• Journal of Digital Convergence
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• v.17 no.9
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• pp.185-192
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• 2019

The introduction of smart technology provides accuracy, safety, and efficiency to both physicians and patients. Although interest in a clear aligner is increasing among users worldwide, the current clear aligner requires a visit to the hospital every one or two weeks for replacement, which is a very cumbersome process. There is also confusion among dentists and patients because about 40 to 80 devices are made, and calibration is done based on the order and duration of the clear aligner. Therefore, this study designed and developed a clear aligner management system so that communication between the patient and dentist can be smoothly performed by inserting the QR code into the transparent correction device. As a result, the size of the QR code was recognized as $6{\ast}6mm^2$ which can be used in the oral and the recognition distance was 100% within 12 cm. Since the dentist can remotely manage the patient with the proposed system and improve the correction effect, it is possible to manage patients abroad, as well as domestically.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.2
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• pp.29-38
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• 2021

The Internet including mobile networks is developing to overcoming the limitation of physical distance and providing or acquiring information from remote locations. However, the systems that use video as primary information require higher bandwidth for recognizing the situation in remote places more accurately through high-quality video as well as lower latency for faster interaction between devices and users. The emergence of the 5th generation mobile network provides features such as high bandwidth and precise location recognition that were not experienced in previous-generation technologies. In addition, the Mobile Edge Computing that minimizes network latency in the mobile network requires a change in the traditional system architecture that was composed of the existing smart device and high availability server system. However, even with 5G and MEC, since there is a limit to overcome the mobile network state fluctuations only by enhancing the network infrastructure, this study proposes a high-definition video streaming system in ultra-low latency based on the SRT protocol that provides Forward Error Correction and Fast Retransmission. The proposed system shows how to deploy software components that are developed in consideration of the nature of 5G and MEC to achieve sub-1 second latency for 4K real-time video streaming. In the last of this paper, we analyze the most significant factor in the entire video transmission process to achieve the lowest possible latency.

• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.122-132
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• 2010

AT-DMB system has been developed to increase data rate up to double of conventional T-DMB in the same bandwidth while maintaining backward compatibility. The AT-DMB system adopted hierarchical modulation which adds BPSK or QPSK signal as enhancement layer to existing DQPSK signal. The enhancement layer signal should be small enough to maintain backward compatibility and to minimize the coverage loss of conventional T-DMB service coverage. But this causes the enhancement layer signal of AT-DMB susceptible to fading effect in transmission channel. A turbo code which has improved error correction capability than convolutional code, is applied to the enhancement layer signal of the AT-DMB system for compensating channel distortion. However there is a need for other solutions for better reception of AT-DMB signal in receiver side without increasing transmitting power. In this paper, we propose adaptive array antenna system with Eigen-space beamforming algorithm which benefits beamforming gain along with diversity gain. We analyzed the reception performances of AT-DMB system in indoor and mobile environments when this new smart antenna system and algorithm is introduced. The computer simulation results are presented along with analysis comments.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.701-715
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• 2021

The drastic advance of recent deep learning technologies is heavily dependent on training datasets which are essential to train models by themselves with less human efforts. In comparison with the work to design deep learning models, preparing datasets is a long haul; at the moment, in the domain of vision intelligent, datasets are still being made by handwork requiring a lot of time and efforts, where workers need to directly make labels on each image usually with GUI-based labeling tools. In this paper, we overview the current status of vision datasets focusing on what datasets are being shared and how they are prepared with various labeling tools. Particularly, in order to relieve the repetitive and tiring labeling work, we present an interactive smart image annotating system with which the annotation work can be transformed from the direct human-only manual labeling to a correction-after-checking by means of a support of automatic labeling. In an experiment, we show that automatic labeling can greatly improve the productivity of datasets especially reducing time and efforts to specify regions of objects found in images. Finally, we discuss critical issues that we faced in the experiment to our annotation system and describe future work to raise the productivity of image datasets creation for accelerating AI technology.

• Smart Media Journal
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• v.11 no.9
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• pp.30-38
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• 2022

It is important to grasp biometric data in real time for prompt action in the event of a safety accident at a work site where the risk of safety accidents exists. Among them, blood oxygen saturation is the most important factor in maintaining human life, so real-time oxygen saturation measurement and monitoring is necessary according to the situation as a preemptive response for worker safety management. By receiving real-time bio-signals from workers wearing health and life-risk protective clothing, and sharing and analyzing the worker's risk status in an external system, it is possible to diagnose the worker's current condition and efficiently respond to emergencies that may occur to the worker. In this paper, we propose a wearable oxygen saturation measurement platform technology that can monitor the risk of harmful gases and oxygen saturation of the wearer in real time and ensure the wearer's activity and safety in order to cope with emergency situations at the scene of an accident. If we overcome the limitations identified through the results of the proposed system later and apply improved biodata such as motion correction to the platform, we expect that it will be usable not only in hazardous gas environments, but also in hospitals and homes for emergency patients.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.93-98
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• 2016

Recently, the project related to the smart grid are being actively studied around the developed world. In particular, the long-term stabilization measures distributed power supply problem has been highlighted. In this paper, we propose a three-dimensional numerical weather prediction models to compare the error rate information which combined with the physical models and statistical models to predict the output of distributed power. Proposed model can predict the system for a stable power grid-can improve the prediction information of the distributed power. In performance evaluation, proposed model was a generation forecasting accuracy improved by 4.6%, temperature compensated prediction accuracy was improved by 3.5%. Finally, the solar radiation correction accuracy is improved by 1.1%.

• Smart Structures and Systems
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• v.21 no.4
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• pp.397-405
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• 2018

In this paper, a novel simple shear deformation theory for buckling analysis of single layer graphene sheet is formulated using the nonlocal differential constitutive relations of Eringen. The present theory involves only three unknown and three governing equation as in the classical plate theory, but it is capable of accurately capturing shear deformation effects, instead of five as in the well-known first shear deformation theory (FSDT) and higher-order shear deformation theory (HSDT). A shear correction factor is, therefore, not required. Nonlocal elasticity theory is employed to investigate effects of small scale on buckling of the rectangular nano-plate. The equations of motion of the nonlocal theories are derived and solved via Navier's procedure for all edges simply supported boundary conditions. The results are verified with the known results in the literature. The influences played by Effects of nonlocal parameter, length, thickness of the graphene sheets and shear deformation effect on the critical buckling load are studied. Verification studies show that the proposed theory is not only accurate and simple in solving the buckling nanoplates, but also comparable with the other higher-order shear deformation theories which contain more number of unknowns.

• Smart Structures and Systems
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• v.19 no.2
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• pp.115-126
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• 2017

In this paper, size dependent bending and free flexural vibration behaviors of functionally graded (FG) nanobeams are investigated using a nonlocal quasi-3D theory in which both shear deformation and thickness stretching effects are introduced. The nonlocal elastic behavior is described by the differential constitutive model of Eringen, which enables the present model to become effective in the analysis and design of nanostructures. The present theory incorporates the length scale parameter (nonlocal parameter) which can capture the small scale effect, and furthermore accounts for both shear deformation and thickness stretching effects by virtue of a hyperbolic variation of all displacements through the thickness without using shear correction factor. The material properties of FG nanobeams are assumed to vary through the thickness according to a power law. The neutral surface position for such FG nanobeams is determined and the present theory based on exact neutral surface position is employed here. The governing equations are derived using the principal of minimum total potential energy. The effects of nonlocal parameter, aspect ratio and various material compositions on the static and dynamic responses of the FG nanobeam are discussed in detail. A detailed numerical study is carried out to examine the effect of material gradient index, the nonlocal parameter, the beam aspect ratio on the global response of the FG nanobeam. These findings are important in mechanical design considerations of devices that use carbon nanotubes.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.48-55
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• 2016

Ambient-light sensor system, which changes the brightness of a display as ambient light change, was studied to reduce the power consumption of the mobile applications such as note PC, tablet PC and smart phone. The ambient-light sensor system should be integrated on a display panel to improve the complexity and cost of mobile applications, so the ambient-light sensor and readout circuit was integrated on a display panel using low-temperature poly-silicon thin film transistors (LTPS-TFT). We proposed the new compensation method to correct the panel-to-panel variation of the ambient-light sensors, without additional equipment. We designed and investigated the new readout circuit with the proposed compensation method and the analog-to-digital converter for the final digital output of ambient light. The readout circuit has very simple structure and control timing to be integrated with LTPS-TFT, and the input luminance ranges from 10 to 10,000 lux. The readout rate is 100 Hz, and maximum differential non-uniformity with 20 levels of the final output below 0.5 LSB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.366-381
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• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.