• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.869-876
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• 2006

In the case of earthenware cultural heritages that are found in the form of fragments, the major task is quick and precise restoration. The existing method, which follows the rule of trial and error, is not only greatly time consuming but also lacked precision. If this job could be done by three dimensional scanning, matching up pieces could be done with remarkable efficiency. In this study, the original earthenware was modeled through three-dimensional pattern scanning and photogrammetry, and each of the fragments were scanned and modeled. In order to obtain images from the photogrammetry, we calibrated and used a Canon EOS 1DS real size camera. We analyzed the relationship among the sections of the formed model, efficiently compounded them, and analyzed the errors through residual and color error map. Also, we built a web-based three-dimensional simulation environment centering around the users, for the virtual museum.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.325-333
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• 2006

Recently, in the case of observing the global environment, satellite remote sensing technology has been important. It's because satellite remote sensing is valuable for assessing relatively large areas. But now, small scale remote sensing techniques are needed which can be applicable to the detail investigation of plant tree areas which afforest land after the large scale construction of roads, dams and airports. In this study, we tried to develop and propose a lower altitude sensing technique which can be used in ground remote sensing by using a CCD camera. As a result of this investigation the following can be concluded: We recognized the transference characteristics of filters which were used in comparative tests about the four ground remote sensing devices. We also found that the near-IR camera could be used for an imaging spectral radiometer in the extraction of the vegetation index. Furthermore, we found that the vegetation index has varied hour by hour during the day of the experiment. Finally, we brought about an increase phase of the NDVI in a forest fire, which caused considerable damage, by developing new ground remote sensing technology.

• Design Convergence Study
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• v.13 no.5
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• pp.131-145
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• 2014

Considering the number of population commuting from Gyeonggi to Seoul, the development of the bus system is relatively slow. There are many problems to be solved but it has been overlooked, leaving the users in discomfort. The goal of this research is to improve the current system to satisfy the unmet needs, using HCI methods, which is composed of five steps, including Define, Find, Analyze & Synthesize, Ideation, Develop. In Define, we recruited the interviewees using the extreme user method to find their needs. In Find, we proceeded 'be the customer', shadowing for background research and in-depth interview. In Analyze & Synthesis, we analyzed the customer needs and clustered to similar needs to draw useful ideas. After that we suggested sophisticated ideas based on design principles from previous stage and visualized in user scenarios and mock-ups. Overall, the research suggests with text message service, informative light service and mobile application to improve the existing bus station.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.113-114
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• 2022

This study demonstrates the performance of flexible surface wave resonators in spaces on a ship to overcome environmental limits like non-metallic walls where conventional surface wave resonators cannot installable. Although test results in plane structures show that the performance of conventional surface wave resonators are better than the flexible ones, the results are reversed in curved structures. Flexible surface wave resonators can be installed on metal-pipes that connects all spaces in a ship, and this will allow to build ultimate communication network all over the ship including the rooms like cabins or bridges that are enclosed in non-metallic walls.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.47-52
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• 2024

The efficiency and quality of healthcare services rely heavily on the secure protection and transparent management of individuals' medical information, which is becoming increasingly important in the digital age. To address this issue, we propose a distributed identity management (DID) and electronic medical record (EMR) integration system based on Hyperledger Indy, which aims to ensure the ownership of medical information to individuals and increase the accessibility and utilization of medical information. The system will allow individuals to manage their own medical information and share it transparently when necessary, which will improve the efficiency of healthcare services. In addition, the system will securely protect and transparently manage medical information, increasing the transparency of medical services and strengthening individuals' control over their medical information. Thus, the system will contribute significantly to improving the quality of medical services, protecting individuals' medical information, and improving the efficiency of medical services.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.490-501
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• 2023

Laboratory column tests were conducted to analyze infiltration and water redistribution processes on the basis of rainfall. To efficiently measure moisture content within soil layers, this research developed a predictive model grounded in a convolutional neural network (CNN), a deep learning technique. The digital images obtained during the column tests were incorporated into the established CNN. The moisture content of each soil layer over time was effectively measured. The measured values were also in relatively good agreement with the moisture content determined using the moisture sensors installed for each soil layer. The use of CNN enabled a comprehensive understanding of continuous moisture distribution within the soil layers, as well as the infiltration process according to soil texture and initial moisture content conditions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.770-775
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• 2023

Recently, as the amount of digital data increases exponentially, the importance of data processing systems is being emphasized. In this situation, the selection and construction of data processing systems are becoming more important. In this study, the performance of cloud computing (CC), edge computing (EC), and UAV-based intelligent edge computing (UEC) was compared as a way to solve this problem. The characteristics, strengths, and weaknesses of each method were analyzed. In particular, this study focused on real-time large-capacity data processing situations such as corridor monitoring. When conducting the experiment, a specific scenario was assumed and a penalty was given to the infrastructure. In this way, it was possible to evaluate performance in real situations more accurately. In addition, the effectiveness and limitations of each computing method were more clearly understood, and through this, the help was provided to enable more effective system selection.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.31-37
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• 2024

Conventional secret sharing techniques often derive shares from randomly generated polynomials or planes, resulting in lengthy and complex shares that are challenging to memorize and/or manage without the aid of a separate computer or specialized device. Modifying existing secret sharing methods to use a predetermined value, such as a memorizable password or bio-metric information, offers a solution. However, this approach raises concerns about security, especially when the predetermined value lacks randomness or has low entropy. In such cases, adversaries may deduce a secret S with just (t - 1) shares by guessing the predetermined value or employing brute force attacks. In this paper, we introduce a share hardening method designed to ensure the security of secret sharing while enabling the use of memorizable passwords or biometric information as predetermined shares.