• Textile Coloration and Finishing
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• v.35 no.4
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• pp.196-205
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• 2023

Water-dispersed pigment is on-going study for without air pollution in the textile and print industry. Primer treatment is essential for the substrate to improve the printing quality of eco-friendly water-dispersed pigment ink. Otherwise in the case of untreated primer, the water-dispersed pigment ink will dry onto the surface and cause defective images. This study was conducted on film substrate coating (primer) to fix eco-friendly water-dispersed pigment ink on film substrate. The drying, bleeding, and color strength of the pigment ink were examined depending on the composition and mixing ratio of the coating solution. The mixing ratio of silica gel in the coating film is increased to 0, 0.5, 1, 2 and 3 and results that DK-1-3 of silica gel ratio of 1 showed the lowest bleeding such as 52%, the letter thickness of 0.76mm and DK-1-5 of SG ratio of 3 showed the highest bleeding such as 304%, the letter thickness of 2.02mm. The mixing ratio of SPA in the coating film is increased to 2.5, 5, 7.5, SPA ratio of 7.5 has a bleeding ratio of 9% and letter thickness of 0.544mm. It showed the closest value to 0.5mm. According to the result, the optimal mixing ratio of binder, polymer coagulant, silica gel is 100:7.5:1.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1235-1243
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• 2023

This study proposes the detection algorithm for the cold water mass (CWM) along the eastern coast of the Korean Peninsula using sea surface temperature (SST) data provided by the Korea Institute of Ocean Science and Technology (KIOST). Considering the occurrence and distribution of the CWM, the eastern coast of the Korean Peninsula is classified into 3 regions("Goseong-Uljin", "Samcheok-Guryongpo", "Pohang-Gijang"), and the K-means clustering is first applied to SST field of each region. Three groups, K-means clusters are used to determine CWM through applying a double threshold filter predetermined using the standard deviation and the difference of average SST for the 3 groups. The estimated sea area is judged by the CWM if the standard deviation in the sea area is 0.6℃ or higher and the average water temperature difference is 2℃ or higher. As a result of the CWM detection in 2022, the number of CWM occurrences in "Pohang-Gijang" was the most frequent on 77 days and performance indicators of the confusion matrix were calculated for quantitative evaluation. The accuracy of the three regions was 0.83 or higher, and the F1 score recorded a maximum of 0.95 in "Pohang-Gijang". The detection algorithm proposed in this study has been applied to the KIOST SST system providing a CWM map by email.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.867-873
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• 2023

In this study, for the purpose of standardized quality control of a cold medicine, we simultaneous analyzed four main chemical components of a cold medicine: acetaminophen, caffeine, methyl paraben, and propyl paraben. The sample was subjected to quantitative analysis using high performance liquid chromatography (HPLC), after pretreatment of four components. The experiment was carried out by using Isocratic elution at wavelength of 270nm. Acetonitrile and water (H₂O) were used as a mobile phase at a flow rate of 1.0mL/min in a commercial C18 reversed-phase column. A volume of 10uL cold medicine were injected into the column with column oven temperature at 35℃. As a result of the experiment, the values of Resolution were 4.983, 1.596, 5.519, and 1.678 respectively-well over Rs >1.5, which indicates that the separation of four components were efficient. In addition, value of symmetry factor of the components was 1.056, 1.069, 1.032, and 1.133 respectively, to show its symmetrical stability. The calibration curve of all four components exhibits good linearity with R² >0.9995 to 0.9999. Furthermore, the limit of detection(LOD) were between 0.0118 to 1.5973 mg/mL, while the limit of quantification (LOQ) were between 0.0353 to 4.7919 ㎍/mL with the recovery rate of 79.6% ~ 120.5%. The results of this study showed an efficient quality evaluation of a simultaneous analysis method for cold medicine components.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.445-471
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• 2023

Long-term safety over millennia is the top priority consideration in the construction of disposal sites. However, ensuring the mechanical stability of deep geological repositories for spent fuel, a.k.a. radwaste, disposal during construction and operation is also crucial for safe operation of the repository. Imposing restrictions or limitations on tunnel support and lining materials such as shotcrete, concrete, grouting, which might compromise the sealing performance of backfill and buffer materials which are essential elements for the long-term safety of disposal sites, presents a highly challenging task for rock engineers and tunnelling experts. In this study, as part of an extensive exploration to aid in the proper selection of disposal sites, the anticipation of constructing a deep geological repository at a depth of 500 meters in an unknown state has been carried out. Through a review of 2D and 3D numerical analyses, the study aimed to explore the range of properties that ensure stability. Preliminary findings identified the potential range of rock properties that secure the stability of central and disposal tunnels, while the stability of the vertical tunnel network was confirmed through 3D analysis, outlining fundamental rock conditions necessary for the construction of disposal sites.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Applied Chemistry for Engineering
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• v.34 no.5
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• pp.515-521
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• 2023

In this study, waste carbon fiber obtained by pyrolysis of carbon fiber reinforced plastic (CFRP) was used to produce carbon fluoride through vapor phase fluorination and recycled as a reducing electrode material for lithium primary batteries. First, the physicochemical properties of the waste carbon fiber obtained by pyrolysis were determined, and the structural and chemical properties of carbon fluoride were analyzed to evaluate the effect of vapor phase fluorination on the waste carbon fiber. XRD analysis confirmed that the hexagonal network carbon laminated structure (002 peak) of the waste carbon fiber was gradually converted into a carbon fluoride structure (CF_X, 001 peak) as the temperature of gas phase fluorination increased. The discharge capacity of the lithium primary battery produced using this carbon fluoride was up to 862 mAh/g. This was compared to the discharge capacity of carbon fluoride-based Li-ion batteries made of other carbon materials. These results suggest that carbon fluoride made from waste CFRP-based carbon fibers can be used as a reducing electrode material for Li-ion batteries.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.597-603
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• 2023

The paradigm of military operations has evolved from platform-centric warfare to network-centric warfare and further to information-centric warfare, driven by advancements in information technology. In recent years, with the development of cutting-edge technologies such as big data, artificial intelligence, and the Internet of Things (IoT), military operations are transitioning towards knowledge-centric warfare (KCW), based on artificial intelligence. Consequently, the military places significant emphasis on integrating advanced information and communication technologies (ICT) to establish reliable C4I (Command, Control, Communication, Computer, Intelligence) systems. This research emphasizes the need to apply data mining techniques to analyze and evaluate various aspects of C4I systems, including enhancing combat capabilities, optimizing utilization in network-based environments, efficiently distributing information flow, facilitating smooth communication, and effectively implementing knowledge sharing. Data mining serves as a fundamental technology in modern big data analysis, and this study utilizes it to analyze real-world cases and propose practical strategies to maximize the efficiency of military command and control systems. The research outcomes are expected to provide valuable insights into the performance of C4I systems and reinforce knowledge-centric warfare in contemporary military operations.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.