• Journal of the Korean Mathematical Society
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• v.35 no.3
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• pp.765-782
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• 1998

The purpose of this paper is to give convergence theorems both for closed convex set valued and relative fuzzy valued martingales, and sub- and super- martingales. These kinds of martingales, sub- and super-martingales are the extension of classical real valued martingales, sub- and super-martingales. Here we compare two kinds of convergences, in the Hausdorff metric and in the Kuratowski-Mosco sense. We also introduce a new convergence for the fuzzy valued case in the graph sense and obtain convergence theorems.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.513-514
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• 2023

초해상은 저해상도의 영상을 고해상도 영상으로 합성하는 기술이다. 이 기술에 딥러닝이 적용되어, 2014년에는 SRCNN(Super Resolution Convolutional Neural Network) 모델이 발표됐다. 이후에는 SRCAE(Super Resolution Convolutional Autoencoders)와 GAN(Generative Adversarial Networks)을 기반으로 한 SRGAN(Super Resolution Generative Adversarial Networks) 등, SRCNN의 성능을 능가하는 모델들이 발표됐다. ESRGAN(Enhanced Super Resolution Generative Adversarial Networks)은 SRGAN 모델의 성능을 개선했지만, 완벽한 성능을 내지 못하는 문제점이 있다. 이에 본 논문에서는 판별자(Discriminator) 구조를 변경하여 ESRGAN의 성능을 개선한다. 실험 결과, 제안하는 모델이 ESRGAN보다 더 높은 성능을 보일 것으로 기대된다.

• Journal of applied mathematics & informatics
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• v.5 no.2
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• pp.507-516
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• 1998

In this study we are concerned with the problem of ap-proximating a locally unique solution of an equation on a Banach space. A semilocal convergence theorem is given for the Super-Halley method in Banach spaces. Earlier results have shown that the order of convergence is four for a certain class of operators [4] [5] [8] These results were not given in affine invariant form and made use of a real quadratic majorizing polynomial. Here we provide our re-sults in affine invariant form showing that the order of convergence is at least four. In cases that it is exactly four the rate of convergence is improved. We achieve these results by using a cubic majorizing polynomial. Some numerical examples are given to show that our error bounds are better than earlier ones.

• Journal of applied mathematics & informatics
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• v.24 no.1_2
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• pp.367-376
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• 2007

In this paper we consider the global convergence of a new super memory gradient method for unconstrained optimization problems. New trust region radius is proposed to make the new method converge stably and averagely, and it will be suitable to solve large scale minimization problems. Some global convergence results are obtained under some mild conditions. Numerical results show that this new method is effective and stable in practical computation.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.501-503
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• 2022

초해상이란 해상도가 낮은 영상을 해상도가 높은 영상으로 합성하는 기술이다. 딥러닝은 영상의 해상도를 높이는 초해상 기술에도 응용되며 실현은 2아4년에 발표된 SRCNN(Super Resolution Convolutional Neural Network) 모델로부터 시작됐다. 이후 오토인코더 (Autoencoders) 구조로는 SRCAE(Super Resolution Convolutional Autoencoders), 합성된 영상을 실제 영상과 통계적으로 구분되지 않도록 강제하는 GAN (Generative Adversarial Networks) 구조로는 SRGAN(Super Resolution Generative Adversarial Networks) 모델이 발표됐다. 모두 SRCNN의 성능을 웃도는 모델들이나 그중 가장 높은 성능을 끌어내는 SRGAN 조차 아직 완벽한 성능을 내진 못한다. 본 논문에서는 SRGAN의 성능을 개선하기 위해 사전 훈련된 특징 추출기(Pre-trained Feature Extractor) VGG(Visual Geometry Group)-19 모델을 변경하고, 기존 모델과 성능을 비교한다. 실험 결과, VGG-19 모델보다 윤곽이 뚜렷하고, 실제 영상과 더 가까운 영상을 합성할 수 있는 모델을 발견할 수 있을 것으로 기대된다.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.343-356
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• 2024

Super app is an application that provides a variety of services in a unified interface within a single platform. With the acceleration of digital transformation, super apps are becoming more prevalent. This study aims to suggest service enhancement measures by analyzing the user review data before and after the transition to a super app. To this end, user review data from a payment-based super app(Shinhan Play) were collected and studied via topic modeling. Moreover, a matrix for assessing the importance and usefulness of topics is introduced, which relies on the eigenvector centrality of the inter-topic network obtained through topic modeling and the number of review recommendations. This allowed us to identify and categorize topics with high utility and impact. Prior to the transition, the factors contributing to user satisfaction included 'payment service,' 'additional service,' and 'improvement.' Following the transition, user satisfaction was associated with 'payment service' and 'integrated UX.' Conversely, dissatisfaction factors before the transition encompassed issues related to 'signup/installation,' 'payment error/response,' 'security authentication,' and 'security error.' Following the transition, user dissatisfaction arose from concerns regarding 'update/error response' and 'UX/UI.' The research results are expected to be used as a basis for establishing strategies to strengthen service competitiveness by making super app services more user-oriented.

• Kyungpook Mathematical Journal
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• v.59 no.3
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• pp.403-414
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• 2019

Theta functions are the sections of line bundles on a complex torus. Noncommutative versions of theta functions have appeared as theta vectors and quantum theta operators. In this paper we describe a super version of theta vectors and quantum theta operators. This is the natural unification of Manin's result on bosonic operators, and the author's previous result on fermionic operators.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.7-13
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• 2020

Meteorology and climate are closely related to human life. By using high-resolution weather data, services that are useful for real-life are available, and the need to produce high-resolution weather data is increasing. We propose a method for super-resolution temperature data using SRCNN. To evaluate the super-resolution temperature data, the temperature for a non-observation point is obtained by using the inverse distance weighting method, and the super-resolution temperature data using interpolation is compared with the super-resolution temperature data using SRCNN. We construct an SRCNN model suitable for super-resolution of temperature data and perform super-resolution of temperature data. As a result, the prediction performance of the super-resolution temperature data using SRCNN was about 10.8% higher than that using interpolation.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.95-101
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• 2019

Recently, there have been many issues related to text recognition using Tesseract. One of these issues is that the text recognition accuracy is significantly lower for smaller fonts. Tesseract extracts text by creating an outline with direction in the image. By searching the Tesseract database, template matching with characters with similar feature points is used to select the character with the lowest error. Because of the poor text extraction, the recognition accuracy is lowerd. In this paper, we compared text recognition accuracy after applying various super-resolution methods to smaller text images and experimented with how the recognition accuracy varies for various image size. In order to recognize small Korean text images, we have used super-resolution algorithms based on deep learning models such as SRCNN, ESRCNN, DSRCNN, and DCSCN. The dataset for training and testing consisted of Korean-based scanned images. The images was resized from 0.5 times to 0.8 times with 12pt font size. The experiment was performed on x0.5 resized images, and the experimental result showed that DCSCN super-resolution is the most efficient method to reduce precision error rate by 7.8%, and reduce the recall error rate by 8.4%. The experimental results have demonstrated that the accuracy of text recognition for smaller Korean fonts can be improved by adding super-resolution methods to the OCR preprocessing module.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1477-1485
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• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.