• Nuclear Engineering and Technology
• /
• 제54권5호
• /
• pp.1851-1859
• /
• 2022

Experimental research on flow characteristics of open natural circulation system was performed, to figure out the mechanism of the open natural circulation behaviors. The influence factors, such as the heating power, the inlet subcooled and the level of cooling tank on the flow characteristics of the system were examined. It was shown that within the scope of the experimental conditions, there are five flow types: single-phase stable flow, flash and geyser coexisting unstable flow, flash stable flow, flash unstable flow, and flash and boiling coexisting unstable flow. The geyser flow in flash and geyser coexisting unstable flow is different from classic geysers flow. The flow oscillation period and amplitude of the former are more regular, is a newly discovered flow pattern. By drawing the flow instability boundary diagram and sorting out the flow types, it is found that the two-phase unstable flow is mainly characterized by boiling and flash, which determine the behavior of open natural circulation respectively or jointly. Moreover, compared with full liquid level system, non-full liquid level system is more prone to boiling phenomenon, and the range of heat flux density and undercooling degree corresponding to unstable flow is larger.

• 산업과 과학
• /
• 1권1호
• /
• pp.1-6
• /
• 2022

일상 생활에서 매일 다른 뉴스가 보도되듯이 우리 주변에서 발생되는 데이터의 양은 엄청나다고 할 수 있다. 이런 데이터에는 개인적인 것도 있지만, 날씨나 교통 정보와 같이 모든 사람들에게 공통적인 것도 존재한다. 본 논문은 이런 정보를 효과적이며, 신속하게 사용하기 위해 공공데이터를 활용한 정보전달에 관해 연구로서, 공공데이터와 API기술을 바탕으로 한 오픈API에 대해 조사하였다. 이를 바탕으로 일상생활에서 쉽게 활용 가능한 오픈API를 사용하여 정보를 전달하는 방법을 설명하고, 이를 응용하여 다양한 오픈API를 활용하여 정보를 전달하는 방안을 제시한다. 제시된 방법을 사용하여 공공데이터를 활용한다면 보다 쉽고 정확한 정보를 전달할 수 있으므로 다양한 분야에 응용이 가능할 것으로 생각한다.

• 한국융합학회논문지
• /
• 제12권12호
• /
• pp.93-106
• /
• 2021

블렌더봇 2.0 대화모델은 인터넷 검색 모듈과 멀티 세션의 도입을 통해 실시간 정보를 반영하고, 사용자에 대한 정보를 장기적으로 기억할 수 있도록 함으로써 오픈 도메인 챗봇을 대표하는 대화모델로 평가받고 있다. 그럼에도 불구하고 해당 모델은 아직 개선점이 많이 존재한다. 이에 본 논문은 블렌더봇 2.0의 여러 가지 한계점 및 오류들을 모델, 데이터, 대화의 세 가지 관점으로 분석하였다. 모델 관점에서 검색엔진의 구조적 문제점, 서비스 시 모델 응답 지연시간에 대한 오류를 주로 분석하였다. 데이터 관점에서 크라우드 소싱 과정에서 워커에게 제공된 가이드라인이 명확하지 않았으며, 수집된 데이터의 증오 언설을 정제하고 인터넷 기반의 정보가 정확한지 검증하는 과정이 부족한 오류를 지적하였다. 마지막으로, 대화 관점에서 모델과 대화하는 과정에서 발견한 아홉 가지 유형의 문제점을 면밀히 분석하였고 이에 대한 원인을 분석하였다. 더 나아가 각 관점에 대하여 실질적인 개선방안을 제안하였으며 오픈 도메인 챗봇이 나아가야 할 방향성에 대한 분석을 진행하였다.

• 한국군사과학기술학회지
• /
• 제25권1호
• /
• pp.30-44
• /
• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• 한국정보통신학회논문지
• /
• 제26권12호
• /
• pp.1794-1799
• /
• 2022

최근 증가하고 있는 도로 위 적재 불량 화물차는 비정상적인 무게 중심으로 인해 물체 낙하, 도로 파손, 연쇄 추돌 등 교통안전에 위해가 되고 한번 사고가 발생하면 큰 피해가 유발할 수 있다. 하지만 이러한 비정상적인 무게 중심은 적재 불량 차량 인식을 위한 주행 중 축중 시스템으로는 검출이 불가능하다는 한계점이 있다. 본 논문에서는 이러한 사회 문제를 야기하는 적재 불량 차량을 관리하기 위한 객체 인식 기반 AI 모델을 구축하고자 한다. 또한 AI-Hub에 공개된 약 40만 장의 데이터셋을 비교 분석하여 전처리를 통해 적재 불량 차량 검지 AI 모델의 성능을 향상시키는 방법을 제시한다. 또한 객체 추적을 통해 실시간 검지를 수행하는 방법을 제안한다. 이를 통해, 원시 데이터를 활용한 학습 성능 대비 약 23% 향상된 적재 불량 차량의 검출 성능을 나타냄을 보였다. 본 연구 결과를 통해 공개 빅데이터를 보다 효율적으로 활용하여, 객체 인식 기반 적재 불량 차량 탐지 모델 개발에 적용할 수 있을 것으로 기대된다.

• Asia pacific journal of information systems
• /
• 제33권4호
• /
• pp.1058-1092
• /
• 2023

This study investigates the perception and adoption of ChatGPT (a large language model (LLM)-based chatbot created by OpenAI) among Korean users and assesses its potential as the next disruptive innovation. Drawing on previous literature, the study proposes perceived intelligence and perceived anthropomorphism as key differentiating factors of ChatGPT from earlier AI-based chatbots. Four individual motives (i.e., perceived usefulness, ease of use, enjoyment, and trust) and two societal motives (social influence and AI anxiety) were identified as antecedents of ChatGPT acceptance. A survey was conducted within two Korean online communities related to artificial intelligence, the findings of which confirm that ChatGPT is being used for both utilitarian and hedonic purposes, and that perceived usefulness and enjoyment positively impact the behavioral intention to adopt the chatbot. However, unlike prior expectations, perceived ease-of-use was not shown to exert significant influence on behavioral intention. Moreover, trust was not found to be a significant influencer to behavioral intention, and while social influence played a substantial role in adoption intention and perceived usefulness, AI anxiety did not show a significant effect. The study confirmed that perceived intelligence and perceived anthropomorphism are constructs that influence the individual factors that influence behavioral intention to adopt and highlights the need for future research to deconstruct and explore the factors that make ChatGPT "enjoyable" and "easy to use" and to better understand its potential as a disruptive technology. Service developers and LLM providers are advised to design user-centric applications, focus on user-friendliness, acknowledge that building trust takes time, and recognize the role of social influence in adoption.

• 대한임베디드공학회논문지
• /
• 제19권3호
• /
• pp.151-158
• /
• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Korean Journal of Radiology
• /
• 제25권3호
• /
• pp.224-242
• /
• 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

• 디지털산업정보학회논문지
• /
• 제19권1호
• /
• pp.139-151
• /
• 2023

This study aims to gain insights into ChatGPT, which has recently received significant attention. The study utilized a mixed method involving case studies and news big data analysis. ChatGPT can be described as an optimized language model for dialogue. The question arises whether ChatGPT will replace Google search services, posing a potential threat to Google. It could hurt Google's advertising business, which is the foundation of its profits. With AI-based chatbots like ChatGPT likely to disrupt the web search industry, Google is establishing a new AI strategy. The study used the BIG KINDS service and analyzed 2,136 articles over six months, from August 23, 2022, to February 22, 2023. Thirty of these articles were written in 2022, while 2,106 have been reported recently as of February 22, 2023. Also, the study examined the contents of ChatGPT by utilizing literature research, news big data analysis, and use cases. Despite limitations such as the potential for false information, analyzing news big data and use cases suggests that ChatGPT is worth using.

• International Journal of Internet, Broadcasting and Communication
• /
• 제16권1호
• /
• pp.99-105
• /
• 2024

Runway released an updated version, Gen-2, in March 2023, which introduced new features that are different from Gen-1: it can convert text and images into videos, or convert text and images together into video images based on text instructions. This update will be officially open to the public in June 2023, so more people can enjoy and use their creativity. With this new feature, users can easily transform text and images into impressive video creations. However, as with all new technologies, comes the instability of AI, which also affects the results generated by Runway. This article verifies the feasibility of using Runway to generate the desired video from several aspects through personal practice. In practice, I discovered Runway generation problems and propose improvement methods to find ways to improve the accuracy of Runway generation. And found that although the instability of AI is a factor that needs attention, through careful adjustment and testing, users can still make full use of this feature and create stunning video works. This update marks the beginning of a more innovative and diverse future for the digital creative field.