• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.701-702
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• 2013

전국 50여개의 AI(Artificial Insemination)센터는 국내 양돈산업 인공수정 기술을 개발하고 보급하는 중추적인 역할을 수행하고 있다. 이에 반해 AI센터의 숫자 규모는 전국적으로 매우 제한되어 있어, AI센터의 운영 및 AI 기술에 대한 정보관리는 각 센터별 독자적인 운영시스템으로 관리되어 상호 정보융합을 통한 양돈산업 발전에 활용되는 사례가 매우 적다. 또한 개별 AI센터에서 관리하고 있는 소비자들의 지역분포도가 매우 폭넓어 실시간으로 수요자에 대한 판매 관리정보를 제공함에 있어 그 한계를 가지고 있다. 이에 본 논문에서는 전국의 AI센터 관리운영에 통합적이고 효율성을 지원할 수 있는 모바일 웹기반 개선된 AI PigMoS(Pig Monitoring System, PigMoS) 시스템을 제안하고 구현하였다. 본 논문에서 제안한 모바일 웹기반 개선된 AI PigMoS 시스템은 이동성, 실시간 정보서비스 등에 해당되는 시스템 모듈을 모바일 웹을 기반으로 구현하여 개별 AI센터에서 운영할 수 있게 하였다. 이에 본 논문에서는 기존의 AI PigMoS 시스템을 개선하여 재구축하였으며, 이동성, 실시간 정보서비스 등이 필요한 모듈을 중심으로 모바일 기능을 설계하고 구현하여, 원거리 소비자들에게 실시간으로 생성된 AI정보를 제공하여 AI센터의 정보관리 효율성과 경쟁력 향상을 높일 것으로 기대한다.

• Journal of Engineering Education Research
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• v.17 no.4
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• pp.15-20
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• 2014

The non-contact easy detecting system of nursing person's body vital information and their behaviors monitoring system are developed, which consist of "Kinect" sensor and thermography camera. The "Kinect" sensor can catch the body contour and the body moving behavior, and output their imaging data realtime. The thermography camera can detect respiration state and body temperature, etc. In this study, the practicability of this system was verified.

• 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 the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.642-648
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• 2019

Vessels sail according to the COLREG to prevent a collision. However, it is difficult to apply COLREG under special situation as heavy traffic, at this time personal skills of the operator are required. In this case, traffic control is required through the maritime traffic monitoring system. Therefore, maritime traffic management is globally implemented by VTS. In this system, VTS of icer uses the VTS system to assess risks and recommends possible safety operation to vessels with radio systems. This study considers that the risk analysis method with AI (Artificial Intelligence) technology from the operator's aspect. In addition, the research explains the Maritime Traffic Safety Monitoring System, Including AR (Augmented Reality) technology to increase vessel control efficiency. This system is able to predict hazards and risk priorities, and it leads to sequential elimination of dangerous situations. Especially, the hazard situations can be analyzed from operator's perspective of each vessel instead of the VTS officer's aspect, which is more practical than the conventional method. Furthermore, the result of analysis enables to comprehend quantitative hazardous areas and support recommended routes to avoid a collision. As a result, I firmly believe that the system will support to prevent a collision in complex traffic waters. In particular, it could be adopted as a collision prevention system for Maritime Autonomous Surface Ship, which occupies a significant proportion in Maritime 4th industrial revolution.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.473-474
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• 2011

본 논문은 해상 안전과 보안등의 목적으로 선박에 설치되어 사용 중인 AIS(Automation Identification System)를 이용하여 선박의 위치를 모니터링하는 시스템의 구현에 대해 기술한다. 이 시스템은 웹기반으로 구현되었으며 위성통신을 이용하는 VMS (Vessel Monitoring System)에 비해 경제적인 구현이 가능하다.

• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.445-446
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• 2022

In this paper, in order to improve port work preparation and berth scheduling efficiency in an artificial intelligence-based berthing monitoring system that can monitor the ship's berthing process, we develop a mooring line recognition model to check an exact berthing time. By improving the pre-designed AI model, it is possible to segment the mooring line from the input image, and to recognize when the mooring line arrives or falls on the berth, thereby providing the correct ship's berthing time. The proposed AI model confirmed by the results that mooring line recognition is possible with evaluation data about the actual berthing situation.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.107-108
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• 2023

본 논문에서는 교량의 변위를 IoT 장치를 이용하여 실시간 측정하고 추출된 데이터를 이용하여 교량의 이상징후를 AI 기반으로 진단 및 모니터링 하는 방법을 제안한다. AI 모델 학습 학습을 위해서 비정상 상태의 교량이 필요하지만, 실제 교량에 인위적으로 비정상 상태를 만들 수 없으므로, 탄성 받침을 이용하여 모의 교량을 제작하였다. 탄성 받침을 이용하여 제작에 반영 및 모의교량에 적합한 모의 차량도 제작하여 정상적 데이터와 비정상적 데이터를 수집하였다. 수집된 데이터를 전처리 과정을 통해 AI 분석을 통해 교량의 이상 징후를 진단 및 모니터링하였으며, 제안 모델을 실험한 결과 96.7%의 정확도가 도출되었다.