• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.110-116
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• 2024

Despite the continuous advancement of science and technology, fire accidents continue to occur without decreasing over time, so there is a constant need for a system that can accurately detect fires at an early stage. However, because most existing fire detection systems detect fire in the early stage of combustion when smoke is generated, rapid fire prevention actions may be delayed. Therefore we propose an early fire detection system that can perform early fire detection at a reasonable cost using LSTM, a deep learning model based on multi-gas sensors with high selectivity in the early stage of decomposition rather than the smoke generation stage. This system combines multiple gas sensors to achieve faster detection speeds than traditional sensors. In addition, through window sliding techniques and model light-weighting, the false alarm rate is low while maintaining the same high accuracy as existing deep learning. This shows that the proposed fire early detection system is a meaningful research in the disaster and engineering fields.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.19-28
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• 2017

False data injection attacks have recently been introduced as one of important issues related to cyber-attacks on electric power grids. These attacks aim to compromise the readings of multiple power meters in order to mislead the operation and control centers. Recent studies have shown that if a malicious attacker has complete knowledge of the power grid topology and branch admittances, s/he can adjust the false data injection attack such that the attack remains undetected and successfully passes the bad data detection tests that are used in power system state estimation. In this paper, we investigate that a practical false data injection attack is essentially a cyber-attack with uncertain information due to the attackers lack of knowledge with respect to the power grid parameters because the attacker has limited physical access to electric facilities and limited resources to compromise meters. We mathematically formulated a method of identifying the most vulnerable locations to false data injection attack. Furthermore, we suggest minimum topology changes or phasor measurement units (PMUs) installation in the given power grids for mitigating such attacks and indicate a new security metrics that can compare different power grid topologies. The proposed metrics for performance is verified in standard IEEE 30-bus system. We show that the robustness of grids can be improved dramatically with minimum topology changes and low cost.

• Annual Conference of KIPS
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• 2005.05a
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• pp.1185-1188
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• 2005

비정상 행위에 대한 true/false 방식의 공격 탐지 및 대응방법은 높은 오탐지율(false-positive)을 나타내기 때문에 이를 대체할 새로운 공격 탐지방법과 공격 대응방법이 연구되고 있다. 대표적인 연구로는 트래픽 제어 기술을 이용한 단계적 대응방법으로, 이 기술은 비정상 트래픽에 대해 단계적으로 대응함으로써 공격의 오탐지로 인하여 정상 서비스를 이용하는 트래픽이 차단되지 않도록 하는 기술이다. 비정상 트래픽 중 포트스캔 공격은 네트워크 기반 공격을 위해 공격대상 호스트의 서비스 포트를 찾아내는 공격으로 이 공격을 탐지하기 위해서는 일정 시간동안 특정 호스트의 특정 포트에 보내지는 패킷 수를 모니터링 하여 임계치와 비교하는 방식의 true/false 방식의 공격 탐지방법이 주로 사용되었다. 비정상 트래픽 제어 프레임워크(Abnormal Traffic Control Framework)는 true/false 방식의 공격 탐지방법을 이용하여 공격이 탐지되었을 때, 처음에는 트래픽 제어로 대응하고 같은 공격이 재차 탐지되었을때, 차단하여 기존의 true-false 방식의 공격 탐지 및 대응방법이 가지는 높은 오탐지율을 낮춘다. 하지만 포트스캔 공격의 특성상, 공격이 탐지된 후 바로 차단하지 못하였을 경우, 이미 공격자가 원하는 모든 정보를 유출하게 되는 문제가 있다. 본 논문에서는 기존의 True/False 방식의 포트스캔 공격 탐지방법에 퍼지 로직 개념을 추가하여 공격 탐지의 정확성을 높이고 기존의 탐지방법을 이용하였을 때보다 신속한 트래픽 제어 및 차단을 할 수 있는 방법을 제안한다.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.327-338
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• 2018

The possibility of ship detection monitoring at operational level using KOMPSAT-5 Synthetic Aperture Radar (SAR) and Automatic Identification System (AIS) data is investigated. For the analysis, the KOMPSAT-5 SLC images, which are collected from the west coast of Shinjin port and the northern coast of Jeju port are used along with portable AIS data from near the coast. The ship detection algorithm based on HVAS (Human Visual Attention System) was applied, which has significant advantages in terms of detection speed and accuracy compared to the commonly used CFAR (Constant False Alarm Rate). As a result of the integrated analysis, the ship detection from KOMPSAT-5 and AIS were generally consistent except for small vessels. Some ships detected in KOMPSAT-5 but not in AIS are due to the data absence from AIS, while it is clearly visible in KOMPSAT-5. Meanwhile, SAR imagery also has some false alarms due to ship wakes, ghost effect, and DEM error (or satellite orbit error) during object masking in land. Improving the developed ship detection algorithm and collecting reliable AIS data will contribute for building wide integrated surveillance system of marine territory at operational level.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.313-326
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• 2018

This paper introduces core techniques on ship detection and tracking based on a compact HF radar platform which is necessary to establish a wide-area surveillance network. Currently, most HF radar sites are primarily optimized for observing sea surface radial velocities and bearings. Therefore, many ship detection systems are vulnerable to error sources such as environmental noise and clutter when they are applied to these practical surface current observation purpose systems. In addition, due to Korea's geographical features, only compact HF radars which generates non-uniform antenna response and has no information on target information are applicable. The ship detection and tracking techniques discussed in this paper considers these practical conditions and were evaluated by real data collected from the Yellow Sea, Korea. The proposed method is composed of two parts. In the first part, ship detection, a constant false alarm rate based detector was applied and was enhanced by a PCA subspace decomposition method which reduces noise. To merge multiple detections originated from a single target due to the Doppler effect during long CPIs, a clustering method was applied. Finally, data association framework eliminates false detections by considering ship maneuvering over time. According to evaluation results, it is claimed that the proposed method produces satisfactory results within certain ranges.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.418-426
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• 2019

Recently, many studies on vital sign detection using a radar sensor related to Internet of Things(IoT) smart home systems have been conducted. Because vital signs such as respiration and cardiac rates generally cause micro-motions in the chest or back, the phase of the received echo signal from a target fluctuates according to the micro-motion. Therefore, vital signs are usually detected via spectral analysis of the phase. However, the probability of false alarms in cardiac rate detection increases as a result of various problems in the measurement environment, such as very weak phase fluctuations caused by the cardiac rate. Therefore, this study analyzes the difficulties of vital sign detection and proposes an efficient vital sign detection algorithm consisting of four main stages: 1) phase decomposition, 2) phase differentiation and filtering, 3) vital sign detection, and 4) reduction of the probability of false alarm. Experimental results using impulse-radio ultra-wideband radar show that the proposed algorithm is very efficient in terms of computation and accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.261-268
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• 2014

Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method's classification rate as well as detection rate under equal false positive rate are higher than conventional one.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1880-1884
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• 2004

The wavelet packet transform has been applied for QRS detection with squaring, window integration, and impulse filter techniques to cut down the false detection of QRS complex. This real time QRS detection has been performed on Simulink and Matlab. The correct QRS detection rates have reached to 99.75% in the experiment with 15 sets of ECG data from European ST-T database which are kept in Physionet.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.75-84
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• 2008

A new automatic small target detection and tracking algorithm for the real-time IR surveillance system is presented. The automatic target detection and tracking algorithm of the real-time systems, requires low complexity and robust tracking performance in the cluttered environment. Linear-array and parallel-scan IR systems usually suffer from severe scan noise caused by the detector non-uniformity. After the spatial filtering and thresholding, this scan noise still remains as high amplitude clutter which degrades the target detection rate and tracking performance. In this paper, we propose a new feature which consists of area and validity information of a measurement. By adopting this feature to the measurements selection and track confirmation, we can increase the target detection rate and reduce both the track loss rate and false track rate. From the experimental results, we can validate the feasibility of the proposed method in the noisy IR images.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1272-1275
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• 2002

Watermark detection plays a crucial role in multimedia copyright protection and has traditionally been tackled using correlation-based algorithms. However, correlation-based detection is not actually the best choice, as it does not utilize the distributional characteristics of the image being marked. Accordingly, an efficient watermark detection scheme for DWT coefficients is proposed as optimal for non-additive schemes. Based on the statistical decision theory, the proposed method is derived according to Bayes' decision theory, the Neyman-Pearson criterion, and the distribution of the DWT coefficients, thereby minimizing the missed detection probability subject to a given false alarm probability. The proposed method was tested in the context of robustness, and the results confirmed the superiority of the proposed technique over conventional correlation-based detection method.