• Journal of the Korean Society of Costume
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• v.62 no.6
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• pp.139-153
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• 2012

To prolong the rapid progress of IT, it is necessary to develop contents through IT convergence among the existing goods & service and process areas to create new added-values. In particular, the wedding dress industry has infinite potential in utilizing various contents like virtual fitting by connecting with newly compelling IT areas such as smart phones, Augmented Reality (AR), and application contents. In the meantime, a large scale of the wedding industry has gained global competitiveness due to consulting expertise and the influence of the Korean Wave, whereas most small-sized wedding dress shops in Korea fall short of developing wedding dress designs and receiving relevant information. Accordingly, the purpose of this study was to help brides who have difficulties in choosing a wedding dress by decreasing their time and effort by providing wedding dress designs and information, according their desired image, body type, and circumstances through the utilization of virtual fitting application contents. Not only that, this study aims to diversify and specialize in wedding information and to help users to set a guideline for wedding dresses that are most suitable for them. Moreover, this study has an academic meaning in proposing an interdisciplinary convergence research model through the study of wedding dress design development, AR, and application contents utilization.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.341-345
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• 2009

We present an augmented reality (AR) application for cell phone where users put a virtual pet on their palms and play/interact with the pet by moving their hands and fingers naturally. The application is fundamentally based on hand/palm pose recognition and finger motion estimation, which is the main concern in this paper. We propose a fast and efficient hand/palm pose recognition method which uses natural features (e.g. direction, width, contour shape of hand region) extracted from a hand image with prior knowledge for hand shape or geometry (e.g. its approximated shape when a palm is open, length ratio between palm width and pal height). We also propose a natural interaction method which recognizes natural motion of fingers such as opening/closing palm based on fingertip tracking. Based on the proposed methods, we developed and tested the AR application on an ultra-mobile PC (UMPC).

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.11a
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• pp.575-578
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• 2000

In this work, all solid-state thin film supercapacitor(TFSC) was fabricated using tungsten trioxide (WO$_3$) with a structure WO$_3$/LiPON/WO$_3$/Pt/TiO$_2$/Si (substrate). After TiO$_2$ was deposited on Si(100) wafer by d.c. reactive sputtering, the Pt current collector films were grown on TiO$_2$glue layer without breaking vacuum by d.c. sputtering. Fabrication conditions of WO$_3$ thin film were such that substrate temperature, working pressure, gas ratio of $O_2$/Ar and r.f. power were room temperature, 5 mTorr, 20% (O$_2$(8sccm)/Ar(32sccm)) and 200W, respectively. LiPON electrolyte film were grown on the WO$_3$ film using r.f. magnetron sputtering at room temperature. The XRD pattern of the as-deposited WO$_3$ thin film were shown no crystalline peak (amorphous). The SEM image of as-deposited WO$_3$ thin film showed that the surface is smooth and uniform. The capacitiy of as-fabricated TFSC was 0$\times$10$^{-2}$ F/$\textrm{cm}^2$-${\mu}{\textrm}{m}$.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.80-85
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• 2024

The concept of single-modal digital art originated in the 20th century and has evolved through three key stages. Over time, digital art has transformed into multi-modal interaction, representing a new era in art forms. Based on multi-modal theory, this paper aims to explore the characteristics of interactive digital art in innovative art forms and its impact on user experience. Through an analysis of practical application of multi-modal interactive digital art, this study summarises the impact of creative models of digital art on the physical and mental aspects of user experience. In creating audio-visual-based art, multi-modal digital art should seamlessly incorporate sensory elements and leverage computer image processing technology. Focusing on user perception, emotional expression, and cultural communication, it strives to establish an immersive environment with user experience at its core. Future research, particularly with emerging technologies like Artificial Intelligence(AR) and Virtual Reality(VR), should not merely prioritize technology but aim for meaningful interaction. Through multi-modal interaction, digital art is poised to continually innovate, offering new possibilities and expanding the realm of interactive digital art.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.33-43
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• 2020

Retrieving a 3D model from a 3D database and augmenting the retrieved model in the Augmented Reality system simultaneously became an issue in developing the plausible AR environments in a convenient fashion. It is considered that the sketch-based 3D object retrieval is an intuitive way for searching 3D objects based on human-drawn sketches as query. In this paper, we propose a novel deep learning based approach of retrieving a sketch-based 3D object as for an Augmented Reality Model. For this work, we introduce a new method which uses Sketch CNN, Wasserstein CNN and Wasserstein center loss for retrieving a sketch-based 3D object. Especially, Wasserstein center loss is used for learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. The proposed 3D object retrieval and augmentation consist of three major steps as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we adopt sketch-based object matching method to localize the natural marker of the images to register a 3D virtual object in AR system. Using the detected marker, the retrieved 3D virtual object is augmented in AR system automatically. By the experiments, we prove that the proposed method is efficiency for retrieving and augmenting objects.

• Journal of The Korean Astronomical Society
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• v.52 no.6
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• pp.217-225
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• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.237-243
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• 2018

Most of public underground facilities are installed under the ground, thus it is difficult to recognize the accurate location even with the drawings. Studies are conducted to understand exact position of underground facilities using augmented reality. However, in those studies, establishing of additional 3D object model database is needed when AR system is used at field. Because most of public underground facility information are established as 2 dimensional. In this study, AR system is developed as mobile application which can use original 2D underground facility data to transfer 3D AR data automatically without additional 3D database establishment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1104-1109
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• 2019

Business utilization of holograms is widely used as a similar hologram. The use of holograms has been proposed in many cases. In this paper, we present an outline of similar holograms using up to 3 or 4 facets, and express the similar holograms using the results produced by 3D modeling for a building from dealing with the representation of buildings from hololens to pseudo-hologram by using 3D modeling results. In addition, to reflect the real image of the disadvantage of modeling, we propose a method to enhance the 3D expression of the object by reflecting the actual building surface on the 3D model through photographing. Virtual building seen by the human eye can be virtually shown in space through a hologram among various methods shown in a virtual space such as AR / VR / MR. Through this study, it will be possible to express holograms of various materials such as buildings or cultural properties with enhanced realism.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.569-570
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• 2022

Failure situations occur frequently in the industry, and the existing 2D-based manual for this purpose is not intuitive to understand and it is difficult to immediately interact with maintenance experts. In this paper, we propose a technology that enables workers to perform maintenance in real time with the help of experts without restrictions in time and place based on augmented reality when a failure situation occurs at a remote location. A local virtual human-based expert diagnoses a failure situation based on an adapted panoramic image of a remote failure situation while wearing an HMD headset, and gives instructions to a remote operator. In addition, in an augmented reality (AR) environment in a remote location, for the interaction between the operator and the expert, the HMD's microphone is used to create the expert's hand as well as verbal communication. You can use to point or draw a picture. If this technology is used, it is possible to overcome the limitations of the existing 2D-based manual, and to provide assistance in performing maintenance smoothly remotely even if an expert does not directly visit a remote location.