• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.41-49
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• 2022

Recently interests on the application of thermal cameras have increased with the advance of image analysis technology. Aside from a simple image acquisition, applications such as digital twin and thermal image management systems have gained popularity. To this end, we studied the effect of emissivity on the DN (Digital Number) value in the process of derivation of a relational expression for converting DN to an actual surface temperature. The DN value is a number representing the spectral band value of the thermal image, and is an important element constituting the thermal image data. However, the DN value is not a temperature value indicating the actual surface temperature, but a brightness value indicating high and low heat as brightness, and has a non-linear relationship with the actual surface temperature. The reliable relationship between DN and the actual surface temperature is critical for a thermal image processing. We tested the relationship between the actual surface temperature and the DN value of the thermal image, and then the radiation adjustment was performed to better estimate actual surface temperatures. As a result, the relation graph between the actual surface temperature and the DN value similarly show linear pattern with the relation graph between the radiation-controlled non-contact thermometer and the DN value. And the non-contact temperature after adjusting the emissivity was closer to the actual surface temperature than before adjusting the emissivity.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• Korea Science and Art Forum
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• v.24
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• pp.321-332
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• 2016

As functions of public library has become diversified, proper environment color plan enhancing a supporting environment of user based on a function of public library should be achieved by utilizing space chromatics which is a psychological environmental factor. Therefore, public library's color environment of each space functions should be understood and the foundation of color plan enhancing supporting environment of user should also be established under the premise that public library color environment which supports integrated functions to every local residents by meeting functional roles of library. As functions of public library expands, this study has its purpose to analyze color environment characteristics by mainly focusing on library of Busan region to study color environment supporting function of each space. Through a literature research, function and role of color, environment color have considered, and through a preceding research analysis on public library's present condition analysis and tendency of library color preference, theoretical background on library color environment has deducted. By researching present condition of environment color application at 9 public libraries located at Busan, the environment color characteristics of library has deducted through an image adjective analysis using color system, coloration analysis, IRI(Image Research Institute) color image scale. This study can be provided as a reference data for environment color plan based on spatial function to enhance supporting environment of public library user, and it is expected to utilize in the library facility plan which has been diversified.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.388-393
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• 2023

The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.91-101
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• 2023

In this study, we estimated the concentration of chlorophyll-a (Chl-a) using hyperspectral water surface reflectance in an inland weir (Baekjae weir) and estuarine reservoir (Namyang Reservoir) for monitoring the occurrence of algae in freshwater in South Korea. The hyperspectral reflectance was measured by aircraft in Baekjae Weir (BJW) from 2016 to 2017, and a drone in Namyang Reservoir (NYR) from 2020 to 2021. The 30 reflectance bands (BJW: 400-530, 620-680, 710-730, 760-790 nm, NYR: 400-430, 655-680, 740-800 nm) that were highly related to Chl-a concentration were selected using permutation importance. Artificial neural network based Chl-a estimation model was developed using the selected reflectance in both water bodies. And the performance of the model was evaluated with the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The performance evaluation results of the Chl-a estimation model for each watershed was R²: 0.63, 0.82, RMSE: 9.67, 6.99, and MAE: 11.25, 8.48, respectively. The developed Chl-a model of this study may be used as foundation tool for the optimal management of freshwater algal blooms in the future.

• Journal of Service Research and Studies
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• v.11 no.2
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• pp.21-36
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• 2021

This study investigated the means-end chain theory more concretely through the APT hard laddering technique. This is carrying out a questionnaire survey targeting users by hair salon type, and the items drawn from the qualitative laddering technique are applied. The technique is a comparative analysis of each attribute, consequences, and value item by analyzing each step's questions. The results are as follows. First, hairdresser's ability, acceptance of individual-customized opinions, and cheap price were the most mentioned items in the selection attributes. As for the consequences items, image transformation, neatness, novelty, and psychological stability were drawn in order. The items indicated as important among the value items were satisfaction, followed by happiness, confidence, beauty, and bond. Second, the remarkable selection attributes, irrelevant of hair salon type, was revealed as hairdresser's ability and the key values pursued when using a hair salon were drawn as satisfaction, confidence, and beauty. From this result, it was found that meeting the desire of consumers using hair salons can be linked with ultimately pursued values. It was also verified that partial differences were shown by hair salon type and this meant that consumers' desire and expected benefits were different by hair salon type. Although this study drew value perception through comparison with hair salon types based on the means-end chain theory, it was confirmed that the most important selection attribute was hairdresser's ability and they select and use hair salons to gain satisfaction and confidence.

• Convergence Security Journal
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• v.23 no.2
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• pp.47-54
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• 2023

The global popularity of K-content(Korean Wave) has led to a continuous increase in copyright infringement cases involving domestic works, not only within the country but also overseas. In response to this trend, there is active research on technologies for detecting illegal distribution sites of domestic copyrighted materials, with recent studies utilizing the characteristics of domestic illegal distribution sites that often include a significant number of advertising banners. However, the application of detection techniques similar to those used domestically is limited for overseas illegal distribution sites. These sites may not include advertising banners or may have significantly fewer ads compared to domestic sites, making the application of detection technologies used domestically challenging. In this study, we propose a detection technique based on the similarity comparison of links and text trees, leveraging the characteristic of including illegal sharing posts and images of copyrighted materials in a similar hierarchical structure. Additionally, to accurately compare the similarity of large-scale trees composed of a massive number of links, we utilize Graph Neural Network (GNN). The experiments conducted in this study demonstrated a high accuracy rate of over 95% in classifying regular sites and sites involved in the illegal distribution of copyrighted materials. Applying this algorithm to automate the detection of illegal distribution sites is expected to enable swift responses to copyright infringements.

• Korea Science and Art Forum
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• v.35
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• pp.281-292
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• 2018

Crowd funding is also called social funding because of SNS that it helps early start-up founder and makers to raise money for idea product production. Recently, the funding platform has recorded high growth rates. As a result, the government in Korea has introduced various support policies for the crowd funding. The purpose of this study is to develop a diagnostic design guideline for product design oriented makers based on the historical situation. The paper writer applied literature survey and expert interview as research methods. The literature survey focused on internet news and previous research studies. The expert interview was conducted for 10 specialist people and divided for the second time. As a result of the text survey, the current guideline was lacking in design and in detail. Researchers have been informed through previous paper that information transfer text and images are important factors for funding success. In the first interview with seven special participants, we made a draft design guideline for social funding with a two-step process and nine themes. We, research and three professional people having a evaluation experience, conducted verification and supplementation for establishing the design guider with a three-step process and eight themes in the next interview. The design guideline for crowd funding, it can be used by money funding manager apart from design makers. Through the results of this paper, researchers are expected to prevent problems and contribute to healthy crowd funding ecosystem development.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.67-76
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• 2023

Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.

• Korean Journal of Culture and Social Issue
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• v.16 no.1
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• pp.43-61
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• 2010

In Korea, Pbichim refers to a psychological state caused by emotional damages that can occur within close relationships. In this state, one might feel reluctant to express one's feelings directly to the other party. It is also possible that Pbichim transforms into anger. This study is aimed to define the term Pbichim as an indigenous psychological concept. In Korea, it is common to express one's feelings indirectly and read the other party's inward thoughts. Pbichim reflects those cultural aspects. In order to examine the representation of Pbichim in Korea, we developed a questionnaire consisting of 15 open-ended questions. The participants were 119 undergraduate and graduate students at Korea University, and the data was analyzed qualitatively. As a result, four different aspects of Pbichim (unsatisfied expectation, being ignored, being alienated, and power struggle) could be differentiated by the situation in which people are likely to present Pbichim. The personality traits of Pbichim, the way of relieving it, as well as positive and negative functions of Pbichim were also elicited. In addition, it was found that Pbichim (the concept that has been negatively perceived) has an important function in maintaining and improving an interpersonal relationship in Korea. Lastly, the importance of mind reading within a certain cultural context is discussed.