• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.229-232
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• 2009

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see real-time operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST Company. Thus, our algorithm in the paper can be useful in PMP or other portable players.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.537-542
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• 1994

This paper gives a general survey of model-based fault detection and dignosis methods. Specific applications of these ideas to boiler systems will also be discussed. A novel aspect of the fault detection technique described here is that it explicitly accounts for the effects of using simplified models and errors from linearizing a nonlinear system at an operation point. Inclusion of these effects is shown to lead to novel fault detection procedures which outperform existing methods when applied to typical fault scenarios in boiler systems.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1748-1750
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• 2004

This paper deals with the detection method of the Quench phenomenon for superconducting magnet using the Acoustic Emission (AE) sensor. AE sensor is the elements, which is used to change the Acoustic signal to the voltage value. This signal may be used to detect whether the superconducting magnet has been at the Quench state or not. Recently, the development of the Quench detection technique, which is the using voltage and current signals, fiber-optic sensor, and so on, for the superconducting applications is widely studying. This method for the Quench detection of the superconducting magnet is also studying at some kinds of institute in Japan and the united state. Because of the large-scale superconducting magnet like International Thermonuclear Experimental Reactor(ITER) is charged a lot of energy, when the Quench phenomenon is being at the superconducting magnet it is happen to the problem of the protection for the applications. In this paper, we concluded that the Quench detection was possible when the mechanical stress by means of the local heat is generated at the part of inside superconducting magnets.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.268-269
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• 2008

Moving object detection is a fundamental technique in visual surveillance. Robust technique to enhance performance of moving object detection is required for several bad conditions in real external circumtance. In case of sudden illumination change in outdoor condition, many objects are determined as moving object though they are not really moving, but just their illumination changes. This makes the detection result untrustworthy. In this paper, robust moving object detection to sudden illumination change using gaussian mixture background model and new texture information using background from the weighted sum of recent images is proposed.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.3
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• pp.185-192
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• 2015

Since real-time monitoring systems, such as early fault detection, have been very important, an infrared thermography technique was proposed as a new diagnosis method. This study focused on damage detection and temperature characteristic analysis of ball bearings using the non-destructive, infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with finite element analysis (FEA) results from ANSYS. In this investigation, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally, it was confirmed that the infrared thermography technique was useful for the real-time detection of damage to bearings.

• Proceedings of the KSRS Conference
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• v.1
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• pp.126-129
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• 2006

The marine environment is under considerable threat from intentional or accidental oil spills, ballast water discharged, dredging and infilling for coastal development, and uncontrolled sewage and industrial wastewater discharges. Monitoring spills and illegal oil discharges is an important component in ensuring compliance with marine protection legislation and general protection of the coastal environments. For the monitoring task an image processing system is needed that can efficiently perform the detection and the tracking of oil spills and in this direction a significant amount of research work has taken place mainly with the use of radar (SAR) remote sensing data. In this paper the level set image segmentation technique was tested for the detection of oil spills. Level set allow the evolving curve to change topology (break and merge) and therefore boundaries of particularly intricate shapes can be extracted. Experimental results demonstrated that the level set segmentation can be used for the efficient detection and monitoring of oil spills, since the method coped with abrupt shape’s deformations and splits.

• Journal of Veterinary Clinics
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• v.16 no.2
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• pp.281-288
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• 1999

Helicobacter species have been identified in or isolated from domestic carnivores, but their prevalence in different population of animals and their clinical significance are still unknown. This study was peformed to evaluate the prevalence of Helicobacter in clinically healthy dogs by urease test, culture, morphological examination and polymerase chain reaction (PCR) technique. Tissue samples from 70 dogs in Kangwon and Kyunggi areas from August 1998 to April, 1999, were examined. The detection rates of Helicobacter by urease activity of tissue-samples were 84.6%, 61.3% and 4.8 % in the fundus, the antrum and the duodenum, respectively. One strain of Helicobacter was isolated from the duodenum. It was identified as H canis by biochemical and morphorogical examination. The detection rates of Helicobacter by histological examination were 92.3%, 79.0% and 4.8% in the fundus, antrum and the duodenum, respectively. Helicobacter organisms were colonized more in the gastric pits than in the surface of epithelium, the gastric gland or the parietal cell. Although most of dogs were colonized with Helicobacter in tissue, gross lesions and specific histopathological lesions caused by Helicobacter in these tissues were not observed. The detection rate of Helicobacter by PCR was 78.6%. The histological examination was more sensitive than urease test, culture or PCR technique for the detection of Helicobacter.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.668-670
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• 1999

We have developed a automatic surface inspection system for cold Rolled strips in steel making process for several years. We have experienced the various kinds of surface inspection systems, including linear CCD camera type and the laser type inspection system which was installed in cold rolled strips production lines. But, we did not satisfied with these inspection systems owing to insufficient detection and classification rate, real time processing performance and limited line speed of real production lines. In order to increase detection and computing power, we have used the Dark Field illumination with Infra_Red LED, Bright Field illumination with Xenon Lamp, Parallel Computing Processor with Area typed CCD camera and full software based image processing technique for the ease up_grading and maintenance. In this paper, we introduced the automatic inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms. As a result of experiment, under the situation of the high speed processed line(max 1000 meter per minute) defect detection is above 90% for all occurred defects in real line, defect name classification rate is about 80% for most frequently occurred 8 defect, and defect grade classification rate is 84% for name classified defect.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.4
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• pp.330-335
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• 2013

Since real-time monitoring system like a fault early detection has been very important, infrared thermography technique as a new diagnosis method was proposed. This study is focused on the damage detection and temperature characteristic analysis of ball bearing using the non-destructive infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with the frequency data of the existing. As results, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally it was confirmed that the infrared technique was useful for real-time detection of the bearing damages.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.