• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.143-149
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• 2018

Recently, due to the large-scale damage of natural disasters caused by global climate change, a monitoring system applying remote sensing technology is being constructed in disaster areas. Among remote sensing platforms, the drone has been actively used in the private sector due to recent technological developments, and has been applied in the disaster areas owing to advantages such as timeliness and economical efficiency. This paper deals with the development of a preprocessing system that can map the drone image data in a near-real time manner as a basis for constructing the disaster monitoring system using the drones. For the research purpose, our system is based on the SURF algorithm which is one of the computer vision technologies. This system aims to performs the desired correction through the feature point matching technique between reference images and shot images. The study area is selected as the lower part of the Gahwa River and the Daecheong dam basin. The former area has many characteristic points for matching whereas the latter area has a relatively low number of difference, so it is possible to effectively test whether the system can be applied in various environments. The results show that the accuracy of the geometric correction is 0.6m and 1.7m respectively, in both areas, and the processing time is about 30 seconds per 1 scene. This indicates that the applicability of this study may be high in disaster areas requiring timeliness. However, in case of no reference image or low-level accuracy, the results entail the limit of the decreased calibration.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.143-149
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• 2019

This study investigates the satisfaction of students who participated in 'Disaster Safety and On-Site Emergency Management' weekend course, the high school-university affiliated program, to provide the basic data on university's major linked program developing and teaching methods. 98 high school students attended the courses at D General high school and B University in North Jeonlla Province. Among the participants, 52%(51 students) were sophomores, while 56.1%(55 students) were male and 43.9%(43 students) were female. The collected data was analyzed by using the SPSS statistics version 21.0 program. 80.6%(79 students) among the participants chose the weekend course program by themselves, 85.7%(84 students) were with clear motivation and goal, and 42.9%(42 students) answered "so interested studying Emergency at a college in the future" The most important reasons to choose this program are as follows: score 4.68 for 'the degree to which the useful program for youth', score 4.58 for 'the leader's expertise', and score 4.53 for 'reflecting the opinion of youth.' After the program's experience, the 'certificate for cardiopulmonary resuscitation' was the most important and the most satisfactory with score 4.78 and score 4.83 respectively. As the university career program using various job experience can be a meaningful experience that enhance the level of career status and career decisions of high school students, this program will strengthen the affiliation between high school and university curriculum and establish the sufficient national social system environment.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.183-190
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• 2022

The article analyzes the features of the formation of a person's value attitude to the worldview by means of information technologies. The present considers it necessary to form a person's value attitude to the perception of the world by means of information technologies. The explosive development of information and telecommunications technologies has become a determining factor in the development of modern society, which is called the information or Global Information Society. It is not yet fully formed, and we are all participants in the development of the Global Information Society. The article considers the basics of a harmonious worldview of a person, which is the basis for the formation of outlook ideas, views, knowledge, beliefs about the surrounding world, which determine the place and role and motivate actions in relation to the surrounding reality through the prism of value orientations. Worldview is considered as an integrity of relatively stable schemes, behaviors, feelings, thinking, vision of the surrounding world, inherent in an individual child, ethno-cultural and socio-cultural groups. The concept of "worldview" as a component of the multi-level structure of the individual's outlook is defined. The features that characterize a person's perception of the world are revealed. The main educational value of information technologies in the formation of a person's value attitude to the perception of the world is highlighted, which consists in the fact that they allow you to create an immeasurable brighter multi-sensory interactive learning environment with almost unlimited potential opportunities that fall at the disposal of both the teacher and the student. The trend of forming a person's value attitude to the perception of the world is clearly developing in the direction of mixed learning as a process that creates a comfortable information educational environment, communication systems that provide all the necessary educational information. The approach to student development by means of the educational environment and the formation, while in the person of a value attitude to the perception of the world by means of Information Technologies, has many pedagogical advantages, which is considered in the article.

• Informatization Policy
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• v.31 no.2
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• pp.3-38
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• 2024

Hallucination is a significant barrier to the utilization of large-scale language models or multimodal models. In this study, we collected 654 computer science papers with "hallucination" in the abstract from arXiv from December 2022 to January 2024 following the advent of Chat GPT and conducted frequency analysis, knowledge network analysis, and literature review to explore the latest trends in hallucination research. The results showed that research in the fields of "Computation and Language," "Artificial Intelligence," "Computer Vision and Pattern Recognition," and "Machine Learning" were active. We then analyzed the research trends in the four major fields by focusing on the main authors and dividing them into data, hallucination detection, and hallucination mitigation. The main research trends included hallucination mitigation through supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), inference enhancement via "chain of thought" (CoT), and growing interest in hallucination mitigation within the domain of multimodal AI. This study provides insights into the latest developments in hallucination research through a technology-oriented literature review. This study is expected to help subsequent research in both engineering and humanities and social sciences fields by understanding the latest trends in hallucination research.

• Cartoon and Animation Studies
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• s.49
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• pp.651-676
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• 2017

Shadow images are a representative medium and means of expression for the imagination that exists between consciousness and unconsciousness for thousands of years. Wherever light exists, people create play with their own shadows without special skills, and have made a fantasy at once. Shadow images have long been used as subjects and materials of literacy, art, philosophy, and popular culture. Especially in the field of art, people have been experimenting with visual stimulation through the uniqueness of simple silhouettes images. In the field of animation, it became to be recognized as a form of non - mainstream areas that are difficult to make. However, shadow images have been used more actively in the field of digital arts and media art. In this Environment with technologies, Various formative imaginations are being expressed more with shadow images in a new dimension. This study is to introduce and analyze these trends, the aesthetic transformations and extended methods focusing on digital silhouette animation and recent media art works using shadow images. Screen-based silhouette animation combines digital technology and new approaches that have escaped conventional methods have removed most of the elements that have been considered limitations, and these factors have become a matter of choice for the directors. Especially, in the display environment using various light sources, projection, and camera technology, shadow images were expressed with multiple-layered virtual spaces, and it becomes possible to imagine a new extended imagination. Through the computer vision, it became possible to find new gaze and spatial images and use it more flexibly. These changes have given new possibility to the use shadow images in a different way.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.157-168
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• 2016

In image processing and computer vision fields, mean squared error (MSE) has popularly been used as an objective metric in image quality optimization problems due to its desirable mathematical properties such as metricability, differentiability and convexity. However, as known that MSE is not highly correlated with perceived visual quality, much effort has been made to develop new image quality assessment (IQA) metrics having both the desirable mathematical properties aforementioned and high prediction performances for subjective visual quality scores. Although recent IQA metrics having the desirable mathematical properties have shown to give some promising results in prediction performance for visual quality scores, they also have high computation complexities. In order to alleviate this problem, we propose a new fast IQA metric using a simple Laplace operator. Since the Laplace operator used in our IQA metric can not only effectively mimic operations of receptive fields in retina for luminance stimulus but also be simply computed, our IQA metric can yield both very fast processing speed and high prediction performance. In order to verify the effectiveness of the proposed IQA metric, our method is compared to some state-of-the-art IQA metrics. The experimental results showed that the proposed IQA metric has the fastest running speed compared the IQA methods except MSE under comparison. Moreover, our IQA metric achieves the best prediction performance for subjective image quality scores among the state-of-the-art IQA metrics under test.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.171-182
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• 2022

The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.