• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.650-652
• /
• 2022

Frame rate up-conversion (FRUC) algorithm is an image interpolation technology that improves the frame rate of moving pictures. This solves problems such as screen shake or blurry motion caused by low frame rate video in high-definition digital video systems, and provides viewers with a more free and smooth visual experience. In this paper, we propose a video compression technique using deep learning-based FRUC algorithm. The proposed method compresses and transmits after excluding some images from the original video, and uses a deep learning-based interpolation method in the decoding process to restore the excluded images, thereby compressing them with high efficiency. In the experiment, the compression performance was evaluated using the decoded image and the image restored by the FRUC algorithm after encoding the video by skipping 1 or 3 pages. When 1 and 3 sheets were excluded, the average BD-rate decreased by 81.22% and 27.80%. The reason that excluding three images has lower encoding efficiency than excluding one is because the PSNR of the image reconstructed by the FRUC method is low.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.311-326
• /
• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• Journal of Korea Multimedia Society
• /
• v.24 no.12
• /
• pp.1624-1631
• /
• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• Geomechanics and Engineering
• /
• v.37 no.1
• /
• pp.9-19
• /
• 2024

This study presents an energy analysis for large-strain cavity expansion problem based on the general strength criterion and energy theory. This study focuses on the energy dissipation problem during the cavity expansion process, dividing the soil mass around the cavity into an elastic region and a plastic region. Assuming compliance with the small deformation theory in the elastic region and the large deformation theory in the plastic region, combined with the general strength criterion of soil mass and energy theory, the energy dissipation solution for cavity expansion problem is derived. Firstly, from an energy perspective, the process of cavity expansion in soil mass is described as an energy conversion process. The energy dissipation mechanism is introduced into the traditional analysis of cavity expansion, and a general analytical solution for cavity expansion related to energy is derived. Subsequently, based on this general analytical solution of cavity expansion, the influence of different strength criterion, large-strain, expansion radius, cavity shape and characteristics of soil mass on the stress distribution, displacement field and energy evolution around the cavity is studied. Finally, the effectiveness and reliability of theoretical solution is verified by comparing the results of typical pressure-expansion curves with existing literature algorithms. The results indicate that different strength criterion have a relatively small impact on the displacement and strain field around the cavity, but a significant impact on the stress distribution and energy evolution around the cavity.

• Journal of IKEEE
• /
• v.27 no.1
• /
• pp.116-121
• /
• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.07a
• /
• pp.1061-1064
• /
• 2001

Deep Interests have been paid on the application of non-thermal plasma technique to solve the environmental pollution problems. $CO_2$, is one of the severe pollutants which cause the acid rain and global warming. In this study, in order to improve the conversion efficiency of $CO_2$, the streamer corona discharge plasma and barrier discharge plasma reactors were made, and the conversion characteristics of $CO_2$by the corona discharge plasma and some discharge characteristics of these discharge chambers are studied experimentally.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2000.05b
• /
• pp.207-211
• /
• 2000

Deep Interests have been paid on the application of non-thermal plasma technique to solve the environmental pollution problems. $CO_2$ is one of the severe pollutants which cause the acid rain and global warming. In this study, in order to improve the conversion efficiency of $CO_2$, the streamer corona discharge plasma and barrier discharge plasma reactors were made, and the conversion characteristics of $CO_2$ by the corona discharge plasma and some discharge characteristics of these discharge chambers are studied experimentally.

• Annual Conference of KIPS
• /
• 2022.05a
• /
• pp.497-500
• /
• 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).

• 한국신재생에너지학회:학술대회논문집
• /
• 2010.11a
• /
• pp.170-170
• /
• 2010

The energy conversion from the temperature difference between hot and cold source like ocean thermal energy conversion (OTEC), requires a long and large-diameter pipe (about 1000 to 10,000 meters long) to reach the deep water. The pipe diameter ranges from 2.8 meter for proposed early test systems, to 5 meter for large, commercial power generation systems. The pipe must be designed to resist collapsing pressures produced by water temperature and density differences, and the reduced pressure required to induce flow up the pipe. Other design considerations include the external-drag effect on the pipe due to ocean currents, and the wave-induced motions of the platform to which the pipe is attached. Various approaches to the pipe construction have been proposed, including aluminum, steel, concrete, and fiberglass. More recently, a flexible pipe construction involving the use of fiberglass reinforced plastic has been proposed. This report presents the results of a scaled fixed cold water pipe (CWP) model test program performed by EES(Engineering Equation Solver) to demonstrate the feasibility of this pipe approach.

• New & Renewable Energy
• /
• v.6 no.3
• /
• pp.22-29
• /
• 2010

The concept of Ocean Thermal Energy Conversion (OTEC) is simple and various types of OTEC have been proposed and tried. However the location of OTEC is limited because OTEC requires $20^{\circ}C$ of temperature difference as a minimum, so most of OTEC plants were constructed and experimented in tropical oceans. To solve this we proposed the modified OTEC which uses condenser discharged thermal energy of existing fossil or nuclear power plants. We call this system CTEC (Condenser Thermal Energy Conversion) as this system directly uses $32^{\circ}C$ partially saturated steam in condenser instead of $20{\sim}25^{\circ}C$ surface sea water as heat source. Increased temperature difference can improve thermal efficiency of Rankine cycle, but CTEC should be located near existing plant condenser and the length of cold water pipe between CTEC and deep cold sea water also increase. So friction loss also increases. Calculated result shows the change of efficiency, pumping power, net power and other parameters of modeled 7.9 MW CTEC at given condition. The calculated efficiency of CTEC is little larger than that of typical OTEC as expected. By proper location and optimization, CTEC could be considered another competitive renewable energy system.