All Issue

2023 Vol.55, Issue 3

Original Paper

30 June 2023. pp. 3-14
Abstract
References
1
Chen, T., Wang, Y., and Xiao, C., An apparatus and method for real-time stacked sheets counting with line-scan cameras, IEEE Transactions on Instrumentation and Measurement 64(7):1876-1884 (2014). 10.1109/TIM.2014.2366977
2
Young, R. D., Reed, R. J., and Crosdale, F. H., Apparatus and method for counting sheets, E.P. Pat. 19960420158, Jan 14 (1998).
3
Vincent T. M., Measuring the thickness of stacked sheets of paper, TAAPI Journal 75(12): 118-120 (1992).
4
Numata, T., Matsuura, S., and Sugano, T., Method and device for discriminating paper sheet., U.S. Pat. 2077534, Jul 8 (2009).
5
Sato, J., Yamada, T., and Ito, K., Vision‐based facial oil blotting paper counting, IEEJ Transactions on Electrical and Electronic Engineering 14(6):899-907 (2019). 10.1002/tee.22880
6
Han, X. and Wang, J., Design of paper counting algorithm based on texture image, 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, IEEE Press, Chengdu, pp. 145-148. 10.1109/IAEAC47372.2019.8997773
7
Zhu, H., Xiao, C., and Gao, J., An apparatus and method for stacked sheet counting with camera array, 2013 Chinese Automation Congress, IEEE Press, Changsha, pp. 7-10. 10.1109/CAC.2013.6775692
8
Xiao, C., Qiu, H., and Zhao, H., A count measurement method for low contrast stacked sheets in machine vision, Journal of Hunan University Natural Sciences 45(4):122-128 (2018).
9
Zhao, H., Dai, R., and Xiao, C., A machine vision system for stacked substrates counting with a robust stripe detection algorithm, IEEE Transactions on Systems, Man, and Cybernetics: Systems 49(11):2352-2361 (2019). 10.1109/TSMC.2017.2766441
10
Pham, D., Ha, M., and San, C., Accurate stacked- sheet counting method based on deep learning JOSA A. 37(7):1206-1218 (2020). 10.1364/JOSAA.38739032609680
11
Allport, J., Brouwer, N., and Kramer, R., Backscatter/transmission X-ray thickness gauge, NDT International 20(4):217-223 (1987). 10.1016/0308-9126(87)90244-6
12
Shirakawa, Y., A build-up treatment for thickness gauging of steel plates based on gamma-ray transmission, Applied Radiation and Isotopes 53(4):581-586 (2000). 10.1016/S0969-8043(00)00227-X11003494
13
Xu, G., Wang, L., and Tong, J., Research on Calibration Model of X-ray Thickness Gauge, Atomic Energy Science and Technology 48(5): 925-929 (2014).
14
Sasanpur, M. T. and Kosarina, E. I., Recommendations on selection of anode voltages in X-ray testing of steel specimens, Russian Journal of Nondestructive Testing 47:329-333 (2011). 10.1134/S1061830911050081
15
Hu, B., Zhang, X., and Ouyang, Q., A prototype system to measure X-ray absorption spectra for diagnosis in vivo, Measurement 93:252- 257 (2016). 10.1016/j.measurement.2016.07.038
16
Fang, Z., Wang, M., and Hu, W., Potassium di-hydrogen phosphate identification based on wide energy X-ray absorption spectrum and an artificial neural network, Computers and Electronics in Agriculture 183:106062-1-1060 62-8 (2021). 10.1016/j.compag.2021.106062
17
Mayerhofer, T., Pahlow, S., and Popp, J., The Bouguer-Beer-Lambert Law: Shining Light on the Obscure, Chemphyschem 21(18):2029- 2046 (2020). 10.1002/cphc.20200046432662939PMC7540309
18
Sola, J. and Sevilla, J., Importance of input data normalization for the application of neural networks to complex industrial problems, IEEE Transactions on Nuclear Science 44(3):1464- 1468 (1997). 10.1109/23.589532
19
Tharwat, A., Gaber, T., and Ibrahim, A., Linear discriminant analysis: A detailed tutorial, AI Communications 30(2):169-190 (2017). 10.3233/AIC-170729
20
Gaskin, C. J. and Happell, B., On exploratory factor analysis: a review of recent evidence, an assessment of current practice, and recommendations for future use, Int J Nurs Stud. 51(3):511-521 (2014). 10.1016/j.ijnurstu.2013.10.00524183474
21
Abdi, H. and Williams, L.J., Principal component analysis, Wiley Interdiscipl Rev Com Statistics 2(4):433-59 (2010). 10.1002/wics.101
22
Friedland, S., A new approach to generalized singular value decomposition, SIAM Journal on Matrix Analysis and Applications 27(2): 434-444 (2005). 10.1137/S0895479804439791
23
Hornik, K., Stinchcombe, M., and White, H., Multilayer feedforward networks are universal approximators, Neural Networks 2(5):359-366 (1989). 10.1016/0893-6080(89)90020-8
24
Kotsiantis, S. B., Zaharakis, I. D., and Pintelas, P. E., Machine learning: a review of classification and combining techniques, Artificial Intelligence Review 26(3):159-190 (2006). 10.1007/s10462-007-9052-3
25
Hochreiter, S. and Schmidhuber, J., Long short- term memory, Neural Computation 9(8):1735- 1780 (1997). 10.1162/neco.1997.9.8.17359377276
26
Jolliffe, I. T. and Cadima, J., Principal component analysis: a review and recent developments, Phil. Trans. R. Soc. A 374:20150202 (2016). 10.1098/rsta.2015.020226953178PMC4792409
Information
  • Publisher :Korea Technical Association of The Pulp and Paper Industry
  • Publisher(Ko) :한국펄프종이공학회
  • Journal Title :Journal of Korea TAPPI
  • Journal Title(Ko) :펄프종이기술
  • Volume : 55
  • No :3
  • Pages :3-14
  • Received Date : 2023-05-08
  • Revised Date : 2023-05-24
  • Accepted Date : 2023-05-26