• Korea Science and Art Forum
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• v.37 no.2
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• pp.275-292
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• 2019

This study was initiated with the attention of demanding new and diverse approaches, we're talking familiar with imitations in the design process like a way to draw a image. So I studied a convergence of humanities and visual arts with the understanding and conceptual approach of the formless. The purpose of this study is to develop formless languages and to organize practical courses which are to enable deeper research and design expression on theoretical approaches and explanations of outcomes required before and after the process when we practice in connection with the formless. The method of this study is to draw detailed items from selected words through advanced researches, work and author researches and practice teaching. The results of the study I proposed the formless language that is related to the horizontality in spatial positioning system, and pulse in the separation of space and time, and entropy in structural orders of the system, and base materialism in the limitation of matter as the operating mechanism and parent item of formless. And those elements are related with shape, size, shading, color, texture, space, structure as visual elements of formative elements and those have various adjectival meanings as the subordinate concept. So I presented an education materials of basic design which is to enable understanding and expressing the formless language in the overall process of formless visual art(theoretical approach, practice course, presentation, etc.). Based on these study results, I hope that this educational materials will be used as educational contents that makes them express and understand different new beauties, and a role that reveals social identity, and a reference for research on a formless visual arts.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.33-49
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• 2018

The importance of convergence activities among business is increasing due to the necessity of designing and developing new products to satisfy various customers' needs. In particular, decision makers such as CEOs are required to participate in networks between entrepreneurs for being connected with valuable convergence partners. Moreover, it is important for entrepreneurs not only to make a large number of network connections, but also to understand the networking relationship with entrepreneurs with similar topic information. However, there is a difficult limit in collecting the topic information that can show the lack of current status of business and the technology and characteristics of entrepreneur in industry sector. In this paper, we solve these problems through the topic extraction method and analyze the business network in three aspects. Specifically, there are C, S, T-Layer models, and each model analyzes amount of entrepreneurs relationship, network centrality, and topic similarity. As a result of experiments using real data, entrepreneur need to activate network by connecting high centrality entrepreneur when the corporate relationship is low. In addition, we confirmed through experiments that there is a need to activate the topic-based network when topic similarity is low between entrepreneurs.

• 기호학연구
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• no.56
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• pp.7-44
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• 2018

The objective of this paper is to propose a classification of Korean taste terms, especially Korean taste adjectives, from the perspective of cognitive science. The classification of Korean taste terms is here grounded in the definition of 'taste sense', 'flavor' and 'taste' which is usually employed in disciplines of cognitive science. There have been a large number of domestic researches in field of taste terms. Accordingly, a lot of research findings on the classification of taste terms have steadily been released showing the differences among researchers. These different classifications are largely based on the fact that researchers have applied their subjective criteria rather than their objective in order to categorize taste terms. According to previous studies, it is well-known that, in everyday usage, the term 'taste' covers a much wider range of qualities than those perceived through the taste receptor cells alone. In addition, we take it for granted that as much as 80~90% of taste comes from olfactory modality. It is also important to note that the texture and temperature of food, the color of food, the sounds of food, and atmospheric cues have an essential effect on taste perception. Many scientists have already pointed out that taste evaluations are influenced by a number of individual and sociocultural factors. Eating and tasting are important parts of our everyday life, so that linguistic approaches to taste perception seem to be of great significance. We can assume that a classification of taste terms from the perspective of cognitive sciences may shed light on the perceptive mechanism through which we perceive taste. It should be noted that this paper is an advanced work prepared for the follow-up study which will try to make a geometric model of word field 'taste terms' existing or probably existing in the mental lexicon of human beings.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

• Food Engineering Progress
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• v.21 no.2
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• pp.167-173
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• 2017

Purple-fleshed potato powder (PFPP) was investigated to determine optimal mixing ratio with milk powder and dextrin to produce a ready-to-eat mashed potato powder. The rheological characteristics, color, and anthocyanin contents were studied at a different concentration of ingredients. The power-law model was applied to explain the mechanical spectra of mashed potatoes which represented the change in structure induced by different mixing ratios. Mixture design was used to obtain the experimental points used to establish the empirical models to describe the effects of each ingredient on the characteristic of the mashed potato. The results of mechanical spectra showed that both storage and loss moduli (G' and G'') were significantly influenced by PFPP and milk powder concentration. The power law parameters n' and n'' showed higher values for the mashed potato with a lower concentration of PFPP and a higher concentration of milk powder, which showed that the gel networks involved in the mashed potato were weaker. The optimum mixing ratio with the highest redness and anthocyanin content, while maintaining the rheological properties similar to the commercial mashed potato, was determined as PFPP:milk powder:dextrin = 90.49:4.86:4.65 (w/w). The proportions of PFPP and milk powder in the formulation significantly changed the characteristics of mashed potato, whereas no significant effect of dextrin was observed in this formulation.

• Food Engineering Progress
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• v.21 no.1
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• pp.93-98
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• 2017

The feasibility of incorporating cabbage powder (CP) as a value-added food ingredient into convenient food products was investigated using cookie as a model system. CP was incorporated into cookies at amounts of 0, 2, 4, 6, and 8% (w/w) based on total weight of wheat flour. pH level and moisture content of cookie dough decreased and increased significantly, respectively, with increasing levels of CP (p<0.05), whereas density was not directly affected by levels of CP incorporation. The spread ratio of cookies and their hardness increased significantly while the loss rate decreased significantly with increasing levels of CP (p<0.05). In terms of color, lightness and yellowness decreased while redness increased significantly (p<0.05) with increasing levels of CP. 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis-(3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) radical scavenging activities were significantly elevated by CP addition, and they increased significantly as CP concentration increased in the formulation (p<0.05). Finally, consumer acceptance test indicated that the higher than 4% of CP incorporation had an adverse effect on general consumer preferences. In contrast, cookies with moderate levels of CP (2%) were recommended based on overall scores to take advantage of the antioxidant properties of CP without sacrificing consumer acceptability.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.104-112
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• 2021

In this study, emotional analysis was conducted based on the basic attribute data of music and the emotional model in psychology, and the result was applied to the visualization rules in the formative arts. In the existing studies using musical parameter, there were many cases with more practical purposes to classify, search, and recommend music for people. In this study, the focus was on enabling sound data to be used as a material for creating artworks and used for aesthetic expression. In order to study the music visualization as an art form, a method that can include human emotions should be designed, which is the characteristics of the arts itself. Therefore, a well-structured basic classification of musical attributes and a classification system on emotions were provided. Also, through the shape, color, and animation of the visual elements, the visualization of the musical elements was performed by reflecting the subdivided input parameters based on emotions. This study can be used as basic data for artists who explore a field of music visualization, and the analysis method and work results for matching emotion-based music components and visualizations will be the basis for automated visualization by artificial intelligence in the future.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.363-369
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• 2021

High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.13-23
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• 2022

In this paper, we propose a detailed component image classification algorithm by fashion item for unstructured data retrieval in the fashion field. Due to the COVID-19 environment, AI-based online shopping malls are increasing recently. However, there is a limit to accurate unstructured data search with existing keyword search and personalized style recommendations based on user surfing behavior. In this study, pre-processing using Mask R-CNN was conducted using images crawled from online shopping sites and then classified components for each fashion item through CNN. We obtain the accuaracy for collar of the shirt's as 93.28%, the pattern of the shirt as 98.10%, the 3 classese fit of the jeans as 91.73%, And, we further obtained one for the 4 classes fit of jeans as 81.59% and the color of the jeans as 93.91%. At the results for the decorated items, we also obtained the accuract of the washing of the jeans as 91.20% and the demage of jeans accuaracy as 92.96%.