• Journal of Korea Multimedia Society
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• v.16 no.8
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• pp.954-961
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• 2013

To enhance the efficiency of network, content-centric networking (CCN), one of future Internet architectures, allows network nodes to temporally cache transmitted contents and then to directly respond to request messages which are relevant to previously cached contents. Also, since CCN uses a hierarchical content-name, not a host identity like source/destination IP address, for request/response packet routing and CCN request message does not include requester's information for privacy protection, contents-providers/ network nodes can not identify practical requesters sending request messages. So to send back relevant contents, network nodes in CCN records both a request message and its incoming interfaces on Pending Interest Table (PIT). Then the devices refer PIT to return back a response message. If PIT is exhausted, the device can not normally handle request/response messages anymore. Hence, it is needed to detect/react attack to exhaust PIT. Hence, in this paper, we propose improved detection/reaction schemes against attacks to exhaust PIT. In practice, for fine-grained control, this proposal is applied to each incoming interface. Also, we propose the message framework to control attack traffic and evaluate the performance of our proposal.

• Journal of Soil and Groundwater Environment
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• v.25 no.3
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• pp.74-83
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• 2020

Since fine dust (PM₁₀) has a significant influence on soil and groundwater composition during dry and wet deposition processes, it is of a vital importance to understand the fate and transport of aerosol in geological environments. Fine dust is formed after the chemical reaction of several precursors, typically observed in short intervals within a few hours. In this study, deep learning approach was applied to predict the fate of fine dust in an urban area. Deep learning training was performed by combining convolutional neural network (CNN) and recurrent neural network (RNN) techniques. The PM₁₀ concentration after 1 hour was predicted based on three-hour data by setting SO₂, CO, O₃, NO₂, and PM₁₀ as training data. The obtained coefficient of determination value, R², was 0.8973 between predicted and measured values for the entire concentration range of PM₁₀, suggesting deep learning method can be developed into a reliable and viable tool for prediction of fine dust concentration.

• Journal of Information Technology and Architecture
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• v.11 no.1
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• pp.11-23
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• 2014

Kill Chain is getting attention due to North Korea's recent nuclear test and missile launches and has emerged the need for an early build up. In order to build a materialized kill chain, you should review the unique kill chain to support operations effectively using various sensors and striking weapon system. Especially, you need a suitable network to reduce a reaction time against the enemy attack under joint operations environment etc. Currently there are many communication ways(e.g. data link, voice, video and text message) used in operations through satellite, wired and wireless and so on. Now, this paper contains analysis on various means for target information exchange which are used in the kill chain. And appropriate network of the kill chain for target information transmission is addressed to confirm feasibility of its alternatives, which is developed using AHP(Analytic Hierarchy Process). Finally, this paper is suggesting network and means of its building up for target information transmission of kill chain which can be implemented under the situation of joint battle field.

• The Korean Journal of Vision Science
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• v.20 no.4
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• pp.513-521
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• 2018

Purpose : : In this study, we made dopamine-functionalized hydrogels containing a cross-linked hyaluronic acid (HA) network and investigated their antioxidant activities. Methods : In the first step, we made poly hydroxyethyl methacrylate(p(HEMA))-based hydrogels post-modified with an interpenetrating polymer network(IPN) structure composed of HA polymers and a p(HEMA) network. The subsequent functionalization with dopamine via an amide coupling reaction resulted in the antioxidant hydrogels. Their antioxidant activities were evaluated using 2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) and 2,2-diphenyl-1-picrylhydrazyl radical scavenging assays. Results : The dopamine-modified hydrogels exhibited significant antioxidant activities, when compared to unmodified control. The presence of the HA-IPN structure improved the surface wettability of the hydrogel while dopamine-conjugated IPN hydrogel did not demonstrate the significant difference compared to hydrogel control. Dopamine-modified hydrogels exhibited high transmittance (>88%). Conclusion : The results demonstrate that the development of antioxidant hydrogels based on dopamine-conjugated HA-IPN structures may help develop ophthalmic and biomedical materials.

• Journal of Ginseng Research
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• v.43 no.3
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• pp.475-481
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• 2019

Background: The ginsenoside Rg1 has been shown to exert various pharmacological activities with health benefits. Previously, we have reported that Rg1 promoted myogenic differentiation and myotube growth in C2C12 myoblasts. In this study, the in vivo effect of Rg1 on fiber-type composition and oxidative metabolism in skeletal muscle was examined. Methods: To examine the effect of Rg1 on skeletal muscle, 3-month-old mice were treated with Rg1 for 5 weeks. To assess muscle strength, grip strength tests were performed, and the lower hind limb muscles were harvested, followed by various detailed analysis, such as histological staining, immunoblotting, immunostaining, and real-time quantitative reverse transcription polymerase chain reaction. In addition, to verify the in vivo data, primary myoblasts isolated from mice were treated with Rg1, and the Rg1 effect on myotube growth was examined by immunoblotting and immunostaining analysis. Results: Rg1 treatment increased the expression of myosin heavy chain isoforms characteristic for both oxidative and glycolytic muscle fibers; increased myofiber sizes were accompanied by enhanced muscle strength. Rg1 treatment also enhanced oxidative muscle metabolism with elevated oxidative phosphorylation proteins. Furthermore, Rg1-treated muscles exhibited increased levels of anabolic S6 kinase signaling. Conclusion: Rg1 improves muscle functionality via enhancing muscle gene expression and oxidative muscle metabolism in mice.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Physical Therapy Korea
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• v.12 no.3
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• pp.67-73
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• 2005

Prospective memory (PM) is related to remember to carry out a previously intented behaviour. The purpose of this study was to develop a paradigm for measuring PM function to diagnosis in mild cognitive impairment 1 or brain injury in patients 2. among brain injured patients Thirty-eight normal healthy subjects participated in current study. The paradigm was composed of four conditions: a baseline and three intention conditions (expectation, execution 1 and 2). In the expectation condition, subjects were asked to make a new response to intented stimuli during ongoing task, but the intented stimuli never occurred. In the execution 1 (one type of expected stimulus) and 2 (two types of expected stimuli), the intended stimuli did occur in 20% of trials. The reaction time and error rate were calculated in each condition. Repeated measures using ANOVA of subject's mean reaction times (RTs) and mean error rates (ERs) showed main effects of conditions during ongoing task. The comparison of PM tasks in executive condition 1 and 2 also showed significance in RTs and ERs. This paradigm reflects sufficiently the performance of prospective memory function during ongoing task in normal individuals. Thus, we suggest that the paradigm will be helpful to study neural network of PM function using brain imaging techniques and diagnosis of PM dysfunction.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2017.05a
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• pp.126-126
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• 2017

Using first principles calculations we unveil fundamental mechanism of hydrolysis reactions of two hazardous chemicals $PCl_3$ and $POCl_3$ with molecular water clusters nearby. It is found that the water molecules play a key role as a catalyst significantly lowing the activation barriers by transferring its protons to the reaction intermediates. Interestingly, torsional angles of molecular complexes at transition states are identified as a vital descriptor on the reaction rate. Analysis of charge distribution over the complexes further reinforces the finding with atomic level correlation between the torsional angle and variation of the orbital hybridization state of P in the complex. Electronic charge separation (or polarization) enhances thermodynamic stability of the activated complex at transition state and reduces the activation energy through hydrogen bonding network with water molecules nearby. Calculated potential energy surfaces (PES) for the hydrolysis reactions of $PCl_3$ and $POCl_3$ depict their two contrastingly different profiles of double- and triple-deep wells, respectively. It is ascribed to the unique double-bonding O=P in the $POCl_3$. Our results on the activation free energy show well agreements with previous experimental data within $7kcalmol^{-1}$ deviation.

• Macromolecular Research
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• v.16 no.5
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• pp.424-428
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• 2008

We prepared an ionic gel polymer electrolyte for dye-sensitized solar cells (DSSCs) without leakage problem. Triiodide compound (BTDI) was synthesized by the reaction of benzene tricarbonyl trichloride with diethylene glycol monotosylate and subsequent substitution of tosylate by iodide using NaI. Bisimidazole was prepared by the reaction of imidazole with the triethylene glycol ditosylate under strongly basic condition provided by NaH. BTDI and bisimidazole dissolved in an ionic liquid were injected into the cells and permeated into the $TiO_2$ nanopores. In situ crosslinking was then carried out by heating to form a network structure of poly(imidazolium iodide), thereby converting the ionic liquid electrolytes to a gel or a quasi-solid state. A monomer (BTDI and bisimidazole) concentration in the electrolytes of as low as 30 wt% was sufficient to form a stable gel type electrolyte. The DSSCs based on the gel polymer electrolytes showed a power conversion efficiency of as high as 1.15% with a short circuit current density of $5.69\;mAcm^{-2}$, an open circuit voltage of 0.525 V, and a fill factor of 0.43.

• Journal of Powder Materials
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• v.23 no.6
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• pp.420-425
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• 2016

N-doped carbon nanofibers as catalysts for oxygen-reduction reactions are synthesized using electrospinning and carbonization. Their morphologies, structures, chemical bonding states, and electrochemical performance are characterized. The optimized N-doped carbon nanofibers exhibit graphitization of carbon nanofibers and an increased nitrogen doping as well as a uniform network structure. In particular, the optimized N-doped carbon nanofibers show outstanding catalytic activity for oxygen-reduction reactions, such as a half-wave potential ($E_{1/2}$) of 0.43 V, kinetic limiting current density of $6.2mAcm^{-2}$, electron reduction pathways (n = 3.1), and excellent long-term stability after 2000 cycles, resulting in a lower $E_{1/2}$ potential degradation of 13 mV. The improvement in the electrochemical performance results from the synergistic effect of the graphitization of carbon nanofibers and the increased amount of nitrogen doping.