• Journal of Information Processing Systems
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• 제10권3호
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• pp.459-470
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• 2014

In this paper, the average probability of the symbol error rate (SER) and throughput are studied in the presence of joint spectrum sensing and data transmission in a cognitive relay network, which is in the environment of an optimal power allocation strategy. In this investigation, the main component in calculating the secondary throughput is the inclusion of the spatial false alarms, in addition to the conventional false alarms. It has been shown that there exists an optimal secondary power amplification factor at which the probability of SER has a minimum value, whereas the throughput has a maximum value. We performed a Monte-Carlo simulation to validate the analytical results.

• ETRI Journal
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• 제32권6호
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• pp.881-890
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• 2010

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE $L^*a^*b^*$ color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state-of-the-art fire detection method.

• 제어로봇시스템학회논문지
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• 제22권4호
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• pp.276-280
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• 2016

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling $T^2$ index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.

• 한국위성정보통신학회논문지
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• 제8권4호
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• pp.1-5
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• 2013

Cognitive Radio는 유휴 스펙트럼을 찾아 환경에 맞는 통신방식과 주파수 대역폭을 능동적으로 판단해 재활용하는 지능적인 간섭회피로 방식으로 스펙트럼을 공유하여 전파자원효율을 극대화 하는 기술이다. 기존의 협력 스펙트럼 센싱은 협력하는 사용자들의 채널 상태만을 고려하여 임계치를 정하고 있으며, 모두 같은 고정된 오경보 확률을 적용하여 센싱하게 된다. 오경보 확률은 오류 검출과 관계가 있으며, 고정된 오경보 확률은 검출확률의 감소의 문제를 가진다. 따라서 본 논문에서는 CR사용자의 채널상태를 고려할 뿐만 아니라 채널 상태에 따른 다른 오경보 확률을 차등 적용하여 협력 스펙트럼 센싱을 하는 시스템 모델을 제안하고 분석한다. 시뮬레이션 결과를 통해 검출확률 향상을 보인다.

• 한국위성정보통신학회논문지
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• 제8권1호
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• pp.61-65
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• 2013

본 논문에서는 기존 에너지 스펙트럼 센싱 할 경우 고정된 FA를 기반으로 정해진 임계값에 따라 센싱이 진행된다. 하지만 SNR 상태가 높다면 낮은 레벨의 SNR에서보 비해 오경보 확률이 일어날 확률이 상대적으로 적어진다. 따라서 90%로 이상의 검출확률을 얻는 구간에 대하여 FA를 제어하는 방법으로 오경보 확률을 높게 설정하지 않아도 검출확률이 유지 되는지를 확인한다. 따라서 CR사용자의 SNR 상태에 따라 FA값을 컨트롤하여 적응적 임계값을 얻는 시스템 모델을 제안하고 성능을 분석한다.

• 한국통신학회논문지
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• 제37권2A호
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• pp.105-112
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• 2012

원거리에서의 획득한 영상은 해상도가 낮고 블러링과 잡음에 의한 영향이 크다. 이러한 문제점들은 얼굴 검출 과정에서 보다 많은 오류영역을 산출할 수 있다. 본 논문에서는 AdaBoost 필터와 얼굴의 색상과 외형 정보를 이용한 순차적인 검증 단계를 적용한 얼굴 검출 방법을 제안한다. AdaBoost 방법으로 검출된 오류(false alarm)는 피부색 필터와 가변 경계마스크 필터로 순차적으로 제거된다. 피부색 필터는 사각 윈도우 영역과 화소 별로 적용되는 두 단계로 구성되어 최종적으로 이진 얼굴 클러스터 영상을 구성한다. 기존의 고정된 경계마스크 필터의 단점을 해결하기 위하여 얼굴 클러스터영역에 부합하는 타원을 추정하여 경계마스크의 크기를 산출하고 가로-세로 비율의 적정성을 검토한다. 실험에서는 CCTV와 스마트 폰으로 획득한 영상을 이용하여 제안된 얼굴 검출 방법이 원거리에서 획득한 영상의 얼굴 검출에 효과적임을 보인다.

• 전자공학회논문지A
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• 제32A권3호
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• pp.58-67
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• 1995

In this paper, we present a new HPEJTC system which is capable of real-time multi-target recognition and tracking with better discrimination by extracting the phase signal of reference function from the JTPS of the conventional optical JTC retaining the amplitude signal of the input function. In order to test the correlation discrimination performance of the HPEJTC system, some experiments are carried out on the scenarios susceptible to the false alarms and missing in which many similar targets are periodically loacted. And, the proposed HPEJTC is analyzed to be the real function version of the POF and finally the possibility of the real-time implementation of the POF is suggested, because it can be implemented by using spatial light modulator, CCD detector and some other optical components.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.289-301
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• 2003

In this paper, we propose Shewhart control chart and EWMA control chart using the autocorrelated data which are common in chemical and process industries and lead to increase the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and simulation is conducted to investigate the performances of the proposed control charts.

• 대한산업공학회지
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• 제27권3호
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• pp.289-296
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• 2001

This paper proposes multivariate control chart for autocorrelated data which are common in chemical and process industries and lead to increase in the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a vector autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and therefore the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and Monte Carlo simulation is conducted to investigate the performances of the proposed control charts.

• 한국군사과학기술학회지
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• 제11권1호
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• pp.66-74
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• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.