• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.3
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• pp.289-295
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• 2009

HRR(High Resolution Range) profiles show one-dimensional radar images including electromagnetic scattering phenomena of a target. Thus, they are not only robust to noise, but also easily obtainable in a real-time. However, in order to construct a training database for the success of radar target identification, a huge amount of HRR profiles are needed because HRR profiles are highly dependent on the relative angle between the radar and the target. In order to alleviate this difficulty, a database construction method based on the scenarios of target's movement is proposed. The proposed method is able to provide a reliable target identification performance even with a small amount of training database.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.618-630
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• 2011

Mobile RFID system, that consists of the existing RFID reader mounted on the mobile devices such as smartphones, is able to provide the users a variety of services and convenience. Although the users can get the information about a certain product anytime anywhere, there is high probability that their privacy may be violated because their belongings with RFID tags can be scanned by other mobile readers at any time. Several RFID authentication schemes have been proposed to deal with these privacy issues. However, since the existing solutions require heavy computation on the tag side, most of them is not applicable to the general low-cost passive tags which do not have any processing unit. In this paper, we propose the efficient authentication scheme for mobile RFID system applicable to the passive tags as well as the active ones by the best use of computing capability of mobile devices. The proposed scheme satisfies the import security issues such as tag protection, untraceability, anti-traffic analysis. We also implement the proposed scheme on top of real smartphone for feasibility and show the experimental results from it.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.739-742
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• 2008

Surface Image Velocity (SIV) is a technique which measures the surface velocity of river by using the principle of Paticle Image Velocimetry (PIV). The technique is economical and efficient way to measure velocity in rivers. The present paper aims to apply the technique to three rivers in Korea. It uses pairs of river surface images taken with two digital-cameras and reference points and cross section data which were acquired through plane survey. The performance of SIV was verified with automatic cart on an experimental flume. The test revealed that average error was less than 10 %, which assures that SIV can be used to measure velocity accurately. When it was applied to rivers with low water levels or rough weather condition, however, it showed the error about 20 %. If the problems of SIV technique are settled down, it can be one of the most convenient and economical ways to measure water discharge anytime and anywhere. And then it would be helpful to river management as developing a real-time river information system.

• Journal of Applied Biological Chemistry
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• v.59 no.2
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• pp.165-169
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• 2016

The quantitative analytical method for the bioactive substance, 3-cyano-4-methoxy-N-methyl-2-pyridone (ricinine) and an index compound, ricinoleic acid in castor plant (Ricinus communis) extract or oil was developed. For the determination of a pyridone alkaloid compound, ricinine, successive cartridge cleanup method combined with ultra-performance liquid chromatography was set up with $ENVI-Carb^{TM}$ (0.5 g) and $C_{18}$ SPE cartridges. Accuracy and precision were evaluated through fortification studies of one biopesticide (PE) at 10 and $100mg\;kg^{-1}$. Mean recoveries of ricinine were 98.7 and 96.0 % associated with less than 10 % RSD, respectively. For the determination of ricinoleic acid in castor extract and oil, saponification and methylation were optimized using gas chromatography-time of flight mass spectrometry. Recovery was more than 84.8 % associated with 6.2 % RSD after derivatization procedure. Both methodologies developed were applied to analyze real samples including three castor oil products and six commercially available biopesticides containing R. communis, collected at Korean market. The contents of ricinine and ricinoleic acid in most commercial biopesticides were less than the oil or extract contents indicated by label.

• Korean Journal of Applied Biomechanics
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• v.30 no.2
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• pp.185-195
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• 2020

Objective: The purpose of this study was to identify recent domestic and international research trends regarding shoes carried out in biomechanics field and to suggest the direction of shoe research later. Method: To achieve this goal of research, the Web of Science, Scopus, PubMed, Korea Education and Research Information Service and Korean Citation Index were searched to identify trends in 64 domestic and international research. Also, classified into the interaction of the human body, usability evaluation of functional shoes, smart shoe development research, and suggested the following are the suggestions for future research directions. Conclusion: A study for the coordination of muscle activity, control of motion and prevention of injury should be sought by developing shoes of eco-friendly materials, and scientific evidence such as physical aspects, materials, floor shapes and friction should be supported. Second, a study on elite athletes in various sports is needed based on functional shoes using new materials to improve their performance along with cooperation in muscle activities and prevention of injury. Third, various information and energy production are possible in real time through human behavioral information, and the application of Human Machine Interface (HMI) technology through shoe-sensor-human interaction should be explored.

• Smart Structures and Systems
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• v.16 no.1
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• pp.163-181
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• 2015

This research investigates the structural health monitoring of nonlinear structures after a major seismic event. It considers the identification of flag-shaped or pinched hysteresis behavior in response to structures as a more general case of a normal hysteresis curve without pinching. The method is based on the overall least squares methods and the log likelihood ratio test. In particular, the structural response is divided into different loading and unloading sub-half cycles. The overall least squares analysis is first implemented to obtain the minimum residual mean square estimates of structural parameters for each sub-half cycle with the number of segments assumed. The log likelihood ratio test is used to assess the likelihood of these nonlinear segments being true representations in the presence of noise and model error. The resulting regression coefficients for identified segmented regression models are finally used to obtain stiffness, yielding deformation and energy dissipation parameters. The performance of the method is illustrated using a single degree of freedom system and a suite of 20 earthquake records. RMS noise of 5%, 10%, 15% and 20% is added to the response data to assess the robustness of the identification routine. The proposed method is computationally efficient and accurate in identifying the damage parameters within 10% average of the known values even with 20% added noise. The method requires no user input and could thus be automated and performed in real-time for each sub-half cycle, with results available effectively immediately after an event as well as during an event, if required.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.75-90
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• 2018

Generally, solutions that can increase patients' satisfaction are in conflict with the solutions that can increase the satisfaction and profitability of the medical personnels. In this paper, we compare appointment patterns using multiple performance measures that take into account both hospital's position and customers' position, and propose the best appointment pattern especially for an efficient scheduling in a plastic surgery clinic. A simulation model using ARENA is designed based on real hospital data. Based on this model, we compare 5 appointment patterns. To check the robustness of the appointment patterns, we have experimented on various appointment situations by considering peak, extreme peak seasons, and peak, off-peak days. We found that Triangle-like pattern turned out to be most efficient regardless of appointment situations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.695-712
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• 2021

As the use of smart devices is being increased rapidly by the development of internet and IT technology, the contents production and utilization rate are showing higher increase, too. In addition, the type of contents also shows very diverse forms such as education, game, video, UCC, etc. In the meantime, the contents are reproduced in diverse forms by reprocessing the original contents, and they are being serviced through the contents service platform. Therefore, the platform to make the contents reprocessing easy and fast is needed. As the diverse contents distribution channels such as YouTube, SNS, App Service, etc, easier contents distribution platform is needed, and the development of the relevant area is expected. In addition, as the selective consumption of the contents having easy accessibility through diverse smart devices is distinguished, the demand for the platform and service that can identify the contents consumption propensity by individual is being increased. Therefore, in this study, to vitalize the online contents distribution, the contents reproduction and publishing platform, was designed and materialized, which can reproduce and distribute the contents based on the real-time contents editing technology in URL unit and the consumer propensity analysis technology using the data management-based broadcasting contents distribution metadata technology and the edited image contents streaming technology. In addition, in the results of comparing with other platforms through the experiment, the performance superiority of the suggested platform was verified. If the suggested platform is applied to the areas of education, broadcasting, press, etc, the multi-media contents can be reproduced and distributed easily, through which the vitalization of contents-related industry is expected.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.155-161
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• 2021

Gesture recognition analytics through a camera in real time have been widely studied in recent years. Since a small number of features from human joints are extracted, low accuracy of classifying models is get in conventional gesture recognition studies. In this paper, CBAM (Convolutional Block Attention Module) with high accuracy for classifying images is proposed as a classification model and algorithm calculating the angle of joints depending on actions is presented to solve the issues. Employing five exercise gestures images from the fitness posture images provided by AI Hub, the images are applied to the classification model. Important 8-joint angles information for classifying the exercise gestures is extracted from the images by using MediaPipe, a graph-based framework provided by Google. Setting the features as input of the classification model, the classification model is learned. From the simulation results, it is confirmed that the exercise gestures are classified with high accuracy in the proposed model.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.