• 융합신호처리학회논문지
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• 제25권2호
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• pp.113-118
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• 2024

최근 들어 인공지능, 컴퓨터 그래픽기술이 진화하면서 영화,광고, 방송, 게임, SNS 등 여러매체를 통해 다양한 가상휴먼이 등장하고 있다. 특히, 가상인플루언서를 중심으로 한 광고 마케팅 시장에서 가상휴먼은 시간과 비용 측면에서 기업의 중요한 홍보수단으로 이미 중요성이 입증된 상태이다. 국내는 가상 인플루언서 시장의 태동기 단계로 대기업 및 스타트업 경계없이 가상인플루언서 관련 신규 서비스를 출시 준비를 하고자 하나, 그 개발 프로세스가 공개되어 있지 않아 많은 비용을 지불해야 하는 상황이다. 이런 기업의 요구사항과 애로사항을 해결하기 위해 본 논문에서는 실사기반의 가상휴먼을 제작하기 위한 포토그래메트리기반 페이셜 캡춰 시스템을 구현하고, 이를 활용한 가상휴먼 모델링 및 활용사례에 대하여 고찰한다. 페이셜 캡처 후 실제 애니메이션이 가능한 과정까지의 복잡한 CG 작업 단계를 간소화할 수 있는 언리얼엔진기반의 메타휴먼 모델링을 통해 비용과 품질면에서 최적의 워크플로우에 대해서도 고찰하고, 또한 인스타그램 등 SNS마케팅에 활용한 사례에 대해서도 소개한다. 언리얼엔진기반의 워크플로우를 통해 기존의 CG작업과의 비교를 통해 제안한 워크플로우의 성능을 입증한다.

• 한국의학물리학회지:의학물리
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• 제31권3호
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• 국제초고층학회논문집
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• 제12권3호
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Journal of Information Processing Systems
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• 제15권4호
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• pp.904-919
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• 2019

Many security systems rely solely on solutions based on Artificial Intelligence, which are weak in nature. These security solutions can be easily manipulated by malicious users who can gain unlawful access. Some security systems suggest using fingerprint-based solutions, but they can be easily deceived by copying fingerprints with clay. Image-based security is undoubtedly easy to manipulate, but it is also a solution that does not require any special training on the part of the user. In this paper, we propose a multi-factor security framework that operates in a three-step process to authenticate the user. The motivation of the research lies in utilizing commonly available and inexpensive devices such as onsite CCTV cameras and smartphone camera and providing fully secure user authentication. We have used technologies such as Argon2 for hashing image features and physically unclonable identification for secure device-server communication. We also discuss the methodological workflow of the proposed multi-factor authentication framework. In addition, we present the service scenario of the proposed model. Finally, we analyze qualitatively the proposed model and compare it with state-of-the-art methods to evaluate the usability of the model in real-world applications.

• Korean Journal of Radiology
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• 제24권7호
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• pp.698-714
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• 2023

In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.

• KEPCO Journal on Electric Power and Energy
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• 제6권4호
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• pp.413-418
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• 2020

초기 학습 데이터의 과적합으로 인한 전력망 상태예측 모델의 성능 감소를 방지하고 예측모델의 예측 정확도 유지를 통한 계속적인 현장활용을 위해서는 기계학습 모델의 예측 정확도를 지속적으로 관리할 필요가 있다. 이를 위해, 본 논문에서는 다양한 요인에 의해 끊임없이 변화하는 전력망 상태 데이터의 특성을 고려하여 예측모델의 정확성과 신뢰성을 높이고 현장 적용 가능한 수준의 품질을 유지하기 위한 기계학습 기반 전력망 상태예측 모델의 성능 유지관리 자동화 기법을 제안한다. 제안 기법은 워크플로우 관리 기술의 적용을 통해 전력망 상태예측 모델 성능 유지관리를 위한 일련의 태스크들을 워크플로우의 형태로 모델링하고 이를 자동화하여 업무를 효율화 하였다. 또한, 기존 기술에서는 시도되지 않았던 학습데이터의 통계적 특성 변화 정도와 예측의 일반화 수준을 모두 고려한 예측모델의 성능 평가를 통해 성능 결과의 신뢰성을 확보하고 이를 통해 예측 모델의 정확도를 일정 수준으로 유지관리하고 더욱 성능이 우수한 예측모델의 신규 개발이 가능하다. 결과적으로 본 논문에서 제안하는 전력망 상태예측 모델 성능 유지관리 자동화 기법을 통해 예측모델의 성능 저하문제를 해결하여 분산자원 연계 등 외부 환경의 변화에 유연한 예측모델 관리를 통해 정확성과 신뢰성이 보장된 예측 모델의 지속적인 활용이 가능하다.

• 한국산업융합학회 논문집
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• 제25권2_2호
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• pp.247-255
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• 2022

Robot Process Automation (RPA) is a computer technology called Robotic Process Automation, a form of business process automation based on the concept of software robots or artificial intelligence (AI) walkers. In general, in traditional workflow automation tools, software developers design software that creates a set of actions to automate tasks and interfaces for the back-end systems using internal APIs or dedicated script languages. However, in RPA software, automation can be implemented by configuring an operating processor as if the general user is directly performing the task of the application. In other words, it can be said that it is a suitable development method for automating simply repetitive tasks rather than developing specific programs in which all necessary functions are implemented, as in general software development. Thus, this is more appropriate for configuring and automating RPA software in traditional manufacturing companies that are not easy to develop and apply smart factories or high-end AI software. Therefore, this research aims to analyze the requirements required at the actual manufacturing companies, focusing on the manufacturer's case in Changwon, Gyeongsangnam-do, called SinceWin Co., Ltd., and to examine the possibility of RPA software in the manufacturing companies by implementing actual RPA software that supports office automation. Through the research, it was confirmed that the actually implemented RPA software met the requirements of the company and helped manufacturer practice significantly by automating the parts that were worked error-prone and manually periodically.

• 패션비즈니스
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• 제27권4호
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• pp.141-162
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• 2023

With the prospect that integrating creative AI in the fashion design field will become more visible, this study considered the case of creative fashion design development through Human-AI Co-creation (HAIC). Methodologically, this research encompasses a literature review and empirical investigations. In the literature review, the fashion design and creative HAIC processes, and the possibilities of integrating AI in fashion design were considered. In the empirical study, based on the case analysis of generating fashion design through HAIC, the HAIC type according to the role and interaction method, and characteristics of humans and AI was considered, and the HAIC process for fashion design was derived. The results of this study are summarized as follows. First, HAIC types in fashion design are divided into four types: AI-driven passive HAIC, human-driven passive HAIC, flexible interaction-based HAIC, and integrated interaction-based value creation HAIC. Second, the stages of the HAIC process for creative fashion design can be broadly divided into semantic data integration, visual ideation, design creation and expansion, design presentation, and design/manufacturing solution and UX platform creation. Third, in fashion design, HAIC contributes to human ability, enhancement of creativity, achievement of efficient workflow, and creation of new values. This research suggests that HAIC has the potential to revolutionize the fashion design industry by facilitating collaboration between humans and AI; consequently, enhancing creativity, and improving the efficiency of the design process. It also offers a framework for understanding the different types of HAIC and the stages involved in the creative fashion design process.

• 한국방사선학회논문지
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• 제17권6호
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• pp.939-945
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• 2023

본 연구는 영상의학 분야에서 인공지능(AI) 기술 기반의 판독 보조 시스템의 'Time Behavior(시간반응성)' 속성을 측정하여 '성능 효율성'을 분석하였다. 의료 영상의 증가와 영상의학 전문의 수의 한계로 인해 인공지능(AI) 기술 기반의 솔루션이 증가하고 있으며, 관련된 연구가 많이 수행되고 있다. 하지만 대부분의 선행 연구가 인공지능의 진단 정확도에 초점을 맞췄다면, 본 연구는 Time Behavior의 중요성을 강조하여 수행하였다. 50개의 흉부 엑스레이 PA 이미지를 사용하여 측정한 결과, 평균 15.24초 만에 영상을 처리하여 높은 일관성과 안정성을 보여주었고, 이 처리 속도는 유명 글로벌 AI 플랫폼과 동등한 수준으로 영상의학과 워크플로우 효율성 부분에 크게 개선될 수 있는 가능성을 제시하였다. 앞으로 인공지능 기술이 영상의학 분야에서 큰 역할을 담당하여, 전반적인 의료 품질 향상과 효율성을 개선하는 데 도움이 될 것으로 기대한다.

• 한국인공지능학회지
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• 제11권2호
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• pp.39-44
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• 2023

The creativity of thesis is that the significance of cyber security challenges in blockchain. The variety of enterprises, including those in the medical market, are the targets of cyberattacks. Hospitals and clinics are only two examples of medical facilities that are easy targets for cybercriminals, along with IoT-based medical devices like pacemakers. Cyberattacks in the medical field not only put patients' lives in danger but also have the potential to expose private and sensitive information. Reviewing and looking at the present and historical flaws and vulnerabilities in the blockchain-based IoT and medical institutions' equipment is crucial as they are sensitive, relevant, and of a medical character. This study aims to investigate recent and current weaknesses in medical equipment, of blockchain-based IoT, and institutions. Medical security systems are becoming increasingly crucial in blockchain-based IoT medical devices and digital adoption more broadly. It is gaining importance as a standalone medical device. Currently the use of software in medical market is growing exponentially and many countries have already set guidelines for quality control. The achievements of the thesis are medical equipment of blockchain-based IoT no longer exist in a vacuum, thanks to technical improvements and the emergence of electronic health records (EHRs). Increased EHR use among providers, as well as the demand for integration and connection technologies to improve clinical workflow, patient care solutions, and overall hospital operations, will fuel significant growth in the blockchain-based IoT market for linked medical devices. The need for blockchain technology and IoT-based medical device to enhance their health IT infrastructure and design and development techniques will only get louder in the future. Blockchain technology will be essential in the future of cybersecurity, because blockchain technology can be significantly improved with the cybersecurity adoption of IoT devices, i.e., via remote monitoring, reducing waiting time for emergency rooms, track assets, etc. This paper sheds the light on the benefits of the blockchain-based IoT market.