• 융합보안논문지
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• 제16권2호
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• pp.63-70
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• 2016

최근 인터넷 등 정보통신 기술의 발달로 인해 언제 어디서나 인터넷을 이용할 수 있는 환경이 구축 되었으며, 이로 인한 사이버위협은 다양한 경로를 통해 시도되고 있다. 본 연구에서는 사이버위협 중 지속적으로 증가 추세인 DDoS 예측 모델링하기 위해 이벤트 데이터를 근거로 하여 통계적 기법을 통해 DDoS 위험지수 예측식을 도출하였고, 도출된 위험지수를 정량화하였다. 제시된 위험지수를 활용하여 DDoS 위협에 대해 사전 대응정책을 세움으로써 피해를 최소화시킬 수 있는 객관적이고 효율적인 예측 모델이 될 것으로 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1583-1598
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• 2019

Recently, software has increased in complexity and been applied in various industrial fields. As a result, the presence of software bugs cannot be avoided. Various bug severity prediction methodologies have been proposed, but their performance needs to be further improved. In this study, we propose a novel technique for bug severity prediction in cross projects such as Eclipse, Mozilla, WireShark, and Xamarin by using topic modeling and similarity (i.e., KL-divergence). First, we construct topic models from bug repositories in cross projects using Latent Dirichlet Allocation (LDA). Then, we find topics in each project that contain the most numerous similar bug reports by using a new bug report. Next, we extract the bug reports belonging to the selected topics and input them to a Naïve Bayes Multinomial (NBM) algorithm. Finally, we predict the bug severity in the new bug report. In order to evaluate the performance of our approach and to verify the difference between cross projects and single project, we compare it with the Naïve Bayes Multinomial approach; the Lamkanfi methodology, which is a well-known bug severity prediction approach; and an emotional similarity-based bug severity prediction approach. Our approach exhibits a better performance than the compared methods.

• 한국정보통신학회논문지
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• 제24권4호
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• pp.522-528
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• 2020

엄청난 속도로 확산하는 랜섬웨어, 트로이목마, 인터넷 웜과 같은 악성코드는 인터넷의 주요한 위협이 되고 있다. 이러한 악성코드의 행위에 대응하기 위해서는 악성코드의 확산 방식과 영향을 끼치는 영향 요인을 이해하는 것이 필수적이다. 본 논문에서는 악성코드 확산 모델링에 기반을 둔 확산 예측 도구를 개발하였다. 이를 위하여 관련 연구를 살펴보고, 시스템 구성과 구현 방법을 살펴본 후 확산 예측 도구를 이용하여 워머블 악성코드 확산 실험을 수행하였다. 제안 확산 예측 도구를 잘 활용한다면, 최근 악명을 떨치는 워머블 악성코드에 대한 기본 지식만으로도 거시적 관점의 여러 조건에서 확산 형태를 예측하고 다양한 대응 방안을 모색할 수 있게 해준다.

• 대한안전경영과학회지
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• 제26권1호
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• pp.39-44
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• 2024

In order to prevent accidents via defective ammunition, this paper analyzes recent research on ammunition life prediction methodology. This workanalyzes current shelf-life prediction approaches by comparing the pros and cons of physical modeling, accelerated testing, and statistical analysis-based prediction techniques. Physical modeling-based prediction demonstrates its usefulness in understanding the physical properties and interactions of ammunition. Accelerated testing-based prediction is useful in quickly verifying the reliability and safety of ammunition. Additionally, statistical analysis-based prediction is emphasized for its ability to make decisions based on data. This paper aims to contribute to the early detection of defective ammunition by analyzing ammunition life prediction methodology hereby reducing defective ammunition accidents. In order to prepare not only Korean domestic war situation but also the international affairs from Eastern Europe and Mid East countries, it is very important to enhance the stability of organizations using ammunition and reduce costs of potential accidents.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2863-2865
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• 1999

In this paper we present the modeling of ozone prediction system using polynomial neural network. The Polynomial Neural Network is a useful tool for data learning, nonlinear function estimation and prediction of dynamic system. The mechanism of ozone concentration is highly complex, nonlinear, nonstationary. The purposed method shows that the prediction to the ozone concentration based upon a polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation.

• Structural Engineering and Mechanics
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• 제16권6호
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• pp.643-654
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• 2003

Two real-time modeling prediction (RMP) schemes are presented in this paper for analyzing the behavior of deep excavations during construction. The first RMP scheme is developed from the traditional AR(p) model. The second is based on the simplified Elman-style recurrent neural networks. An on-line learning algorithm is introduced to describe the dynamic behavior of deep excavations. As a case study, in-situ measurements of an excavation were recorded and the measured data were used to verify the reliability of the two schemes. They proved to be both effective and convenient for predicting the behavior of deep excavations during construction. It is shown through the case study that the RMP scheme based on the neural network is more accurate than that based on the traditional AR(p) model.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.205-215
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• 2015

Common mode (CM) conducted interference are predicted and compared with experiments in a motor drive system of Electric vehicles in this study. The prediction model considers each part as an equivalent circuit model which is represented by lumped parameters and proposes the parameter extraction method. For the modeling of the inverter, a concentrated and equivalent method is used to process synthetically the CM interference source and the stray capacitance. For the parameter extraction in the power line model, a computation method that combines analytical method and finite element method is used. The modeling of the motor is based on measured date of the impedance and vector fitting technique. It is shown that the parasitic currents and interference voltage in the system can be simulated in the different parts of the prediction model in the conducted frequency range (150 kHz-30 MHz). Experiments have successfully confirmed that the approach is effective.

• 마이크로전자및패키징학회지
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• 제20권3호
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• pp.7-10
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• 2013

This study presents an approach to predict insulation resistance failure of multilayer ceramic capacitors (MLCCs) using non-linear modeling. A capacitance aging model created by non-linear modeling allowed for the prediction of insulation resistance failure. The MLCC data tested under temperature-humidity-bias testing conditions showed that a change in capacitance, when measured against a capacitance aging model, was able to provide a prediction of insulation resistance failure.

• 한국컴퓨터정보학회논문지
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• 제18권5호
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• pp.147-156
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• 2013

현대 사회의 각 분야는 산업화와 과학기술의 발전으로 빠르게 변화한다. 그러나 빠른 사회 변화의 부작용으로 다양한 문제가 발생하고 있는데, 그 중 범죄는 큰 문제이다. 본 논문은 범죄를 예측하기 위한 모델로 마코프 체인을 적용한 범죄 예측 모델링을 제안한다. 기존의 마코프 체인 모델링은 한 사건의 전체 상태만으로 미래 예측 확률을 구하였으나, 본 논문은 사건 발생 확률 예측을 높이기 위해 전체 상태 예측 확률과 최근 상태 예측 확률로 나누었다. 그리고 전체 상태 예측 확률과 최근 상태 예측 확률의 평균값을 적용하여 미래 예측 확률 모델링으로 구현했다. 데이터는 범죄 발생 건수를 적용하였다. 그 결과 전체 상태만을 대상으로 예측확률을 적용 하였을 때 보다, 전체 상태와 최근상태로 나누어 확률 값을 구한 후, 그 평균값을 예측 확률로 적용하였을 때, 범죄 발생 예측에 근접하다는 결론을 얻었다.

• 전기학회논문지
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• 제59권6호
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• pp.1126-1130
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• 2010

Speech production can be viewed as a filtering operation in which a sound source excites a vocal tract filter. The vocal tract is modeled as a chain of cylinders of varying cross-sectional area in linear prediction acoustic tube modeling. In this modeling the most common implementation assumes equal length of tube sections. Therefore, to model complex vocal tract shapes, a large number of tube sections are needed. This paper proposes a new vocal tract model with unfixed sectionlengths, which uses the reduced lattice filter for modeling the vocal tract. This model transforms the lattice filter to reduced structure and the Burg algorithm to modified version. When the conventional and the proposed models are implemented with the same order of linear prediction analysis, the proposed model can produce more accurate results than the conventional one. To implement a system within similar accuracy level, it may be possible to reduce the stages of the lattice filter structure. The proposed model produces the more similar vocal tract shape than the conventional one.