• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.10a
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• pp.79-82
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• 2003

In case of performing resin transfer molding (RTM), race track effects and non-uniform fiber volume fraction may cause undesirable resin flow pattern and thus result in dry spots, which affect the mechanical properties of the finished parts. In this study, a real time RTM control strategy to reduce these unfavorable effects is proposed. Through numerical simulations and experiments, the validity of the proposed scheme is demonstrated.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2116-2119
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• 2003

In this paper, we present a DirectShow-based retrieval and playback subsystem of DVR(Digital Video Recorder), which supports real-time playback of stored video data and synchronized playback among several video channel data. The effectiveness of out proposed design is verified through experiments with a DVR system implementing the proposed design.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.506-507
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• 2007

Lateral Information Propagation Neural Networks (LIPN) is proposed for on-line interpolation. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.326-329
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• 2004

Content adaptation is an effective solution to support the quality of service over multimedia services over heterogeneous environments. This paper deals with the accuracy and the real-time requirement, two important issues in making decision on content adaptation. From our previous problem formulation, we propose an optimal algorithm and a fast approximation based on the Viterbi algorithm of dynamic programming. Through extensive experiments, we show that the proposed algorithms can enable accurate adaptation decisions, and especially they can support the real-time processing.

• Corrosion Science and Technology
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• v.18 no.6
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• pp.253-257
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• 2019

A deep learning based real-time painting surface inspection algorithm is proposed herein, designed for developing an autonomous inspection drone. The painting surface inspection is usually conducted manually. However, the manual inspection has a limitation in obtaining accurate data for correct judgement on the surface because of human error and deviation of individual inspection experiences. The best method to replace manual surface inspection is the vision-based inspection method with a camera, using various image processing algorithms. Nevertheless, the visual inspection is difficult to apply to surface inspection due to diverse appearances of material, hue, and lightning effects. To overcome technical limitations, a deep learning-based pattern recognition algorithm is proposed, which is specialized for painting surface inspections. The proposed algorithm functions in real time on the embedded board mounted on an autonomous inspection drone. The inspection results data are stored in the database and used for training the deep learning algorithm to improve performance. The various experiments for pre-inspection of painting processes are performed to verify real-time performance of the proposed deep learning algorithm.

• Journal of the Korea Computer Graphics Society
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• v.16 no.2
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• pp.1-7
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• 2010

This paper proposes a real-time simulation technique for thin rods undergoing large rotational deformation. Rods are thin objects such as ropes and hairs that can be abstracted as 1D structures. Development of a satisfactory physical model that runs in real-time but produces visually convincing animation of thin rods has been remaining a challenge in computer graphics. We adopt the energy formulation based on continuum mechanics, and develop a modal warping technique for rods that can integrate the governing equation in real-time. This novel simulation framework results from making extensions to the original modal warping technique, which was developed for the simulation of 3D solids. Experiments show that the proposed method runs in real-time even for large meshes, and that it can simulate large bending and/or twisting deformations with acceptable realism.

• Journal of Microbiology and Biotechnology
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• v.20 no.3
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• pp.569-573
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• 2010

A quantitative detection method for Salmonella in seafood was developed using a SYBR Green-based real-time PCR assay. The assay was developed using pure Salmonella DNA at different dilution levels [i.e., 1,000 to 2 genome equivalents (GE)]. The sensitivity of the real-time assay for Salmonella in seeded seafood samples was determined, and the minimum detection level was 20 CFU/g, whereas a detection level of 2 CFU/ml was obtained for pure culture in water with an efficiency of ${\geq}85%$. The real-time assay was evaluated in repeated experiments with seeded seafood samples and the regression coefficient ($R^2$) values were calculated. The performance of the real-time assay was further assessed with naturally contaminated seafood samples, where 4 out of 9 seafood samples tested positive for Salmonella and harbored cells <100 GE/g, which were not detected by direct plating on Salmonella Chromagar media. Thus, the method developed here will be useful for the rapid quantification of Salmonella in seafood, as the assay can be completed within 2-3 h. In addition, with the ability to detect a low number of Salmonella cells in seafood, this proposed method can be used to generate quantitative data on Salmonella in seafood, facilitating the implementation of control measures for Salmonella contamination in seafood at harvest and post-harvest levels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4117-4135
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• 2020

Due to the angle of view, illumination and scene diversity, real-time dynamic face detection and recognition is no small difficulty in those unrestricted environments. In this study, we used the intrinsic correlation between detection and calibration, using a multi-task cascaded convolutional neural network(MTCNN) to improve the efficiency of face recognition, and the output of each core network is mapped in parallel to a compact Euclidean space, where distance represents the similarity of facial features, so that the target face can be identified as quickly as possible, without waiting for all network iteration calculations to complete the recognition results. And after the angle of the target face and the illumination change, the correlation between the recognition results can be well obtained. In the actual application scenario, we use a multi-camera real-time monitoring system to perform face matching and recognition using successive frames acquired from different angles. The effectiveness of the method was verified by several real-time monitoring experiments, and good results were obtained.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7A
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• pp.654-661
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• 2011

RTLS (Real-time locating systems) are used for tracking the location of people or assets in real time. In this system, RTLS readers continuously communicate with RTLS tags for measuring time or ranges and location engine tries to calculate accurate location of tags. However, when we attempt to apply this system to real world, the non-line-of-sight(NLOS) problem can be critical to the system performance because of the obstacles. In this paper, we suggest a new location estimation method for an NLOS environment using a reader-selection strategy. We have implemented all components of the locating system and carried out experiments in a test-bed. The accuracy of the system is 50% better than that of the existing general locating system.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2130-2133
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• 2003

For real-time recognizing of the load vehicles a new Neural Network algorithm is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a Processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through severa simulation experiments, real time reconstruction nonlinear image information is Processed. 1-D hardware has been composed and various experi with static and dynamic signals have implemented.