• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1283-1295
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• 2013

This study proposed an improvement in synchronously rotating reference frame-based voltage sag detection under distorted grid voltages. In the past, the conventional synchronously rotating reference frame (CSRRF)-based voltage sag detection was generally used in the voltage sag compensation applications. Its disadvantage is a long delay of detection time. The modified synchronously rotating reference frame (MSRRF)-based voltage sag detection is able to detect the voltage sag with only a short delay in detection time. However, its operation under distorted grid voltage conditions is unavailable. This paper proposed the improvement of modified synchronously rotating reference frame (IMSRRF)-based voltage sag detection for use in distorted grid voltages with very fast operation of voltage sag detection. The operation of the proposed voltage sag detections is investigated via simulations and experimentations to verify the performance of the IMSRRF-based voltage sag detection.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.62-70
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• 2012

In this paper, we propose the gunnery detection method based on reference frame modeling and frame difference method. The frame difference method is basic method in target detection, and it's applicable to the detection of moving targets. The goal of proposed method is the detection of gunnery target which has huge variation of energy and size in the time domain. So, proposed method is based on frame difference, and it guarantee real-time processing and high detection performance. In the method of frame difference, it's important to generate reference frame. In the proposed method, reference frame is modeled and updated in real time processing using statistical values for each pixels. We performed the simulation on 73 IR video data that has gunnery targets, and the experimental results showed that the proposed method has 95.7% detection ratio under condition that false alarm is 1 per hour.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.38-43
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• 2001

This Paper suggests a new feature for shot detection, using the proposed robust feature from the DC image constructed by DCT DC coefficients in the MPEG video stream, and proposes the characterizing value that reflects the characteristic of kind of video (movie, drama, news, music video etc.). The key frames are pulled out from many frames by using the local minima and maxima of differential of the value. After original frame(not do image) are reconstructed for key frame, indexing process is performed through computing parameters. Key frames that are similar to user's query image are retrieved through computing parameters. It is proved that the proposed methods are better than conventional method from experiments. The retrieval accuracy rate is so high in experiments.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.27-33
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• 2020

Backward compatibility is one of the key issues for radio equipment supporting IEEE 802.11, the typical wireless local area networks (WLANs) communication protocol. For a successful packet decoding with the backward compatibility, the frame format detection is a core precondition. This paper presents a novel frame format detection method based on a deep learning procedure for WLANs affiliated with IEEE 802.11. Considering that the detection performance of conventional methods is degraded mainly due to the poor performances in the symbol synchronization and/or channel estimation in low signal-to-noise-ratio environments, we propose a novel detection method based on convolutional neural network (CNN) that replaces the entire conventional detection procedures. The proposed deep learning network provides a robust detection directly from the receive data. Through extensive computer simulations performed in the multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), the proposed method exhibits superb improvement in the frame format detection compared to the conventional method.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.101-104
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• 2001

This paper proposes a unique shot boundary detection algorithm for the video indexing and/or browsing. Conventional methods based on the frame differences and the histogram differences are improved. Instead of using absolute frame differences, block by block based relative frame differences are employed. Frame adaptive thresholding values are also employed for the better detection. for the cases that the frame differences are not enough to detect the shot boundary, histogram differences are selectively applied. Experimental results show that the proposed algorithm reduces both the “false positive” errors and the “false negative” errors especially for the videos of dynamic local and/or global motions

• Journal of IKEEE
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• v.26 no.4
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• pp.622-627
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• 2022

This paper proposes an intrusion detection system for in-vehicle network to improve detection performance considering attack counts and attack types. In intrusion detection system, both FNR (False Negative Rate), where intrusion frame is misjudged as normal frame, and FPR (False Positive Rate), where normal frame is misjudged as intrusion frame, seriously affect vechicle safety. This paper proposes a novel intrusion detection algorithm to improve both FNR and FPR, where data frame previously detected as intrusion above certain attack counts is automatically detected as intrusion and the automatic intrusion detection method is adaptively applied according to attack types. From the simulation results, the propsoed method effectively improve both FNR and FPR in DoS(Denial of Service) attack and spoofing attack.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.601-617
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• 2014

In digital radio broadcasting systems, long delays are incurred in service start time when tuning to a particular frequency because several synchronization steps, such as symbol timing synchronization, frame synchronization, and carrier frequency offset and sampling frequency offset compensation are necessary. Therefore, the operation of the synchronization blocks causes delays ranging from several hundred milliseconds to a few seconds until the start of the radio service after frequency tuning. Furthermore, if spectrum inversed signals are transmitted in digital radio broadcasting systems, the receivers are unable to decode them, even though most receivers can demodulate the spectral inversed signals in analog radio broadcasting systems. Accordingly, fast synchronization techniques and a method for spectral inversion detection are required in digital radio broadcasting systems that are to replace the analog radio systems. This paper presents a joint detection method of frame, integer carrier frequency offset, and spectrum inversion for DRM Plus digital broadcasting systems. The proposed scheme can detect the frame and determine whether the signal is normal or spectral inversed without any carrier frequency offset and sampling frequency offset compensation, enabling fast frame synchronization. The proposed method shows outstanding performance in environments where symbol timing offsets and sampling frequency offsets exist.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.109-114
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• 2015

Frame synchronization is achieved by correlation between received symbols and a preamble pattern which is periodically appended at a frame header. In this paper, we deal with a frame detection method complaint with satellite-based DVB-S2 system. In DVB-S2, frame synchronization is performed under the low signal-to-noise ratio(SNR), a large frequency offset which can be up to 20% of a symbol transmission rate and unknown modulation schemes ranging from QPSK to 32-APSK. In this environment, we propose a method combining differential correlation based on SOF and PLSC with an accumulated correlation method for the detection of frame structures. In addition, detection performances about mean acquisition time(MAT) and detection error probability are evaluated via computer simulations.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.43-50
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• 2017

In this paper, we are proposing improved vanishing points detection and segments labeling methods for inside wall frame detection from indoor image of a piece of having a colour RGB. A lot of research related to recognizing the frame of artificial structures from the image is being performed due to increase in demand for AR technology. But detect the inside wall frame in indoor images have many objects that caused the occlusion is still a difficult issue. Inner wall frame detection methods are usually segment labeling methods and detect vanishing point methods are used together. In order to improve the vanishing point detection method we proposed using inner wall orthogonality which forms the cube. Also we proposed labeling method using tree based learning and superpixel based segmentation method for labelingthe segments in indoor images. Finally, in experiments have shown improved results about inside wall frame detection according to our methods.