• Journal of the Korean Society of Mechanical Technology
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• v.20 no.6
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• pp.872-878
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• 2018

Steam tables including superheated, saturated and compressed region were simultaneously modeled using the neural networks. Pressure and temperature were used as two inputs for superheated and compressed region. On the other hand Pressure and dryness fraction were two inputs for saturated region. The outputs were specific volume, specific enthalpy and specific entropy. The neural network model were compared with the linear interpolation model in terms of the percentage relative errors. The criterion of judgement was selected with the percentage relative error of 1%. In conclusion the neural networks showed better results than the interpolation method for all data of superheated and compressed region and specific volume of saturated region, but similar for specific enthalpy and entropy of saturated region.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.110-114
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• 2015

This paper reports a novel object-detection technique in video sequences. The proposed algorithm consists of detection of objects in illuminated and occluded videos by using object features and a neural network technique. It consists of two functional modules: region-based object feature extraction and continuous detection of objects in video sequences with region features. This scheme is proposed as an enhancement of the Lowe's scale-invariant feature transform (SIFT) object detection method. This technique solved the high computation time problem of feature generation in the SIFT method. The improvement is achieved by region-based feature classification in the objects to be detected; optimal neural network-based feature reduction is presented in order to reduce the object region feature dataset with winner pixel estimation between the video frames of the video sequence. Simulation results show that the proposed scheme achieves better overall performance than other object detection techniques, and region-based feature detection is faster in comparison to other recent techniques.

• Journal of Communications and Networks
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• v.12 no.1
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• pp.11-18
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• 2010

Diversity-multiplexing tradeoff (DMT) is an efficient tool to measure the performance of multiple-input and multiple-output (MIMO) systems and cooperative systems. Recently, cooperative multicast system with wireless network coding stretched tremendous interesting due to that it can drastically enhance the throughput of the wireless networks. It is desirable to apply DMT to the performance analysis on the multicast system with wireless network coding. In this paper, DMT is performed at the three proposed wireless network coding protocols, i.e., non-regenerative network coding (NRNC), regenerative complex field network coding (RCNC) and regenerative Galois field network coding (RGNC). The DMT analysis shows that under the same system performance, i.e., the same diversity gain, all the three network coding protocols outperform the traditional transmission scheme without network coding in terms of multiplexing gain. Our DMT analysis also exhibits the trends of the three network coding protocols' performance when multiplexing gain is changing from the lower region to the higher region. Monte-Carlo simulations verify the prediction of DMT.

• The KIPS Transactions:PartC
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• v.14C no.4
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• pp.359-364
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• 2007

Current researches on the overlay multicast mechanism in the mobile ad hoc network (MANET) maintain the network topology information of the dynamically changing MANET, which may cause severe overhead. In this paper, we propose a new overlay multicast mechanism, the region-based overlay multicast in MANET(ROME), using the geometric locations of group members. In ROME, the physical topology is divided into small regions and the scope of location updates of group members is limited to a single region. ROME provides scalability by using the coordinate of the center point of a destination region as the destination of a data packet instead of the list of member addresses of that region. Our simulation results show that ROME gives better performance in terms of the packet overhead than other schemes.

• Electrical & Electronic Materials
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• v.9 no.4
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• pp.364-371
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• 1996

In this study, in order to investigate the applicability of IPN structure to epoxy resin which has been widely used as electrical and electronic insulating materials, DC dielectric breakdown properties and morphology were compared and analyzed according to variation of network structure, using the single network structure specimen formed of epoxy resin alone, interpenetrating polymer network specimen formed of epoxy resin/methacrylic acid resin, and interpenetrating polymer network specimen formed of epoxy resin/methacrylic acid resin/polyurethane resin. As results of the measunnent of DC dielectric breakdown strength at 50[.deg. C] and 130[>$^{\circ}C$], IPN specimen formed of epoxn, resin 100[phr] and methacrylic acid resin 35[phr] was the most excellent, and which corresponded to the SEM phenomena. The effect of IPN was more remarkable at high temperature region than at low temperature region. It is supposed that the defect of epoxy resin, dielectric breakdown strength is lowered remarkably at high temperature region, be complemented according to introducing IPN method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.877-893
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• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1109-1116
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• 2023

Object tracking is used to track a goal in a video sequence by using coordinate information provided as annotation in the first frame of the video. In this paper, we propose a tracking algorithm that combines deep features and region inference modules to improve object tracking accuracy. In order to obtain sufficient object information, a convolution neural network was designed with a Siamese network structure. For object region inference, the region proposal network and overlapping confidence module were applied and used for tracking. The performance of the proposed tracking algorithm was evaluated using the Object Tracking Benchmark dataset, and it achieved 69.1% in the Success index and 89.3% in the Precision Metrics.

• Journal of the Economic Geographical Society of Korea
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• v.18 no.1
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• pp.103-114
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• 2015

This research aims to identify the network formation process of wine industry based on patents for technological innovation focused on Shandong region. China government initiated supporting policy upbringing wine cluster in Shandong for regional economic development since 2006. First of all, China government focused on upbringing wine industry on the network formation process. Especially, it is evaluated that Shangdong region develops wine industry by research-oriented. Secondly, for the purpose of this research, the formation of network in Shandong region explained the strategy of Chinese government corresponded with growing wine industry. As a implication from the network formation process, this research confirmed that the wine industry grows industry-university-government linkages strategically according to government strategy with government policy integration integrating regional development.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.51-57
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• 2021

In this paper, we propose a method to find the DoF(Depth of field) that is blurred in an image by focusing and out-focusing the camera through a efficient convolutional neural network. Our approach uses the RGB channel-based cross-correlation filter to efficiently classify the DoF region from the image and build data for learning in the convolutional neural network. A data pair of the training data is established between the image and the DoF weighted map. Data used for learning uses DoF weight maps extracted by cross-correlation filters, and uses the result of applying the smoothing process to increase the convergence rate in the network learning stage. The DoF weighted image obtained as the test result stably finds the DoF region in the input image. As a result, the proposed method can be used in various places such as NPR(Non-photorealistic rendering) rendering and object detection by using the DoF area as the user's ROI(Region of interest).

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.388-395
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• 2009

The objectives of this study are to analyze the railway traffic conditions of Korea, China and Japan, and to appropriate the railway network planning for international logistics in Northeast Asia. Korea is located geographically on the main trunk route in Northeast Asia. Considering the geographical advantage and the overall potential of the economic and trade in Northeast Asia region, this area is required to connect the railway network. Recently, the scale of economic in Northeast Asia, including Korea, China and Japan, is increasing, also Northeast Asia's gross domestic product(GDP) is 22 percent of global and containers trade come up to 36 percent. Each country intend about integration of economic region for activity of mutual economic cooperation, for steady development and prosperity of each country economic all over the world, and Northeast Asia countries are in debate. There is a quite possibility of integration by a single economic region in Korea, China and Japan. Accordingly these countries should have expansion of traffic infrastructure, when the economic region is going to integration.