• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.132-141
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• 2019

The necessity of transmitting video data over a narrow-bandwidth exists steadily despite that video service over broadband is common. In this paper, we propose a scalable video coding framework for low-resolution video transmission over a very narrow-bandwidth network by super-resolution of decoded frames of a base layer using a convolutional neural network based super resolution technique to improve the coding efficiency by using it as a prediction for the enhancement layer. In contrast to the conventional scalable high efficiency video coding (SHVC) standard, in which upscaling is performed with a fixed filter, we propose a scalable video coding framework that replaces the existing fixed up-scaling filter by using the trained convolutional neural network for super-resolution. For this, we proposed a neural network structure with skip connection and residual learning technique and trained it according to the application scenario of the video coding framework. For the application scenario where a video whose resolution is $352{\times}288$ and frame rate is 8fps is encoded at 110kbps, the quality of the proposed scalable video coding framework is higher than that of the SHVC framework.

• Journal of Life Science
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• v.29 no.2
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• pp.181-190
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• 2019

The purpose of this study was to acquire the active compounds of Anisi stellati fructus (ASF) and to analyze the genes and diseases it targets, focusing on its antibacterial effects using a system pharmacological analysis approach. Active compounds of ASF were obtained through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database and Analysis Platform. This contains the pharmacokinetic properties of active compounds and related drug-target-disease networks, which is a breakthrough in silico approach possible at the network level. Gene information of targets was gathered from the UnitProt Database, and gene ontology analysis was performed using the David 6.8 Gene Functional Classification Tool. A total of 201 target genes were collected, which corresponded to the nine screened active compounds, and 47 genes were found to act on biological processes related to antimicrobial activity. The representative active compounds involved in antibacterial action were luteolin, kaempferol, and quercetin. Among their targets, Chemokine ligand2, Interleukin-10, Interleukin-6, and Tumor Necrosis Factor were associated with more than three antimicrobial biological processes. This study has provided accurate evidence while saving time and effort to select future laboratory research materials. The data obtained has provided important data for infection prevention and treatment strategies.

• Korean Journal of Food Science and Technology
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• v.51 no.3
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• pp.221-226
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• 2019

This study investigated the optimal amount of shell powder (0.1, 0.2, 0.3, and 0.4%) to add to rice noodles containing 20% rice flour and compared their quality characteristics to those of wheat noodles containing a commercial alkaline reagent (added at 0.4%). As the amount of shell powder was increased, the L and b values (Hunter's color) increased. The pH, turbidity, and water absorption also increased as the amount of shell powder was increased. However, when the shell powder content exceeded 0.3%, the hardness, chewiness, springiness, and tension tended to decrease below acceptable levels. This might be because the shell powder inhibited network formation. The textural properties and pH value of rice noodles containing 0.2% shell powder were similar to those of the wheat noodles. This suggests that 0.2% shell powder may be the optimal amount to add to rice noodles when used as the alkaline reagent.

• Applied Chemistry for Engineering
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• v.30 no.3
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• pp.384-388
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• 2019

In winter season, the snow and ice accretion on the bottom of the high speed railway rolling stock and boogie part has fallen at a high speed from the ballast section (gravel section for the transmission of the rolling stock load received by sleepers and fixing sleepers), causing the gravel to be scattered, thereby damaging the railway rolling stock structures and facilities. In order to solve these problems, the gravel scattering prevention net, manual de-icing, and movable hot air machine were used, but their efficiency was low. For the more efficient de-icing than ever before, an optimum material for de-icing fluid for high speed railway rolling stock was developed by evaluating the ice melting capacity, kinematic viscosity, evaporation of the material used as a chemical de-icing fluid. Four kinds of organic acid salts (sodium formate, sodium acetate, potassium formate and potassium acetate) and two different alcohols (propylene glycol, glycerol) were used as evaluation materials. Potassium formate, potassium acetate, and propylene glycol had similar ice melting capacities in the indoor test, but the propylene glycol showed the best ice melting capacity in spraying the system simulation test. This is because the kinematic viscosity of propylene glycol was 2.989029 St, which is higher than those of other materials therefore, it could stay longer on the ice and de-icing. In addition, potassium formate and potassium acetate were difficult to be used since the crystals precipitated and adversely affected the appearance of the rolling stock. The propylene glycol is the most optimum as an de-icing fluid for the high speed railway rolling stock.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.2
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• pp.155-161
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• 2019

Currently, numerous companies and institutes provide services using the Internet, and establish and operate Information Systems to manage them efficiently and reliably. The Information System implies the possibility of losing the ability to provide normal services due to a disaster or disability. It is preparing for this by utilizing a disaster recovery system. However, existing disaster recovery systems cannot perform normal recovery if files for system recovery are corrupted. In this paper, we proposed a system that can verify the integrity of the system recovery file and proceed with recovery by utilizing hyper-ledger fabric blockchain. The PBFT consensus algorithm is used to generate the blocks and is performed by the leader node of the blockchain network. In the event of failure, verify the integrity of the recovery file by comparing the hash value of the recovery file with the hash value in the blockchain and proceed with recovery. For the evaluation of proposed techniques, a comparative analysis was conducted based on four items: existing system recovery techniques and data consistency, able to data retention, recovery file integrity, and using the proposed technique, the amount of traffic generated was analyzed to determine whether it was actually applicable.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.137-143
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• 2019

Fake news is a form of misinformation that has the advantage of rapid spreading of information on media platforms that users interact with, such as social media. There has been a lot of social problems due to the recent increase in fake news. In this paper, we propose a method to detect such false news. Previous research on fake news detection mainly focused on text analysis. This research focuses on a network where social media news spreads, generates qualities with DeepWalk, a network embedding method, and classifies fake news using logistic regression analysis. We conducted an experiment on fake news detection using 211 news on the Internet and 1.2 million news diffusion network data. The results show that the accuracy of false network detection using network embedding is 10.6% higher than that of text analysis. In addition, fake news detection, which combines text analysis and network embedding, does not show an increase in accuracy over network embedding. The results of this study can be effectively applied to the detection of fake news that organizations spread online.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.48-63
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• 2019

We tried new analysis including social network analysis(SNA) on the transaction network centered on electronic companies using more than 50 thousand company transaction data obtained from Korean enterprise data (KED) for the year of 2015. We found 97 clusters having more than 10 firms and remarkable 13 clusters having more than 90% sales of the electronic industry in Korea. Clusters are the groups of companies having most of their transactions in the clusters they belong to. We found 5 clusters have 83% of sales in the electronic industry. Most of clusters have main single firms having most of the sales in each clusters except a few clusters. However, we found a few firms to have high rear production linkage effect and found the firms with high linkage effect specially for the small and medium size enterprise (SME). The companies with high production linkage (specially on SMEs) should be managed in terms of (SME) growth policy. The last firm group consisting of the small clusters with less than 10 firms has high employment coefficients. The clusters or company having high production linkage effect on this last firm group should be noted in the terms of employment policy. We also note that there exist the firms with the high value of betweenness coefficients meaning high potential of technology development. They should be managed carefully in terms of technology development policy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.400-402
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• 2019

The electric point machine, which is used for the control of the turnout used to change the track of the train, is very important in the railway system. Various wired and wireless real-time monitoring systems are used to check the status of the point machine, but there is a possibility of malfunction due to sensor or network error. In this paper, a redundant monitoring system was designed that incorporates the point machine monitoring system and the CCTV camera control system to double check the operation of the point machine. In the point machine monitoring system, the operating state of the railway converter is monitored, alarmed and transmitted over the network. The CCTV camera control system, which received this information, was required to record the status of the turnout and the point machine in question and send it to the administrator. The manager of the railway line can check the conversion status of the railway through the monitoring screen for the railway line switcher first, and then confirm the switching status directly through the CCTV camera image, thereby improving the reliability of the point machine operation. It will also enable the safe and efficient operation of personnel for management. It is expected to contribute to preventing a derailment caused by a malfunction of the point machine.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.104-110
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• 2019

In this paper, a complementary metal oxide semiconductor(CMOS) power amplifier(PA) integrated circuit operating in the 900 MHz band for long-term evolution(LTE) communication systems is presented. The output matching network based on a transformer was implemented on a printed circuit board for low loss. Simultaneously, to achieve high efficiency of the PA, the second harmonic impedances are controlled. The CMOS PA was fabricated using a $0.18{\mu}m$ CMOS process and measured using an LTE uplink signal with a bandwidth of 10 MHz and peak to average power ratio of 7.2 dB for verification. The implemented CMOS PA module exhibits a power gain of 24.4 dB, power-added efficiency of 34.2%, and an adjacent channel leakage ratio of -30.1 dBc at an average output power level of 24.3 dBm.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.277-284
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• 2021

Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.