• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1039-1046
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• 2022

After the COVID-19 pandemic, the spread of the untact culture and the Fourth Industrial Revolution, which generates various types of data, generated so much data that it was not compared to before. This led to higher data throughput, revealing little by little the limitations of the existing network system centered on vendors and hardware. Recently, SDN technology centered on users and software that can overcome these limitations is attracting attention. In addition, SDN-based load balancing techniques are expected to increase efficiency in the load balancing area of the server cluster in the data center, which generates and processes vast and diverse data. Unlike existing SDN load distribution studies, this paper proposes a load distribution technique in which a controller checks the state of a server according to the occurrence of an event rather than periodic confirmation through a monitoring technique and allocates a user's request by weighting it according to a load ratio. As a result of the desired experiment, the proposed technique showed a better equal load balancing effect than the comparison technique, so it is expected to be more effective in a server cluster in a large and packet-flowing data center.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3738-3760
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• 2022

Due to increasing spectrum demand for new wireless devices applications, cooperative spectrum sensing (CSS) paradigm is the most promising solution to alleviate the spectrum shortage problem. However, in the interweave cognitive radio (CR) system, the inherent nature of CSS opens a hole to Byzantine attack, thereby resulting in a significant drop of the CSS security and efficiency. In view of this, a weighted differential sequential single symbol (WD3S) algorithm based on MATLAB platform is developed to accurately identify malicious users (MUs) and benefit useful sensing information from their malicious reports in this paper. In order to achieve this, a dynamic Byzantine attack model is proposed to describe malicious behaviors for MUs in an interweave CR system. On the basis of this, a method of data transmission consistency verification is formulated to evaluate the global decision's correctness and update the trust value (TrV) of secondary users (SUs), thereby accurately identifying MUs. Then, we innovatively reuse malicious sensing information from MUs by the weight allocation scheme. In addition, considering a high spectrum usage of primary network, a sequential and differential reporting way based on a single symbol is also proposed in the process of the sensing information submission. Finally, under various Byzantine attack types, we provide in-depth simulations to demonstrate the efficiency and security of the proposed WD3S.

• Journal of the Korean Institute of Rural Architecture
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• v.25 no.1
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• pp.35-44
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• 2023

This study aimed to understand the driver change of recent research in relation to rural and migrant and draw overarching issues as well as to provide implications to contribute to migrants' social integration in Korean rural areas. As for the scope and method of the study, data through quantitative bibliographic analysis (quantitative data) and research keywords by period were derived. To address the aim this study employed bibliometric analysis utilising netwok mapping interface analysis by VOSviewer and topic modeling analysis by Netminer. The findings were revealed that firstly mental health issues in abroad research and employment and discrimination in domestic research both derived from migrant mobility constituted staple key issues, secondly internal and external research differed two issues in health and violence where Korea has overlooked the issues seriously. Therefore this study presented implications which are about first, health and violence-related sections for migrants should be specified into domestic law, second domestic-focused MIPEX index should be developed in which the two issues are over-weighted and last such newly emerging approach 'inclusive formation of social psychological mechanisms should be widely spread. Concluding remark is that delivering the implications can be foster to migrants' integration in rural area underlining that this will ultimately contribute to migrants' quality of life.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.94-101
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• 2023

A deep neural network composed of encoders and decoders, such as U-Net, used for speech enhancement, concatenates the encoder to the decoder through skip-connection. Skip-connection helps reconstruct the enhanced spectrum and complement the lost information. The features of the encoder and the decoder connected by the skip-connection are incompatible with each other. In this paper, for complex-valued spectrum based speech enhancement, Self-Attention (SA) method is applied to skip-connection to transform the feature of encoder to be compatible with the features of decoder. SA is a technique in which when generating an output sequence in a sequence-to-sequence tasks the weighted average of input is used to put attention on subsets of input, showing that noise can be effectively eliminated by being applied in speech enhancement. The three models using encoder and decoder features to apply SA to skip-connection are studied. As experimental results using TIMIT database, the proposed methods show improvements in all evaluation metrics compared to the Deep Complex U-Net (DCUNET) with skip-connection only.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Journal of Digital Policy
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• v.3 no.3
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• pp.9-14
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• 2024

In this paper, proposes a method using a multi block structure composed of residual blocks with adaptive weights to improve the quality of results in single image super resolution. In the process of generating super resolution images using deep learning, the most critical factor for enhancing quality is feature extraction and application. While extracting various features is essential for restoring fine details that have been lost due to low resolution, issues such as increased network depth and complexity pose challenges in practical implementation. Therefore, the feature extraction process was structured efficiently, and the application process was improved to enhance quality. To achieve this, a multi block structure was designed after the initial feature extraction, with nested residual blocks inside each block, where adaptive weights were applied. Additionally, for final high resolution reconstruction, a multi kernel image reconstruction process was employed, further improving the quality of the results. The performance of the proposed method was evaluated by calculating PSNR and SSIM values compared to the original image, and its superiority was demonstrated through comparisons with existing algorithms.

• Korean journal of communication and information
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• v.74
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• pp.109-135
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• 2015

This study investigated the news report tendency and frame by three major Korean television networks(KBS, MBC, and SBS) in their news reports on issues involving their own interests and considering in social level. The three issues chosen for this study are 'advertising regulations for total amount', 'the reallocation of 700MHz spectrum' and 'raising TV license fee'(this issue applicable only to KBS). By using content analysis method, this study identified extremely weighted tendency toward themselves by the network television channels. They have used highly biased tone and sources to reinforce their private interests in news reports about the three controversial issues. In terms of story content, the news reports have used attribution of responsibility frame dominantly in 'advertising regulations for total amount' issue, whereas the moral frame was dominant in the other two issues.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.10
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• pp.4251-4256
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• 2015

Background: Exposure to cigarette may affect human health and increase risk of a wide range of diseases including pulmonary diseases, such as chronic obstructive pulmonary disease (COPD), asthma, lung fibrosis and lung cancer. However, the molecular mechanisms of pathogenesis induced by cigarettes still remain obscure even with extensive studies. With systemic view, we attempted to identify the specific gene modules that might relate to injury caused by cigarette smoke and identify hub genes for potential therapeutic targets or biomarkers from specific gene modules. Materials and Methods: The dataset GSE18344 was downloaded from the Gene Expression Omnibus (GEO) and divided into mouse cigarette smoke exposure and control groups. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network for each group and detected specific gene modules of cigarette smoke exposure by comparison. Results: A total of ten specific gene modules were identified only in the cigarette smoke exposure group but not in the control group. Seven hub genes were identified as well, including Fip1l1, Anp32a, Acsl4, Evl, Sdc1, Arap3 and Cd52. Conclusions: Specific gene modules may provide better understanding of molecular mechanisms, and hub genes are potential candidates of therapeutic targets that may possible improve development of novel treatment approaches.

• Journal of KIISE:Databases
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• v.37 no.3
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• pp.127-136
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• 2010

With rapid growth of information and computer communication technologies, the numbers of digital documents including multimedia data have been recently exploded. In particular, news video database and news video mining have became the subject of extensive research, to develop effective and efficient tools for manipulation and analysis of news videos, because of their information richness. However, many research focus on browsing, retrieval and summarization of news videos. Up to date, it is a relatively early state to discover and to analyse the plentiful latent semantic knowledge from news videos. In this paper, we propose the news video mining system based on multi-modal approach and text mining, which uses the visual-textual information of news video clips and their scripts. The proposed system systematically constructs a taxonomy of news video stories in automatic manner with hierarchical clustering algorithm which is one of text mining methods. Then, it multilaterally analyzes the topics of news video stories by means of time-cluster trend graph, weighted cluster growth index, and network analysis. To clarify the validity of our approach, we analyzed the news videos on "The Second Summit of South and North Korea in 2007".

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.63-73
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• 2015

In the end to end data transfer protocols, it is very important to correctly estimate available bandwidth. In UDT (UDP based Data Transfer), receiver estimates the MTR (Maximum Transfer Rate) of the current link using pair packets transmitted periodically from sender and, then sender finally decides the MTR through EWMA (Exponential Weighted Moving Average) algorithm. Here, MTR has to be exactly estimated because available bandwidth is calculated with difference of MTR and current transfer rate. However, when network is congested due to traffic load and where competing flows are coexisted, it bring about a severe fairness problem. This paper proposes a congestion degree based MTR estimation algorithm. Here, the congestion degree stands a relative index for current congestion status on bottleneck link, which is calculated with arriving intervals of a pair packets. The algorithm try to more classify depending on the congestion degree to estimate more actual available bandwidth. With the network simulation results, our proposed method showed that the fairness problem among the competing flows is significantly resolved in comparison with that of UDT.