• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.77-81
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• 2002

In many high performance engineering systems such as automated production system and transportation systems, AC-servo drives are employed as the most Important driving parts. And the faults of servo drives result in overall system performance deterioration or an unscheduled shutdown In critical situations. The real-time fault detection and isolation(FDI) scheme Is very useful to prevent them and to guarantee the desired reliability of the overall system. In this paper, the FDI schemes which can be applied to AC servo drives are introduced and some new results are presented.

• Structural Engineering and Mechanics
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• v.15 no.3
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• pp.285-297
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• 2003

Complementing recent advances made in the field of structural health monitoring and damage detection, the concept of a wireless sensing network with distributed computational power is proposed. The fundamental building block of the proposed sensing network is a wireless sensing unit capable of acquiring measurement data, interrogating the data and transmitting the data in real time. The computational core of a prototype wireless sensing unit can potentially be utilized for execution of embedded engineering analyses such as damage detection and system identification. To illustrate the computational capabilities of the proposed wireless sensing unit, the fast Fourier transform and auto-regressive time-series modeling are locally executed by the unit. Fast Fourier transforms and auto-regressive models are two important techniques that have been previously used for the identification of damage in structural systems. Their embedment illustrates the computational capabilities of the prototype wireless sensing unit and suggests strong potential for unit installation in automated structural health monitoring systems.

• Food Science of Animal Resources
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• v.29 no.4
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• pp.506-512
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• 2009

Salmonellosis is an important worldwide foodborne infectious disease that is transmitted by many food vehicles including raw and processed animal products and fresh produce. In this study, the effectiveness of automated ELISA ($VIDAS^{(R)}$) and realtime PCR in the detection of Salmonella spp. in steamed pork and raw broccoli sprouts was evaluated by comparing their results with those of a conventional culture method. Bulk samples (500 g) of steamed pork and raw broccoli sprouts were inoculated with various levels of Salmonella and divided into 20 samples (25 g each). All the samples, including the controls, were analyzed using a conventional culture method, $VIDAS^{(R)}$, and real-time PCR to detect the presence of Salmonella. In addition, the levels of background flora in the steamed pork and the raw broccoli sprouts were determined. In the steamed pork that contained less than 100 CFU/g of aerobic bacteria, all three methods detected low levels of Salmonella without a statistical difference in their performance. In the broccoli sprouts with high quantities of background flora (ca. $6.7{\times}10^7$ CFU/g), however, all three methods were unable to detect low levels of Salmonella, and real-time PCR and $VIDAS^{(R)}$ more sensitively detected Salmonella than the culture method, with significant statistical differences. In conclusion, $VIDAS^{(R)}$ and real-time PCR could be superior to conventional culture methods in detecting Salmonella in food with high levels of background flora.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.471-477
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• 2004

In this study, we introduce a 3-dimensional simulation to support the Design on ACT(Automated Container Terminal). This simulation system developed to simulate virtual operations of ACT using 3-dimensional simulation and animate the simulated results with real time. And the developed system applied an object-oriented design and C++ programming to increase the reusability and extensibility. We select several items of performance evaluation for objects used in ACT in terms of problem detection, problem forecast, and logic feasibility, and provide evaluation points for the design of ACT.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.986-1005
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• 2020

In this paper, we propose AnoVid, an automated video annotation tool based on deep neural networks, that automatically generates various meta data for each scene or shot in a long drama video containing rich elements. To this end, a novel meta data schema for drama video is designed. Based on this schema, the AnoVid video annotation tool has a total of six deep neural network models for object detection, place recognition, time zone recognition, person recognition, activity detection, and description generation. Using these models, the AnoVid can generate rich video annotation data. In addition, AnoVid provides not only the ability to automatically generate a JSON-type video annotation data file, but also provides various visualization facilities to check the video content analysis results. Through experiments using a real drama video, "Misaeing", we show the practical effectiveness and performance of the proposed video annotation tool, AnoVid.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.745-750
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• 2014

The chatter vibration in the machining process plays bad role in machining quality such as high roughness as well as tool life and machine failure. And the grinding process under this risk in the fully automated factory is exposed to the unexpected mass machining quality problem. Studying the vibration signal of the hub bearing grinding process, the reason of chatter vibration was explained with the specific machining pattern of chatter. And this study suggests the chatter detecting method in the production line, which is monitoring the peak acceleration level around the natural frequencies of the specimen, and calculating kurtosis value by assuming the chatter is related to the resonance of the specimen. The suggested method was applied to the vehicle hub bearing grinding process and proved good to detecting the chatter induced machining quality problem.

• International Journal of High-Rise Buildings
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• v.9 no.4
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• pp.335-342
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• 2020

This article comes up with a new method which is based on the visual characteristic of the objects and machine learning technology to achieve semi-automated recognition of the personnel, machine & materials of the construction sites. Balancing the real-time performance and accuracy, using Faster RCNN (Faster Region-based Convolutional Neural Networks) with transfer learning method appears to be a rational choice. After fine-tuning an ImageNet pre-trained Faster RCNN and testing with it, the result shows that the precision ratio (mAP) has so far reached 67.62%, while the recall ratio (AR) has reached 56.23%. In other word, this recognizing method has achieved rational performance. Further inference with the video of the construction of Huoshenshan Hospital also indicates preliminary success.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.146-154
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• 2019

Recently, a system has been developed and applied to establish and utilize HD maps through R&D. The biggest problem, however, is the lack of a proper HD map update system, which requires the development and adoption of such a system as soon as possible. In addition, in the case of updating HD maps for automated driving, integrity and accuracy of maps are required for safe driving, so an test of these technologies and data quality is required. In April 2018, the Ministry of Land, Infrastructure and Transport implemented a project to 'Develop Technology to Demonstrate and Share the Instant Road Change Detection and Update Technology for automated driving. This paper analyzed the technology for updating map based on the investigation and analysis of relevant technology trends for the development of integrated demonstration and sharing technology of road change rapid detection and updating map technology, and put forward the criteria for road change rapid detection, integrated quality verification of update technology. It is expected that the results of this study will contribute to quality assurance of HD maps that support safety driving for automated vehicles.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.373-380
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• 2004

This paper presents the feasibility of an impedance-based damage detection technique using piezoelectric (PZT) transducers for civil infrastructures such as steel bridges. The impedance-based damage detection method is based on monitoring the changes in the electrical impedance. Those changes in the electrical impedance are due to the electro-mechanical coupling property of the piezoelectric material and structure. An effective integrated structural health monitoring system must include a statistical process of damage detection that is automated and real time assessment of damage in the structure. Once measured, damage sensitive features from this impedance change can be statistically quantified for various damage cases. The results of the experimental study on three kinds of structural members show that cracks or loosened bolts/nuts near the PZT sensors may be effectively detected by monitoring the shifts of the resonant frequencies. The root mean square (RMS) deviations of impedance functions between before and after damages were also considered as a damage indicator. The subsequent statistical methods using the impedance signature of the PZT sensors were investigated.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.129-134
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• 2015

Since automatic social engineering based spam attacks induce for users to click or receive the short message service (SMS), e-mail, site address and make a relationship with an unknown friend, it is very easy for them to active in online social networks. The previous spam detection schemes only apply manual filtering of the system managers or labeling classifications regardless of the features of social networks. In this paper, we propose the spam detection metric after reflecting on a couple of features of social networks followed by analysis of real social network data set, Twitter spam. In addition, we provide the online social networks based unsupervised scheme for automated social engineering spam with self organizing map (SOM). Through the performance evaluation, we show the detection accuracy up to 90% and the possibility of real time training for the spam detection without the manager.