• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.6
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• pp.487-496
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• 2017

Ground moving target indicator(GMTI) using syntheticaperture radar(SAR) used for finding moving targets on wide background clutter in short time is one of good ways to monitor a traffic situation or an enemy's threat. Although displaced phase center antenna (DPCA) is a real time method with low computational complexity, there have been few studies about its performance against various ground clutters. Thus, we need to analyze GMTI performance for various ground clutters in order to design a suitable DPCA detector. In this paper, simulation results show that the conventional DPCA detector produces different performance in terms of detection rate and false alarm rate. In particular, the false alarm rate of heterogeneous or extremely heterogeneous clutter from urban area is higher than one of homogeneous clutter from natural area.

• MALSORI
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• no.43
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• pp.151-161
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• 2002

This study presents performance analysis results of speaker adaptation for keyword spotting system. In this paper, we implemented MLLR (Maximum Likelihood Linear Regression) method on our middle size vocabulary keyword spotting system. This system was developed for directory services of universities and colleges. The experimental results show that speaker adaptation reduces the false alarm rate to 1/3 with the preservation of the mis-detection ratio. This improvement is achieved when speaker adaptation is applied to not only keyword models but also non-keyword models.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.1037-1044
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• 2012

Multivariate control charts for effectively monitoring every component in the dispersion matrix of multivariate normal process are considered. Through the numerical results, we noticed that the multivariate control charts based on sample statistic $V_i$ by Hotelling or $W_i$ by Alt do not work effectively when the correlation coefficient components in dispersion matrix are increased. We propose a combined procedure monitoring every component of dispersion matrix, which operates simultaneously both control charts, a chart controlling variance components and a chart controlling correlation coefficients. Our numerical results show that the proposed combined procedure is efficient for detecting changes in both variances and correlation coefficients of dispersion matrix.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.3-9
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• 1999

In this paper, the characteristics of LFM and CW signals in active sonar environment is investigated. CA, OS and TM CFAR processors are applied to the received CW/LFM signals which are plotted in the range/doppler domain. The performances of detection are analyzed. Particularly, using the real data, we certified that the results of the experiments are identical with the theoretical performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1109-1112
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• 2006

DDoS(Distributed Denial-of-Service) 공격은 인터넷 침해가운데 가장 위협적인 공격들 중 하나이며 이러한 공격을 실시간으로 탐지하기 위한 연구는 활발히 이루어져 왔다. 하지만 기존의 탐지 메커니즘이 가지고 있는 높은 오탐지율은 여전히 보완해야할 과제로 남아 있다. 따라서 본 논문에서는 DDoS공격 탐지의 근거로 사용된 기존의 트래픽 볼륨(traffic volume), 엔트로피(entropy), 그리고 카이제곱(chi-square)을 이용한 비정상 행위탐지(Anomaly detection)방식의 침임탐지시스템이 가지는 오탐지율(false alarm rate)을 개선할 수 있는 방안을 제안한다. 또한 공격 탐지 시 프로토콜, TCP 플래그(flag), 그리고 포트 번호를 이용하여 네트워크 관리자에게 보다 자세한 공격 정보를 제공함으로써 효율적으로 공격에 대처할 수 있는 시스템을 설계한다.

• Phonetics and Speech Sciences
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• v.5 no.1
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• pp.11-16
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• 2013

This paper proposes robust feature extraction for accurate voice activity detection (VAD). VAD is one of the principal modules for speech signal processing such as speech codec, speech enhancement, and speech recognition. Noisy environments contain nonstationary noises causing the accuracy of the VAD to drastically decline because the fluctuation of features in the noise intervals results in increased false alarm rates. In this paper, in order to improve the VAD performance, harmonic-weighted energy is proposed. This feature extraction method focuses on voiced speech intervals and weighted harmonic-to-noise ratios to determine the amount of the harmonicity to frame energy. For performance evaluation, the receiver operating characteristic curves and equal error rate are measured.

• Journal of Multimedia Information System
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• v.7 no.3
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• pp.205-214
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• 2020

Crime has become one of the major problems in modern society. Even though visual surveillances through closed-circuit television (CCTV) is extensively used for solving crime, the number of crimes has not decreased. This is because there is insufficient workforce for performing 24-hour surveillance. In addition, CCTV surveillance by humans is not efficient for detecting dangerous situations owing to accuracy issues. In this paper, we propose the autonomous detection of dangerous situations in CCTV scenes using a deep learning model with relational inference. The main feature of the proposed method is that it can simultaneously perform object detection and relational inference to determine the danger of the situations captured by CCTV. This enables us to efficiently classify dangerous situations by inferring the relationship between detected objects (i.e., distance and position). Experimental results demonstrate that the proposed method outperforms existing methods in terms of the accuracy of image classification and the false alarm rate even when object detection accuracy is low.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.305-308
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• 1998

본 논문에서는 핵심어 검출의 성능을 향상시키기 위한 새로운 후처리 방법을 제안한다. 일반적으로 핵심어 검출 시스템에 의해 검출된 상위 n개의 후보 단어들의 우도(likelihood)는 비슷한 경우가 많다. 따라서, 한 음성구간에 대해 음향학적으로 유사한 핵심어들간의 오인식 가능성이 높아진다. 그러나 기존의 핵심어 검출에 사용된 후처리 방법은 음성의 모든 구간에 같은 비중을 두고 우도를 평가하므로 비슷한 음향학적 특징을 가지는 유사한 핵심어들의 비교에 적합하지 못하다. 이를 해결하기 위하여, 본 논문에서는 후보단어들의 부분적인 음향학적 특징 차이에 기반한 가중치를 우도 계산 시에 반영함으로써 보다 변별력을 높이는 알고리즘을 제안한다. 실험 결과, 제안된 방법을 이용하여 유사한 후보단어들간의 변별력을 높일 수 있었고, 인식율이 93%일 때, 우도비검사 방법에 비해 19.6%의 false alarm rate을 감소시킬 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3E
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• pp.124-132
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• 2003

We propose a new method for speech/non-speech classifiers based on concepts of the adaptive boosting (AdaBoost) algorithm in order to detect speech for robust speech recognition. The method uses a combination of simple base classifiers through the AdaBoost algorithm and a set of optimized speech features combined with spectral subtraction. The key benefits of this method are the simple implementation, low computational complexity and the avoidance of the over-fitting problem. We checked the validity of the method by comparing its performance with the speech/non-speech classifier used in a standard voice activity detector. For speech recognition purpose, additional performance improvements were achieved by the adoption of new features including speech band energies and MFCC-based spectral distortion. For the same false alarm rate, the method reduced 20-50% of miss errors.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.76-84
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• 2000

As first step of any acoustic defence system, a attacking target warning system needs to be extremely reliable. This means the system must ensure a high probability of target classification together with a very low false alarm rate. In this paper, a algorithms for underwater target automatic classification is available for use in the passive sonar will be presented. In first, we will describe the precise automatic extraction of frequency lines for the detection of acoustic signatures. Also, a neural network and fuzzy based algorithms for target classification will be described. Thus the performances of these algorithms are very good with a high probability of classification.