• Proceedings of the Korea Water Resources Association Conference
• /
• 2022.05a
• /
• pp.61-61
• /
• 2022

최근 머신러닝 기술의 발달에 따라 이를 활용한 레이더 자료기반 강우예측기법이 활발히 개발되고 있다. 기존 머신러닝을 이용한 강우예측모델 개발 관련 연구는 주로 한 지역에 대해 수행되며, 데이터 기반으로 훈련되는 머신러닝 기법의 특성상 개발된 모델이 훈련된 지역에 대해서만 좋은 성능을 보인다는 한계점이 존재한다. 이러한 한계점을 해결하기 위해 사전 훈련된 모델을 이용하여 새로운 데이터에 대해 모델을 훈련하는 전이학습 기법 (transfer learning)을 적용하여 여러 유역에 대한 강우예측모델을 개발하고자 하였다. 본 연구에서는 사전 훈련된 강우예측 모델로 생성적 적대 신경망 기반 기법(Generative Adversarial Network, GAN)을 이용한 미래 강우예측모델을 사용하였다. 해당 모델은 기상청에서 제공된 2014년~2017년 여름의 레이더 이미지 자료를 이용하여 초단기, 단기 강우예측을 수행하도록 학습시켰으며, 2018년 레이더 이미지 자료를 이용한 단기강우예측 모의에서 좋은 성능을 보였다. 본 연구에서는 훈련된 모델을 이용해 새로운 댐 유역(안동댐, 충주댐)에 대한 강우예측모델을 개발하기 위해 여러 전이학습 기법을 적용하고, 그 결과를 비교하였다. 결과를 통해 새로운 데이터로 처음부터 훈련시킨 모델보다 전이학습 기법을 사용하였을 때 좋은 성능을 보이는 것을 확인하였으며, 이를 통해 여러 댐 유역에 대한 모델 개발 시 전이학습 기법이 효율적으로 적용될 수 있음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.9
• /
• pp.2589-2604
• /
• 2024

Brain tumors, characterized by uncontrollable cellular growths, are a significant global health challenge. Navigating the complexities of tumor identification due to their varied dimensions and positions, our research introduces enhanced methods for precise detection. Utilizing advanced learning techniques, we've improved early identification by preprocessing clinical dataset-derived images, augmenting them via a Generative Adversarial Network, and applying an Improved Privacy-Preserving Collaborative Convolutional Neural Network (IPC-CNN) for segmentation. Recognizing the critical importance of data security in today's digital era, our framework emphasizes the preservation of patient privacy. We evaluated the performance of our proposed model on the Figshare and BRATS 2018 datasets. By facilitating a collaborative model training environment across multiple healthcare institutions, we harness the power of distributed computing to securely aggregate model updates, ensuring individual data protection while leveraging collective expertise. Our IPC-CNN model achieved an accuracy of 99.40%, marking a notable advancement in brain tumor classification and offering invaluable insights for both the medical imaging and machine learning communities.

• Korean Journal of Cognitive Science
• /
• v.19 no.4
• /
• pp.369-396
• /
• 2008

The purpose of this article is to investigate the effect of visual working memory, the types and contents of graphic annotations on English learning. The participants of the experiments were 5th and 6th graders. The result showed that animation was effective only in the word test for children with large visual working memory, whereas text-only-annotation yielded better performance in the comprehension test in Experiment 1. The effect of relevance of animations was tested in Experiment 2. Context-relevant-animations yielded better comprehension than the animations denoting the typical meaning, whereas the opposite pattern was reported in the word test. The result of the two experiments was interpreted in terms of cognitive load theory and the generative theory of multimedia learning.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.05a
• /
• pp.522-524
• /
• 2019

In spite of a rapid development in the quality of built-in mobile cameras, their some physical restrictions hinder them to achieve the satisfactory results of digital single lens reflex (DSLR) cameras. In this work we propose an end-to-end deep learning method to translate ordinary images by mobile cameras into DSLR-quality photos. The method is based on the framework of generative adversarial networks (GANs) with several improvements. First, we combined the U-Net with DenseNet and connected dense block (DB) in terms of U-Net. The Dense U-Net acts as the generator in our GAN model. Then, we improved the perceptual loss by using the VGG features and pixel-wise content, which could provide stronger supervision for contrast enhancement and texture recovery.

• Journal of Korea Multimedia Society
• /
• v.25 no.11
• /
• pp.1582-1590
• /
• 2022

Due to the situation of the widespread of the coronavirus, which causes the problem of lack of face image data occluded by masks at recent time, in order to solve the related problems, this paper proposes a method to generate face images with masks using a combination of generative adversarial networks and spatial transformation networks based on CNN model. The system we proposed in this paper is based on the GAN, combined with multi-scale convolution kernels to extract features at different details of the human face images, and used Wasserstein divergence as the measure of the distance between real samples and synthetic samples in order to optimize Generator performance. Experiments show that the proposed method can effectively put masks on face images with high efficiency and fast reaction time and the synthesized human face images are pretty natural and real.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.871-891
• /
• 2019

Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.

• Journal of Multimedia Information System
• /
• v.8 no.2
• /
• pp.85-92
• /
• 2021

In a face, there is much information of person's identity. Because of this property, various tasks such as expression recognition, identity recognition and deepfake have been actively conducted. Most of them use the exact frontal view of the given face. However, various directions of the face can be observed rather than the exact frontal image in real situation. The profile (side view) lacks information when comparing with the frontal view image. Therefore, if we can generate the frontal face from other directions, we can obtain more information on the given face. In this paper, we propose a combined style model based the conditional generative adversarial network (cGAN) for generating the frontal face from multi-view images that consist of characteristics that not only includes the style around the face (hair and beard) but also detailed areas (eye, nose, and mouth).

• Journal of Information Processing Systems
• /
• v.17 no.1
• /
• pp.1-13
• /
• 2021

In this research, we study the problem of font image skeletonization using an end-to-end deep adversarial network, in contrast with the state-of-the-art methods that use mathematical algorithms. Several studies have been concerned with skeletonization, but a few have utilized deep learning. Further, no study has considered generative models based on deep neural networks for font character skeletonization, which are more delicate than natural objects. In this work, we take a step closer to producing realistic synthesized skeletons of font characters. We consider using an end-to-end deep adversarial network, SkelGAN, for font-image skeletonization, in contrast with the state-of-the-art methods that use mathematical algorithms. The proposed skeleton generator is proved superior to all well-known mathematical skeletonization methods in terms of character structure, including delicate strokes, serifs, and even special styles. Experimental results also demonstrate the dominance of our method against the state-of-the-art supervised image-to-image translation method in font character skeletonization task.

• International journal of advanced smart convergence
• /
• v.12 no.2
• /
• pp.90-95
• /
• 2023

With the rapid development of deep learning and artificial intelligence, generative models have achieved remarkable success in the field of image generation. By combining the stable diffusion method with Web UI technology, a novel solution is provided for the application of AI painting generation. The application prospects of this technology are very broad and can be applied to multiple fields, such as digital art, concept design, game development, and more. Furthermore, the platform based on Web UI facilitates user operations, making the technology more easily applicable to practical scenarios. This paper introduces the basic principles of Stable Diffusion Web UI technology. This technique utilizes the stability of diffusion processes to improve the output quality of generative models. By gradually introducing noise during the generation process, the model can generate smoother and more coherent images. Additionally, the analysis of different model types and applications within Stable Diffusion Web UI provides creators with a more comprehensive understanding, offering valuable insights for fields such as artistic creation and design.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.673-674
• /
• 2021

본 논문은 집중력 향상 놀이인 다른 그림 찾기를 자신이 좋아하는 주제를 배경으로 쉽게 생성할 수 있는 시스템을 제안한다. 아동기에 주로 진단이 되고 성인기까지 이어질 수 있는 주의력 결핍 과다활동 증후군(ADHD)을 조기에 예방하기 위해 본 논문에서는 선택한 그림의 일부분을 가지고 생성적 적대 신경망을 활용하여 새로운 물체를 생성해 낸 뒤 자연스럽게 원본 그림에 융화될 수 있도록 하는 것이 목표이다. 하나의 다른 그림 찾기 콘텐츠를 만드는 것은 포토샵과 같이 전문성을 가진 툴을 전문가가 오랜 시간 작업해야 하는 내용이다. 전문적인 기술이 필요한 작업 과정을 본 연구를 통해 일반인도 쉽게 작업할 수 있도록 하는 것을 최종 목표로 한다.