• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.122-126
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• 1988

This paper deals with an application of an expert system to transmission line fault diagnosis using alarm information line possible solution can be obtained even in case that the cause of alarms is due to relays, circuit breakers or alarm systems. The expert system diagnoses not only any possible fault element, but also normal or abnormal misoperations. Also, this system can give any possible answers only when the sum of appropriate error indices assigned to false operation of devices is less than the appropriate criterion specified in advance. This paper is written in Official Projection System-Version 5 (OPS-5) which is one of the AI languages.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• Fire Protection Technology
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• s.19
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• pp.17-21
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• 1995

This is to study installation standards of aspirating devices and detectors, which are components of ADS(aspirating fire detection system). The BFPSA(British Fire Protection System Association) code was mainly referred is studying. ADS aspirated air and smoke through the pipe and then checks if there is fire or not. It is now in the limelight because it can early alarm in case of fire and prevent false-alarming.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.141-145
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• 2008

Wireless sensor network (WSN) is expected to be used in many applications. However, sensor nodes still have some secure problems to use them in the real applications. They are typically deployed on open, wide, and unattended environments. An adversary using these features can easily compromise the deployed sensor nodes and use compromised sensor nodes to inject fabricated data to the sensor network (false data injection attack). The injected fabricated data drains much energy of them and causes a false alarm. To detect and drop the injected fabricated data, a filtering-based security method and adaptive methods are proposed. The number of different partitions is important to make event report since they can make a correctness event report if the representative node does not receive message authentication codes made by the different partition keys. The proposed methods cannot guarantee the detection power since they do not consider the filtering scheme. We proposed clustering method for filtering-based secure methods. Our proposed method uses fuzzy system to enhance the detection power of a cluster.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.101-109
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• 2008

In this paper, we introduce clustering scheme to calculate probability of detection which is practically required for conventional weighted-collaborative sensing technique. We also propose an improved weighted-collaborative spectrum sensing scheme using new weight generation algorithm to achieve better performance in Cognitive Radio systems. We calculate Pd in each cluster which is a CR users group with similar channel situation. New weight factor is generated using square sum of all cluster's Pds. Simulations under slow fading show that we can get better total detection probability and lower false alarm rate when PU (Primary User) suddenly terminates their transmission.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3712-3729
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• 2020

Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.172-180
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• 2012

In multi-static sonar for detecting an underwater target, received signals contain the target echo, reverberation and clutter. Clutter and reverberation are main causes of increasing the false alarm rate. MRAL classifies received signals according to the spatial similarity, and it regards classified signal as reflected signals from a reflector. MRAL reduces the false alarm rate this way. However, the results of MRAL can have localization errors. In this paper, an MRAL post processing algorithm is proposed to reduce the localization errors with the least square (LS) method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.227-236
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• 2001

This paper proposes a frequency-domain partially adaptive algorithm, called a censoring algorithm, to reduce the computational complexity of the frequency domain adaptive array. The proposed censoring algorithm determines the existence of interferences in the frequency-domain at each frequency bin using a constant false alarm rate (CFAR) processor. The censoring algorithm adapts only those parts of the weights that correspond to the frequency bins expected to contain interferences. The censoring algorithm is also expanded to overcome the signal cancellation phenomenon caused by smart jammers. Accordingly, a censoring spatial smoothing, which combines the censoring algorithm with spatial smoothing, is proposed. Simulation results show that the proposed algorithms are effective in removing interferences with only part of the computational complexity of conventional algorithms yet with the same level of performance.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.49-58
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• 2014

This paper proposes a new assessment and reliability validation method of Lane Departure Assistance System based on DGPS-GIS by measuring lanes with camera vision. Assessment of lane departure is performed with yaw speed measurement and determination method for false alarm of ISO 17361 and performance validation is executed after generating departure warning boundary line by considering deviation error of LDAS using DGPS. Distance between the wheel and the lane is obtained through line abstraction using Hough transformation of the lane image with camera vision. Evaluation validation is obtained by comparing this value with the distance obtained with LDAS. The experimental result shows that the error of the extracted distance of the LDAS is within 5 cm. Also it proves performance of LDAS based on DGPS-GIS and assures effectiveness of the proposed validation method for system reliability using camera vision.

• Journal of Astronomy and Space Sciences
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• v.27 no.3
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• pp.245-252
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• 2010

Korea Astronomy and Space Science Institute (KASI) has developed a mobile satellite laser ranging (SLR) system called ARGO-M since 2008 for space geodesy research and precise orbit determination technologies using SLR with mm level accuracy. ARGO-M is capable of night tracking and daylight tracking for which requires spatial, spectral and time filters due to high background noises. In this study, characteristics and specifications of ARGO-M are discussed and its tracking capabilities of night and daylight tracking are analyzed for STSAT-2B and KOMPSAT-5 through link budget. Additionally false alarm and signal detection probabilities are also analyzed depending on spectral and time filters for daylight tracking for these satellites.