• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.753-755
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• 2021

본 논문에서 기존의 치아가공기는 회전하는 모터를 사용하여 구성하였으나 이러한 모터는 정밀도, 반복정밀도가 50um 이하로 가공물 가공시에 치기공사나 치과의사가 사람에 맞추어 다시 작업을 해야하는 불편함과 시간적, 작업자의 피로도를 높일수 있는데 이러한 모터에 스크류나 밸트를 연결하여 선형적으로 움직일 수 있는 리니어모듈과 리니어모터를 적용하게되면 20um수준의 고정밀의 위치제어가 가능한 5축 치아가공기를 만들 수 있었다. 또한 MEMS센서를 이용하여 스핀들의 상태를 모니터링 하고 임계값을 지정하여 이상 신호 발생시 모터를 멈추어 위험상황에 대해서 인공지능기법을 이용하여 정지하거나 관리자에게 알림을 주어 효과적으로 5축치아가공기를 운영할 수 있도록 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.282-283
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• 2022

본 논문에서 기존의 혈액투석기는 회전하는 모터를 사용하여 구성하였으나 이러한 모터는 정밀도, 반복정밀도가 50um 이하로 가공물 가공시에 치기공사나 치과의사가 사람에 맞추어 다시 작업을 해야하는 불편함과 시간적, 작업자의 피로도를 높일수 있는데 이러한 모터에 스크류나 밸트를 연결하여 선형적으로 움직일 수 있는 리니어모듈과 리니어모터를 적용하게되면 20um수준의 고정밀의 위치 제어가 가능한 혈액투석기를 만들 수 있었다. 또한 MEMS센서를 이용하여 모터의 상태를 모니터링하고 임계값을 지정하여 이상 신호 발생시 모터를 멈추어 위험상황에 대해서 인공지능기법을 이용하여 정지하거나 관리자에게 알림을 주어 효과적으로 혈액투석기를 운영할 수 있도록 하였다.

• Korean Journal of Ecology and Environment
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• v.54 no.3
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• pp.190-198
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• 2021

Human Aichivirus (Aichivirus A; AiV-A) is a positive-sense single-strand RNA non-enveloped virus that has been detected worldwide in various water environments including sewage, river, surface, and ground over the past decade. To develop a method with excellent sensitivity and specificity for AiV-A diagnosis from water environments such as groundwater, a combination capable of reverse transcription (RT)-nested polymerase chain reaction (PCR) was developed based on existing reported and newly designed primers. A selective method was applied to evaluate domestic drinking groundwater samples. Thus, a procedure was devised to select and subsequently identify RT-nested PCR primer sets that can successfully detect and identify AiV-A from groundwater samples. The findings will contribute to developing a better monitoring system to detect AiV-A contamination in water environments such as groundwater.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.1-7
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• 2022

Explainable AI (XAI) is an approach that leverages artificial intelligence to support human decision-making. Recently, governments of several countries including Korea are attempting objective evidence-based analyses of R&D investments with returns by analyzing quantitative data. Over the past decade, governments have invested in relevant researches, allowing government officials to gain insights to help them evaluate past performances and discuss future policy directions. Compared to the size that has not been used yet, the utilization of the text information (accumulated in national DBs) so far is low level. The current study utilizes a text mining strategy for monitoring innovations along with a case study of smart-farms in the Honam region.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.22-31
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• 2023

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1615-1622
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• 2016

This research aims to develop a voyage performance monitoring system based on international standards. The developed system is equipped with an electronic navigational chart(ENC) that provides onshore and offshore information, as well as supports standardized interfaces with navigational equipment, such as a gyro compass, a differential global positioning system(DGPS), and an automatic identification system(AIS), to monitor the navigation route in real time. In addition, the proposed system adopts a car navigation system to provide a graphical user interface, an intuitive menu-driven configuration, and an easy guide for safer sea navigation. The system, interfaced with the gyro compass and DGPS, was verified without any data loss, and passed a test conducted under extreme conditions by the Korea Laboratory Accreditation Scheme(KOLAS). Finally, the system contributes to preventing collision of vessels and minimizing casualties by maximizing the convenience of mariners which a conventional system does not provide.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.342-342
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• 2020

최근 소형 무인비행장치(UAV; unmaned aerial vehicle)인 드론을 이용한 신산업 육성 및 지원에 관한 관심도가 높아지고 있다. 국외에서는 이미 드론을 이용한 농업관리와 물류배송, 공공부문 모니터링 등 다양한 산업 분야의 드론 이용을 적극 장려하고 있다. 드론 이용에 관한 관심도가 높아짐에 따라 국내외적으로 드론 응용 관련 기술 개발과 연구가 활발하게 진행되고 있지만, 국내에서는 환경모니터링과 시설물 점검 등 일부 제한적으로 활용되고 있다. 국내에서는 2024년까지 드론 응용서비스로 확장되는 산업 변화에 대응, DNA(Data, Network, AI) 기술을 접목한 새로운 개방형 플랫폼 구축을 목표로 기술개발 및 산업 육성을 촉진하고 있다. 이러한 국내 기술 개발 방향에 맞추어 드론과 첨단기술을 이용한 하천조사와 관련해 드론을 연계한 하천관리 플랫폼 개발의 필요성이 높아지고 있다. 본 연구에서는 드론 기반 하천조사 및 모니터링 수행을 위한 하천관리 운영플랫폼 개발을 목표로 국내외 요소기술을 분석하고 기술수준을 조사했다. 특히, 드론 기반 하천관리에 필요한 임무를 영역별로 분리해 요소기술 기반의 플랫폼 서비스를 정의하고 하천관리 부문 개방형 플랫폼 구축을 위한 시스템 구성 및 운영에 필요한 요소기술을 선정했다. 최종적으로 선정된 플랫폼 서비스와 요소기술을 기초로 시스템 적용방안을 검토하고 하천관리 운영플랫폼 구축을 위한 시스템 아키텍처를 정의 및 설계했다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.633-640
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• 2024

This study develops AI-based 3D body shape modeling technology that can be utilized in the healthcare sector, proposing a system that enables monitoring of users' body shape changes and health status. Utilizing data from Size Korea, the study developed a model to generate 3D body shape images from 2D images, and compared various models to select the one with the best performance. Ultimately, by proposing a system process through the developed technology, including personalized health management, exercise recommendations, and dietary suggestions, the study aims to contribute to disease prevention and health promotion.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.381-394
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• 2022

Recently, due to the personalization and customization of data, Internet-based services have increased requirements for real-time processing, such as real-time AI inference and data analysis, which must be handled immediately according to the user's situation or requirement. Real-time tasks have a set deadline from the start of each task to the return of the results, and the guarantee of the deadline is directly linked to the quality of the services. However, traditional container systems are limited in operating real-time tasks because they do not provide the ability to allocate and manage deadlines for tasks executed in containers. In addition, tasks such as AI inference and data analysis basically utilize graphical processing units (GPU), which typically have performance impacts on each other because performance isolation is not provided between containers. And the resource usage of the node alone cannot determine the deadline guarantee rate of each container or whether to deploy a new real-time container. In this paper, we propose a monitoring technique for tracking and managing the execution status of deadlines and real-time GPU tasks in containers to support real-time processing of GPU tasks running on containers, and a node list management technique for container placement on appropriate nodes to ensure deadlines. Furthermore, we demonstrate from experiments that the proposed technique has a very small impact on the system.