three-dimensional model in an augmented reality environment. The next study provides a variety of educational content so that children can use AR technology as well as shapes to improve learning effectiveness. We also believe that studies are needed to quantitatively measure the efficacy of which educational content is more effective when utilizing AR technology.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.130-135
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• 2024

The Stable diffsuion AI tool is popular among designers because of its flexible and powerful image generation capabilities. However, due to the diversity of its AI models, it needs to spend a lot of time testing different AI models in the face of different design plans, so choosing a suitable general AI model has become a big problem at present. In this paper, by comparing the AI images generated by two different Stable diffsuion models, the advantages and disadvantages of each model are analyzed from the aspects of the matching degree of the AI image and the prompt, the color composition and light composition of the image, and the general AI model that the generated AI image has an aesthetic sense is analyzed, and the designer does not need to take cumbersome steps. A satisfactory AI image can be obtained. The results show that Playground V2.5 model can be used as a general AI model, which has both aesthetic and design sense in various style design requirements. As a result, content designers can focus more on creative content development, and expect more groundbreaking technologies to merge generative AI with content design.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.297-304
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• 2022

The models used for ionosphere error correction in positioning using Global Navigation Satellite System (GNSS) are representatively Klobuchar model and NeQuick model. Although these models can correct the ionosphere error in real time, the disadvantage is that the accuracy is only 50-60%. In this study, a method for polynomial modeling of Global Ionosphere Map (GIM) which provides Vertical Total Electron Content (VTEC) in grid type was studied. In consideration of Ionosphere Pierce Points (IPP) of satellites with a receivable elevation angle of 15 degrees or higher on the Korean Peninsula, the target area for model generation and provision was selected, and the VTEC at 88 GIM grid points was modeled as a polynomial. The developed VTEC polynomial model shows a data reduction rate of 72.7% compared to GIM regardless of the number of visible satellites, and a data reduction rate of more than 90% compared to the Slant Total Electron Content (STEC) polynomial model when there are more than 10 visible satellites. This VTEC polynomial model has a maximum absolute error of 2.4 Total Electron Content Unit (TECU) and a maximum relative error of 9.9% with the actual GIM. Therefore, it is expected that the amount of data can be drastically reduced by providing the predicted GIM or real-time grid type VTEC model as the parameters of the polynomial model.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.287-293
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• 2023

With the rapid development of domestic and international over-the-top markets, a large amount of video content is being created. As the volume of video content increases, consumers tend to increasingly check data concerning the videos before watching them. To address this demand, video summaries in the form of plot descriptions, thumbnails, posters, and other formats are provided to consumers. This study proposes an approach that automatically generates posters to effectively convey video content while reducing the cost of video summarization. In the automatic generation of posters, face recognition and clustering are used to gather and classify character data, and keyframes from the video are extracted to learn the overall atmosphere of the video. This study used the facial data of the characters and keyframes as training data and employed technologies such as DreamBooth, a text-to-image generation model, to automatically generate video posters. This process significantly reduces the time and cost of video-poster production.

• Journal of Astronomy and Space Sciences
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• v.27 no.2
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• pp.117-122
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• 2010

An ionospheric error simulation is needed for creating precise Global Positioning System (GPS) signal using GPS simulator. In this paper we developed Klobuchar coefficients n ${\alpha}_n$ and ${\beta}_n$ (n = 1, 2, 3, 4) generation algorithms for simulator and verified accuracy of the algorithm. The algorithm extract those Klobuchar coefficients from broadcast (BRDC) messages provided by International GNSS Service during three years from 2006 through 2008 and curve-fit them with sinusoidal and linear functions or constant. The generated coefficients from our developed algorithms are referred to as MODL coefficients, while those coefficients from BRDC messages are named as BRDC coefficients. The maximum correlation coefficient between MODL and BRDC coefficients was found for ${\alpha}_2$ and the value was 0.94. On the other hand, the minimum correlation was 0.64 for the case of ${\alpha}_1$. We estimated vertical total electron content using the Klobuchar model with MODL coefficients, and compared the result with those from the BRDC model and global ionosphere maps. As a result, the maximum RMS was 3.92 and 7.90 TECU, respectively.

• Journal of Astronomy and Space Sciences
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• v.28 no.1
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• pp.71-77
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• 2011

Ionospheric error modeling is necessary to create reliable global navigation satellite system (GNSS) signals using a GNSS simulator. In this paper we developed algorithms to generate Klobuchar coefficients ${\alpha}_n$, ${\beta}_n$ (n = 1, 2, 3, 4) for a GNSS simulator and verified accuracy of the algorithm. The eight Klobuchar coefficients were extracted from three years of global positioning system broadcast (BRDC) messages provided by International GNSS service from 2006 through 2008 and were fitted with Fourier series. The generated coefficients from our developed algorithms are referred to as Fourier Klobuchar model (FOKM) coefficients, while those coefficients from BRDC massages are named as BRDC coefficients. The correlation coefficient values between FOKM and BRDC were higher than 0.97. We estimated total electron content using the Klobuchar model with FOKM coefficients and compared the result with that from the BRDC model. As a result, the maximum root mean square was 1.6 total electron content unit.

• Geomechanics and Engineering
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• v.21 no.6
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• pp.537-549
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• 2020

The presence of cracks changes the water content pattern during seepage through a cracked soil as compared to that of intact soil. In addition, several different crack networks may form in one soil type. These two factors result in a variation of water contents in the soil matrix part of a cracked soil during seepage. This paper presents an investigation of the effect of crack network representation on the water content of the soil matrix part of cracked soil using numerical models. A new method for the numerical generation of crack networks incorporating connections among crack endpoints was developed as part of the investigation. Numerical analysis results indicated that the difference in the point water content was large, whereas the difference in the average water content was relatively small, indicating the uniqueness of the crack network representation on the average water content of the soil matrix part of cracked soil.

• The KIPS Transactions:PartA
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• v.11A no.2
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• pp.207-212
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• 2004

In this paper, based on SCORM Sequencing Model, we propose the learning content structure which has structure informations of learning object and decision rules how to transfer learning object to learner. It is intended to provide the technical means for learning content objects to be easily shared and reused across multiple learning delivery environment. We develop the generation tool of learning object sequencing, for processing the learning with variable teaching methodologies. The teaming objects also are automatically packaged the PIE(Package Interchange File) to transmit with SCORM RTE(Run-Time Environment) and attached SCO(Sharable Content Object) function for tracking learner information.

• Journal of information and communication convergence engineering
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• v.20 no.4
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• pp.288-294
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• 2022

The media content market has been growing, as various types of content are being mass-produced owing to the recent proliferation of the Internet and digital media. In addition, platforms that provide personalized services for content consumption are emerging and competing with each other to recommend personalized content. Existing platforms use a method in which a user directly inputs video metadata. Consequently, significant amounts of time and cost are consumed in processing large amounts of data. In this study, keyframes and audio spectra based on the YCbCr color model of a movie trailer were extracted for the automatic generation of metadata. The extracted audio spectra and image keyframes were used as learning data for genre recognition in deep learning. Deep learning was implemented to determine genres among the video metadata, and suggestions for utilization were proposed. A system that can automatically generate metadata established through the results of this study will be helpful for studying recommendation systems for media super-personalization.

• ETRI Journal
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• v.41 no.3
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• pp.348-357
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• 2019

Fog radio access network (F-RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm-based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F-RAN. First, an F-RAN system model is presented that includes a certain number of randomly distributed fog access points (F-APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F-RANs is described. Third, the details of the proposed optimal genetic algorithm-based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.