• Bulletin of the Korean Mathematical Society
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• v.49 no.1
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• pp.75-88
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• 2012

A new algorithm is proposed for the construction of Brezing-Weng-like elliptic curves such that polynomials defining the CM discriminant are linear. Using this construction, new families of curves with variable discriminants and embedding degrees of $k{\in}\{8,16,20,24\}$, which were not covered by Freeman, Scott, and Teske [9], are presented. Our result is useful for constructing elliptic curves with larger and more flexible discriminants.

• Journal of the Korean Mathematical Society
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• v.61 no.2
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• pp.357-375
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• 2024

The smooth quintic del Pezzo variety Y is well-known to be obtained as a linear sections of the Grassmannian variety Gr(2, 5) under the Plücker embedding into ℙ⁹. Through a local computation, we show the Hilbert scheme of conics in Y for dimY ≥ 3 can be obtained from a certain Grassmannian bundle by a single blowing-up/down transformation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.733-735
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• 2014

Example based Super Resolution(SR) is using the correspondence between the low and high resolution image from a database. This method uses only one image to estimate a high resolution image and can get the larger image than 2 times. Example based SR is proposed to solve the problem of classical SR. Neighbor embedding(NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the poor generalization of NE decreases the performance of such algorithm. The sizes of local training sets are always too small to improve the performance of NE. We propose the advanced NE baesd on SVR having an excellent generalization ability to solve this problem. Given a low resolution image, we estimate a pixel in its high resolution version by using SVR based NE. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed algorithm when comparing with conventional interpolation methods and NE.

• Journal of Digital Contents Society
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• v.15 no.6
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• pp.737-743
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• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2946-2952
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• 2014

Among the Example based Super Resolution(SR) techniques, Neighbor embedding(NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the poor generalization of NE decreases the performance of such algorithm. The sizes of local training sets are always too small to improve the performance of NE. We propose the Learning Sparse-Neighbor Image Representation baesd on SVR having an excellent generalization ability to solve this problem. Given a low resolution image, we first use bicubic interpolation to synthesize its high resolution version. We extract the patches from this synthesized image and determine whether each patch corresponds to regions with high or low spatial frequencies. After the weight of each patch is obtained by our method, we used to learn separate SVR models. Finally, we update the pixel values using the previously learned SVRs. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed algorithm when comparing with conventional interpolation methods and NE.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.202-209
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• 2005

The rapid development of digital media and communication network urgently brings about the need of data certification technology to protect IPR (Intellectual property right). This paper proposed a new watermarking method for embedding owner's audio signal. Because this method uses an audio signal as a watermark to be embedded, it is very useful to claim the ownership aurally. And it has the advantage of restoring audio signal modified and especially removed by image removing attacks by applying our LBX(Linear Bit-expansion) interleaving. Three basic stages of our watermarking include: 1) Encode . analogue owner's audio signal by PCM and create new digital audio watermark, 2) Interleave an audio watermark by our LBX; and 3) Embed the interleaved audio watermark in the low frequency band on DTn (Discrete Haar Wavelet Transform) of image. The experimental results prove that this method is resistant to lossy JPEG compression as standard image compression and especially to cropping and rotation which remove a part of Image.

• Journal of Electrical Engineering and Technology
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• v.8 no.2
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• pp.271-281
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• 2013

In order to improve the performance of analysis, it is important to consider the nonlinearity in power system. The Carleman embedding technique (linearization procedure) provides an effective approach in reduction of nonlinear systems. In the approach, a group of differential equations in which the state variables are formed by the original state variables and the vector monomials one can build with products of positive integer powers of them, is constructed. In traditional Carleman linearization technique, the tensor matrix is truncated to form a square matrix, and then regular linear system theory is used to solve the truncated system directly. However, it is found that part of nonlinear information is neglected when truncating the Carleman model. This paper proposes a new approach to solve the problem, by combining the Poincar$\acute{e}$ transformation with the Carleman linearization. Case studies are presented to verify the proposed method. Modal analysis shows that, with traditional Carleman linearization, the calculated contribution factors are not symmetrical, while such problems are avoided in the improved approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.949-952
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• 2009

In this paper, a watermarking method based on the trellis code with multi-layer is proposed. An image is divided $8{\times}8$ block with no overlapping, and compute the discrete cosine transform(DCT) of each block, and the 12 medium-frequency AC terms from each block are extracted. Next it is compared with gaussian random vectors with zero mean and unit variance. As these processing, the embedding vectors with minimum linear correlation can be obtained by Viterbi algorithm at each layer of trellis coding. To evaluate the performance of proposed method, the average bit error rate of watermark message is calculated from different several images.

• ETRI Journal
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• v.44 no.5
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• pp.794-804
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• 2022

Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.706-715
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• 2004

In this paper, to decide the watermark embedding positions and embed the watermark we use the subband tee structure which is presented in the wavelet domain and the edge information in the spatial domain. The significant frequency region is estimated by the subband searching from the higher frequency subband to the lower frequency subband. LH1 subband which has the higher frequency in tree structure of the wavelet domain is divided into 4${\times}$4 submatrices, and the threshold which is used in the watermark embedding is obtained by the blockmatrix which is consists by the average of 4${\times}$4 submatrices. Also the watermark embedding position, Keymap is generated by the blockmatrix for the energy distribution in the frequency domain and the edge information in the spatial domain. The watermark is embedded into the wavelet coefficients using the Keymap and the random sequence generated by LFSR(Linear feedback shift register). Finally after the inverse wavelet transform the watermark embedded image is obtained. the proposed watermarking algorithm showed PSNR over 2㏈ and had the higher results from 2％ to 8％ in the comparison with the previous research for the attack such as the JPEG compression and the general image processing just like blurring, sharpening and gaussian noise.