• International journal of advanced smart convergence
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• 제8권3호
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• pp.87-94
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• 2019

IoT-based services are being released in accordance with the aging population and the demand for well-being pursuit needs. In addition to medical device companies, companies with ideas ranging from global ICT companies to startup companies are accelerating their market entry. The areas where these services are most commonly applied are health/medical, life/safety, city/energy, automotive and transportation. Furthermore, by expanding IoT technology convergence into the area of life care services, it contributes greatly to the development of service models in the public sector. It also provides an important opportunity for IoT-related companies to open up new markets. By addressing the problems of life care services that are still insufficient. We are providing opportunities to pursue the common interests of both users and workers and improve the quality of life. In order to establish IoT-based digital life care services, it is necessary to develop convergence technologies using cloud computing systems, big data analytics, medical information, and smart healthcare infrastructure.

• International journal of advanced smart convergence
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• 제10권3호
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• pp.149-156
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• 2021

Today, 3D softwares have become an essential tool in all areas of Video, including Movies, Animations, CFs, Motion Graphics and Games. One of the most commonly used fields is the 3D character video part. However, these 3D character animations and motion graphics softwares are difficult to learn and too much to learn, making it difficult to learn them all in a university education with a limited time of four years. In this paper, many Inverse kinematics tools, which are essential in the 3D character animations and motion graphics field, compare and analyze the strengths and weaknesses of each tool, focusing on Bone, Character Studio, and Character Animation Toolkit, which are most commonly used in work fields. And use Delphi techniques for 3D experts to secure objectivity. Therefore, for universities that require large amounts of teaching in a limited time, I propose an analysis of which of the above three Inverse Kinetics tools is advantageous for students to select and focus on for efficient education.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.120-123
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• 2020

The surveillance system to prevent crime and accidents in advance has become a necessity, not an option in real life. Not only public institutions but also individuals are installing surveillance cameras to protect their property and privacy. However, since the installed surveillance camera cannot be monitored for 24 hours, the focus is on the technology that tracks the video after an accident occurs rather than prevention. In this paper, we propose a system model that monitors abnormal behaviors that may cause crimes through real-time video, and when a specific behavior occurs, the surveillance system automatically detects it and responds immediately through an alarm. We are a model that analyzes real-time images from surveillance cameras and uses I3D models from analysis servers to analyze abnormal behavior and deliver notifications to web servers and then to clients. If the system is implemented with the proposed model, immediate response can be expected when a crime occurs.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.10-14
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• 2021

Today people adopt various contents from their mobile devices which lead to numerous platforms. As technology of 5G, IOT, and smart phone develops, the number of people who create, edit, collect, and share their own videos, photos, and articles continues to increase. As more contents are shared online, the numbers of data being stolen continue to increase too. To prevent these cases, an authentication method is needed to encrypt the content and prove it as its own content. In the report, we propose a few methods to secure various misused content with secondary security. A unique private key is designed when people create new contents through sending photos or videos to platforms. The primary security is to encrypt the "Private Key" with a public key algorithm, making its data-specific "Timeset" that doesn't allow third-party users to enter. For the secondary security, we propose to use Message Authentication Codes(MACs) to certify that we have produced the content.

• International journal of advanced smart convergence
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• 제10권2호
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• pp.53-58
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• 2021

In this paper, based on the real estate registration model in the Chinese internet environment, we propose a model for the joint business of banking collateral registration. This is to increase the efficiency and service level of the real estate mortgage registration process. And it can solve the problems that in the process of registering a mortgage loan, difficulty of data sharing between the real estate registration agency and the bank, and ordinary users and bank clerks duplicate unnecessary work. In addition, it realizes joint processing and data sharing of real estate registration work with real estate registration agencies and banks, increases the efficiency and level of government affairs services, and offers an optimized solution to realize a one-stop service for real estate security registration. The results of this study are expected to provide theoretical support for the application and innovation of the Internet environment real estate registration model.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.179-183
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• 2020

The online education platform market is developing rapidly after the coronavirus infection-19 pandemic. As school classes at various levels are converted to non-face-to-face classes, interest in non-face-to-face online education is increasing more than ever. However, the majority of online platforms currently used are limited to the fragmentary functions of simply delivering images, voice and messages, and there are limitations to online hands-on training. Indeed, digital transformation is a traditional business method for increasing coding education and a corporate approach to service operation innovation strategy computing thinking power and platform model. There are many ways to evaluate a computer programmer's ability. Generally, piecemeal evaluation methods are used to evaluate results in time through coding tests. In this study, the purpose of this study is to propose a comprehensive evaluation of not only the results of writing, but also the execution process of the results, etc., and to evaluate the programmer's propensity habits based on the programmer's coding experience to evaluate the programmer's ability and productivity.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.34-46
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• 2023

A growing number of large scale knowledge graphs raises several issues how knowledge graph data can be organized, discovered, and integrated efficiently. We present a novel semantic-based mashup platform for contents convergence which consists of acquisition, RDF storage, ontology learning, and mashup subsystems. This platform servers a basis for developing other more sophisticated applications required in the area of knowledge big data. Moreover, this paper proposes an entity matching method using graph convolutional network techniques as a preliminary work for automatic classification and discovery on knowledge big data. Using real DBP15K and SRPRS datasets, the performance of our method is compared with some existing entity matching methods. The experimental results show that the proposed method outperforms existing methods due to its ability to increase accuracy and reduce training time.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.260-267
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• 2023

Due to the development of science and technology in the 4th Industrial Revolution, a variety of content is being developed and utilized through educational courses linked to digital textbooks. Students use smart devices to engage in realistic virtual learning experiences, interacting with the content in digital textbooks. However, while many realistic contents offer visual and auditory effects like 3D VR, AR, and holograms, olfactory content that evokes actual sensations has not yet been introduced. Therefore, in this paper, we designed and implemented 4D educational content by adding the sense of smell to existing content. This implemented content was tested in classrooms through a curriculum-based evaluation. Classes taught with olfactory-enhanced content showed a higher percentage of correct answers compared to those using traditional audio-visual materials, indicating improved understanding.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.195-204
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• 2024

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.