• The Research Journal of the Costume Culture
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• v.31 no.1
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• pp.53-69
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• 2023

This study aims to analyze research trends regarding outdoor wear. For this purpose, the data-collection period was limited to January 2002-October 2022, and the collection consisted of titles of papers, academic names, abstracts, and publication years from the Research Information Sharing Service (RISS). Frequency analysis was conducted on 227 papers in total to check academic journals and annual trends, and LDA topic-modeling analysis was conducted using 20,964 tokens. Data pre-processing was performed prior to topic-modeling analysis; after that, topic-modeling analysis, core topic derivation, and visualization were performed using a Python algorithm. A total of eight topics were obtained from the comprehensive analysis: experiential marketing and lifestyle, property and evaluation of outdoor wear, design and patterns of outdoor wear, outdoor-wear purchase behavior, color, designs and materials of outdoor wear, promotional strategies for outdoor wear, purchase intention and satisfaction depending on the brand image of outdoor wear, differences in outdoor wear preferences by consumer group. The results of topic-modeling analysis revealed that the topic, which includes a study on the design and material of outdoor wear and the pattern of jackets related to the overall shape, was the highest at 30.9% of the total topics. The next highest topic was also the design and color of outdoor wear, indicating that design-related research was the main research topic in outdoor wear research. It is hoped that analyzing outdoor wear research will help comprehend the research conducted thus far and reveal future directions.

• The Research Journal of the Costume Culture
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• v.31 no.1
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• pp.124-140
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• 2023

This study analyzes traditional women's Hu costumes of the Tang dynasty, and deploys a creative fashion design to converge contemporary and traditional styles. In this costume, women wear a robe with striped or plain patterns in the lower part of the pants, and it appears frequently in red and yellow colors. Depending on the sleeve, it is either a round collar or a turn down collar robe. In the Hu hat, the huntuomao and juanyanxumao were leather and mili and weimao were used to prevent the sand from flowing. This study uses the CLO 3D program with the "moment" theme based on the Hu costume for women to deploy 4 pairs of fashion design and to produce works for 2 pairs. The 3D virtual clothing program demonstrates important effects in design deployment and pattern arrangement through its efficiency and convenience of clothing production. The CLO 3D program was closely combined with the 2D design and the 3D affect, and it heightened the efficiency in saving the processing time and energy of the sample clothes. Through facilitating the 3D digital fashion design, the production may reduce time needed and contribute to an effective economy, and it may compare digital fashion design to actual products as well as illustrate the potential of digital fashion design.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3775-3786
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• 2023

Mixing Vane Grid (MVG) is one of the most important structures in fuel assembly due to its high performance in mixing the coolant and ultimately increasing Critical Heat Flux (CHF), which avoids the temperature rising suddenly of fuel rods. To evaluate the mixing performance of the MVG, a Total Diffusion Coefficient (TDC) mixing coefficient is defined in the subchannel analysis code. Conventionally, the TDC of the spacer grid is obtained from the combination of experiments and subchannel analysis. However, the processing of obtaining and determine a reasonable TDC is much challenging, it is affected by boundary conditions and MVG geometries. In is difficult to perform all the large and costing rod bundle tests. In this paper, the CFD method was applied in TDC analysis. A typical 5 × 5 MVG was simulated and validated to estimate the mixing performance of the MVG. The subchannel code was used to calculate the TDC. Firstly, the CFD method was validated from the aspect of pressure drop and lateral temperature distribution in the subchannels. Then the effect of boundary conditions including the inlet temperature, inlet velocities, heat flux ratio between hot and cold rods and the arrangement of hot and cold rods on MVG mixing and TDC were studied. The geometric effects on mixing are also carried out in this paper. The effect of vane pattern on mixing was investigated to determine which one is the best to represent the grid's mixing performance.

• Tribology and Lubricants
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• v.39 no.5
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• pp.197-202
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• 2023

Recently, research on experimental and analytical techniques utilizing microfluidic devices has been pursued. For example, lab-on-a-chip devices that integrate micro-devices onto a single chip for processing small sample quantities have gained significant attention. However, during sample preparation, unnecessary gases can be introduced into the internal channels, thus, impeding device flow and compromising specific function efficiency, including that of analysis and separation. Several methods have been proposed to mitigate this issue, however, many involve cumbersome procedures or suffer from complexities owing to intricate structures. Recently, some approaches have been introduced that utilize hydrophobic device structures to remove gases within channels. In such cases, the permeability of gases passing through the structure becomes a crucial performance factor. In this study, a method involving the deposition and sintering of diluted Ag-ink onto a silicon wafer surface is presented. This is followed by unstructured nano-pattern creation using a Metal Assisted Chemical Etching (MACE) process, which yields a nanostructured surface with unstructured pillar shapes. Subsequently, gas permeability in the spaces formed by these surface structures is investigated. This is achieved by experiments conducted to incorporate a pressure chamber and measure gas permeability. Trends are subsequently analyzed by comparing the results with existing theories. Finally, it can be confirmed that the significance of this study primarily lies in its capability to effectively evaluate gas permeability through unstructured pillar-like nanostructures, thus, providing quantitative values for the appropriate driving pressure and expected gas removal time in practical device operation.

• Informatization Policy
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• v.30 no.3
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• pp.69-91
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• 2023

The impact of technological advancements on employment is a matter of ongoing debate, with discussions on the effects of AI technology development on employment being particularly scarce. This study employs the natural language processing technique (SBERT) and patents to calculate an occupation-based AI exposure score and to analyze employment trends by group. It proposes a method for calculating the AI exposure score based on the similarity between Korean patent information and US job descriptions and linking SOC(U.S.) and KSCO(Korea). The analysis of domestic AI patent applications and regional employment data in the KOSIS Database since 2013 reveals a K-shaped polarization pattern in Korean employment trends among groups with above and below average levels of AI exposure.

• Computers and Concrete
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• v.33 no.5
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• pp.535-544
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• 2024

In the rapidly advancing landscape of computer vision (CV) technology, there is a burgeoning interest in its integration with the construction industry. Camera calibration is the process of deriving intrinsic and extrinsic parameters that affect when the coordinates of the 3D real world are projected onto the 2D plane, where the intrinsic parameters are internal factors of the camera, and extrinsic parameters are external factors such as the position and rotation of the camera. Camera pose estimation or extrinsic calibration, which estimates extrinsic parameters, is essential information for CV application at construction since it can be used for indoor navigation of construction robots and field monitoring by restoring depth information. Traditionally, camera pose estimation methods for cameras relied on target objects such as markers or patterns. However, these methods, which are marker- or pattern-based, are often time-consuming due to the requirement of installing a target object for estimation. As a solution to this challenge, this study introduces a novel framework that facilitates camera pose estimation using standardized materials found commonly in construction sites, such as concrete forms. The proposed framework obtains 3D real-world coordinates by referring to construction materials with certain specifications, extracts the 2D coordinates of the corresponding image plane through keypoint detection, and derives the camera's coordinate through the perspective-n-point (PnP) method which derives the extrinsic parameters by matching 3D and 2D coordinate pairs. This framework presents a substantial advancement as it streamlines the extrinsic calibration process, thereby potentially enhancing the efficiency of CV technology application and data collection at construction sites. This approach holds promise for expediting and optimizing various construction-related tasks by automating and simplifying the calibration procedure.

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

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.305-311
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• 2006

In this paper the performance of a array invariant method is evaluated for source-range estimation in horizontally stratified shallow water ocean waveguide. The method has advantage of little computationally effort over existing source-localization methods. such as matched field processing or the waveguide invariant and array gain is fully exploited. And. no knowledge of the environment is required except that the received field should not be dominated by purely interference This simple and instantaneous method is applied to simulated acoustic propagation filed for testing range estimation performance. The result of range estimation according to the SNR for the underwater impulsive source with broadband spectrum is demonstrated. The spatial smoothing method is applied to suppress the effect of mutipath propagation by high frequency signal. The result of performance test for range estimation shows that the error rate is within 20% at the SNR above 10dB.

• Information Systems Review
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• v.25 no.3
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• pp.27-41
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• 2023

Unlike the previous works focusing on the state-of-the-art methodologies to improve the performance of machine learning models, this study improves the 'quality' of training data used in machine learning. We propose a method to enhance the quality of training data through the processing of 'local grammar,' frequently used in corpus analysis. We collected a vast amount of unstructured corporate review text data posted by employees working in the top 100 companies in Korea. After improving the data quality using the local grammar process, we confirmed that the classification model with local grammar outperformed the model without it in terms of classification performance. We defined five factors of work engagement as classification categories, and analyzed how the pattern of reviews changed before and after the COVID-19 pandemic. Through this study, we provide evidence that shows the value of the local grammar-based automatic identification and classification of employee experiences, and offer some clues for significant organizational cultural phenomena.

• The Korean Journal of Pesticide Science
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• v.10 no.2
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• pp.99-106
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• 2006

Chlorothalonil and imidacloprid sprayed onto the green peppers were degraded more faster at outdoor than in greenhouse. These results were affected by dew and photodegration, considering no rain during the experimental period. Chlorothalonil, esfenvalerate and imidacloprid in green pepper, green twist pepper and sweet pepper did not show any residual pattern, because green peppers are one of the continuous harvesting crops and pesticides could not be sprayed homogeneously on them. When green peppers were pickled with soy sauce and green twist peppers were fried with vegetable oil, the amounts of pesticides such as alpha-cypermethrin, bifenthrin, chlorfenapyr, esfenvalerate and imidacloprid were diminished to the levels of about $30{\sim}71$ and $20{\sim}41%$, respectively. Esfenvalerate and imidacloprid could not be detected in 2 month-old hot pepper paste. The removal rates of pesticide residues in leaves of green peppers were about $22{\sim}37%$ by washing, about $74{\sim}95%$ by parboiling, and about $17{\sim}55%$ by drying after parboiling.