• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.318-323
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• 1992

The improvement of the production capability of multi PCB assembly line can not be simply done by improving the capacities of each assembly robot cells but must be done by controlling the production line effectively with the line host computer which controls over the whole assembly line. A real time production control, a real time model change and a real time trouble shooting compose the specific concepts of this technique. In this paper, we present and analyze the definition and application method of real time assembly concept. The meaning of real time model change, troubles and error sooting and its algorithm will be introduced. Also, the function of the host computer which is in charge of all of many different tasks mentioned above and the method are presented. The improvement of the productivity is mainly focused on the efficiency of multi-PCB production control. The importance of this aspect is gradually increasing, which we have presented the analysis and the solution.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.317-324
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• 2006

Performance-based design concept is needed to evaluate the seismic capacity of building. In this study, the method estimating the performance point of the spatial structures based on capacity spectrum method (CSM) is proposed. And for efficient evaluation for the performance point of the spatial structures, the algorithm to convert spatial structural system to ESDOF system is simulated Its efficiency is confirmed by comparing with time history analysis of full model. And dynamic behaviors of spatial structures are examined by using this method. At last, evaluation of structural performance according to variation of stiffness after plastic deformation on the substructures is carried out.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.82.3-82.3
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• 2019

In this study, we generate future full disk magnetograms in 12, 24, 36 and 48 hours advance from SDO/HMI images using deep learning. To perform this generation, we apply the convolutional generative adversarial network (cGAN) algorithm to a series of SDO/HMI magnetograms. We use SDO/HMI data from 2011 to 2016 for training four models. The models make AI-generated images for 2017 HMI data and compare them with the actual HMI magnetograms for evaluation. The AI-generated images by each model are very similar to the actual images. The average correlation coefficient between the two images for about 600 data sets are about 0.85 for four models. We are examining hundreds of active regions for more detail comparison. In the future we will use pix2pix HD and video2video translation networks for image prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.630-631
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• 2021

In this paper, we propose a high-resolution fringe pattern generation technique using deep learning networks. Generating a hologram using a computer requires a very large amount of computation. Therefore, in order to replace this, it was shown that it can be replaced through deep learning, but there was a limitation in the resolution of the output fringe pattern. To improve this, we propose an algorithm for generating a high-resolution fringe pattern.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.565-586
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• 2022

Cloud storage's elastic expansibility not only provides flexible services for data owners to store their data remotely, but also reduces storage operation and management costs of their data sharing. The data outsourced remotely in the storage space of cloud service provider also brings data security concerns about data integrity. Data integrity verification has become an important technology for detecting the integrity of remote shared data. However, users without data access rights to verify the data integrity will cause unnecessary overhead to data owner and cloud service provider. Especially malicious users who constantly launch data integrity verification will greatly waste service resources. Since data owner is a consumer purchasing cloud services, he needs to bear both the cost of data storage and that of data verification. This paper proposes a verification control algorithm in data integrity verification for remotely outsourced data. It designs an attribute-based encryption verification control algorithm for multiple verifiers. Moreover, data owner and cloud service provider construct a common access structure together and generate a verification sentinel to verify the authority of verifiers according to the access structure. Finally, since cloud service provider cannot know the access structure and the sentry generation operation, it can only authenticate verifiers with satisfying access policy to verify the data integrity for the corresponding outsourced data. Theoretical analysis and experimental results show that the proposed algorithm achieves fine-grained access control to multiple verifiers for the data integrity verification.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.618-629
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• 2017

Mobile nodes can't always connect each other in DTNs (delay tolerant networks). Many DTN routing protocols that favor the "multi-hop forwarding" are proposed to solve these network problems. But they also lead to intolerant delivery cost so that designing a overhead-efficient routing protocol which is able to perform well in delivery ratio with lower delivery cost at the same time is valuable. Therefore, we utilize the small-world property and propose a new delivery metric called multi-probability to design our relay node selection principles that nodes with lower delivery predictability can also be selected to be the relay nodes if one of their history nodes has higher delivery predictability. So, we can find more potential relay nodes to reduce the forwarding overhead of successfully delivered messages through our proposed algorithm called HESnW. We also apply our new messages copies allocation scheme to optimize the routing performance. Comparing to existing routing algorithms, simulation results show that HESnW can reduce the delivery cost while it can also obtain a rather high delivery ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.366-385
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• 2013

In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2384-2392
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• 2015

The technical development and practical applications of big-data for health is one hot topic under the banner of big-data. Big-data medical image fusion is one of key problems. A new fusion approach with coding based on Spherical Coordinate Domain (SCD) in Wireless Sensor Network (WSN) for big-data medical image is proposed in this paper. In this approach, the three high-frequency coefficients in wavelet domain of medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on the multi-scale edge of medical image, it can be fused and reconstructed. Experimental results indicate the novel approach is effective and very useful for transmission of big-data medical image(especially, in the wireless environment).

• Structural Engineering and Mechanics
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• v.68 no.4
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• pp.399-408
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• 2018

Cable supported structures have been widely used in civil engineering. Cable tension estimation has great importance in cable supported structures' analysis, ranging from design to construction and from inspection to maintenance. Even though the Bernoulli-Euler beam element is commonly used in the traditional finite element method for calculation of frequency and cable tension estimation, many elements must be meshed to achieve accurate results, leading to expensive computation. To improve the accuracy and efficiency, a dynamic finite element method for estimation of cable tension is proposed. In this method, following the dynamic stiffness matrix method, frequency-dependent shape functions are adopted to derive the stiffness and mass matrices of an exact beam element that can be used for natural frequency calculation and cable tension estimation. An iterative algorithm is used for the exact beam element to determine both the exact natural frequencies and the cable tension. Illustrative examples show that, compared with the cable tension estimation method using the conventional beam element, the proposed method has a distinct advantage regarding the accuracy and the computational time.