Artificial intelligence-enabled evaluating for computer-aided drawings (AMCAD)

被引:1
|
作者
Jianwu, Lisa Wang [1 ,2 ]
Yew, Lim See [1 ]
On, Lin Kok [1 ]
Keong, Tan Chee [1 ]
Sheng, Ricky Tan Yuan [1 ]
Sani, Sairin Bin [1 ]
Agnes, Tan Hwee Juan [1 ]
机构
[1] ITE Coll Cent, Sch Engn, Singapore, Singapore
[2] ITE Coll Cent, Sch Engn, 2 Ang Mo Kio Dr, Singapore 567720, Singapore
关键词
CAD exercises; automatic evaluating; automatic assessing; artificial intelligence; computer vision;
D O I
10.1177/03064190231175231
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
AMCAD (www.amcad.ai) is a web-enabled artificial intelligence (AI) system created to auto-evaluate students' computer-aided drawing (CAD) work assignments and help students in self-directed learning. It compares two engineering drawings and highlights the differences (errors) between the two. The errors are classified into two categories: "Missing Line" errors shown in red and "Erroneous Line" errors in blue. AMCAD works in tandem with all CAD software. It uses computer vision technology to convert PDF drawings to raster subimages, and XOR to evaluate quantitatively the best-matched with best-aligned views. AMCAD, whose AI algorithms and web application were developed in Python, shows the comparison results in two display modes: superimpose and side-by-side. To encourage students in self-directed leaning, instructional videos can be included in each CAD work assignment.
引用
收藏
页码:3 / 31
页数:29
相关论文
共 50 条
  • [1] Artificial Intelligence for Computer-Aided Drug Discovery
    Kate, Aditya
    Seth, Ekkita
    Singh, Ananya
    Chakole, Chandrashekhar Mahadeo
    Chauhan, Meenakshi Kanwar
    Singh, Ravi Kant
    Maddalwar, Shrirang
    Mishra, Mohit
    DRUG RESEARCH, 2023, 73 (07) : 369 - 377
  • [2] Artificial intelligence-enabled smart city construction
    Jiang, Yanxu
    Han, Linfei
    Gao, Yifang
    JOURNAL OF SUPERCOMPUTING, 2022, 78 (18) : 19501 - 19521
  • [3] Artificial intelligence-enabled smart city construction
    Yanxu Jiang
    Linfei Han
    Yifang Gao
    The Journal of Supercomputing, 2022, 78 : 19501 - 19521
  • [4] Prediction of certainty in artificial intelligence-enabled electrocardiography
    Demolder, Anthony
    Nauwynck, Maxime
    De Pauw, Michel
    De Buyzere, Marc
    Duytschaever, Mattias
    Timmermans, Frank
    De Pooter, Jan
    JOURNAL OF ELECTROCARDIOLOGY, 2024, 83 : 71 - 79
  • [5] A BREAKTHROUGH IN ARTIFICIAL INTELLIGENCE-ENABLED MATERIALS DISCOVERY
    Bailey, Mary Page
    Chemical Engineering (United States), 2021, 128 (01):
  • [6] Clinical Evaluation of Artificial Intelligence-Enabled Interventions
    Hogg, H. D. Jeffry
    Martindale, Alexander P. L.
    Liu, Xiaoxuan
    Denniston, Alastair K.
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2024, 65 (10)
  • [7] Artificial Intelligence-Enabled Traffic Monitoring System
    Mandal, Vishal
    Mussah, Abdul Rashid
    Jin, Peng
    Adu-Gyamfi, Yaw
    SUSTAINABILITY, 2020, 12 (21) : 1 - 21
  • [8] Smart Infrastructures: Artificial Intelligence-Enabled Lifecycle Automation
    Fortuna, Carolina
    Yetgin, Halil
    Mohorcic, Mihael
    IEEE INDUSTRIAL ELECTRONICS MAGAZINE, 2023, 17 (02) : 37 - 47
  • [9] Clinical perspectives on the adoption of the artificial intelligence-enabled electrocardiogram
    Khurshid, Shaan
    JOURNAL OF ELECTROCARDIOLOGY, 2023, 81 : 142 - 145
  • [10] Computer-aided detection in chest radiography based on artificial intelligence: a survey
    Qin, Chunli
    Yao, Demin
    Shi, Yonghong
    Song, Zhijian
    BIOMEDICAL ENGINEERING ONLINE, 2018, 17