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

• Smart Media Journal
• /
• v.12 no.4
• /
• pp.41-46
• /
• 2023

UWB is a type of technology used for indoor positioning and is characterized by higher accuracy than RSSI-based schemes. Mobile equipment operating based on ROS can monitor the environment around the equipment using lidar and cameras. When applying the loop closing technique to determine the starting position in this monitoring process, the existing method has a problem of low accuracy because the closing operation occurs only when there are feature points on the image. In this paper, to solve this problem, we designed a system that increases the accuracy of loop closing work by providing location information by mounting a UWB tag on a mobile device. In addition, the accuracy of the UWB-based indoor positioning system was evaluated through experiments, and it was verified that it could be used for loop closing techniques.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2022.06a
• /
• pp.1253-1255
• /
• 2022

예능 프로그램에서 관찰예능 포맷은 널리 사용되는 형태이다. 본 연구에서는 AI기반 리프레이밍 기술을 활용하는 새로운 관찰 예능 제작 기법을 제안한다. 제안방식은 실제 방송프로그램 KBS2 신상출시 편스토랑에 적용되었다. 연구의 조건에 부합하는 촬영 장비의 기능조건과 조사결과를 다룬다. 센서타입와 연속녹화시간은 장비선정에 있어 핵심 고려요소로 나타났다. 시스템 구성은 제작 워크플로우에 따라 촬영파트와 편집파트로 나누어 소개한다. 촬영파트는 실제 제작현장의 기록을 바탕으로 작성되었다. 편집파트의 경우 자체 개발한 편집도구로 이루어지며, 핵심모듈인 AI엔진과 고속렌더링모듈에 대한 소개를 하였다. 향후 최신 촬영 장비의 도입, 처리성능의 향상 등을 통해 제안방식의 적용처를 넓혀갈 수 있을 것으로 기대한다.

• Nuclear Engineering and Technology
• /
• v.56 no.8
• /
• pp.3167-3179
• /
• 2024

In abnormal states of nuclear power plants (NPPs), operators undertake mitigation actions to restore a normal state and prevent reactor trips. However, in abnormal states, the NPP condition fluctuates rapidly, which can lead to human error. If human error occurs, the condition of an NPP can deteriorate, leading to reactor trips. Sudden shutdowns, such as reactor trips, can result in the failure of numerous NPP facilities and economic losses. This study develops a remaining trip time (RTT) prediction system as part of an operator support system to reduce possible human errors and improve the safety of NPPs. The RTT prediction system consists of an algorithm that utilizes artificial intelligence (AI) and explainable AI (XAI) methods, such as autoencoders, light gradient-boosting machines, and Shapley additive explanations. AI methods provide diagnostic information about the abnormal states that occur and predict the remaining time until a reactor trip occurs. The XAI method improves the reliability of AI by providing a rationale for RTT prediction results and information on the main variables of the status of NPPs. The RTT prediction system includes an interface that can effectively provide the results of the system.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.27 no.4
• /
• pp.297-304
• /
• 2022

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.7
• /
• pp.105-111
• /
• 2018

Last March, the world Go competition between AlphaGo, AI Go program developed by Google Deep Mind and professional Go player Lee Sedol has shown us that the 4th industrial revolution using AI has come close. Especially, there ar many system combined with AI hae been developing including program for researching legal information, system for expecting jurisdiction, and processing big data, there is saying that even AI legal person is ready for its appearance. As legal field is mostly based on text-based document, such characteristic makes it easier to adopt artificial intelligence technology. When a legal person receives a case, the first thing to do is searching for legal information and judical precedent, which is the one of the strength of AI. It is very difficult for a human being to utilize a flow of legal knowledge and figures by analyzing them but for AI, this is nothing but a simple job. The ability of AI searching for regulation, precedent, and literature related to legal issue is way over our expectation. AI is evaluated to be able to review 1 billion pages of legal document per second and many people agree that lot of legal job will be replaced by AI. Along with development of AI service, legal service is becoming more advanced and if it devotes to ethical solving of legal issues, which is the final goal, not only the legal field but also it will help to gain nation's trust. If nations start to trust the legal service, it would never be completely replaced by AI. What is more, if it keeps offering advanced, ethical, and quick legal service, value of law devoting to the society will increase and finally, will make contribution to the nation. In this time where we have to compete with AI, we should try hard to increase value of traditional legal service provided by human. In the future, priority of good legal person will be his/her ability to use AI. The only field left to human will be understanding and recovering emotion of human caused by legal problem, which cannot be done by AI's controlling function. Then, what would be the attitude of legal people in this period? It would be to learn the new technology and applying in the field rather than going against it, this will be the way to survive in this new AI period.

• Journal of The Korean Association of Information Education
• /
• v.25 no.6
• /
• pp.973-985
• /
• 2021

The purpose of this study is to suggest the direction and implications of learning with AI in the future by analyzing the trends of research learning with AI in the field of education. For doing this, the final 78 papers published in domestic journals over the past three years from 2019 to July 2021 were selected for analysis through review. The analysis results are as follows. First of all, papers in 2020 among the three years were most published, and the most utilized research method was the qualitative research. In addition, according to the analysis by study subject, studies on elementary school students were the most common, followed by studies on college and graduate students. In the analysis by subject, research related to foreign language education was most utilized and chatbot was most used in the AI technology type. Finally, the research learning with AI accounted for the majority, and student support accounted for the majority as the type of education system learning with AI at the implementation stage among the areas of teaching and learning and evaluation. Based on these results, the direction and implications of learning with AI in the future were presented. This study is meaningful in that it grasped research trends of learning with AI in domestic from an overall perspective, and examined learning with AI focusing on the instructor-learner and the teaching and learning design process.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.1
• /
• pp.27-38
• /
• 2023

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user's characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.

• Journal of KIBIM
• /
• v.14 no.3
• /
• pp.22-30
• /
• 2024

Recently, LLM (Large Language Model), a rapidly developing generative AI technology, is receiving much attention in the smart construction field. This study proposes a methodology for implementing an knowledge expert system by linking BIM (Building Information Modeling), which supports data hub functions in the smart construction domain with LLM. In order to effectively utilize LLM in a BIM expert system, excessive model learning costs, BIM big data processing, and hallucination problems must be solved. This study proposes an LLM-based BIM expert system architecture that considers these problems. This study focuses on the RAG (Retrieval-Augmented Generation) document generation method and search algorithm for effective BIM data retrieval, with the goal of implementing an LLM-based BIM expert system within a small GPU resource. For performance comparison and analysis, a prototype of the designed system is developed, and implications to be considered when developing an LLM-based BIM expert system are derived.

• Safety and Health at Work
• /
• v.15 no.3
• /
• pp.373-378
• /
• 2024

Introduction: Following the Republic of Korea electric power industry site-specific safety management system, this paper proposes a novel safety autonomous platform (SAP) architecture that can automatically and precisely manage on-site safety through ensemble artificial intelligence (AI) models. The ensemble AI model was generated from video information and worker's biometric information as learning data and the estimation results of this model are based on standard operating procedures of the workplace and safety rules. Methods: The ensemble AI model is designed and implemented by the Hadoop ecosystem with Kafka/NiFi, Spark/Hive, HUE, and ELK (Elasticsearch, Logstash, Kibana). Results: The functional evaluation shows that the main function of this SAP architecture was operated successfully. Discussion: The proposed model is confirmed to work well with safety mobility gateways to provide some safety applications.