• 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%.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.28 no.3
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• pp.183-192
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• 2022

With the growth of the packaging industry, demand on the packaging printing comes in various forms. Customers' orders are diversifying and the standards for quality are increasing. Offset printing is mainly used in the packaging printing since it is easy to print in large quantities. However, productivity of the offset printing decreases when printing various order. This is because it takes time to change colors for each printing unit. Therefore, scheduling that minimizes the color replacement time and shortens the overall makespan is required. By the existing manual method based on workers' experience or intuition, scheduling results may vary for workers and this uncertainty increase the production cost. In this study, we propose an automated scheduling method of parallel offset printing process for packaging printing. We decompose the original problem into assigning and sequencing orders, and ink arrangement for printing problems. Vehicle routing problem and assignment problem are applied to each part. Mixed integer programming is used to model the problem mathematically. But it needs a lot of computational time to solve as the size of the problem grows. So guided local search algorithm is used to solve the problem. Through actual data experiments, we reviewed our method's applicability and role in the field.

• Journal of Life Science
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• v.32 no.5
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• pp.355-361
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• 2022

Melanin pigments are the main cause of skin color. They are produced in melanocytes and then transferred to keratinocytes, which eventually gives the skin surface a variety of colors. Although many skin-lightening or depigmenting agents have been developed, the demand for materials to reduce pig- mentation is still increasing. Here, we tried to find materials for skin-lightening or depigmentation using natural compounds and found that mistletoe (Viscum album var. coloratum) extracts (ME) had an inhibitory effect on tyrosinase activity. As a result, ME significantly reduced pigmentation in human primary melanocytes. In addition, a promoter reporter assay revealed that ME inhibited the transcription of microphthalmia-associated transcription factor (MITF), melanophilin (MLPH), tyrosinase-related protein-2 (TRP-2), and tyrosinase (TYR) genes in HM3KO melanoma cells. In addition, ME decreased the protein level for pigmentation-related molecules, such as TYR and TRP-1. Furthermore, it markedly inhibited the melanogenesis of zebrafish embryos, an in vivo evaluation model for pigmentation. To elucidate the action mechanism of ME, we investigated its effects on intracellular signaling. Eventually, the ME dramatically decreased the phosphorylation of the cAMP responsive element binding protein (CREB), AKT, and ERK. The data suggest that ME may inhibit the melanogenesis pathway by regulating the signaling pathway related to pigmentation. Taken together, these data propose that ME can be developed as a depigmenting or skin-lightening agent.

• Knowledge Management Research
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• v.23 no.2
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• pp.123-142
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• 2022

The non-face-to-face environment accelerated by COVID-19 has speeded up the dissemination of digital virtual ecosystems and metaverse. In order for the metaverse to be sustainable, digital twins that are compatible with the real world are key, and critical technology for that is AR (Augmented Reality). In this study, we examined research trends about AR, and will propose the directions for future AR research. We conducted LDA based topic modeling on 11,049 abstracts of published domestic and foreign AR related papers from 2009 to Mar 2022, and then looked into AR that was comprehensive research trends, comparison of domestic and foreign research trends, and research trends before and after the popularity of metaverse concepts. As a result, the topics of AR related research were deduced from 11 topics such as device, network communication, surgery, digital twin, education, serious game, camera/vision, color application, therapy, location accuracy, and interface design. After popularity of metaverse, 6 topics were deduced such as camera/vision, training, digital twin, surgical/surgical, interaction performance, and network communication. We will expect, through this study, to encourage active research on metaverse AR with convergent characteristics in multidisciplinary fields and contribute to giving useful implications to practitioners.

• Journal of Bio-Environment Control
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• v.32 no.1
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• pp.72-80
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• 2023

Melons, a rich source of vitamins and fibers, are commonly grown in the soil. Hydroponic cultivation could improve yield and quality of melon and selection of substrate volume and the kind of substrates is important for hydroponic cultivation of melons. This study investigated the effect on melon growth according to volume of granular rockwool and substrates of coir and rockwool slab. 'Geumsegye' melon (Cucumis melo L. cv. Geumsegye) was cultivated hydroponically according to volume of granular rockwool to 1.0, 1.5, 2.0, 3.0, and 4.0 L, and was also cultivated using coir and rockwool slabs. Logistic model was applied to estimate the growth characteristics of melons such as plant height, leaf length, leaf width, and the characteristics of fruit. The growth characteristics of melons were significantly increased at 4.0 L compared to those grown of 1.0 L volume of on granular rockwool, and the results were the highest in coir and rockwool slabs. Melons grown in rockwool slabs showed the largest fruit fresh weight, fruit length, and fruit width. During hydroponic cultivation, growth characteristics of melon appropriate at the 4.0 L volume of granular rockwool, and the highest at coir and rockwool slabs. This study provides a basis for understanding the effect of root zone environment to the growth characteristics and fruit quality of non-netted melon.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.27-35
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• 2024

Fuel gases such as methane and propane are used in explosion hazardous area of domestic plants and can form non-uniform mixtures with the influence of process conditions due to leakage. The fire-explosion risk assessment using literature data measured under uniform mixtures, damage prediction can be obtained the different results from actual explosion accidents by gas leaks. An explosion characteristics such as explosion pressure and flame velocity of non-uniform gas mixtures with concentration change similar to that of facility leak were examined. The experiments were conducted in a closed 0.82 m long stainless steel duct with observation recorded by color high speed camera and piezo pressure sensor. Also we proposed the quantification method of non-uniform mixtures from a regression analysis model on the change of concentration difference with time in explosion duct. For the non-uniform condition of this study, the area of flame surface enlarged with increasing the concentration non-uniform in the flame propagation of methane and was similar to the wrinkled flame structure existing in a turbulent flame. The time to peak pressure of methane decreased as the non-uniform increased and the explosion pressure increased with increasing the non-uniform. The ranges of K_G (Deflagration index) of methane with the concentration non-uniform were 1.30 to 1.58 [MPa·m/s] and the increase rate of K_G was 17.7% in methane with changing from uniform to non-uniform.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.525-532
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• 2024

This study explores the impact of physical characteristics (e.g., shape, color, material, size, weight, technical features) of smart wristbands and smartwatches on consumers' perceived functional, aesthetic, and symbolic values using an extended technology acceptance model. An online survey was conducted with adult residents of the United States who had experience using smart wristbands or smartwatches. Participants were asked about various physical characteristics of products they had used in the past year or were currently using, and their evaluations of these characteristics. The results revealed that the shape of the front display shape significantly influenced symbolic value, with circle shape and square shpae showing significantly higher symbolic value than rectangle shape. Wristband materials also had a significant impact on symbolic value, with metal and leather showing higher symbolic value among various materials. Additionally, an increase in product size was associated with higher symbolic value. Moreover, certain technical features such as activity tracker, alarm clock, and distance tracking influenced perceived functional value, while functions like time display, GPS, and email influenced perceived aesthetic value. Pedometer, GPS, and email were found to enhance perceived symbolic value. These findings provide valuable insights into consumer preferences for smart wristbands and smartwatches, serving as valuable information for product improvement and new product development.