• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.55-65
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• 2020

As corona began to spread around the world, it had such a big impact on many people's lives that the word "Untact Culture" was born. Among them, non-face-to-face meetings naturally became a daily routine as educational institutions and many domestic and foreign companies used video conferencing service platforms. Among many video conferencing service platforms, Zoom, the company with the largest number of downloads, caused many security issues and caused many concerns about Zoom's security. In this paper, Zoom's security problems and vulnerabilities were classified into five categories, and Zoom's latest update to solve those problems and the 90-day security planning project were compared and analyzed. And the problem was solved and classified as unresolved. Three of the five parts have been resolved but are still described as how they should be resolved and improved in the future for the two remaining parts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.581-583
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• 2021

Due to the pendemic of Corona 19, the use of mobile services is increasing. However, since anomalies in most mobile devices are recognized by the device's alarm, it is difficult to intuitively determine the problem of the device when a complex failure occurs. To compensate for this, in this study, the Anomaly Score was created by RRCF algorithm to intuitively recognize the problem by combining the alarm and performance information of the equipment, and the effect of detecting 97% of the past failure history was verified.

• ETRI Journal
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• v.46 no.2
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• pp.290-306
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• 2024

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transferlearning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.62-70
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• 2021

2020 was a year in which the world spent in disorder due to the pandemic of Coronavirus infection-19(COVID-19). The pandemic was at the beginning of a turning point in history. For examples, the Black Death(Pest) that destroyed the feudal system of medieval Europe in the 14th century, smallpox that led to the destruction of the Inca Empire by Spain in the 17th century, and the Spanish flu that ended World War I early. The great transformation that will come after COVID-19 is presented from various fields and perspectives, but the understanding and direction of the transformation is ambiguous. This study attempts to derive and to analyze core terms based on a network of the future of globalization after COVID-19. Four Networks related to globalization, anti-globalization, and globalization and digitalization after COVID-19 were established respectively. A network integrating four networks was also constructed. The core terms were extracted from the hub nodes, the stress centrality, and the simplified network to which the K-core algorithm was applied. After COVID-19, the changes in globalization were analyzed from the extracted core terms. This study is thought to be meaningful to propose a method of deriving and analyzing core terms based on a network in understanding social changes after COVID-19.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.4
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• pp.112-117
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• 2004

This paper describes the analysis of the UHF signal characteristics due to the partial discharge sources which can exist in a GIS. For the experiment, a model GIS was made and 5 types of discharge source were created as follows; corona discharge, surface discharge, void discharge, discharge due to free particle, discharge from floating electrode. The frequency spectra and the phase characteristics of UHF signals were induced by UHF signal analysis. The results were quantified to systematically adapt to analyze the PD sources in the GIS and utilized as algorithm data based on the neural network for Back-Propagation Algorithm with a multi-layer structure. The perception rate of the constructed algorithm showed approximately 94[%] and 82[%] in learning and testing data, respectively.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.4
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• pp.54-61
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• 2013

UV-IR camera is being used for predictive maintenance of high voltage equipment together with measurement of temperature on localized heat and corona discharge. This paper was suggested the judgement method that is the discharge count, UV image pattern and discharge matching rate to apply the UV-IR camera on power facility. The discharge count method is counted by UV image pixel value. the UV image pattern method is determined by the UV image shape using neural network algorithm method, separated by Sunflower, Jellyfish, Ameba. The UV discharge matching is compare the breakdown the UV image size and measuring UV image size according to distance.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.1
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• pp.19-23
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• 2000

Partial discharges(PD)in air insulated electric power apparatus often lead to deterioration of solid insulation by electron bombardments and electrochemical reaction. The PD caused to reduce the life time of power apparatus and to increase power losses. Thus understanding and classification of PD patterns in air are very important to discern sources of PD. In this paper, PD in air by using statistical methods was investigated. We classified air discharges, corona, surface discharges and cavity discharges by Kohonen network. For classification of PD patterns, we used statistical operators and parameters such as skewness$(S^+,\; S^-),\; kurtosis(K^+, K^-),\; mean phase(AP^+, AP^-)$, cross-correlation factor(CC) and asymmetry derived from the mean pulse-height phase distribution$(H_{avg}(\phi))$, the max pulse-height phase distribution $(H_{qmax}(\phi))$, the pulse count phase distribution $(H_n(\phi))$ and the pulse height vs. Repetition rate $(H_q(n))$.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.66-71
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• 2021

The purpose of this paper is to identify keywords related to museums, gamification, and visitors, and provide basic data that the museum market can be expanded by using gamification. That used to collect data for blogs, news, cafes, intellectuals, academic information by Naver and Daum which is Web documents in Korea, and Google Web, news, Facebook, Baidu, YouTube, and Twitter for analysis. For the data analysis period, a total of one year of data was selected from April 16, 2020 to April 16, 2021, after Corona. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analysed and visualized using the Netdraw function in the UCINET6 program. In addition, We performed CONCOR analysis to derive clusters for similar keywords. As a result, a total of 25,761 cases that analysing the keywords of museum, gamification and visitors were derived. This shows that the museum, gamification, and spectators are related to each other. Furthermore, if a system using gamification is developed for museums, the museum market can be developed.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.43-53
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• 2021

Super-realistic content technology has recently attracted attention as a core of the "new normal" that can overcome the spatial constraints caused by pandemics. It is moreover the core that allows users in remote locations to meet and engage in various social, cultural, and economic activities based on a network. Content technology is rapidly spreading beyond the existing entertainment area to various industries as an innovative tool that can be used to overcome space-time constraints and improve the productivity of industrial sites, because reality and virtual reality are now super-connected with ultra-low latency. However, existing services such as teleconferencing and tele-collaboration do not provide a level of realism that replaces face-to-face services, and various technical requirements have been proposed to overcome this. The trends in core technologies such as XR twins, hyper-realistic reproduction, sensory interaction, and emotional recognition technology, which are necessary for interactive realistic content that leads to feelings, from reproduction to experience and emotion, are explained. In this article, our aim is to present the future of realistic content that enables human care and can even overcome psychological difficulties such as the "Corona blues".

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.