Angle analysis of fabric wrinkle by projected profile light line method, image processing and neuro-fuzzy system

References

• AATCC test method 128-1999. 2000. “Wrinkle Recovery of Fabrics.” Appearance Method, AATCC Technical Manual 75: 213–214.
   Google Scholar
• Abrishami, A., F. Mousazadeghan, and G. Kipchirchir. 2019. “Evaluating the Crease Recovery Performance of Woven Fabrics considering Bending Behaviour in Various Directions.” Journal of the Textile Institute 110 (5): 690–699. doi:10.1080/00405000.2018.1511229.
   Web of Science ®Google Scholar
• Amirbayat, J., and M. J. Alagha. 1996. “Objective Assessment of Wrinkle Recovery by Means of Laser Triangulation.” Journal of the Textile Institute 87.2 (2): 349–355. doi:10.1080/00405009608659087.
   Web of Science ®Google Scholar
• Atalie, D., A. Ferede, and N. Ezazshahabi. 2019. “Effect of Weft Yarn Twist Level on Mechanical and Sensorial Comfort of 100% Woven Cotton Fabrics.” Journal of the Fashion & Textile Research 6 (3): 1–12. doi:10.1186/s40691-018-0169-6.
   Google Scholar
• Behera, B. K., and R. Guruprasad. 2012. “Predicting Bending Rigidity of Woven Fabrics Using Adaptive Neuro-fuzzy Inference System (ANFIS).” Journal of the Textile Institute 103 (11): 1205–1212. doi:10.1080/00405000.2012.673296.
   Web of Science ®Google Scholar
• Can, Y., M. Akaydin, Y. Turhan, and E. Ay. 2009. “Effect of Wrinkle Resistance Finish on Cotton Fabric Properties.” Indian Journal of Fibre & Textile Research 34: 183–186.
   Web of Science ®Google Scholar
• Dehdar, M. M., M. J. Rezaee, M. Zarinbal, and H. Izadbakhsh. 2018. “Integrating Fuzzy Inference System, Image Processing and Quality Control to Detect Defects and Classify Quality Level of Copper Rods.” International Journal of Industrial Engineering & Production Research 29 (4): 461–469. doi:10.22068/ijiepr.29.4.461.
   Google Scholar
• Fathi, E., M. J. Rezaee, R. Tavakkoli-Moghaddam, A. Alizadeh, and A. Montazer. 2020. “Design of an Integrated Model for Diagnosis and Classifying of Pediatrics Acute Leukemia Using Machine Learning.” Part H: Journal of Engineering in Medicine. doi:10.1177/0123456789123456.
   PubMedGoogle Scholar
• Hadizadeh, M., M. Amani Tehran, and A. A. Jedi. 2010. “Application of an Adaptive Neuro-fuzzy System for Prediction of Initial Load—Extension Behavior of Plain-woven Fabrics.” Textile Research Journal 80 (10): 981–990. doi:10.1177/0040517509346451.
   Web of Science ®Google Scholar
• Hesarian, M. S. 2010. “Evaluation of Fabric Wrinkle by Projected Profile Light Line Method.” The Journal of the Textile Institute 101 (5): 463–470. doi:10.1080/13598130802528238.
   Web of Science ®Google Scholar
• Hu, J., B. Xin, and H. Yan. 2002. “Measuring and Modeling 3D Wrinkles in Fabrics.” Textile Research Journal 72 (10): 863–869. doi:10.1177/004051750207201003.
   Web of Science ®Google Scholar
• Hu, X., F. Sun, Q. Wang, and W. Gao. 2020. “In Situ Characterization of the Morphological Wrinkling of Woven Fibrous Materials by a Mechanical Test.” Textile Research Journal 90 (3–4): 333–343. doi:10.1177/0040517520910709.
   Google Scholar
• Hussain, T., Z. A. Malik, Z. Arshad, and A. Nazir. 2014. “Comparison of Artificial Neural Network and Adaptive Neuro-fuzzy Inference System for Predicting the Wrinkle Recovery of Woven Fabrics.” The Journal of the Textile Institute 106 (9): 934–938. doi:10.1080/00405000.2014.953790.
   Web of Science ®Google Scholar
• Jahan, I. 2017. “Effect Of Fabric Structure On The Mechanical Properties Of Woven Fabrics.” Indian Journal of Advanced Research in Textile Engineering 2 (2): 1018.
   Google Scholar
• Jamshaid, H., T. Hussain, and Z. A. Malik. 2013. “Comparison of Regression and Adaptive Neuro-fuzzy Models for Predicting the Bursting Strength of Plain Knitted Fabrics.” Fibers and Polymers 14 (7): 1203–1207. doi:10.1007/s12221-013-1203-3.
   Web of Science ®Google Scholar
• Jeguirim, S. E. G., A. B. Dhouib, M. Sahnoun, M. Cheikhrouhou, L. Schacher, and D. Adolphe. 2011. “The Use of Fuzzy Logic and Neural Networks Models for Sensory Properties Prediction from Process and Structure Parameters of Knitted Fabrics.” Journal of Intelligent Manufacturing 22.6 (6): 873–884. doi:10.1007/s10845-009-0362-y.
   Web of Science ®Google Scholar
• Joliffe, I. T., and B. J. T. Morgan. 1992. “Principal component analysis and exploratory factor analysis.” Statistical methods in medical research 1 (1): 69–95.
   Google Scholar
• Kim, E. H. 1999. “Objective Evaluation of Wrinkle Recovery.” Textile Research Journal 69 (11): 860–865. doi:10.1177/004051759906901110.
   Web of Science ®Google Scholar
• Lim, H., J. Lee, and D.-W. Kim. 2017 “Optimization approach for feature selection in multi-label classification.” Pattern Recognition Letters 89 (2017): 25–30.
   Web of Science ®Google Scholar
• Liu, C. 2017. “Investigation on the Novel Measurement for Fabric Wrinkle Simulating Actual Wear.” Textile Institute of Journal 108 (2): 279–286. doi:10.1080/00405000.2016.1165384.
   Web of Science ®Google Scholar
• Liu, C., and Y. Fu. 2014. “Novel Measurement for Multidirectional Fabric Wrinkling Using Wavelet Analysis.” Journal of Fibers and Polymers 15 (6): 1337–1342. doi:10.1007/s12221-014-1337-y.
   Web of Science ®Google Scholar
• Liu, J., X. Wang, and Y. Lu. 2017. “A Novel Hybrid Methodology for Short-term Wind Power Forecasting Based on Adaptive Neuro-fuzzy Inference System.” Renewable Energy 103: 620–629. doi:10.1016/j.renene.2016.10.074.
   Web of Science ®Google Scholar
• Lu, Y., X. Hu, F. Sun, F. Peng, and W. Gao. 2020. “Determination of Optimal System Parameters to Characterize the Wrinkle Recovery of Fabrics by an Integrated Shape Retention Evaluation System.” Textile Research Journal 90 (1): 91–100. doi:10.1177/0040517519858770.
   Web of Science ®Google Scholar
• Manogaran, G., R. Varatharajan, and M. K. Priyan. 2018. “Hybrid Recommendation System for Heart Disease Diagnosis Based on Multiple Kernel Learning with Adaptive Neuro-fuzzy Inference System.” Multimedia Tools and Applications 77 (4): 4379–4399. doi:10.1007/s11042-017-5515-y.
   Web of Science ®Google Scholar
• Mori, T., and J. Komiyama. 2002. “Evaluation Wrinkle Fabrics with Image Analysis and Neural Networks.” Textile Research Journal 72 (5): 412–423. doi:10.1177/004051750207200508.
   Web of Science ®Google Scholar
• Nguyen, P. T., L. C. Chua, A. Talei, and Q. H. Chai. 2018. “Water Level Forecasting Using Neuro-fuzzy Models with Local Learning.” Neural Computing & Applications 30 (6): 1877–1887. doi:10.1007/s00521-016-2803-9.
   Web of Science ®Google Scholar
• Papageorgiou, E. I., K. Aggelopoulou, T. A. Gemtos, and G. D. Nanos. 2018. “Development and Evaluation of a Fuzzy Inference System and a Neuro-Fuzzy Inference System for Grading Apple Quality.” Applied Artificial Intelligence 32 (3): 253–280. doi:10.1080/08839514.2018.1448072.
   Web of Science ®Google Scholar
• Rajab, S., and V. Sharma. 2018. “A Review on the Applications of Neuro-fuzzy Systems in Business.” Artificial Intelligence Review 49 (4): 481–510. doi:10.1007/s10462-016-9536-0.
   Web of Science ®Google Scholar
• Rezaee, M. J., and A. Moini. 2013. “Reduction Method Based on Fuzzy Principal Component Analysis in Multi-objective Possibilistic Programming.” The International Journal of Advanced Manufacturing Technology 67 (1–4): 823–831. doi:10.1007/s00170-012-4526-0.
   Web of Science ®Google Scholar
• Rezaee, M. J., M. Dadkhah, and M. Falahinia. 2019. “Integrating Neuro-fuzzy System and Evolutionary Optimization Algorithms for Short-term Power Generation Forecasting.” International Journal of Energy Sector Management 13 (4): 828–845. doi:10.1108/IJESM-09-2018-0015.
   Web of Science ®Google Scholar
• El-Ghezal, J. S., A. B. Dhouib, M. Sahnoun, M. Cheikhrouhou, L. Schacher, and D. Adolphe. 2011.“The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics.” Journal of Intelligent Manufacturing 22 (6): 873–884.
   Google Scholar
• Sheikhpour, R., M. A. Sarram, M. A. Z. Chahooki, and R. Sheikhpour. 2017. “A kernelized non-parametric classifier based on feature ranking in anisotropic Gaussian kernel.” Neurocomputing 267: 545–555
   Web of Science ®Google Scholar
• Solgi, A., A. Pourhaghi, R. Bahmani, and H. Zarei. 2017. “Improving SVR and ANFIS Performance Using Wavelet Transform and PCA Algorithm for Modeling and Predicting Biochemical Oxygen Demand (BOD).” Ecohydrology & Hydrobiology 17 (2): 164–175. doi:10.1016/j.ecohyd.2017.02.002.
   Web of Science ®Google Scholar
• Sun, J. J., M. Yao, B. G. Xu, and B. Patricia. 2011. “Fabric Wrinkle Characterization and Classification Using Modified Wavelet Coefficients and Support-vector-machine Classifiers.” Textile Research Journal 81 (9): 902–913. doi:10.1177/0040517510391702.
   Web of Science ®Google Scholar
• Tekin, E., and S. O. Akbas. 2019. “Predicting Groutability of Granular Soils Using Adaptive Neuro-fuzzy Inference System.” Neural Computing & Applications 31 (4): 1091–1101. doi:10.1007/s00521-017-3140-3.
   Web of Science ®Google Scholar
• Wang, J., K. Shi, L. Wang, R. Pan, and W. Gao. 2020b. “A Computer Vision System for Objective Fabric Smoothness Appearance Assessment with an Ensemble Classifier.” Textile Research Journal 90 (3–4): 333–343. doi:10.1177/0040517519866951.
   Web of Science ®Google Scholar
• Wang, J., K. Shi, L. Wang, Z. Li, F. Sun, R. Pan, W. Gao, et al. 2020a. “Automatic Assessment of Fabric Smoothness Appearance Based on a Compact Convolutional Neural Network with Label Smoothing.” IEEE Access Journal 8:26966–26974. doi:10.1109/ACCESS.2020.2971506.
   Web of Science ®Google Scholar
• Wang, L., Q. Tang, X. Zhang, and W. Gao. 2020c. Instrumental Evaluation of Fabric Shape Retention by Image Analysis. doi:10.1177/0040517520921505.
   Google Scholar
• Xu, B., and D. F. Cuminato. 1998. “Evaluation Fabric Smoothness Appearance with a Laser Profilometer.” Textile Research Journal 68 (12): 900–906. doi:10.1177/004051759806801204.
   Web of Science ®Google Scholar
• Xu, B., and J. A. Reed. 1995. “Instrumental Evaluation of Fabric Wrinkle.” Textile Research Journal 86 (1): 123–134. doi:10.0.4.56/00405009508631316.
   Google Scholar
• Xu, P., X. Ding, X. Wu, and R. Wang. 2018. “Characterization and Assessment of Fabric Smoothness Appearance Based on Sparse Coding.” Textile Research Journal 88 (4): 357–366. doi:10.1177/0040517516679148.
   Web of Science ®Google Scholar
• Yang., X. 2011. “Evaluate Fabric Wrinkle Grade Based on Subtractive Clustering Adaptive Network Fuzzy Inference Systems.” Advanced Materials Research Journal 332–334. www.scientific.net/AMR.332-334.1505.
   PubMedGoogle Scholar