• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.482-491
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• 2020

This study proposed a research model based on self-determination theory and unified theory of acceptance and use of technology to explain the factors influencing intention to use sharing economy services. A total of 392 responses were collected, and structural equation analysis was performed with AMOS 22.0. The results are summarized as follows. First, self-technological aptness and trust had a positive effect on intention to use sharing economy services. Second, access bigger market and environmental friendliness had a positive effect on intention to use sharing economy services. Third, intention to use sharing economy services had a positive effect on actual usage of sharing economy services. Finally, benefits was found to strengthen the relationship between intention to use sharing economy services and actual usage of sharing economy services. The findings of this study would provide a theoretical framework for sharing economy services and important information for understanding individuals using the sharing economy services.

• Journal of the Korea Computer Graphics Society
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• v.26 no.5
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• pp.1-13
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• 2020

In this paper, we propose a new method to stable simulation the directional ice shape by coupling of freezing solver and viscous water flow. The proposed ice modeling framework considers viscous fluid flow in the direction of ice growth, which is important in freezing simulation. The water simulation solution uses the method of applying a new viscous technique to the IISPH(Implicit incompressible SPH) simulation, and the ice direction and the glaze effect use the proposed anisotropic freezing solution. The condition in which water particles change state to ice particles is calculated as a function of humidity and new energy with water flow. Humidity approximates a virtual water film on the surface of the object, and fluid flow is incorporated into our anisotropic freezing solution to guide the growth direction of ice. As a result, the results of the glaze and directional freezing simulations are shown stably according to the flow direction of viscous water.

• Smart Media Journal
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• v.10 no.1
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• pp.99-107
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• 2021

In addition to the 4th Industrial Revolution and Industry 4.0, the recent megatrends in the ICT field are Big-data, IoT, Cloud Computing, and Artificial Intelligence. Therefore, rapid digital transformation according to the convergence of various industrial areas and ICT fields is an ongoing trend that is due to the development of technology of AI services suitable for the era of the 4th industrial revolution and the development of subdivided technologies such as (Business Intelligence), IA (Intelligent Analytics, BI + AI), AIoT (Artificial Intelligence of Things), AIOPS (Artificial Intelligence for IT Operations), and RPA 2.0 (Robotic Process Automation + AI). This study aims to integrate and advance various machine learning services of infrastructure-side GPU, CDA (Connected Data Architecture) framework, and AI based on mass distributed Abyss storage in accordance with these technical situations. Also, we want to utilize AI business revenue model in various industries.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.312-318
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• 2022

Currently, the Republic of Korea is facing the problem of a decrease in military service resources due to the demographic cliff, and is pursuing military restructuring and changes in the military force structure in order to respond to this. In this situation, the Army is pushing forward the deployment of a drone-bot combat system that will lead the future battlefield. The battlefield of the future will be changed into an integrated battlefield concept that combines command and control, surveillance and reconnaissance, and precision strike. According to these changes, unmanned combat system, including dronebots, will be widely applied to combat situations that are high risk and difficult for humans to perform in actual combat. In this paper, as one of the countermeasures to these changes, autonomous behavior software with a BDI architecture-based decision-making system was developed. The autonomous behavior software applied a framework structure to improve applicability to multiple models. Its function was verified in a PC-based environment by assuming that the target UAV is a battalion-level surveillance and reconnaissance UAV.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.789-802
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• 2022

The purpose of this study is to present implications for effective operation of domestic school-based mental health services by comparing those in Singapore the United States, Australia. Based on reviewing the public data and previous article, researchers conducted a comparative study on the background, history, goal, and managing process of overseas and domestic organizations. The result of comparison in overseas cases suggests that the foundation of school-based mental health project was laid with the national health plan and detailed frame work for implementation. It was also discussed that domestic school-based mental health project is still in introduction stage, while foreign services were delivered efficiently through the leading agency and the cooperation between government ministries and institutions were active. It suggests that cooperation between government ministries, preparation of an effective operating system, and various approaches for students, guardians and teachers are need for domestic school-based mental health project.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.285-293
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• 2022

The COVID-19 outbreak, which took longer than expected, caused considerable damage to students' basic academic ability in mathematics. In this paper, a multiplication table practice application that can help students improve their basic multiplication arithmetic skills has been developed based on a cloud-service. The performance of the application was improved by integrating the Flutter framework, Google Cloud, and Google Sheets. As a result of applying this application to 72 6th graders in elementary schools located in K Metropolitan City, for one week. students' spending time required for solving multiplication table problems was reduced by more than 28% compared to the initial period, while students' learning data was able to be accurately collected without errors. It is hoped that the development case conducted through the Flutter framework in this study can lead to the development of other educational learning applications.

• 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 Intelligent Transport Systems
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• v.22 no.6
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• pp.299-312
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• 2023

Recently, the electronic control unit (ECU) has been integrating several functions into one beyond simple convenience functions. Accordingly, ECUs have more functions and external interfaces than before, and various cybersecurity problems are arising. The United Nations Economic Commission for Europe (UNECE) World Forum for Harmonization of Vehicle Regulations (WP.29) issued UN Regulation No.155 to establish international standards for vehicle cybersecurity management systems in light of the growing threats to vehicle cybersecurity. According to international standards, vehicle manufacturers are required to establish a Cybersecurity Management System (CSMS) and receive a Vehicle Type Approval (VTA). However, opinions were raised that the implementation period should be adjusted because domestic preparations for this are insufficient. Therefore, in this paper, we propose a web-based solution that maps a checklist to check the status of CSMS in the requirement and various vehicle security companies and solutions to mitigate the identified gap.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.