• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.291-315
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• 2019

The aim of the present study was to compare the research trends on disinformation between Korean and abroad. To achieve this objective, a total of 283 author-assigned English keywords in 104 Korean papers and 3,551 author-assigned English keywords in 861 abroad papers were collected from the whole research fields and the publication periods. The collected data were analyzed using NetMiner V.4 to discover their 'degree centrality' and 'betweenness centrality'of the keyword network. The result are as follows. First, the major research topics of disinformation in Korea were drawn such as 'Freedom of Expression', 'Fact Check', 'Regulation', 'Media Literacy', and 'Information Literacy' in order; whereas, in abroad were shown like 'Social Media', 'Post Truth', 'Propaganda', 'Information Literacy', and 'Journalism' in order. Second, in terms of the influence of research topics related to disinformation, in Korea were identified such as 'Fact Check', 'Freedom of Expression', and 'Hoax' in order; whereas, in abroad were shown such as 'Social Media' and 'Detection' in order. Finally, in an aspect of intervention of research topics related to disinformation, in Korea were 'Fact Check', 'Polarization', 'Freedom of Expression', and 'Commercial'; whereas, in abroad were 'Social Media', 'Detection', and 'Machine Learning' in order.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1127-1130
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• 1999

In this paper, using one method of Additive Synthesis, Analysis-by-synthesis/Overlap-Add (ABS/OLA) method, analysis and synthesis of musical tones is processed. But peak detection of frequency domain is processed by proposed method considering the view of acoustics. It is that that harmonics frequency is times of main frequency. Using this fact, peak detection of frequency domain is useful for detection of tonal component identified musical note. It is possible to realize high-quality lour bit rate audio.

• Proceedings of the KIEE Conference
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• 1997.07c
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• pp.883-885
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• 1997

High impedance fault(HIF) is random in its behavior even in a similar environment. The detection of Ire HIF has focused on the development of algorithms based on harmonic, parameters of the arc currents. However, a fact that proper selection of the harmonic parameters, rather than algorithm selection, is more important is shown in this paper by applying three different performance evaluation methods on two HIF detection algorithms using eight harmonic parameters.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.418-421
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• 2003

In this thesis, a new channel estimation technique is proposed for orthogonal frequency division multiplexing (OFDM) over time varying channels. The channel estimation algorithm exploits the fact that the estimated channel impulse response (CIR) by using pilot signal is the average value of the CIR variation within an OFDM symbol period. With this fact, the CIR variation is simply estimated through lowpass interpolation of the CIRs of the adjacent OFDM symbols. For signal detection, a time domain equalizer is used in this thesis. Simulation results show that the proposed system improves the bit error rate (BER) over time varying channels.

• Current Optics and Photonics
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• v.8 no.3
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• pp.282-299
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• 2024

Target detection (TD) is a research hotspot in the field of hyperspectral imaging (HSI). Traditional TD methods often mine targets from HSIs under a single imaging condition, without considering the influence of imaging conditions. In fact, the spectra of ground objects in HSIs are uncertain and affected by the imaging conditions (weather, atmospheric, light, time, and other angle conditions including zenith angle). Hyperspectral data changes under different imaging conditions. Therefore, the detection result for a single imaging condition cannot accurately reflect the effectiveness of the detection method used. It is necessary to analyze the performance of various detection methods under different imaging conditions, to find a more applicable detection method. In this paper, we study the performance of TD methods under various land-based imaging conditions. We first summarize classical TD methods and evaluation methods. Then, the detection effects under various imaging conditions are analyzed. Finally, the concepts of the stability coefficient (SC) and effective area under the curve (EAUC) are proposed to comprehensively evaluate the applicability of detection methods under land-based imaging conditions, in terms of both detection accuracy and stability. This is conducive to our selection of detection methods with better applicability in land-based contexts, to improve detection accuracy and stability.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.189-194
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• 2018

In this paper, we propose the drowsiness detection method for car driver. This paper determines whether or not the driver's eyes are closed using the depth and infrared videos. The proposed method has the advantage to detect drowsiness without being affected by illumination. The proposed method detects a face in the depth picture by using the fact that the nose is closest to the camera. The driver's eyes are detected by using the extraction of harr-like feature within the detected face region. This method considers to be drowsiness if eyes are closed for a certain period of time. Simulation results show the drowsiness detection performance for the proposed method.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.127-130
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• 2009

We propose a method for detecting DDoS (Distributed Denial of Service) traffic in real-time inside the backbone network. For this purpose, we borrow the concepts of MACD (Moving Average Convergence Divergence) and RoC (Rate of Change), which are used for technical analysis in the stock market Due to the fact that the method is based on a quantitative, rather than a heuristic, detection level, DDoS traffic can be detected with greater accuracy (by reducing the false alarm ratio). Through simulation results, we show how the detection level is determined and demonstrate how much the accuracy of detection is enhanced.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.3
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• pp.275-280
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• 2002

A new algorithm for gradual scene change detection in MPEG based frame sequences is proposed in this paper. The proposed algorithm is based on the fact that most of gradual curves can be characterized by variance distributions of edge information in the frame sequences. Average edge frame sequences are obtained by performing "sober" edge detection. Features are extracted by comparing variances with those of local blocks in the average edge frames. Those features are further processed by the opening operation to obtain smoothing variance curves. The lowest variance in the local frame sequences is chosen as a gradual detection point. Experimental results show that the proposed method provides 85% precision and 86% recall rate fur gradual scene changes.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.165-171
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• 2007

This paper proposes a new islanding detection method for inverter-interfaced distributed generators (DG). To detect islanding conditions, this paper calculates the phase angle variation of the system voltage by using the phase-locked loop (PLL) in the inverter controllers. Because almost all inverter systems are equipped with the PLL, the implementation of this method is fairly simple and economical for inverter-interfaced DGs. The detection time can also be shortened by reducing communication delay between the relays and the DGs. The proposed method is based on the fact that islanding conditions result in the frequency and voltage variation of the islanded area. The variation depends on the amount of power mismatch. To improve the accuracy of the detection algorithm, this paper injects small low-frequency reactive power mismatch to the output power of DG.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1071-1082
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• 2021

Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.