• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.702-704
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• 2017

In case of dismantling of nuclear power generation facility or radiation accident, the accurate information of gammaray source is essential for rapid decontamination. In order to more efficiently represent the position of the gamma ray to be removed, we create a spatial domain based on the real image. And we can perform decontamination of gamma-ray source more quickly by expressing the distribution of radiation source. The developed gamma ray imaging device overlaps with the visible image after gamma - ray detection and provides only two - dimensional image, but it does not show the distance information to the source. In this paper, we have developed a operation environment using the 3D visualization model for reporting effective decontamination operation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.6
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• pp.507-517
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• 2019

Multi-function radar(MFR) is a system that uses various functions such as detection, tracking, and classification. To operate the functions in real-time, the detection stage in MFR usually uses radar signals for short measurement time. We can utilize several conventional detectors in the MFR system to detect low radar cross section maritime targets in the sea-clutter; however, the detectors, which have been developed to be effective for radar signals measured for a longer time, may be inappropriate for MFR. In this study, we proposed a modelling technique of sea-clutter short measurement time. We combined the modeled sea-clutter signal with the maritime-target signal, which was obtained by the numerical analysis method. Using this combined model, we exploited four independent detectors and analyzed the detection performances.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.24-32
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• 2021

Recently, ransomware attacks have been infected through various channels such as e-mail, phishing, and device hacking, and the extent of the damage is increasing rapidly. However, existing known malware (static/dynamic) analysis engines are very difficult to detect/block against novel ransomware that has evolved like Advanced Persistent Threat (APT) attacks. This work proposes a method for modeling ransomware malicious behavior based on graph databases and detecting novel multi-complex malicious behavior for ransomware. Studies confirm that pattern detection of ransomware is possible in novel graph database environments that differ from existing relational databases. Furthermore, we prove that the associative analysis technique of graph theory is significantly efficient for ransomware analysis performance.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.264-265
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• 2008

기존 영상감시 시스템의 물체 탐지 알고리즘은 주로 배경 모델링 기법을 기반으로 하고 있다. 이 기법은 차영상 기법보다는 성능이 뛰어나기는 하지만 여전히 정지 카메라에서만 활용이 가능하고, 주변 환경에 따라 알고리즘 상의 많은 임계값을 현재 상황에 맞춰 일일이 조절해 주어야 한다는 한계점이 있다. 따라서 이 논문에서는 배경모델링 기법을 사용하지 않고 입력되는 영상의 Color 정보를 이용하여 영상 내에 있는 여러 대상을 직접 판단하여 관심 있는 물체를 탐지하는 방법을 제안하고자 한다. 제안된 알고리즘은 먼저 현재의 영상을 하나의 물체로 추정되는 영역이 하나의 영역으로 구분되어지게 간단하게 분할해낸다 그리고 나누어진 영역마다 대표 Color 값을 계산하여 미리 학습된 데이터를 기준으로 Support Vector Domain Description (SVDD) 알고리즘을 사용하여 구별해내고 그 결과를 바탕으로 영역이 무엇인지를 판별해낸다. 이 방법은 정지되어 있는 카메라뿐만 아니라 움직이는 카메라 상에서도 사용되어질 수 있으며 알고리즘 상에서 사용되는 임계값의 종류가 적기 때문에 많은 상황에서 일반적으로 쓰일 수 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11B
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• pp.1020-1029
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• 2008

This paper proposes an high speed pattern matching algorithm and its implementation. The pattern matcher is used to check patterns from realtime input packet. The proposed algorithm can find exact string, range of string values, and combination of string values from input packet at high speed. Given string and rule set are modelled as a state transition graph which can find overlapped strings simultaneously, and the state transition graph is partitioned according to input implicants to reduce implementation complexity. The pattern matcher scheme uses the transformed state transition graph and input packet as an input. The pattern matcher was modelled and implemented in VHDL language. Experimental results show the proprieties of the proposed approach.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.107-116
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• 2022

Recently, research has been actively conducted on the technology of inspection facilities through image-based analysis assessment of human-inaccessible facilities. This research was conducted to study the conditions of deep learning-based imaging data on bridges and to develop an evaluation prototype program for bridges. To develop a deep learning-based bridge damage detection prototype, the Semantic Segmentation model, which enables damage detection and quantification among deep learning models, applied Mask-RCNN and constructed learning data 5,140 (including open-data) and labeling suitable for damage types. As a result of performance modeling verification, precision and reproduction rate analysis of concrete cracks, stripping/slapping, rebar exposure and paint stripping showed that the precision was 95.2 %, and the recall was 93.8 %. A 2^nd performance verification was performed on onsite data of crack concrete using damage rate of bridge members.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.495-503
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• 2014

In recent days, a study of the intrusion detection system collecting and analyzing network data, packet or logs, has been actively performed to response the network threats in computer security fields. In particular, Bayesian network has advantage of the inference functionality which can infer with only some of provided data, so studies of the intrusion system based on Bayesian network have been conducted in the prior. However, there were some limitations to calculate high detection performance because it didn't consider the problems as like complexity of the relation among network packets or continuos input data processing. Therefore, in this paper we proposed two methodologies based on K-menas clustering to improve detection rate by reforming the problems of prior models. At first, it can be improved by sophisticatedly setting interval range of nodes based on K-means clustering. And for the second, it can be improved by calculating robust CPT through applying weighted-leaning based on K-means clustering, too. We conducted the experiments to prove performance of our proposed methodologies by comparing K_WTAN_EM applied to proposed two methodologies with prior models. As the results of experiment, the detection rate of proposed model is higher about 7.78% than existing NBN(Naive Bayesian Network) IDS model, and is higher about 5.24% than TAN(Tree Augmented Bayesian Network) IDS mode and then we could prove excellence our proposing ideas.

• Journal of the Korea Society for Simulation
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• v.24 no.1
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• pp.25-34
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• 2015

It is necessary for us to undergo trial and error for eliciting the rational requirement of the acquisition of weapon systems, but the M&S is general approach due to costs and risk of the development. In addition to the acquisition of weapon systems, M&S is extensively employed in the analysis and the training of developed weapon systems. The ADD (Agency for Defense Development) has developed DEVS integrated development environment (QUEST) that provides M&S general ground technique composed of simulation model implementation services, simulation result analysis services, and simulation interface services. This paper describes the interface architecture and the implementation of torpedo target detection section interface simulation system using QUEST. The torpedo target detection section interface simulation system is composed of torpedo target detection section which calculates a result of target detection and the QUEST scenario generator which provides simulation scenario for performance test of the torpedo target detection section. The interface architecture of torpedo target detection section interface simulation system is designed to verify the interface and performance of the torpedo target detection section by linking with the QUEST scenario generator.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.92-98
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• 2008

Network data modeling is a essential research for the evaluation for intrusion detection systems performance, network modeling and methods for analyzing network data. In network data modeling, real data from the network must be analyzed and the modeled data must be efficiently composed to reflect a sufficient amount of the original data. In this parer the useful elements of real network data were quantified from packets captured from a huge network. Futhermore, a statistical analysis method was used to find the most effective element for efficiently classifying the modeled data.