• 정보보호학회논문지
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• 제28권3호
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• pp.617-623
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• 2018

본 연구는 안드로이드 정적분석을 기반으로 추출된 AndroidManifest 권한 특징을 통해 악성코드를 탐지하고자 한다. 특징들은 AndroidManifest의 권한을 기반으로 분석에 대한 자원과 시간을 줄였다. 악성코드 탐지 모델은 1500개의 정상어플리케이션과 500개의 악성코드들을 학습한 SVM(support vector machine), NB(Naive Bayes), GBC(Gradient Boosting Classifier), Logistic Regression 모델로 구성하여 98%의 탐지율을 기록했다. 또한, 악성앱 패밀리 식별은 알고리즘 SVM과 GPC (Gaussian Process Classifier), GBC를 이용하여 multi-classifiers모델을 구현하였다. 학습된 패밀리 식별 머신러닝 모델은 악성코드패밀리를 92% 분류했다.

• 한국통신학회논문지
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• 제34권3C호
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• pp.304-310
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• 2009

본 논문은 M-레벨 QAM 계층 변조 시스템에서 반복 수신기의 복잡도를 줄이기 위한 연속 MAP(maximum a posteriori probability) 검파 방식을 제안한다. 계층 변조 신호 내의 특정 우선 순위를 갖는 신호는 계층 변조 신호를 구성하는 각 신호를 우선 순위에 따라 간섭 신호 성분으로서 제거하거나 가우시안 잡음으로 간주한 후 MAP 방식에 의해 순차적으로 검파된다. 검파 과정을 순차적으로 진행함으로써 반복 수신의 복잡도를 신호 당 전송되는 비트 수에 선형적으로 증가하도록 감소시킬 수 있으며 각 부호화 비트의 연판정 값 계산 시 간섭 제거와 가우시안 가정의 효과를 검파 과정에 반영하여 잡음 분산을 조정함으로써 순차적 검파 방식에 의해 발생할 수 있는 성능 열화를 최소화한다. 전산 모의 실험을 통하여 제안하는 순차적 MAP 검파 방식의 성능이 최적 MAP 검파 방식과 비교하여 0.5dB 미만의 성능열화를 나타내는 것을 보인다.

• Current Optics and Photonics
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• 제1권4호
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• pp.308-314
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• 2017

A particular optical receiver has its own optical receiver modes (ORMs) determined by its optical and electrical filters. Superposing the ORM waveforms at the transmitter, we can generate a new type of optical signals, called ORM signals. After optical detection, they produce pre-specified voltage waveforms accurately, which is advantageous for digital signal processing. Assuming a Gaussian optical receiver, where the optical and electrical filters are Gaussian, we illustrate various phase-shift keying ORM signals using two ORMs by changing their relative phase. We also illustrate multi-level ORM signal patterns using two or more ORMs.

• Environmental Engineering Research
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• 제11권2호
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• pp.63-76
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• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

• Journal of Communications and Networks
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• 제16권1호
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• pp.92-97
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• 2014

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2018년도 제30회 한글 및 한국어 정보처리 학술대회
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• pp.550-551
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• 2018

바이오마커는 체내의 상태 및 변화를 파악할 수 있는 지표이다. 이는 암을 비롯한 다양한 질병에 대하여 진단하는데 활용도가 높은 것으로 알려져 있으나, 새로운 바이오마커를 찾아내기 위한 임상 실험은 많은 시간과 비용을 소비되며, 모든 바이오마커가 실제 질병을 진단하는데 유용하게 사용되는 것은 아니다. 따라서 본 연구에서는 자연어처리 기술을 활용해 바이오마커를 발굴할 때 요구되는 많은 시간과 비용을 줄이고자 한다. 이 때 다양한 의미를 가진 어휘들이 해당 질병과 연관성이 높은 것으로 나타나며, 이들을 분류하는 것은 매우 어렵다. 따라서 우리는 Word2Vec과 가우시안 혼합 모델을 사용하여 바이오마커를 분류하고자 한다. 실험 결과, 대다수의 바이오마커 어휘들이 하나의 군집에 나타나는 것을 확인할 수 있었다.

• 한국통신학회논문지
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• 제21권4호
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• pp.1077-1088
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• 1996

In this paper, we propose a new method to analyze the performance degradation by timing jitters in the receivers of intensity modulation/direct detection digital optical communication systems where pulse-shaping filters are used to minimize intersymbol interference. The results obtained from the proposed analytical method show that conventional analytical methods underestimate the influence of timing jitters on the receiver performance. Using the proposed anlaytical method, we derive an analytic equation for approximated power penalty due to timing itters and obtain an exact power penalty by numerical analyses. Assuming Gaussian or uniform probability density function for timing jitters, we also show that assumption of Gaussian distribution for timing jitters yields more performance degration than that of uniform distribution.

• Journal of information and communication convergence engineering
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• 제13권3호
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• pp.197-204
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• 2015

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective.

• International journal of advanced smart convergence
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• 제1권2호
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• pp.39-42
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• 2012

This paper proposed a method that detects emergency situations in a video stream using MHI (Motion History Image) and template matching for a video-based intelligent security system. The proposed method creates a MHI of each human object through image processing technique such as background removing based on GMM (Gaussian Mixture Model), labeling and accumulating the foreground images, then the obtained MHI is compared with the existing MHI templates for detecting an emergency situation. To evaluate the proposed emergency detection method, a set of experiments on the dataset of video clips captured from a security camera has been conducted. And we successfully detected emergency situations using the proposed method. In addition, the implemented system also provides MMS (Multimedia Message Service) so that a security manager can deal with the emergency situation appropriately.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권2호
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• pp.52-61
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• 2006

For the detection of moving objects, background subtraction methods are widely used. An adaptive Gaussian mixture model combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, probabilistic learning approach does not work well in high traffic regions. In this paper, we Propose a reliable learning method of complex and dynamic backgrounds in high traffic regions.