• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.865-870
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• 2018

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.243-250
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• 2022

The purpose of this study was to propose technology to detect the wave in the image in real-time, and calculate the height of the wave-overtopping through image analysis using artificial intelligence. It was confirmed that the proposed wave overtopping detection system proposed in this study could detect the occurring of wave overtopping, even in severe weather and at night in real-time. In particular, a filtering algorithm for determining if the wave overtopping event was used, to improve the accuracy of detecting the occurrence of wave overtopping, based on a convolutional neural networks to catch the wave overtopping in CCTV images in real-time. As a result, the accuracy of the wave overtopping detection through AP50 was reviewed as 59.6%, and the speed of the overtaking detection model was 70fps based on GPU, confirming that accuracy and speed are suitable for real-time wave overtopping detection.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1438-1446
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• 2019

Recently, the number of mobile users as well as high-speed Internet user has been increasing rapidly. Moreover, traffic is growing fast as services that provide real-time content such as Youtube and Netflix become popular. The problem of traffic control in real-time content services is important because many people use cell phones to receive real-time content. In this regard, the field of CCN is currently being studied. We studied the mobility of nodes among CCN research fields. Node mobility can be divided into three categories : consumer mobility, intermediate node, and provide mobility. In this paper, we propose Mobility Node Support(MD-INS) to support the intermediate-node mobility in CCN real-time services. Experimental results show that the proposed scheme shows better performance than CCN in terms of service disconnection time and packet loss.

• Journal of Microbiology and Biotechnology
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• v.15 no.6
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• pp.1353-1358
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• 2005

A rapid and quantitative real-time PCR was developed to target the invasion A (invA) gene of Salmonella spp. We developed quantitative standard curves based on plasmids containing the invA gene. Based on these curves, we detected Salmonella spp. in artificially contaminated buffered peptone water (BPW) and milk samples. We were able to determine the invA gene copy number per ml of food samples, with the minimum detection limit of $4.1{\times}10^{3}$ copies/ml of BPW and $3.3{\times}10^{3}$ copies/ml of milk. When applied directly to detect and quantify Salmonella spp. in BPW and milk, the present real-time PCR assay was as sensitive as the plate count method; however, copy numbers were one to two logs higher than the colony-forming units obtained by the plate count methods. In the present work, the real-time PCR assay was shown to significantly reduce the total time necessary for the detection of Salmonella spp. in foods and to provide an important model for other foodborne pathogens.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.304-309
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• 2015

On construction sites, it is important to monitor the performance of construction equipment and workers to achieve successful construction project management; especially, vision-based detection methods have advantages for the real-time site data collection for safety and productivity analyses. Although many researchers developed vision-based detection methods with acceptable performance, there are still limitations to be addressed: 1) sensitiveness to the shape and appearance changes of moving objects in difference working postures, and 2) high computation time. To deal with the limitations, this paper proposes a detection algorithm of construction equipment based on Integral Channel Features. For validation, 16,850 frames of video streams were recorded and analyzed. The results showed that the proposed method worked in high performance in terms of accuracy and processing time. In conclusion, the developed method can help to understand useful site information including working pattern, working time and input manpower analyses.

• Journal of Life Science
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• v.18 no.3
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• pp.374-380
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• 2008

Rapid detection of enterovirus (EVs) is important in the management of aseptic meningitis. We examined the relative efficiency and specificity of the real-time nucleic acid sequence-based amplification (NASBA) comparing with the established reverse transcription polymerase chain reaction (RT-PCR) and viral culture method which were used for the detection of enterovirus RNA in clinical specimens. Of the total 292 samples, 145 were found to be positive to enterovirus RNA by real-time NASBA, 101 were positive by viral culture, and 86 were positive by RT-PCR. 147 samples and 46 samples were determined to be negative and positive by all methods respectively, but 4 samples were positive only by real-time NASBA. To compare the specificity of each method, various clinical samples which were diagnosed for herpes simplex virus (HSV)-1, HSV-2, adenovirus, mumps, and rhinovirus were applied. Except one rhinovirus sample which was false positive to enterovirus RNA by RT-PCR, the other different samples were negative to all three methods. The real-time NASBA procedure can be completed within 5 hours in contrast with 9 hours for the RT-PCR and 3-14 days for the viral culture. From this study, it was suggested that the real-time NASBA assay could be a standardized, rapid, specific, and sensitive procedure for the detection of enterovirus RNA.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.118.3-118
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• 2002

$\textbullet$ Introduction $\textbullet$ The Structure and Operation of an Electronic Jacquard $\textbullet$ Design of Real-Time Error Detection System $\textbullet$ The System Realization and Verification $\textbullet$ Conclusion

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.1-6
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• 2019

In the field of industrial applications, the real-time performance of the target detection problem is very important. The most serious time consumption in the pedestrian detection process is the extraction phase of the candidate window. To accelerate the speed, in this paper, a fast extraction of pedestrian candidate window based on the BING (Binarized Normed Gradients) algorithm replaces the traditional sliding window scanning. The BING features are extracted with the positive and negative samples and input into the two-stage SVM (Support Vector Machine) classifier for training. The obtained BING template may include a pedestrian candidate window. The trained template is loaded during detection, and the extracted candidate windows are input into the classifier. The experimental results show that the proposed method can extract fewer candidate window and has a higher recall rate with more rapid speed than the traditional sliding window detection method, so the method improves the detection speed while maintaining the detection accuracy. In addition, the real-time requirement is satisfied.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.72-77
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• 2011

The checkpointing scheme is a well-known technique to cope with transient faults in digital systems. This paper proposes an adaptive checkpointing scheme for the reliability improvement of real-time control systems with concurrent fault detection capability. With concurrent fault detection capability the effect of transient faults are assumed to be detected with no latency. The proposed adaptive checkpointing scheme is based on the reliability analysis of an equidistant checkpointing scheme. Numerical data show the proposed adaptive scheme outperforms the equidistant scheme from a reliability point of view.