• 제목/요약/키워드: Machine Learning Education

검색결과 324건 처리시간 0.027초

Empirical evaluations for predicting the damage of FRC wall subjected to close-in explosions

  • Duc-Kien Thai;Thai-Hoan Pham;Duy-Liem Nguyen;Tran Minh Tu;Phan Van Tien
    • Steel and Composite Structures
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    • 제49권1호
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    • pp.65-79
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    • 2023
  • This paper presents a development of empirical evaluations, which can be used to evaluate the damage of fiber-reinforced concrete composites (FRC) wall subjected to close-in blast loads. For this development, a combined application of numerical simulation and machine learning approaches are employed. First, finite element modeling of FRC wall under blast loading is developed and verified using experimental data. Numerical analyses are then carried out to investigate the dynamic behavior of the FRC wall under blast loading. In addition, a data set of 384 samples on the damage of FRC wall due to blast loads is then produced in order to develop machine learning models. Second, three robust machine learning models of Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) are employed to propose empirical evaluations for predicting the damage of FRC wall. The proposed empirical evaluations are very useful for practical evaluation and design of FRC wall subjected to blast loads.

대학 방사선학과 인공지능 교육 활성화를 위한 Orange 플랫폼 이용 사례 (Utilizing the Orange Platform for Enhancing Artificial Intelligence Education in the Department of Radiological Science at Universities)

  • 최경호
    • 대한방사선기술학회지:방사선기술과학
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    • 제47권4호
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    • pp.255-262
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    • 2024
  • Although a universally accepted definition of artificial intelligence (AI) remains elusive, the terminology has gained widespread familiarity owing to its pervasive integration across diverse domains in our daily lives. The application of AI in healthcare, notably in radiographic imaging, is no longer a matter of science fiction but a reality. Consequently, AI education has emerged as an indispensable requirement for radiological technologists responsible for the field of radiology. This paper underscores this imperative and advocates for the incorporation of AI education, using the Orange platform in university radiology department as part of the solution. Furthermore, this paper presents a case study featuring machine learning analysis using structured data on exposure doses for radiation related workers and unstructured data consisting of X-ray data encompassing 69 COVID-19-infected cases and 25 individuals with normal findings. The emphasized importance of AI education for radiology professionals in this research is expected to contribute to the job stability of radiologic practitioners in the future.

교육대학원에서의 인공지능 교과목 운영 사례 (A Case Study of Artificial Intelligence Education Course for Graduate School of Education)

  • 한규정
    • 정보교육학회논문지
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    • 제25권5호
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    • pp.673-681
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    • 2021
  • 본 연구는 교육대학원에서의 인공지능 교육 과목의 운영사례이다. 교육 과정은 머신러닝의 이해와 실습, 데이터 분석, 엔트리를 이용한 인공지능의 실제, 인공지능과 피지컬 컴퓨팅 등으로 구성되었다. 교육효과에 대한 설문 조사 결과, 수강생들은 초등학교 현장으로의 적용 용이성과 수업 우선순위로 엔트리 인공지능 블록의 활용, 피지컬 컴퓨팅 도구로써 대장장이 보드의 활용을 선호하였다. 데이터 분석 영역은 수학교과의 데이터와 그래프 교육과의 연계 등에서 그 효과성이 있으며. 피지컬 컴퓨팅 도구로 허스키 렌즈는 고유의 이미지 처리 기능을 활용하면 자율주행차 메이커 교육에 유용한 것으로 나타났다. 바람직한 인공지능교육으로는 수준별 교육과정, 데이터 수집 및 분석 교육의 강화 등이 요구되었다.

Machine Learning Algorithm Accuracy for Code-Switching Analytics in Detecting Mood

  • Latib, Latifah Abd;Subramaniam, Hema;Ramli, Siti Khadijah;Ali, Affezah;Yulia, Astri;Shahdan, Tengku Shahrom Tengku;Zulkefly, Nor Sheereen
    • International Journal of Computer Science & Network Security
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    • 제22권9호
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    • pp.334-342
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    • 2022
  • Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

Global Citizenship Education(GCED) and Engineering for Non-Majors Convergence D-SteamRobot(DSR) Educational Model

  • Kibbm Lee;Seok-Jae Moon
    • International Journal of Advanced Culture Technology
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    • 제11권1호
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    • pp.312-319
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    • 2023
  • This study aims to enhance the engineering education for non-majors by incorporating the concept of Global Citizenship Education and addressing the need for education that responds to climate and ecological changes. The study uses robot programming as a tool to foster the development of global citizens. Non-majors often struggle with producing more than just motionless forms or solid productions, due to a lack of understanding of mechanisms and coding. The study proposes the use of the Convergence D-SteamRobot (DSR) to address this issue by blending humanities and engineering. This is achieved by presenting problems through books to increase empathy, integrating simple machine mechanisms, and creating prototypes to solve self-defined problems. Through this process, learners determine the SDGs topic they want to solve and learn about the simple mechanical mechanism involved in producing the prototype. The educational model provides a constructivist learning environment that emphasizes empathy and exploration, encourages peer-learning, and improves divergent thinking and problem-solving skills.

교육 대학원에서의 인공지능 교육 사례 (A Case Study of Artificial Intelligence Education for Graduate School of Education)

  • 한규정
    • 한국정보교육학회:학술대회논문집
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    • 한국정보교육학회 2021년도 학술논문집
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    • pp.401-409
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    • 2021
  • 본 연구는 교육 대학원의 인공 지능 교육 과목의 운영사례이다. 주요 교육내용은 머신러닝의 이해와 실습, 데이터 분석, 엔트리를 이용한 인공지능의 실제, 인공지능과 피지컬 컴퓨팅 등으로 구성되었다. 교육과정 적용후 교육효과에 대한 설문 조사 결과, 수강생들은 초등교육 현장에 적용 용이성 등을 고려하여 우선순위로 엔트리 인공지능 블록의 활용, 피지컬 컴퓨팅 도구로써 대장장이 보드의 활용 등을 선호함을 알 수 있었다. 데이터 분석 영역은 수학교과의 데이터와 그래프 교육과의 연계 등에서 그 효과성이 있으며. 피지컬 컴퓨팅 도구로 허스키 렌즈는 고유의 이미지 처리 기능을 활용하면 자율주행차 메이커 교육에 유용하다고 하였다. 그 외의 바람직한 인공지능교육으로 수준별 교육과정, 데이터 수집 및 분석 교육의 강화 등의 필요성이 대두되었다.

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공업계 고등학교를 위한 전자신문활용교육 학습 모형의 설계 및 구현 (Design and Implementation of an e-NIE Learning Model for Technical High Schools)

  • 강오한;이경환
    • 한국산업정보학회논문지
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    • 제11권2호
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    • pp.18-28
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    • 2006
  • 최근에 새로운 교수 학습 방법의 하나로 대두되고 있는 e-NIE는 학습자 중심의 흥미와 적성, 창의성 개발, 비판적 사고력의 함양을 통한 문제해결능력과 의사결정 능력을 키워 준다. 본 논문에서는 공업계 고등학교 전기과의 '전기 전자 측정' 과목 중에서 'I. 측정 일반' 단원을 중심으로 보충 심화 학습지를 개발하였으며, 이를 활용한 e-NIE 수업 모형을 설계하고 구현하였다. 본 논문에서 제안한 e-NIE 수업 모형의 효과를 검증하기 위하여 통제 집단에게는 전통적 수업을 실시하고 실험 집단에게는 e-NIE 수업을 실시한 후 결과를 분석하였다. 그 결과 e-NIE 수업을 실시한 학습자들이 학습 동기, 학습 태도, 자기 주도적 탐구력 분야에서 긍정적인 효과가 있는 것으로 확인되었다.

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A Comprehensive Literature Study on Precision Agriculture: Tools and Techniques

  • Bh., Prashanthi;A.V. Praveen, Krishna;Ch. Mallikarjuna, Rao
    • International Journal of Computer Science & Network Security
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    • 제22권12호
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    • pp.229-238
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    • 2022
  • Due to digitization, data has become a tsunami in almost every data-driven business sector. The information wave has been greatly boosted by man-to-machine (M2M) digital data management. An explosion in the use of ICT for farm management has pushed technical solutions into rural areas and benefited farmers and customers alike. This study discusses the benefits and possible pitfalls of using information and communication technology (ICT) in conventional farming. Information technology (IT), the Internet of Things (IoT), and robotics are discussed, along with the roles of Machine learning (ML), Artificial intelligence (AI), and sensors in farming. Drones are also being studied for crop surveillance and yield optimization management. Global and state-of-the-art Internet of Things (IoT) agricultural platforms are emphasized when relevant. This article analyse the most current publications pertaining to precision agriculture using ML and AI techniques. This study further details about current and future developments in AI and identify existing and prospective research concerns in AI for agriculture based on this thorough extensive literature evaluation.

Remarks on Education Method to Turn Failure Experience to Instructions for Engineering Design

  • Arimitsu, Yutaka;Yagi, Hidetsugu
    • 공학교육연구
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    • 제13권2호
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    • pp.74-77
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    • 2010
  • This article proposes to examine how the study of failure differs from other technical subjects, and how to turn failure experiences to one's advantage. The authors surveyed the properties of failures in PBL (Project Based Learning) and also examined students' interest and understanding of failure, after introducing failure examples. To investigate how students communicate failure experiences to third parties, reports of the failure experience in PBL were evaluated. From above mentioned surveys, we get the following results. The typical causes of failure in educational institutions are lack of skill in manufacturing and inadequate planning, which conversely are minor causes of failure in the industry. A knowledge database on failure, employed commonly in industry, is not effective in PBL, because projects in educational institutes are usually changed every year. Case studies in failure can be approached from many points of view including causes, processes, effects and safety measures. While teachers should emphasize the notable points in the failure examples in introducing examples of specific topics in machine design, teachers should explain the multiple aspects in the failure examples to educate students about the complexity of actual accidents.

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기계 학습 방법을 이용한 직장 생활 프로파일 기반의 퇴직 예측 모델 개발 (Development of Retirement Prediction Model based on Work Life Profile Using Machine Learning Method)

  • 윤유동;이설화;지혜성;임희석
    • 컴퓨터교육학회논문지
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    • 제20권1호
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    • pp.87-97
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    • 2017
  • 최근 대부분의 기업에서 인적 자원의 유출이 조직에 미칠 부정적인 영향을 인지하게 되면서 조직 구성원의 이직 및 퇴직의도에 대해 많은 연구가 이루어졌다. 그러나 대부분 설문조사의 형태로 이루어지며, 직장 생활 데이터를 기반으로 이직 또는 퇴직의도를 살펴본 연구는 아직까지 미비했다. 이에 본 연구에서는 직장 생활 프로파일을 기반으로 직원의 퇴직 여부에 영향을 미치는 요인에 대한 분석을 실시하고, 기계 학습 방법을 활용하여 퇴직 예측 모델을 생성했다. 이 결과, 기존의 설문조사를 중심으로 수행되었던 연구에서 접근하지 못했던 다양한 요인들을 파악할 수 있었다. 또한, 우수한 성능의 퇴직 예측 모델 생성을 통해 기업의 인적 자원 유출에 대한 해결방안을 제시할 수 있는 연구의 발판을 마련했다.