• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.450-453
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• 2011

분석 경보에 따른 점층적인 분석을 수행하는 버퍼 오버런 분석 기법을 제안한다. 구간 도메인을 사용한 분석은 비용이 낮지만 정확도도 낮다. 변수들 간의 관계를 고려하는 팔각형 도메인을 사용한 분석은 정확도가 높지만 비용도 높다. 점층적인 분석 방법으로 정적 분석기의 비용과 정확도 사이에서 균형을 잡을 수 있다. 먼저 (비용이 낮은) 구간 도메인을 사용한 분석을 수행하고, 증명하지 못한 부분 코드에 대해서만 (정확도가 높은) 관계 도메인을 사용한 분석을 적용한다. 정확도가 높은 분석이 필요한 부분에만 관계 분석을 적용함으로써, 낮은 분석 비용을 유지하면서 정확도를 높일 수 있다.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.04a
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• pp.102-105
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• 2010

본 논문은 전산센터에서 발생하는 비화재보를 분석하여 전산센터에 미치는 장애요소를 개선하고자 한 것이다. 전산센터의 중요성에 의해 설치된 감지기의 감도는 모두 예민하게 되어 있어 비화재보 발생이 많았으며 노이즈발생, 미세먼지, 항온항습기의 바람, 온도 등이 비화재보의 주원인 이었으나 이외에 11가지가 발생되었다. 이들 비화재보는 소화약제 방출에 따른 충전비용 발생, 시스템 정치에 따른 전 시스템이나 시설의 정지등으로 파급되어 생산성 저하, 신뢰성 저하등을 초래하는 것으로 나타났다.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.339-355
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• 2015

Corrosion of prestressed concrete structures is one of the main challenges that engineers face today. In response to this national need, this paper presents the results of a long-term project that aims at developing a structural health monitoring (SHM) technology for the nondestructive evaluation of prestressed structures. In this paper, the use of permanently installed low profile piezoelectric transducers (PZT) is proposed in order to record the acoustic emissions (AE) along the length of the strand. The results of an accelerated corrosion test are presented and k-means clustering is applied via principal component analysis (PCA) of AE features to provide an accurate diagnosis of the strand health. The proposed approach shows good correlation between acoustic emissions features and strand failure. Moreover, a clustering technique for the identification of false alarms is proposed.

• Structural Engineering and Mechanics
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• v.41 no.1
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• pp.139-155
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• 2012

Structural health monitoring systems are often installed on bridges to provide assessments of the need for structural maintenance and repair. Damage or deterioration may be detected by observation of changes in bridge characteristics evaluated from measured structural responses. However, construction materials such as concrete and steel cables exhibit certain time-dependent behaviour, which also results in changes in structural characteristics. If these are not accounted for properly, false alarms may arise. This paper proposes a systematic and efficient method to study the time-dependent effects on the dynamic properties of cable-stayed bridges. After establishing the finite element model of a cable-stayed bridge taking into account geometric nonlinearities and time-dependent behaviour, long-term time-dependent analysis is carried out by time integration. Then the dynamic properties of the bridge after a certain period can be obtained. The effects of time-dependent behaviour of construction materials on the dynamic properties of typical cable-stayed bridges are investigated in detail.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.119-131
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• 2014

In recent literature on traffic scheduling, the combination of the two-dimensional discrete-time Markov chain (DTMC) and the Markov modulated Poisson process (MMPP) is used to analyze the capacity of VoIP traffic in the cognitive radio system. The performance of the cognitive radio system solely depends on the accuracy of spectrum sensing techniques, the minimization of false alarms, and the scheduling of traffic channels. In this paper, we only emphasize the scheduling of traffic channels (i.e., traffic handling techniques for the primary user [PU] and the secondary user [SU]). We consider the following three different traffic models: the cross-layer analytical model, M/G/1(m) traffic, and the IEEE 802.16e/m scheduling approach to evaluate the performance of the VoIP services of the cognitive radio system from the context of blocking probability and throughput.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1187-1198
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• 2019

In this study, we propose a new $PM_{10}$ forecasting model for Seoul region using DNN(Deep Neural Network) and secondary data. The previous numerical and Julian forecast model have been developed using primary data such as weather and air quality measurements. These models give excellent results for accuracy and false alarms, but POD is not good for the daily life usage. To solve this problem, we develop four secondary factors composed with primary data, which reflect the correlations between primary factors and high $PM_{10}$ concentrations. The proposed 4 models are A(Anomaly), BT(Back trajectory), CB(Contribution), CS(Cosine similarity), and ALL(model using all 4 secondary data). Among them, model ALL shows the best performance in all indicators, especially the PODs are improved.

• Journal of the Korean Magnetics Society
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• v.5 no.5
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• pp.579-582
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• 1995

The use of amorphous magnetic alloys as tags or targets in electronic article surveillance systems such as antishoplifting desvices is briefly reviewed. Improved tags became possible with the discovery in 1988 of asymmetric magnetization reversal (AMR) in certain amorphous alloys annealed in applied field approximately equal to the earth's field. These asymmetric hysteresis loops are highly unusual, if not unique, and so greatly diminish the probability of false alarms in a detection system. furthermore, the jump field Hj, which is the coercive field in negative applied fields, can be controlled over a useful range by controlling the field applied to the sample during annealing. By applying several tags to an object, each with a different jump field, it is possible to identify the object with a numeric code that can be remotely read by nonoptical means.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.159-168
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• 2019

Accurate forest fires detection algorithms remain a challenging issue, because, some of the objects have the same features with fire, which may result in high false alarms rate. This paper presents a new video-based, image processing forest fires detection method, which consists of four stages. First, a background-subtraction algorithm is applied to detect moving regions. Secondly, candidate fire regions are determined using CIE $L{\ast}a{\ast}b{\ast}$ color space. Thirdly, special wavelet analysis is used to differentiate between actual fire and fire-like objects, because candidate regions may contain moving fire-like objects. Finally, support vector machine is used to classify the region of interest to either real fire or non-fire. The final experimental results verify that the proposed method effectively identifies the forest fires.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.138-148
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• 1995

While Model-based Monitoring systems based on state observer theory have shown much promise in the laboratory, they have not been widely accepted by industry because, inpractice, these systems often have poor performance with respect to accuracy, band-width, reliability(false alarms), and robustness. In this paper, the linitations of the deterministic discrete-time state observer are investigated quantitatively from the machine monitoring viewpoint. The limitations in the transient and steady-state observer performance are quantified as estimation error bounds from which performance indices are selected. Each index represents the conditioning of the corresponding performance. By utilizing matrix norm theory, an unified main index is determined, that dominates all the indices. This index could from the basis for an observer design methodology that should improve the performance of model-based monitoring systems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.125-137
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• 2009

Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems used in service. For reliable fault monitoring related to the crack growth, it is important to identify the dynamical characteristics as well as transient crack-related signals. Widely used methods which are based on physical phenomena of the three damage stages for detecting the crack growth have a problem that crack-related acoustic emission activities overlap in time, therefore it is insufficient to estimate the exact crack growth time. The proposed pattern recognition method uses the dynamical characteristics of acoustic emission as inputs for minimizing false alarms and miss alarms and performs the temporal clustering to estimate the crack growth time accurately. Experimental results show that the proposed method is effective for practical use because of its robustness to changes of acoustic emission caused by changes of pressure levels.