• Title/Summary/Keyword: 인공지능 개발자

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Development of AI-based Hemodialysis machine monitoring system (AI 기반의 혈액투석기 모니터링 시스템 개발)

  • Kim, Hong-youn;Kim, Seu-hong;Piao, Hai-lian
    • Annual Conference of KIPS
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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.

A Study on the Development of an Automatic Classification System for Life Safety Prevention Service Reporting Images through the Development of AI Learning Model and AI Model Serving Server (AI 학습모델 및 AI모델 서빙 서버 개발을 통한 생활안전 예방 서비스 신고 이미지 자동분류 시스템 개발에 대한 연구)

  • Young Sic Jeong;Yong-Woon Kim;Jeongil Yim
    • Journal of the Society of Disaster Information
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    • v.19 no.2
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    • pp.432-438
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    • 2023
  • Purpose: The purpose of this study is to enable users to conveniently report risks by automatically classifying risk categories in real time using AI for images reported in the life safety prevention service app. Method: Through a system consisting of a life safety prevention service platform, life safety prevention service app, AI model serving server and sftp server interconnected through the Internet, the reported life safety images are automatically classified in real time, and the AI model used at this time An AI learning algorithm for generation was also developed. Result: Images can be automatically classified by AI processing in real time, making it easier for reporters to report matters related to life safety.Conclusion: The AI image automatic classification system presented in this paper automatically classifies reported images in real time with a classification accuracy of over 90%, enabling reporters to easily report images related to life safety. It is necessary to develop faster and more accurate AI models and improve system processing capacity.

Case Study for the Application of PBL in Engineering School : Focused on an Artificial Intelligence Class (공과대학에서 문제중심학습 적용 사례 연구 : 인공지능 과목을 중심으로)

  • Lee, Keunsoo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.4
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    • pp.154-160
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    • 2018
  • This thesis aims to develop PBL (Problem-Based-Learning) problems. Its goal is for some groups of students to creative their own problems and to confirm the effectiveness of PBL as they apply it to AI (Artificial Intelligence) in engineering schools. Modern industrial society needs competent people who have abilities in cooperative learning, self-controlled learning, united knowledge application, and creative problem-solving. Universities need to offer their students the opportunity to improve their problem-solving and cooperative learning abilities in order to train the competent people that society demands. PBL activity is an appropriate learning method for the accomplishment of these goals. The study subjects are 37 sophomore students in H University who are studying 'AI'. Five PBL problems were submitted to the class over a period of 15 weeks. The students wrote and submitted a reflective journal after they finished each PBL activity. In addition, they filled out a class evaluation form to assess the performances of each member when the $5^{th}$ PBL problem activity was accomplished. The study shows that the students experienced the effectiveness of PBL in many fields, such as the comprehension of the studied contents (86.48%), comprehension of cooperative learning (94.59%), authentic experience (75.67%), problem-solving skills (89.18%), presentation skills (97.29%), creativity improvement (81.08%), knowledge acquisition ability (86.48%), communication ability (97.29%), united knowledge application (78.37%), self-directed study ability (86.48%) and confidence (97.29%). Through these methods, the students were able to realize that PBL learning activities play an important role in their learning. These methods prepare and enhance their ability to think creatively, work systematically and speak confidently as they learn to become competitive engineers equipped with the knowledge and skills that modern industrial society demands.

A COVID-19 Chest X-ray Reading Technique based on Deep Learning (딥 러닝 기반 코로나19 흉부 X선 판독 기법)

  • Ann, Kyung-Hee;Ohm, Seong-Yong
    • The Journal of the Convergence on Culture Technology
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    • v.6 no.4
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    • pp.789-795
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    • 2020
  • Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

Educational Industry Use of Metaverse Platform as a Learning Space in the Cultural Content Area (문화콘텐츠 영역에서 학습공간 메타버스 플랫폼의 교육산업적 활용)

  • Hye Kyoung Lee
    • Industry Promotion Research
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    • v.9 no.4
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    • pp.181-190
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    • 2024
  • This study aims to examine the educational use of the metaverse platform according to changes in the social environment. In addition, it will examine how and with what the learning space to be realized through artificial intelligence should be filled as the learner's demand and the reality of the education industry are intertwined.(Interestingly, in the virtual world, each person can create their own character and participate in educational activities by utilizing various contents.) To this end, it will explore materials expressed in various forms and add educational interpretation. In particular, it will examine literature and examples related to the metaverse platform to understand and explain its functioning method. As is well known, the 4th industrial revolution and the COVID-19 pandemic have presented a social environment for applying cutting-edge technologies using artificial intelligence to the field of education, even though they have surpassed the predictability of the development process. Edu-tech, which emerged in this situation, has not only developed the education industry but also transformed online spaces into actual educational spaces. In this reality, this study confirmed the possibility that the metaverse as an educational platform can reshape the field of the education industry by showing how it can provide differentiated experiences and values by utilizing cultural content developed through the complex application of various technologies. And this could be an alternative that can overcome the limitations of existing educational methods.

A Study on the Effectiveness of AI-based Learner-led Assessment in Elementary Software Education (초등 소프트웨어 교육에서 AI기반의 학습자 주도 평가의 효과성 고찰)

  • Shin, Heenam;Ahn, Sung Hun
    • Journal of Creative Information Culture
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    • v.7 no.3
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    • pp.177-185
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    • 2021
  • In future education, the paradigm of education is changing due to changes in learner-led and assessment methods. In addition, AI-based learning infrastructure and software education are increasingly needed. Thus, this study aims to examine the effectiveness of AI-based evaluation in future education by combining it with learner-led assessment. Using AI education and evaluation literature and Step 7 of the Learner-Driven Software Assessment Method, we sought to extract evaluation elements tailored to elementary school level in conjunction with the 2015 revised elementary practical course content elements, software understanding, procedural problem solving, and structural evaluation elements. In the future, we will develop a grading system that applies AI-based learner-led evaluation elements in software education and continuously demonstrate its effectiveness, and help the school site prepare for future education independently through AI-based learner-led assessment in software education.

A Study on Teaching the Method of Lagrange Multipliers in the Era of Digital Transformation (라그랑주 승수법의 교수·학습에 대한 소고: 라그랑주 승수법을 활용한 주성분 분석 사례)

  • Lee, Sang-Gu;Nam, Yun;Lee, Jae Hwa
    • Communications of Mathematical Education
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    • v.37 no.1
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    • pp.65-84
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    • 2023
  • The method of Lagrange multipliers, one of the most fundamental algorithms for solving equality constrained optimization problems, has been widely used in basic mathematics for artificial intelligence (AI), linear algebra, optimization theory, and control theory. This method is an important tool that connects calculus and linear algebra. It is actively used in artificial intelligence algorithms including principal component analysis (PCA). Therefore, it is desired that instructors motivate students who first encounter this method in college calculus. In this paper, we provide an integrated perspective for instructors to teach the method of Lagrange multipliers effectively. First, we provide visualization materials and Python-based code, helping to understand the principle of this method. Second, we give a full explanation on the relation between Lagrange multiplier and eigenvalues of a matrix. Third, we give the proof of the first-order optimality condition, which is a fundamental of the method of Lagrange multipliers, and briefly introduce the generalized version of it in optimization. Finally, we give an example of PCA analysis on a real data. These materials can be utilized in class for teaching of the method of Lagrange multipliers.

A Study on the Improvement Scheme of University's Software Education

  • Lee, Won Joo
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.3
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    • pp.243-250
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    • 2020
  • In this paper, we propose an effective software education scheme for universities. The key idea of this software education scheme is to analyze software curriculum of QS world university rankings Top 10, SW-oriented university, and regional main national university. And based on the results, we propose five improvements for the effective SW education method of universities. The first is to enhance the adaptability of the industry by developing courses based on the SW developer's job analysis in the curriculum development process. Second, it is necessary to strengthen the curriculum of the 4th industrial revolution core technologies(cloud computing, big data, virtual/augmented reality, Internet of things, etc.) and integrate them with various fields such as medical, bio, sensor, human, and cognitive science. Third, programming language education should be included in software convergence course after basic syntax education to implement projects in various fields. In addition, the curriculum for developing system programming developers and back-end developers should be strengthened rather than application program developers. Fourth, it offers opportunities to participate in industrial projects by reinforcing courses such as capstone design and comprehensive design, which enables product-based self-directed learning. Fifth, it is necessary to develop university-specific curriculum based on local industry by reinforcing internship or industry-academic program that can acquire skills in local industry field.

What factors drive AI project success? (무엇이 AI 프로젝트를 성공적으로 이끄는가?)

  • KyeSook Kim;Hyunchul Ahn
    • Journal of Intelligence and Information Systems
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    • v.29 no.1
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    • pp.327-351
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    • 2023
  • This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

PCB Level EMC Expert System의 소개와 연구동향

  • 곽호철;조성건;조일제
    • The Proceeding of the Korean Institute of Electromagnetic Engineering and Science
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    • v.14 no.3
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    • pp.103-111
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    • 2003
  • 전자/통신기기에 대한 전자파 장해(EMl/EMS) 문제를 제품개발 기간 내에 완전하게 해결하기란 이론만큼 쉽지 않으며, 전자기적 적합성(EMC)에 대한 지식이 충분하지 못한 회로/기구 설계자들은 전자 파장해 문제를 반복적인 설계 수정 및 디버깅 작업으로 밖에 해결할 수 없는 골치 아픈 Black Magic으로 생각하고 있다. 그러나 분명히 전자기적 간섭(EMI) 문제도 이론 및 해석적인 접근으로 그 해답을 충분히 찾을 수가 있다, 본 고에서는 이러한 PCB Level에서의 전자파장해 문제를 해결하기 위한 체계적인 접근 방법과 오랜 현장 경험에서 나오는 EMC 전문가의 경험적인 지식을 통합한 인공 지능형 EMC 전문가 시스템에 대한 소개와 연구개발 동향 및 극복 과제 등에 대해서 기술하고자 한다.