최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기소성가공 = Transactions of materials processing : Journal of the Korean society for technology of plastics, v.33 no.2, 2024년, pp.132 - 150
김영석 (경북대학교 기계공학부)
초록이 없습니다.
Y. LeCun, Y. Bengio, Geoffrey Hinton, Deep?learning, Review Nature. 28(521) (2015) 436-444,?https://doi.org/10.1038/nature14539
D. P. Kroese, et al, 2023, Data Science and Machine?Learning; Mathematical and Statistical Methods,?ISBN-13978-1138492530
M. di Nuzzo, 2021, Data Science and Machine?Learning: From Data to Knowledge,?ISBN-13979-8779849456
V.N. Vapnik, 1998, Statistical Learning Theory. New?York: Wiley, ISBN-978-0471030034
E. Alpaydin, 2016, Introduction to Machine Learning.?The MIT Press, Cambridge, Massachusetts, USA;?London, England, 3rd edn.
S.W. Bae, J.S. Yu, Estimation of the apartment?housing price using the machine learning methods:?The case of Gangnam-gu, Seoul, J. Korea Real?Estate Analysts Assoc., 24(1) (2018), 69-85,?http://dx.doi.org/10.19172/KREAA.24.1.5
T. Hastie, R. Tibshirani, J. Friedman, 2009, The?elements of statistical learning, data mining,?inference, and prediction, Springer 2nd Ed.,?ISBN 978-0-387-84857-0
Y.S. Kim, J.J. Kim, Basics of Artificial Neural?Network and its Applications to Material Forming?Process I, Trans. Mater. Process., 30(4) (2021), 201-210.
Y.S. Kim, J.J. Kim, Basics of Artificial Neural?Network and its Applications to Material Forming?Process II, Trans. Mater. Process., 30(6) (2021),?311-322.
M. Soori et. al., Machine learning and artificial?intelligence in CNC machine tools, A review,?https://doi.org/10.1016/j.smse.2023.100009
V.C. Do, Y.S. Kim et al., Effect of hole lancing on?the forming characteristics of single point?incremental forming, Procedia Engng., 84(2017) 35-42, https://doi.org/10.1016/j.proeng.2017.04.068
Pham Q. Tuan, Y.S. Kim et al., A machine learning-based methodology for identification of the plastic?flow in aluminum sheets during incremental sheet?forming processes, Int. J. Adv. Manuf. Technol., 120?(2022) 3559-3584,?https://doi.org/10.1007/s00170-022-08698-z
H. Salmenjoki, M. J. Alava, L. Laurson, Machine?learning plastic deformation of crystals, Nature?Communications, 9, (2018) 5307,?https://doi.org/10.1038/s41467-018-07737-2
T. Tancogne-Dejean et al., Recurrent neural network?modeling of the large deformation of lithium-ion?battery cells, Int. J. Plast., 146 (2021), 103072,?https://doi.org/10.1016/j.ijplas.2021.103072
X. Chen et al., Research on fault early warning and?the diagnosis of machine tools based on energy fault?tree analysis, Proc. IMech. Engng., Part B: J Eng.?Manuf., 233(11) (2019) 2147-2159,?https://doi.org/10.1177/0954405418816848
F. Tao et al, Data-driven smart manufacturing, J.?Manuf. Systems-C, 48(7) (2018), 157-169,?https://doi.org/10.1016/j.jmsy.2018.01.006
www.kamp-ai.kr
Ministry of SMEs and Startups, Precision processing?resource optimization AI dataset, analysis practice?guidebook, 2021.
Y. Freund, R.E. Schapire. A decision-theoretic?generalization of on-line learning and an application?to boosting. J. Comp. Syst. Sci., 55(1), (1997) 119-139, https://doi.org/10.1006/jcss.1997.1504
Y. Freund, R.E. Schapire., A short introduction to?boosting, J. Japan. Soc. Artificial Intell., 14(5)?(1999) 771-780 (In Japanese, translation by Naoki?Abe.)
Y.S. Kim, Review paper for key algorithms of?machine learning and its application to material?processing problems I, Trans. Mater. Process., 33(1) (2024), 50-67.
Andrew Ng,?https://www.coursera.org/learn/machine-learning
J.H. Friedman, Greedy function approximation: A?gradient boosting machine, Ann. Statist. 29 (5) 1189-1232, https://doi.org/10.1214/aos/1013203451
C. Cortes, V. Vapnik, Support-vector networks,?Machine Learning, 20, (1995), 273-297,?https://doi.org/10.1007/BF00994018
F. Parrella, On-line support vector regression, Thesis,?Department of Information Science Univ. Genoa?Italy June 2007.
A.J. Smola, B. Scholkopf, 1998. A Tutorial on?Support Vector Regression, NeuroCOLT Technical?Report NC-TR-98-030, Royal Holloway College,?Univ. London, UK.
C. Kubik et al., Smart sheet metal forming:?importance of data acquisition, preprocessing and?transformation on the performance of a multiclass?support vector machine for predicting wear states?during blanking , J. Intell. Manuf., 33 (2022) 259-282, https://doi.org/10.1007/s10845-021-01789-w
P.E. Romero et al., Use of the support vector?machine (SVM) algorithm to predict geometrical?accuracy in the manufacture of molds via single?point incremental forming (SPIF) using aluminized?steel sheets, J. Mater. Res. Technol., 15 (2021),?1562-1571,?https://doi.org/10.1016/j.jmrt.2021.08.155
S.M. Najm, I. Paniti, Predict the effects of forming?tool characteristics on surface roughness of?aluminum foil components formed by SPIF using?ANN and SVR. Int. J. Prec. Engng. Manuf., 22(1),?(2021) 13-26,?https:// doi.org/10.1007/s12541-020-00434-5
S. M. Najm, I. Paniti, Investigation and machine?learning-based prediction of parametric effects of?single point incremental forming on pillow effect?and wall profile of AlMn1Mg1 aluminum alloy?sheets, J. Intell. Manuf. 34 (2023) 331-367,?https://doi.org/10.1007/s10845-022-02026-8
J. Wang et al., A neural networks approach to?investigating the geometrical influence on wrinkling?in sheet metal forming. J. Mater. Process. Technol.?105, (2000), 215-220,?https://doi.org/10.1016/S0924-0136(00)00534-3
F. Kara et al., ANN and multiple regression method-based modelling of cutting forces in orthogonal?machining of AISI 316L stainless steel, Neural?Comput. Appl., (2014)?https://doi.org/10.1007/s00521-014-1721-y
J.Y. Lee, Technology for collecting, processing,?analyzing, and utilizing data for intelligent die-casting processes, J. Korean. Soc. Manuf. Eng.,?29(6) (2020), 441-448,?https://doi.org/10.7735/ksmte.2020.29.6.441
J.Y. Lee, Development of intelligence data analytics?system for quality enhancement of die-casting?process, J. Korean Soc. Precis. Eng., 37(4) (2020),?247-254, https://doi.org/10.7736/JKSPE.019.136
https://www.youtube.com/@statquest
https://colah.github.io/
S. Raschka, V. Mirjalili, 2021, Python Machine?Learning, 3rd Edition (H.S. Park, Korean?Translation, Gilbut) ISBN9791165215187
https://www.kaggle.com/c/titanic/data
https://www.kaggle.com/datasets/uciml/iris
https://www.kaggle.com/code/agileteam/t2-2-pima-indians-diabetes
https://www.kaggle.com/datasets/altavish/boston-housing-dataset
http://www.timeseriesclassification.com/description.php?DatasetFordA
www.kamp-ai.kr 「Ford Engine Vibration AI?Dataset」 Analysis Practice Guidebook
https://fordatis.fraunhofer.de/handle/fordatis/151.2?modefull&localeen?
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.