تشخیص نواحی معیوب پارچه مبتنی برخوشه‌بندی و عملگرهای ریخت شناسی
محورهای موضوعی : انرژی های تجدیدپذیر
اکرم محمدی سومار
1
,
مهران عمادی
2
1 - گروه مهندسی برق- واحد مبارکه، دانشگاه آزاد اسلامی، مبارکه، اصفهان، ایران
2 - گروه مهندسی برق- واحد مبارکه، دانشگاه آزاد اسلامی، مبارکه، اصفهان، ایران
کلید واژه: پارچه معیوب, ناحیه بندی, عملگرهای ریخت شناسی, کانتورفعال,
چکیده مقاله :
در مراحل مختلف تولید پارچه، خرابی‌هایی متعددی برسطح پارچه ظاهر می‌شود. با چشم پوشی از دلایل ایجاد خرابی ها، تشخیص دقیق انواع آن‌ها به طبقه بندی صحیح پارچه کمک می‌کند و در نتیجه درصد بالایی از فرآیند کنترل کیفیت را به خود اختصاص می‌دهد. کنترل کیفیت پارچه به منظور بهبود کیفیت محصول و حفظ بازار رقابتی از اهمیت بالایی برخوردار است. همچنین شناسایی نواحی معیوب در روش های خودکار از اهمیت ویژه ای برخورداراست. در این مقاله، یک روش جدید، جهت ناحیه بندی نواحی معیوب پارچه ، مبتنی بر خوشه بندی و همچنین عملگرهای ریخت شناسی ارائه شده است. در روش پیشنهادی، پس از پیش پردازش های لازم جهت بهبود کیفیت تصویر، در مرحله اول روی تصویر خوشه بندی صورت می‌گیرد، تا نواحی مشابه ایجاد شوند. سپس عملگرهای ریخت شناسی به کار گرفته می شوند تا ناحیه معیوب استخراج شود. استفاده از ترکیب هوشمندانه عملگرهای ریخت شناسی، سبب شناسایی دقیق نواحی معیوب درتصویر پارچه شده است. نمایش ناحیه معیوب به کمک الگوریتم کانتور فعال صورت می گیرد. اگرچه تاکنون روش های متعددی همچون الگوهای محلی دودویی و سایر روش ها ارائه شده است، اما سرعت شناسایی این الگوریتم ها پایین بوده و پیچیدگی محاسباتی بالایی دارند. روش پیشنهادی روی پایگاه داده CMUPIE، پیاده سازی شده و به کمک معیارهای صحت و دقت ارزیابی شده است. صحت شناسایی نواحی معیوب در روش پیشنهادی، 82/93درصد و دقت روش پیشنهاد شده، 33/98 درصد حاصل گردیده است که در مقایسه با روش های مشابه، بهبود چشم گیری داشته است
At various stages of fabric production, there are numerous damages to the surface of the fabric. Regardless of the causes of the failures, precise identification of their types helps to correctly classify the fabric and thus provides a high percentage of the quality control process. Quality control of fabrics is of great importance in order to improve product quality and maintain a competitive market. Identification of faulty areas in automated methods is of great importance. In this paper, a new method is presented in the clustering of faulty zones based on clustering as well as morphological operators. In the proposed method, after preprocessing necessary to improve image quality, the first step is to cluster the image to create similar areas. Then morphological operators are applied to extract the defective area. The defective area is represented by the active contour algorithm. Although many methods such as local binary patterns and other methods have been proposed, the speed of detection of these algorithms is low and has high computational complexity. The proposed method is implemented on the CMUPIE database and evaluated using accuracy assessment criteria and accuracy criteria. The accuracy of identifying defective areas in the proposed method is 93.82%, and the precision of detecting defective areas in the suggested method is 98.33% which are significantly improved compared to similar methods.
[1] S. Torabi, A. Bahrami, M. Sabahi, “Overhead reduction in cooperative spectrum sensing via sequential detection in cognitive radio networks under bandwidth constraint”, Journal of Intelligent Procedures in Electrical Technology, vol. 5, no. 19, pp. 11-16., Autumn 2014 (in Persian).
[2] S. E. Razavi, P. Poursoltani, N. Pariz, "Optimal observer path planning in tracking two targets using side angle measurements", Journal of Intelligent Procedures in Electrical Technology, Vol. 10, No. 38, pp. 33-42, Summer 2019.
[3] S. Saberian-Borojeni, “Fuzzy second order sliding mode speed observer for a synchronous reluctance motor with predictive control”, Journal of Intelligent Procedures in Electrical Technology, vol. 4, no. 13, pp. 45-52, winter 2013 (in Persian).
[4] M. Hashemi, G. Shahgholian, "Distributed robust adaptive control of high order nonlinear multi agent systems", ISA Trans., Vol. 74, pp. 14-27, March 2018 (doi:10.1016/j.isatra.2018.01.023).
[5] A. Casavola, E. Mosca, D. Angeli, "Robust command governors for constrained linear systems", IEEE Trans. on Automatic Control, Vol. 45, No. 11, pp. 2071-2077, Nov. 2000 (doi:10.1109/9.887628).
[6] M. A. Mohammadkhani, F. Bayat, A. A. Jalali, "Design of explicit model predictive control for constrained linear systems with disturbances”, International Journal of Control, Automation and Systems, Vol. 12, No. 2, pp. 294-301, April 2014 (doi:10.1007/s12555-013-0058-0).
[7] E. G. Gilbert, K. T. Tan, "Linear systems with state and control constraints: The theory and application of maximal output admissible sets", IEEE Trans. on Automatic Control, Vol. 36, No. 9, pp. 1008-1020, Sep. 1991 (doi:10.1109/9.83532).
[8] A. Bemporad, M. Morari, V. Dua, E. N. Pistikopoulos, "The explicit linear quadratic regulator for constrained systems", Automatica, Vol. 38, No. 1, pp. 3-20, Jan. 2002 (doi:10.1016/S0005-1098(01)00174-1).
[9] J. A. Primbs, C. H. Sung, "Stochastic receding horizon control of constrained linear systems with state and control multiplicative noise", IEEE Trans. on Automatic Control, Vol. 54, No. 2, pp. 221-230, Feb. 2009 (doi:10.1109/TAC.2008.2010886).
[10] D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. Scokaert, "Constrained model predictive control: Stability and optimality" Automatica, Vol. 36, No. 6, pp. 789-814, June 2000 (doi:10.1016/S0005-1098(99)00214-9).
[11] K. D. Do, "Control of nonlinear systems with output tracking error constraints and its application to magnetic bearings”, International Journal of Control, Vol. 83, pp. 1199-1216, May 2010 (doi: 10.1080/00207171003664828).
[12] W. Meng, Q. Yang, J. Si, Y. Sun, "Consensus control of nonlinear multiagent systems with time-varying state constraints”, IEEE Trans. on cybernetics, Vol. 47, No. 8, pp. 2110-2120, Aug. 2017 (doi:10.1109/TCYB. 2016.2629268).
[13] D. Liu, X. Yang, D. Wang, Q. Wei, "Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints", IEEE Trans. on Cybernetics, Vol. 45, No. 7, pp. 1372- 1385, July 2015 (doi:10.1109/TCYB.2015.2417170).
[14] L. Zhang, C. Hua, H. Yu, X. Guan, "Distributed adaptive fuzzy containment control of stochastic pure-feedback nonlinear multiagent systems with local quantized controller and tracking constraint", IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol. 40, No. 4, April 2019 (doi:10.1109/TSMC.2017.2701344 ).
[15] W. He, H. Huang, S. S. Ge, "Adaptive neural network control of a robotic manipulator with time-varying output constraints”, IEEE Trans. on Cybernetics, Vol. 47, No. 10, pp. 3136-3147, Oct. 2017 (doi:10.1109/TCYB. 2017.2711961).
[16] Y.-J. Liu, S. Lu, S. Tong, "Neural network controller design for an uncertain robot with time-varying output constraint”, IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol. 47, No. 8, Aug. 2017 (doi:10.1109/ TSMC.2016.2606159).
[17] K. P. Tee, S. S. Ge, E. H. Tay, "Barrier Lyapunov functions for the control of output-constrained nonlinear systems", Automatica, Vol. 45, No. 4, pp. 918-927, April 2009 (doi:10.1016/j.automatica.2008.11.017).
[18] K. P. Tee, B. Ren, S. S. Ge, "Control of nonlinear systems with time-varying output constraints", Automatica, Vol. 47, No. 11, pp. 2511-2516, Nov. 2011 (doi:10.1016/j.automatica.2011.08.044).
[19] G. Shahgholian, A. Movahedi, "Modeling and controller design using ANFIS method for non-linear liquid level system", International Journal of Information and Electronics Engineering, Vol. 1, No. 3, pp. 271-275, Nov. 2011 (doi:10.7763/IJIEE.2011.V1.43).
[20] E. Fridman, U. Shaked, "A descriptor system approach to H/sub/spl infin//control of linear time-delay systems", IEEE Trans. on Automatic Control, Vol. 47, No. 2, pp. 253-270, Feb. 2002 (doi:10.1109/9.983353).
[21] L. Yu, J. Chu, "An LMI approach to guaranteed cost control of linear uncertain time-delay systems", Automatica, Vol. 35, No. 6, pp. 1155-1159, June 1999 (doi:10.1016/S0005-1098(99)00007-2).
[22] D.-H. Zhai, Y. Xia, "Adaptive control for teleoperation system with varying time delays and input saturation constraints", IEEE Trans. on Industrial Electronics, Vol. 63, No. 11, pp. 6921-6929, 2016 (doi:10.1109/TIE. 2016.2583199).
[23] Y.-Y. Cao, P. M. Frank, "Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach", IEEE Trans. on Fuzzy Systems, Vol. 8, No. 2, pp. 200-211, April 2000 (doi:10.1109/91.842153).
[24] Li, Da-Peng, et al. "Approximation-based adaptive neural tracking control of nonlinear MIMO unknown timevarying delay systems with full state constraints", IEEE Trans. on Cybernetics, Vol. 47, No. 10, pp. 3100-3109, Oct. 2017 (doi:10.1109/TCYB.2017.2707178).
[25] K. Gu, "An integral inequality in the stability problem of time-delay systems", Proceeding of the IEEE/CDC, pp. 2805-2810, Sydney, NSW, Australia, Dec. 2000 (doi: 10.1109/CDC.2000.914233).
[26] L. Xie, E. Fridman, U. Shaked, "Robust H/sub/spl infin//control of distributed delay systems with application to combustion control", IEEE Trans. on Automatic Control, Vol. 46, No. 12, pp. 1930-1935, Dec. 2001 (doi:10.1109/9 .975483).
[27] S.-I. Niculescu, "H/sub/spl infin//memoryless control with an/spl alpha/-stability constraint for time-delay systems: an LMI approach”, IEEE Trans. on Automatic Control, vol. 43, No. 5, pp. 739-743, May 1998 (doi:10.1109/ 9.668850).
[28] E. Aghadavoodi, G. Shahgholian, "A new practical feed-forward cascade analyze for close loop identification of combustion control loop system through RANFIS and NARX", Applied Thermal Engineering, Vol. 133, pp. 381- 395, March 2018 (doi: 10.1016/j.applthermaleng.2018.01.075).
[29] M. Taslimi, A. Chatraei, M. Hosseini, "A robust neuro-adaptive control of three link scara robot with mass uncertainty", Journal of Intelligent Procedures in Electrical Technology, Vol. 4, No. 15, pp. 11-18, Sep. 2013.
[30] R. Aarthi, R. A. Natarajan, "An integrated fault detection and diagnosis using kaman filter and eigen structure assignment-application to three tank system", Applied Mechanics and Materials, Vol. 704, pp. 252-256, 2015 (doi: 10.4028/www.scientific.net/AMM.704.252).
[31] J. Chen, W. Zhang, Y.-Y. Cao, "Robust reliable feedback controller design against actuator faults for linear parameter-varying systems in finite-frequency domain", IET Control Theory and Applications, Vol. 9, No. 10, pp. 1595-1607, June 2015 (doi:10.1049/iet-cta.2014.1308).
[32] J. Chen, W. Zhang, Y.-Y. Cao, H. Chu, "Observer-based consensus control against actuator faults for linear parameter-varying multiagent systems", IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol. 47, No. 7, pp. 1336-1347, July 2017 (doi:10.1109/TSMC.2016.2587300).
[33] C. Deng, G.-H. Yang, "Cooperative adaptive output regulation for linear multi-agent systems with actuator faults", IET Control Theory and Applications, Vol. 11, No. 14, pp. 2396-2402, Sep. 2017 (doi:10.1049/iet-cta.2016.1571).
[34] A. Fattollahi, “Simultaneous design and simulation of synergetic power system stabilizers and a thyristor-controller series capacitor in multi-machine power systems”, Journal of Intelligent Procedures in Electrical Technology, vol. 8, no. 30, pp. 3-14, Summer 2017 (in Persian).
[35] K. Shojaei, A. Chatraei, S. Nakhkoob, "Fuzzy adaptive control for trajectory tracking of autonomous underwater vehicle", Journal of Intelligent Procedures in Electrical Technology, Vol. 4, No. 16, pp. 51-58, Dec. 2014.
[36] M. Blanke, M. Kinnaert, J. Lunze, M. Staroswiecki, J. Schröder, Diagnosis and fault-tolerant control vol. 691: Springer, 2006.
[37] I. Sadeghzadeh, A. Mehta, Y. Zhang, C.-A. Rabbath, "Fault-tolerant trajectory tracking control of a quadrotor helicopter using gain-scheduled PID and model reference adaptive control", Proceeding of the ACPHMS, Aug. 2011.
[38] M. Hashemi, "Adaptive neural dynamic surface control of MIMO nonlinear time delay systems with time‐varying actuator faults", International Journal of Adaptive Control and Signal Processing, Vol. 31, No. 2, pp. 275-296, 2017 (doi:10.1002/acs.2715).
[39] C. Wen, Y. Zhang, Y. C. Soh, "Robustness of an adaptive backstepping controller without modification", Systems and Control Letters, Vol. 36, No. 2, pp. 87-100, Feb. 1999 (doi:10.1016/S0167-6911(98)00081-4).
[40] M. Hashemi, J. Askari, J. Ghaisari, "Adaptive actuator fault compensation for a class of MIMO nonlinear time delay systems”, Nonlinear Dynamics, Vol. 79, pp. 865-883, 2015 (doi: 10.1007/s11071-014-1708-3).
[41] M. Hashemi, J. Askari, J. Ghaisari, "Adaptive control of uncertain nonlinear time delay systems in the presence of actuator faults and applications to chemical reactor systems", European Journal of Control, Vol. 29, pp. 62-73, May 2016 (doi:10.1016/j.ejcon.2016.03.002).
[42] S. Yin, H. Yang, H. Gao, J. Qiu, O. Kaynak, "An adaptive NN-based approach for fault-tolerant control of nonlinear time-varying delay systems with unmodeled dynamics", IEEE Trans. on Neural Networks and Learning Systems, Vol. 28, No. 8, pp. 1902-1913, Aug. 2017 (doi:10.1109/TNNLS.2016.2558195).
[43] H. Li, Y. Gao, L. Wu, H.-K. Lam, "Fault detection for TS fuzzy time-delay systems: delta operator and input-output methods", IEEE Trans. on Cybernetics, Vol. 45, No. 2, pp. 229-241, Feb. 2015 (doi:10.1109/TCYB.2014. 2323994).
[44] S.-J. Huang, G.-H. Yang, "Fault tolerant controller design for t–s fuzzy systems with time-varying delay and actuator faults: a k-step fault-estimation approach", IEEE Trans. on Fuzzy Systems, Vol. 22, No. 6, pp. 1526-1540, Dec. 2014 (doi:10.1109/TFUZZ.2014.2298053).
[45] E. Hosseini, E. Aghadavoodi, G. Shahgholian, H. Mahdavi-Nasab, "Intelligent pitch angle control based on gainscheduled recurrent ANFIS", Journal of Renewable Energy and Environment, Vol. 6, No. 1, pp. 36-45, 2019.
_||_
