• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.627-634
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• 2003

In this paper, we consider a graph-theoretic problem,, the so-called "graph embedding problem" that maps the vertices and edges of the given guest graph model into the corresponding vertices and paths of the host graph under the condition of maintaining better performance parameters such as dilation, congestion, and expansion. We firstly propose a new mapping function which can embed the pyramid model with height N into the 3-dimensional mesh massively parallel processor system with the height $(4^{(N+1)/3}+2)/3$ and the regular 2-dimensional mesh of one side $2^{(2N-1)/3}$, and analyze the performance of the embedding in terms of the dilation parameter that reflects the number of communication steps between two adjacent vertices under the embedding. We prove that the dilation of the embedding is $2{\cdot}4^{(N-2)/3}+4)/3$. This is superior to the previous result of $4^{N+183}+2)/3$ under the same condition.condition.

• Journal of Korea Multimedia Society
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• v.24 no.6
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• pp.768-788
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• 2021

The reversible data hiding technique safely transmits secret data to the recipient from malicious attacks by third parties. In addition, this technique can completely restore the image used as a transmission medium for secret data. The reversible data hiding schemes have been proposed in various forms, and recently, the reversible data hiding schemes based on interpolation are actively researching. The reversible data hiding scheme based on the interpolation method expands the original image into the cover image and embed secret data. However, the existing interpolation-based reversible data hiding schemes did not embed secret data during the interpolation process. To improve this problem, this paper proposes embedding the first secret data during the image interpolation process and embedding the second secret data into the interpolated cover image. In the embedding process, the original image is divided into blocks without duplicates, and the maximum and minimum values are determined within each block. Three way searching based on the maximum value and two way searching based on the minimum value are performed. And, image interpolation is performed while embedding the first secret data using the PVD scheme. A stego image is created by embedding the second secret data using the maximum difference value and log function in the interpolated cover image. As a result, the proposed scheme embeds secret data twice. In particular, it is possible to embed secret data even during the interpolation process of an image that did not previously embed secret data. Experimental results show that the proposed scheme can transmit more secret data to the receiver while maintaining the image quality similar to other interpolation-based reversible data hiding schemes.

• Journal of Astronomy and Space Sciences
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• v.18 no.2
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• pp.119-128
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• 2001

The oscillation frequency tuning range of waveguide Gunn oscillator and its stability depend sensitively on the dimensions of the resonator. Therefore the embedding impedances with the various dimensions of the resonator for Q-band (33 ∼ 50 GHz) Gunn oscillator are calculated by using HFSS (High Frequency Structure Simulator). In this paper the comparisons between theoretical results of embedding impedances as a function of frequency and that of experimental results are described. And the oscillation frequency range could be predicted by using the theoretical evaluation methods which were proposed in this paper It shows that post size has an effect on the frequency tuning characteristics of Gunn oscillator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2741-2761
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• 2017

With the purpose of copyright protection for digital video, a novel H.264/AVC watermarking algorithm based on JND model is proposed. Firstly, according to the characteristics of human visual system, a new and more accurate JND model is proposed to determine watermark embedding strength by considering the luminance masking, contrast masking and spatial frequency sensitivity function. Secondly, a new embedding strategy for H.264/AVC watermarking is proposed based on an analysis on the drift error of energy distribution. We argue that more robustness can be achieved if watermarks are embedded in middle and high components of $4{\times}4$ integer DCT since these components are more stable than dc and low components when drift error occurs. Finally, according to different characteristics of middle and high components, the watermarks are embedded using different algorithms, respectively. Experimental results demonstrate that the proposed watermarking algorithm not only meets the imperceptibility and robustness requirements, but also has a high embedding capacity.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1511-1514
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• 2002

This paper presents content adaptive image watermark embedding using stochastic visual model based on multiwavelet transform. To embedding watermark, the original image is decomposed into 4 levels using a discrete multiwavelet transform, then a watermark is embedded into the JND(just noticeable differences) of the image each subband. The perceptual model is applied with a stochastic approach fer watermark embedding. This is based on the computation of a NVF(noise visibility function) that have local image properties. The perceptual model with content adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the JND. This method uses stationary Generalized Gaussian model characteristic because watermark has noise properties. The experiment results of simulation of the proposed watermark embedding method using stochastic visual model based on multiwavelet transform techniques was found to be excellent invisibility and robustness.

• International Journal of Advanced Culture Technology
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• v.5 no.4
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• pp.57-62
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• 2017

In this paper, we applied long short term memory(LSTM) for classifying political polarity in cyber public sphere. The data collected from the cyber public sphere is transformed into word corpus data through word embedding. Based on this word corpus data, we train recurrent neural network (RNN) which is connected by LSTM's. Softmax function is applied at the output of the RNN. We conducted our proposed system to obtain experimental results, and we will enhance our proposed system by refining LSTM in our system.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1149-1158
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• 2003

Content adaptive watermark embedding algorithm using a stochastic image model in the multiwavelet transform is proposed in this paper. A watermark is embedded into the perceptually significant coefficients (PSCs) of each subband using multiwavelet transform. The PSCs in high frequency subband are selected by SSQ, that is, by setting the thresholds as the one half of the largest coefficient in each subband. The perceptual model is applied with a stochastic approach based on noise visibility function (NVF) that has local image properties for watermark embedding. This model uses stationary Generalized Gaussian model characteristic because watermark has noise properties. The watermark estimation use shape parameter and variance of subband region. it is derive content adaptive criteria according to edge and texture, and flat region. The experiment results of the proposed watermark embedding method based on multiwavelet transform techniques were found to be excellent invisibility and robustness.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1380-1390
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• 2021

The video face recognition (FR) is one of the most popular researches in the field of computer vision due to a variety of applications. In particular, research using the attention mechanism is being actively conducted. In video face recognition, attention represents where to focus on by using the input value of the whole or a specific region, or which frame to focus on when there are many frames. In this paper, we propose a novel attention based deep learning method. Main novelties of our method are (1) the use of combining two loss functions, namely weighted Softmax loss function and a Triplet loss function and (2) the feasibility of end-to-end learning which includes the feature embedding network and attention weight computation. The feature embedding network has a positive effect on the attention weight computation by using combined loss function and end-to-end learning. To demonstrate the effectiveness of our proposed method, extensive and comparative experiments have been carried out to evaluate our method on IJB-A dataset with their standard evaluation protocols. Our proposed method represented better or comparable recognition rate compared to other state-of-the-art video FR methods.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1807-1810
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• 2003

Steganography based on Just Noticeable Difference(JND) has been used for natural images. However, it has been recognized to have defects for the non-natural images such as scanned text images, cartoons, etc. In this paper, an alternative method is proposed to improve this problem. A new scheme is designed specially for the non-natural images. Instead of JND, Noise Visibility Function(NVF) is used. NVF value and edge strength value of each pixel ate combined to decide the embedding data capacity and the visibility of data embedded images have been improved specially for the non-natural images.

• Information Systems Review
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• v.25 no.1
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• pp.93-110
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• 2023

Recently, attempts have been made to convert unstructured text into vectors and to analyze vast amounts of natural language for various purposes. In particular, the demand for analyzing texts in specialized domains is rapidly increasing. Therefore, studies are being conducted to analyze specialized and general-purpose documents simultaneously. To analyze specific terms with general terms, it is necessary to align the embedding space of the specific terms with the embedding space of the general terms. So far, attempts have been made to align the embedding of specific terms into the embedding space of general terms through a transformation matrix or mapping function. However, the linear transformation based on the transformation matrix showed a limitation in that it only works well in a local range. To overcome this limitation, various types of nonlinear vector alignment methods have been recently proposed. We propose a vector alignment model that matches the embedding space of specific terms to the embedding space of general terms through end-to-end learning that simultaneously learns the autoencoder and regression model. As a result of experiments with R&D documents in the "Healthcare" field, we confirmed the proposed methodology showed superior performance in terms of accuracy compared to the traditional model.