• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.87-92
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• 2024

The rapid development of artificial intelligence technology in recent years is evident, from the emergence of ChatGPT to innovations like Midjourney, Stable Diffution, and the upcoming SORA text-to-video technology by OPENai. Animation capture technology, driven by the AI technology trend, is undergoing significant advancements, accelerating the progress of the animation industry. Through an analysis of the current application of DEEPMOTION, this paper explores the development direction of AI motion capture technology, analyzes issues such as errors in multi-person object motion capture, and examines the vast prospects. With the continuous advancement of AI technology, the ability to recognize and track complex movements and expressions faster and more accurately, reduce human errors, enhance processing speed and efficiency. This advancement lowers technological barriers and accelerates the fusion of virtual and real worlds.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.162-163
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• 2021

Currently, the depth of cracks is measured using ultrasonic detectors in maintenance practice. This method consists of measuring the depth of cracks by attaching ultrasonic depth measuring equipment to the concrete surface, and there are restrictions on the timing and location of the inspection. These limitations can be addressed through the development of image-based crack depth measurement AI technology. If crack depth measurements are made based on images, restrictions on the timing and location of inspections can be lifted because images acquired with simple filming equipment can be used as input information. To efficiently develop these artificial intelligence technologies, it is essential to identify the interrelationship between crack depth measurements and image characteristic information. Thus, this study is a basic study of the development of image-based crack depth measurement AI technology and aims to identify image characteristic information related to crack depth.

• Journal of Cadastre & Land InformatiX
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• v.53 no.1
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• pp.107-121
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• 2023

In order to acquire a wide range of land that changes in real time and quickly and accurately grasp it, we plan to utilize the recently released high-resolution S.Korea's satellite image data and artificial intelligence (AI). Compared to existing satellite images, the spectral and periodic resolutions of S.Korea's satellite are higher, making them a more suitable data source for periodically monitoring changes in land. Therefore, this study aims to acquire S.Korea's satellite, select 8 types of objects to detect land changes, construct data sets for them, and apply AI models to analyze them. In order to confirm the optimal model and variable conditions for detecting 8 types of objects of various types, several experiments are performed and AI-based image analysis is technically reviewed.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.148-153
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• 2024

This article embarks on an exploration of how the burgeoning landscape of AI technology is reshaping and augmenting creative expression within the realm of 3D animation. As AI continues to evolve and mature, its integration into the process of 3D animation creation has become an increasingly focal point of investigation and analysis. The article undertakes a comprehensive examination of the myriad applications of AI within the domain of 3D animation, shedding light on its multifaceted contributions to various aspects of the creative process. Furthermore, it delves into the transformative impact that AI technology has on enhancing creative expression within 3D animation, particularly through increased productivity, personalized content creation, and the expansion of creative boundaries. By automating repetitive and time-consuming tasks inherent in traditional production methods, AI liberates artists and animators to unleash their creative ingenuity and push the boundaries of their craft. Through empirical research and case studies, the article elucidates how AI serves as a catalyst for innovation, fostering a conducive environment for the exploration of novel techniques and artistic styles.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.346-351
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• 2023

With the rapid development of Artificial Intelligence (AI) technology, there is an increasing variety of methods for creating 3D models. These include innovations such as text-only generation, 2D images to 3D models, and combining images with cue words. Each of these methods has unique advantages, opening up new possibilities in the field of 3D modeling. The purpose of this study is to explore and summarize these methods in-depth, providing researchers and practitioners with a comprehensive perspective to understand the potential value of these methods in practical applications. Through a comprehensive analysis of pure text generation, 2D images to 3D models, and images with cue words, we will reveal the advantages and disadvantages of the various methods, as well as their applicability in different scenarios. Ultimately, this study aims to provide a useful reference for the future direction of AI modeling and to promote the innovation and progress of 3D model generation technology.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.939-945
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• 2023

This study analyzes the 'performance efficiency' of AI-based reading assistance systems in the field of radiology by measuring their 'time behavior' properties. Due to the increase in medical images and the limited number of radiologists, the adoption of AI-based solutions is escalating, stimulating a multitude of studies in this area. Contrary to the majority of past research which centered on AI's diagnostic precision, this study underlines the significance of time behavior. Using 50 chest X-ray PA images, the system processed images in an average of 15.24 seconds, demonstrating high consistency and reliability, which is on par with leading global AI platforms, suggesting the potential for significant improvements in radiology workflow efficiency. We expect AI technology to play a large role in the field of radiology and help improve overall healthcare quality and efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1466-1488
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• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.90-95
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• 2023

With the rapid development of deep learning and artificial intelligence, generative models have achieved remarkable success in the field of image generation. By combining the stable diffusion method with Web UI technology, a novel solution is provided for the application of AI painting generation. The application prospects of this technology are very broad and can be applied to multiple fields, such as digital art, concept design, game development, and more. Furthermore, the platform based on Web UI facilitates user operations, making the technology more easily applicable to practical scenarios. This paper introduces the basic principles of Stable Diffusion Web UI technology. This technique utilizes the stability of diffusion processes to improve the output quality of generative models. By gradually introducing noise during the generation process, the model can generate smoother and more coherent images. Additionally, the analysis of different model types and applications within Stable Diffusion Web UI provides creators with a more comprehensive understanding, offering valuable insights for fields such as artistic creation and design.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.372-380
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• 2022

This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.