• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1284-1295
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• 2024

Due to increasing operational security demands, digital and intelligent condition monitoring of nuclear power plants is becoming more significant. However, establishing an accurate and effective anomaly detection model is still challenging. This is mainly because of data characteristics of nuclear power data, including the lack of clear class labels combined with frequent interference from outliers and anomalies. In this paper, we introduce a Transformer-based unsupervised model for anomaly detection of nuclear power data, a modified loss function based on the maximum correntropy criterion (MCC) is applied in the model training to improve the robustness. Experimental results on simulation datasets demonstrate that the proposed Trans-MCC model achieves equivalent or superior detection performance to the baseline models, and the use of the MCC loss function is proven can obviously alleviate the negative effect of outliers and anomalies in the training procedure, the F1 score is improved by up to 0.31 compared to Trans-MSE on a specific dataset. Further studies on genuine nuclear power data have verified the model's capability to detect anomalies at an earlier stage, which is significant to condition monitoring.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.504-514
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• 2009

Recently, fire monitoring application systems have been an active research area due to the safety of industries, historical monuments and so on. The fire monitoring application systems can reduce the damage of properties by providing earlier warning for possible fire situation. However, the existing systems have a drawback that they detect fire with delay due to their uniform epoch in fire detection algorithm. Moreover, they do not provide user-friendly graphical user interfaces in their fire monitoring systems. To resolve the problems, First, we propose a new fire detection algorithm (Early Fire Detection Fire Algorithm) which uses the distribution of sensing data for early fire detection. Our fire detection algorithm is better in terms of fire detection time than the existing work because it can set a start time of fire detection epoch dynamically based on data distribution. Second, we develop a fire monitoring application system which provides users with both a user-friendly graphical user interface and a fire alarm message when fire occurs. Finally, we show from our experiment that our developed system is effectively used for early fire warning in a variety of fire situations.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.391-394
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• 1998

The man-machine interface Is an important factor in the computer system, and it is thought that line-of-sight (LOS) detection technology will allow significant advances in this field. Techniques for detecting LOS for use in human interfaces have been studied［1]［2］. In earlier studies, however, LOS was detected with a head piece, goggles, or through fixing the position of the head. The limitations imposed by these fixed conditions render them unsuitable far use in interfaces, as they have adverse mental or physical effects on humans. Therefore. they have not been sufficiently developed for practical application. Research on non-contact LOS detection is expected to result in a usable LOS man-machine interface［3］［4］, and the current study is intended to be a step in that direction. The authors used color contact lenses for LOS detection, and applied this new method to a computer interface. The use of color contact lenses simplifies image processing. The algorithm used in this study is sufficiently accurate for practical applications. This technique can be used in input devices, in virtual reality applications, and in human engineering research.

• Plant Journal
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• v.16 no.4
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• pp.33-39
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• 2020

For several decades, productivity in construction industry has been regressed and it is inevitable to improve productivity for major EPC players. One of challenges to achieve this goal is automatically extracting information from imaged drawings. Although computer vision technique has been advanced rapidly, it is still a problem to detect pipe lines in a drawing. Earlier works for line detection have problems that detected line elements be broken into small pieces and accuracy of detection is not enough for engineers. Thus, we adopted Contour and Hough Transform algorithm and reinforced these to improve detection results. First, Contour algorithm is used with Ramer Douglas Peucker algorithm(RDP). Weakness of contour algorithm is that some blank spaces are occasionally found in the middle of lines and RDP covers them around 17%. Second, HEC Hough Transform algorithm, we propose on this paper, is improved version of Hough Transform. It adopted iteration of Hough Transform and merged detected lines by conventional Hough Transform based on Euclidean Distance. As a result, performance of Our proposed method improved by 30% than previous.

• The Plant Pathology Journal
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• v.40 no.3
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• pp.282-289
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• 2024

Fire blight is a bacterial disease caused by Erwinia amylovora. In Korea, fire blight was first reported in 2015 in an orchard. If the infection is confirmed, all trees in the orchard must be removed and the orchard must remain closed for 3 years. Since 2020, if the number of trees infected with fire blight is less than 5% of the total trees in the orchard, only the infected tree and adjacent trees are removed in Korea. Three years after removal, the trees can be replanted after confirming that the orchard soil is free from E. amylovora. In this study, a protocol was established for detecting E. amylovora in soil via selective enrichment, using tryptic soy broth with 0.05% bile salts and 50 ㎍/ml cycloheximide, and real-time polymerase chain reaction. This protocol resulted in a 1,000-times improved detection limit for E. amylovora in soil samples compared to that in unenriched samples. Soil monitoring was performed for orchards where fire blight-infected trees had been removed 3-27 months prior; the selected orchards were monitored every 3 months. Monitoring confirmed that E. amylovora was not present in the soil at any site in any of the orchards. A new detection protocol facilitates the monitoring of E. amylovora in soil and could help permit the replanting of trees in orchards. Also monitoring results provide evidence that trees can be planted earlier.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4377-4384
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• 2014

According to the World Health Organization (WHO), 1.37 million people died of lung cancer all around the world in 2008, occupying the first place in all cancer-related deaths. However, this number might be decreased if patients were detected earlier and treated appropriately. Unfortunately, traditional imaging techniques are not sufficiently satisfactory for early detection of lung cancer because of limitations. As one alternative, breath volatile organic compounds (VOCs) may reflect the biochemical status of the body and provide clues to some diseases including lung cancer at early stage. Early detection of lung cancer based on breath analysis is becoming more and more valued because it is non-invasive, sensitive, inexpensive and simple. In this review article, we analyze the limitations of traditional imaging techniques in the early detection of lung cancer, illustrate possible mechanisms of the production of VOCs in cancerous cells, present evidence that supports the detection of such disease using breath analysis, and summarize the advances in the study of E-noses based on gas sensitive sensors. In conclusion, the analysis of breath VOCs is a better choice for the early detection of lung cancer compared to imaging techniques. We recommend a more comprehensive technique that integrates the analysis of VOCs and non-VOCs in breath. In addition, VOCs in urine may also be a trend in research on the early detection of lung cancer.

• Journal of electromagnetic engineering and science
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• v.12 no.1
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• pp.107-114
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• 2012

Microwave imaging (MI) is one of the most promising and attractive new techniques for earlier breast cancer detection. Microwave tomography (MT) realizes configuration of a multistatic multiple-input multiple-output system and reconstructs dielectric properties of the breast by solving a nonlinear inversion scattering problem. In this paper, we describe ETRI 3D MT system with 3D MI reconstruction program and demonstrate its robustness through some examples of the image reconstruction.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.319-323
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• 2007

Acoustic emission (AE) was originally developed for non-destructive testing of static structure, but over the year its application has been extended to health monitoring of rotating machines and bearings. It offers the advantage of earlier defect detection in comparison with monitoring bearing. This study was diagnosed low-speed machine which had a fault bearing for early detection by AE. And the artificial faults in a experimentation bearing was made for the bearing signals from difference speed and load were compared and analyzed by AE.

• The Plant Pathology Journal
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• v.19 no.4
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• pp.205-209
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• 2003

Dispersal of citrus bacterial canker caused by Xanthomonas axonopodis pv. citri on Unshiu orange was investigated in naturally infested nursery plot at Seogwipo in Jeju island, Korea. Based on phage detection, over 2% of the bacterial pathogen over-wintered in canker lesions and started to multiply in late May. However, symptoms were first observed 1 month after the phage detection. The disease dispersed non-directionally to nearby plants possibly because of indirect dissemination of the bacterium by rain splashes. The disease increased from late June to late August and decreased thereafter. Population of phage increased constantly, however, disease occurrence somewhat fluctuated due to environmental factors. Disease incidence and severity were correlated with rainfall with wind that occurred 14-32 days earlier from late May to late August.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.259-262
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• 2012

In this paper, we propose a new fast algorithm for effective scene change detection. The proposed algorithm exploits Otsu threshold matching technique, which was proposed earlier. In this method, the current and the reference frames are divided into square blocks of particular size. After doing so, the pixel histogram of each block is generated. According to Otsu method, every histogram distribution is assumed to be bimodal, i.e. pixel distribution can be divided into two groups, based on within-group variance value. The pixel value that minimizes the within-group variance is said to be Otsu threshold. After Otsu threshold is found, the same procedure is performed at the reference frame. If the difference between Otsu threshold of a block in the current frame and co-located block in the reference frame is larger than predefined threshold, then a scene change between those two blocks is detected.