• The KIPS Transactions:PartD
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• v.14D no.2
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• pp.145-156
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• 2007

Recently, the needs for effective real time change detection algorithms for XML/HTML documents and increased in such fields as the detection of defacement attacks to web documents, the version management, and so on. Especially, those applications of real time change detection for large number of XML/HTML documents require fast heuristic algorithms to be used in real time environment, instead of algorithms which compute minimal cost-edit scripts. Existing heuristic algorithms are fast in execution time, but do not provide satisfactory edit script. In this paper, we present existing algorithms XyDiff and X-tree Diff, analyze their problems and propose algorithm X-tree Diff which improve problems in existing ones. X-tree Diff+ has similar performance in execution time with existing algorithms, but it improves matching ratio between nodes from two documents by refining matching process based on the notion of efficiency of matching.

• Journal of Digital Contents Society
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• v.12 no.3
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• pp.279-289
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• 2011

The latest hardware and software techniques are combined to give safe walking guidance and convenient service of realtime walking assistance system for visually impaired person. This system consists of obstacle detection and perception, place recognition, and sign recognition for pedestrian can safely walking to arrive at their destination. In this paper, we exploit the sign object detection system in subway station for sign recognition that one of the important factors of walking assistance system. This paper suggest the adaptive feature map that can be robustly extract the sign object region from complexed environment with light and noise. And recognize a sign using fast zernike moment features which is invariant under translation, rotation and scale of object during walking. We considered three types of signs as arrow, restroom, and exit number and perform the training and recognizing steps through adaboost classifier. The experimental results prove that our method can be suitable and stable for real-time system through yields on the average 87.16% stable detection rate and 20 frame/sec of operation time for three types of signs in 5000 images of sign database.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.6
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• pp.891-895
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• 2019

Objective: Aim of present study was the set up of a fast and reliable protocol using species-specific markers for the quali-quantitative analysis of DNA and the detection of ruminant biological components in dairy products. For this purpose, the promoter of the gene coding for the ${\alpha}$-lactoalbumin (LALBA) was chosen as possible candidate for the presence of short interspersed nuclear elements (SINEs). Methods: DNA was isolated from somatic cells of 120 individual milk samples of cattle (30), Mediterranean river buffalo (30), goat (30), and sheep (30) and the gene promoter region (about 600/700 bp) of LALBA (from about 600 bp upstream of exon 1) has been sequenced. For the development of a single polymerase chain reaction (PCR) protocol that allows the simultaneous identification of DNA from the four species of ruminants, the following internal primers pair were used: 5'-CACTGATCTTAAAGCTCAGGTT-3' (forward) and 5'-TCAGA GTAGGCCACAGAAG-3' (reverse). Results: Sequencing results of LALBA gene promoter region confirmed the presence of SINEs as monomorphic "within" and variable in size "among" the selected species. Amplicon lengths were 582 bp in cattle, 592 bp in buffalo, 655 in goat and 729 bp in sheep. PCR specificity was demonstrated by the detection of trace amounts of species-specific DNA from mixed sources ($0.25ng/{\mu}L$). Conclusion: We developed a rapid PCR protocol for the quali-quantitative analysis of DNA and the traceability of dairy products using a species-specific marker with only one pair of primers. Our results validate the proposed technique as a suitable tool for a simple and inexpensive (economic) detection of animal origin components in foodstuffs.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1442-1450
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• 2017

In liquid metal fast breeder reactors, postulated failures of the plant protection system may lead to serious unprotected accidental consequences. Unprotected transients are generically categorized as transient overpower accidents and transient under cooling accidents. In both cases, core meltdown may occur and this can lead to a molten fuel coolant interaction (MFCI). The understanding of MFCI phenomena is essential for study of debris coolability and characteristics during post-accident heat removal. Sodium is used as coolant in liquid metal fast breeder reactors. Viewing inside sodium at elevated temperature is impossible because of its opaqueness. In the present study, a methodology to depict MFCI phenomena using a flat panel detector based imaging system (i.e., real time radiography) is brought out using a woods metal-water experimental facility which simulates the $UO_2-Na$ interaction. The developed imaging system can capture attributes of the MFCI process like jet breakup length, jet front velocity, fragmented particle size, and a profile of the debris bed using digital image processing methods like image filtering, segmentation, and edge detection. This paper describes the MFCI process and developed imaging methodology to capture MFCI attributes which are directly related to the safe aspects of a sodium fast reactor.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.122-128
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• 2013

This paper presents a detection algorithm that accurately finds the active area of a video player on monitors or smart TVs. Unlike the previous approaches, temporal difference-based detection algorithms or hooking programs, the proposed detection algorithm can locate the active video player by using the spatial and temporal correlation and a corner detection filter. First, an initial location of the video player is found using conventional temporal difference-based detection. Then, starting from the initial location, the four corners of the active video player are detected by the spatial edge information and the corner detection filter. The experimental results show that proposed algorithm provides fast detection speed and high accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.989-995
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• 2015

Due to the recent increase of the importance and demand of security services, the importance of a surveillance monitor system that makes an automatic security system possible is increasing. As the market for surveillance monitor systems is growing, price competitiveness is becoming important. As a result of this trend, surveillance monitor systems based on an embedded system are widely used. In this paper, an object detection algorithm based on an embedded system for a surveillance monitor system is introduced. To apply the object detection algorithm to the embedded system, the most important issue is the efficient use of resources, such as memory and processors. Therefore, designing an appropriate algorithm considering the limit of resources is required. The proposed algorithm uses two background models; therefore, the embedded system is designed to have two independent processors. One processor checks the sub-background models for if there are any changes with high update frequency, and another processor makes the main background model, which is used for object detection. In this way, a background model will be made with images that have no objects to detect and improve the object detection performance. The object detection algorithm utilizes one-dimensional histogram distribution, which makes the detection faster. The proposed object detection algorithm works fast and accurately even in a low-priced embedded system.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1345-1360
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• 2016

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.77-80
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• 2000

Adaptive filter based code acquisition scheme offers a fast acquisition with a low error probability. However, it has been studied only under a coherent environment. In this paper, the noncoherent adaptive code acquisition scheme employing a differential detection technique is proposed. For the proposed scheme, system probabilities and the mean acquisition time are analyzed numerically. Simulation results show that the proposed system outperforms over the conventional matched filter by 2-4 ㏈ under AWGN channel for 16 taps.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.4
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• pp.113-134
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• 1994

A radial basis hybrid neural network (RHNN) is presented for an on-line detection of machine condition change. Two-phase modeling by RHNN is designed for describing a machine condition process and for predicting future signal. A moving block procedure is also designed for detecting a process change. A fast on-line learning algorithm, the recursive least square estimation, is introduced. Experimental results showed the RHNN could be utilized efficiently for on-line machine condition monitoring.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.157-160
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• 2002

Scene change detection is the fundamental process of automatic video indexing and retrieving. In this paper we propose a method which utilizes both compressed and uncompressed domain methods to detect scene change in a video. Candidate locations for scene change are estimated from DC images and motion vector information in compressed domain. And candidate frames are verified using edge histogram distance and color histogram distance, in uncompressed domain. The experimental results show that scene change can be detected fast and correctly by proposed method.