• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.3
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• pp.150-156
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• 2007

In this paper, we studied security systems for information security of several systems that use in network environment along with information society. Therefore, we designed symmetry style base mixing style cryptographic system that apply block and stream way to solve problems of complexity and lower processing speed etc. Symmetry style base mixing style cryptographic system including authentication operation holds performance that the processing speed and the calculation amount are more superior than asymmetry style. Result that design system by Synopsys 1999.10 and ALTERA MaxPlus 10.1 and do simulation, mixing style password system that we propose is that information security offers very efficient assistance and performance in necessary field in network environment.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.31-38
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• 2023

Style transfer is one of deep learning-based image processing techniques that has been actively researched recently. These research efforts have led to significant improvements in the quality of result images. Style transfer is a technology that takes a content image and a style image as inputs and generates a transformed result image by applying the characteristics of the style image to the content image. It is becoming increasingly important in exploiting the diversity of digital content. To improve the usability of style transfer technology, ensuring stable performance is crucial. Recently, in the field of natural language processing, the concept of Transformers has been actively utilized. Attention maps, which forms the basis of Transformers, is also being actively applied and researched in the development of style transfer techniques. In this paper, we analyze the representative techniques SANet and AdaAttN and propose a novel attention map-based structure which can generate improved style transfer results. The results demonstrate that the proposed technique effectively preserves the structure of the content image while applying the characteristics of the style image.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.483-496
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• 2012

The purpose of this study is to examine difference of information processing style on lesson comprehension scores and usability ratings in e-Learning containing visual information structure. To address this goal, 68 university students were participated in this research. They were asked information processing style test, lesson comprehension test, and usability ratings after completed e-Learning lesson. According to the result, there was not significant difference between visual and verbal information process style on lesson comprehension as learn outcomes. However, students who are visual information processing style were significantly higher ratings than students who are verbal information processing style on 4 of 8 usability scales; awareness of lesson structure, awareness of lesson length, ease of navigation, and ease of lesson learning. These result indicate that there will be needed the design of aptitude treatment interaction for e-Book according to information processing style.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.293-294
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• 2023

이 연구는 사람의 외적인 요소와 마이어스-브릭스가 제안한 16 가지 성격유형을 결부한 연구이다. 기존의 성격유형에 대한 기준을 기반으로 하여, 일반적인 MBTI 판독기와는 다르게 StyleCLIP 을 활용해서 추상적인 단어로 이미지를 변환하고 전이학습 AI 를 이용하여 비교 테스트를 진행한다. 최종적으로 이 연구를 통해 외모와 성격은 연관이 있다는 가설을 증명한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.497-500
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• 2022

High-quality images and videos are being generated as technologies for deep learning-based image style translation and conversion of static images into dynamic images have developed. However, it takes a lot of time and resources to manually transform images, as well as professional knowledge due to the difficulty of natural image transformation. Therefore, in this paper, we study natural style mixing through a style conversion network using GAN and natural dynamic image generation using the First Order Motion Model network (FOMM).

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.904-905
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• 2023

본 논문에서는 의료 영상 생성을 위한 Med-StyleGAN2를 제안한다. 생성적 적대 신경망은 이미지 생성에는 효과적이지만, 의료 영상 생성에는 한계점을 가지고 있다. 따라서 본 연구에서는 의료 영상 생성에 특화된 StyleGAN 기반 학습 모델을 제안한다. 이는 다양한 의료 영상 어플리케이션에 활용할 수 있으며, 생성된 의료 영상에 대한 정량적, 정성적 평가를 수행함으로써 의료 영상 생성 분야의 발전 가능성에 대해 연구한다.

• Journal of the Korean School Mathematics Society
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• v.16 no.2
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• pp.363-381
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• 2013

The constituents of math learning styles are information recognition and information processing in the cognitive domain and attitudes toward math learning and environments of math learning in the affective domain. Each of the constituents has two opposing styles; there are the visual style and verbal style in information recognition; and there are the whole style and analytical style in information processing. And as for attitudes toward math learning, there are two styles which are the authoritative and goal-oriented style and the practical and entertaining style. Also as for attitudes toward environments of math learning, there are two styles which are the interior-oriented style and exterior-oriented style. There can be classified into 16 types of mathematics learning by the combination of a total of 8 styles of mathematics learning. The purpose of this study was to analyze the preference of the students from three middle schools located in Daegu Metropolitan City to the styles and types of mathematics learning.

• Proceedings of the Korea Contents Association Conference
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• 2004.05a
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• pp.56-62
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• 2004

In the case that we do not have enough number of training patterns because of limitation such as time consuming, economic problem, and so on, we geneterate a new patterns using the brain-style Information processing algorithm, that is, supervised and unsupervised learning methods.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.96-102
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• 2019

The purpose of this study was to examine the effects of applying a program to enhance social information processing ability in schizophrenic patients. We confirmed the positive effects of the program on the theories of mind and attribution style, which are the social information elements of patients, and confirmed the effect of decreasing paranoid ideation. We used the theory of mind(hinting task, the false belief task), the attributional style questionnaire(external bias, personal bias), and the paranoia scale to test the effectiveness of the program. Specifically, in theory of mind, hinting task performance was improved(t=4.14, p=.000),. The scores of personal bias(t=-7.9, p=.000) and paranoid ideation(t=-2.98, p=.004) decreased. Further research is needed to verify the effectiveness of meta - cognitive training to enhance social information processing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2509-2528
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• 2019

There are various styles of license plates for different countries and use cases that require style-specific methods. In this paper, we propose and illustrate a multi-style license plate recognition system. The proposed system performs a series of processes for license plate candidates detection, structure classification, character segmentation and character recognition, respectively. Specifically, we introduce a license plate structure classification process to identify its style that precedes character segmentation and recognition processes. We use a K-Nearest Neighbors algorithm with pre-training steps to recognize numbers and characters on multi-style license plates. To show feasibility of our multi-style license plate recognition system, we evaluate our system for multi-style license plates covering single line, double line, different backgrounds and character colors on Korean and the U.S. license plates. For the evaluation of Korean license plate recognition, we used a 50 minutes long input video that contains 138 vehicles of 6 different license plate styles, where each frame of the video is processed through a series of license plate recognition processes. From two experiments results, we show that various LP styles can be recognized under 50 ms processing time and with over 99% accuracy, and can be extended through additional learning and training steps.