• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.201-210
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• 2020

This paper proposes a framework for the battle damage assessment (BDA) of electronic warfare linked to cyber warfare. Thanks to the rapid development of information and communication technology, the importance of cyber warfare and electronic warfare in cyberspace is increasing. Therefore, the BDA for cyber warfare and its associated electronic warfare in cyberspace is an important factor that affects the success or failure of military operations. In this paper, we propose a method to calculate measure of performance and measure of effectiveness by classifying the electronic warfare system into large / medium / small classes according to the type of electronic warfare. By setting up a hypothetical scenario, we show the effectiveness of the proposed framework.

• Explosives and Blasting
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• v.28 no.2
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• pp.37-49
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• 2010

Ground vibration caused by blasting in the urban area close to structures can give some indirect damage to human body and may lead to structural damage to buildings. At the stage of design or when complaints were filed by residents, the test blasting in borehole, which is most practical for expressing simple vibration wave form quantitatively, is usually chosen for assessing the degree of damage to structures. In this paper, some lessons gained from the application of electronic detonator triggering system in borehole test blasting are presented. The difference in delay time of detonator when borehole is blasted by electronic detonator and electric detonator are discussed. The peak particle velocities measured at the structure embedded in the similar rock layer to main line of tunnel at test site and measured at the road surface just above the tunnel having different overburden layers were analysed to draw their relationship. By comparing the results with those appearing in some published literatures, the usefulness of the borehole test blasting and the importance of delay time of detonator are addressed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.71-82
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• 2024

With the advancement of information and communication technology (ICT), the use of electronic document types such as PDF, MS Office, and HWP files has increased. Such trend has led the cyber attackers increasingly try to spread malicious documents through e-mails and messengers. To counter such attacks, AI-based methodologies have been actively employed in order to detect malicious document files. The main challenge in detecting malicious HWP(Hangul Word Processor) files is the lack of quality dataset due to its usage is limited in Korea, compared to PDF and MS-Office files that are highly being utilized worldwide. To address this limitation, data augmentation have been proposed to diversify training data by transforming existing dataset, but as the usefulness of the augmented data is not evaluated, augmented data could end up harming model's performance. In this paper, we propose an effective semi-supervised learning technique in detecting malicious HWP document files, which improves overall AI model performance via quantifying the utility of augmented data and filtering out useless training data.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.289-291
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• 2005

고도화, 정보화시대를 맞이하여 대형인버터, 컴퓨터 등 전력풀질을 저하시키는 비선형 부하의 보급이 확대되고 있으며, 이들 비선형부하에 의한 전력품질 저하시 공정제어기, 마이크로프로세서 등과 같이 전력품질에 매우 민감한 기기의 오동작 등으로 경제적 피해가 증대되고 있다. 따라서 중요부하 및 계통을 보호하기 위한 전력전자 기술을 응용한 전력품질 보상기기 적용이 필요하다. 이에 본 연구에서는 전력품질 향상기기 성능평가 텐 실증기술 개발을 위한 전력풀질 실증시험장 구축 및 전력풀질 향상기기 실계통 적용시 효과분석을 위한 해석기법을 확보하고자 하였다. DSTATCOM, DVR, SSTS 등 전력품질 향상기기의 성능평가를 위해서 실계통에서 발생되는 외란을 모의할 수 있는 외란발생장치 등 시험설비와 전력품질 향상기기 및 실증시험설비를 제어하는 S/W와 시험결과를 분석하기 위한 전력품질 측정 및 분석시스템을 구축하였다. 순간전압강하, 순간전압 상승 파형을 외란발생장치를 구현하고 전력품질 향상기기의 보강효과를 분석하였다. 또한 전력품질 향상기기(DVR, DSTATCOM, SSTS)와 부하 등 시험설비에 대하여 EMTDC 프로그램용 모델 및 해석기법을 개발하였다.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.780-789
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• 2018

Nowadays, gun crimes occur very frequently not only in public places but in alleyways around the world. In particular, it is essential to detect a person armed by a pistol to prevent those crimes since small guns, such as pistols, are often used for those crimes. Because conventional works for armed person detection have treated an armed person as a single object in an input image, their accuracy is very low. The reason for the low accuracy comes from the fact that the gunman is treated as a single object although the pistol is a relatively much smaller object than the person. To solve this problem, we propose a novel algorithm called APDA(Armed Person Detection Algorithm). APDA detects the armed person using in a post-processing the positions of both wrists and the pistol achieved by the CNN-based human body feature detection model and the pistol detection model, respectively. We show that APDA can provide both 46.3% better recall and 14.04% better precision than SSD-MobileNet.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.664-668
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• 2018

Free-standing structures that are especially high are more likely to receive brain attacks caused by lightning. Since special structures are generally part of national industrial structures, lightning strikes mostly cause socio-economic damage. Lightning protection facilities are installed to prevent such lightning damage, but in 2015, support cables on West Sea bridges were hit by lightning, causing a lot of economic damage. Accordingly, the design of a lightning protection system shall establish protective measures after analyzing the risk of debris falling onto the structure. In this thesis, lightning strikes are analyzed directly in relation to the modeling system that operates the actual information collection system for lightning strikes, depending on the location of the tall, free-standing structures, and practical lightning hazard information is provided by a meteorological station. In addition, we propose monitoring and applying a probability correction rate to the calculation of the lightning risk based on the number of lightning strikes directly reaching the ground in order to obtain an effective lightning risk assessment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.4
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• pp.146-154
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• 1998

Broadband noise with frequency components in the range from several kHz up the tens of MHz is a major obstacle factor in normal operation of the AC line to supply the power to electrical and electronic control equipments. Because this kind of noise could damage the device or could be a source of malfunction, many devices such as filter and surge suppressor are used to cut off the noise. But those devices could not disconnected from the power line, so they result in poor common-mode of NCT as well as insulation characteristics as a isolation transformer in addition faraday shielding and proposed analysis model of NCT having tow functions of surge and noise reduction. The simulated and experimental results for the surge suppression characteristics are compared and evaluated for designed protype 1[kVA] NCT.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.249-258
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• 2012

Cyber attacks expose sensitive information and cause fatal damage in both the public and the private sectors. Therefore, MKE (Ministry of Knowledge Economy) Cyber Security Center was founded on July 25, 2008, to perform three major roles. First, it detects and analyzes cyber attacks for the both sectors. Second, its ISAC (Information Sharing & Analysis Center) service analyzes and evaluates the vulnerability of the communication and network infrastructure to security threats, including control systems. Third, it provides CERT/CC (Computer Emergency Response Team Coordination Center) service to prevent and to respond to computer security incidents. This study focuses on the MKE Cyber Security Center's service analysis, which is playing an increasingly larger role in the both sectors. Based on this analysis, after grasping the response services activity and pointing out the problems, this study suggests improvements to the MKE Cyber Security Center.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.