Inspection process for dimensioning through images and fuzzy logic

This paper presents a hybrid methodology based on a type 1 fuzzy model in singleton version using a 2k factorial design that optimizes the model of the expert system and serves to perform in-line inspection. The factorial design method provides the required database for the creation of the rule base...

Full description

Autores:
amelec, viloria
Pineda Lezama, Omar Bonerge
Cabrera, Danelys
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7664
Acceso en línea:
https://hdl.handle.net/11323/7664
https://doi.org/10.1016/j.procs.2020.07.095
https://repositorio.cuc.edu.co/
Palabra clave:
Neural networks
Irrigation control
Instrumentation and image analysis
Micro-greenhouse
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_f5422b453faad13cee78ad8b0638bbff
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7664
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Inspection process for dimensioning through images and fuzzy logic
title Inspection process for dimensioning through images and fuzzy logic
spellingShingle Inspection process for dimensioning through images and fuzzy logic
Neural networks
Irrigation control
Instrumentation and image analysis
Micro-greenhouse
title_short Inspection process for dimensioning through images and fuzzy logic
title_full Inspection process for dimensioning through images and fuzzy logic
title_fullStr Inspection process for dimensioning through images and fuzzy logic
title_full_unstemmed Inspection process for dimensioning through images and fuzzy logic
title_sort Inspection process for dimensioning through images and fuzzy logic
dc.creator.fl_str_mv amelec, viloria
Pineda Lezama, Omar Bonerge
Cabrera, Danelys
dc.contributor.author.spa.fl_str_mv amelec, viloria
Pineda Lezama, Omar Bonerge
Cabrera, Danelys
dc.subject.spa.fl_str_mv Neural networks
Irrigation control
Instrumentation and image analysis
Micro-greenhouse
topic Neural networks
Irrigation control
Instrumentation and image analysis
Micro-greenhouse
description This paper presents a hybrid methodology based on a type 1 fuzzy model in singleton version using a 2k factorial design that optimizes the model of the expert system and serves to perform in-line inspection. The factorial design method provides the required database for the creation of the rule base for the fuzzy model and also generates the database to train the expert system. The proposed method was validated in the process of verifying dimensional parameters by means of images compared with the ANFIS and RBFN models which show greater margins of error in the approximation of the function represented by the system compared with the proposed model. The results obtained show that the model has an excellent performance in the prediction and quality control of the industrial process studied when compared with similar expert system techniques as ANFIS and RBFN.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-07T14:25:32Z
dc.date.available.none.fl_str_mv 2021-01-07T14:25:32Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.issn.spa.fl_str_mv 1877-0509
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7664
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.procs.2020.07.095
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 1877-0509
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7664
https://doi.org/10.1016/j.procs.2020.07.095
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Zhang, C., Craine, W. A., McGee, R. J., Vandemark, G. J., Davis, J. B., Brown, J., ... & Sankaran, S. (2020). Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops. Sensors, 20(5), 1450.
[2] Zhang, F., Zhang, X.: Classification and Quality Evaluation of Tobacco leaves Based in Image Processing and Fuzzy Comprehensive Evaluation. Sensors. 11(3), pp. 2369–2384 (2011)
[3] Mittal, P., Saini, R. K., & Jain, N. K. (2019). Image enhancement using fuzzy logic techniques. In Soft Computing: Theories and Applications (pp. 537-546). Springer, Singapore.
[4] Ramya, H. R., & Sujatha, B. K. (2016, October). A novel approach for medical image fusion using fuzzy logic type-2. In 2016 International Conference on Circuits, Controls, Communications and Computing (I4C) (pp. 1-5). IEEE.
[5] Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018, October). Noise parameter estimation for non-singleton fuzzy logic systems. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2960-2965). IEEE.
[6] Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Advances in Swarm Intelligence, ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham (2018)
[7] Bora, D. J., & Thakur, R. S. (2018). An Efficient Technique for Medical Image Enhancement Based on Interval Type-2 Fuzzy Set Logic. In Progress in Computing, Analytics and Networking (pp. 667-678). Springer, Singapore.
[8] Gonzalez, C. I., Melin, P., Castro, J. R., Castillo, O., & Mendoza, O. (2016). Optimization of interval type-2 fuzzy systems for image edge detection. Applied Soft Computing, 47, 631-643.
[9] Reyes, D., Álvarez, A., Rincón, E. J., Valderrama, J., Noradino, P., & Méndez, G. M. (2017, October). A PID using a non-singleton fuzzy logic system type 1 to control a second-order system. In North American Fuzzy Information Processing Society Annual Conference (pp. 264- 269). Springer, Cham.
[10] Sv, A. K., & Srivatsa, S. K. (2018). An image fusion technique based on sparse wavelet transform and non-singleton type-2 FNN techniques. TAGA J, 14, 76-86.
[11] Bengochea-Guevara, J. M., Andújar, D., Cantuña, K., Garijo-Del-Río, C., & Ribeiro, A. (2019, November). An Autonomous Guided Field Inspection Vehicle for 3D Woody Crops Monitoring. In Iberian Robotics conference (pp. 164-175). Springer, Cham.
[12] Myna, A. N., & Prakash, J. (2018). Medical Image Fusion using Interval Type 2 Fuzzy Logic. International Journal of Applied Engineering Research, 13(14), 11410-11416.
[13] Mohammadzadeh, A., & Kayacan, E. (2019). A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications. Neurocomputing, 338, 63-71.
[14] Desta, H. (2017). Development of Automatic Sesame Grain Classification and Grading System Using Image Processing Techniques (Doctoral dissertation, Addis Ababa University).
[15] Davila, I., Lopez-Juarez, I., Mendez, G. M., Osorio-Comparan, R., Lefranc, G., & Cubillos, C. (2017). A singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding. International Journal of Computers Communications & Control, 12(2), 201- 216.
[16] Varela, N., Silva, J., Pineda, O. B., & Cabrera, D. (2020). Prediction of the Corn Grains Yield through Artificial Intelligence. Procedia Computer Science, 170, 1017-1022.
[17] Viloria A., Varela N., Pérez D.M., Lezama O.B.P. (2020) Data Processing for Direct Marketing Through Big Data. In: Smys S., Tavares J., Balas V., Iliyasu A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham.
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dc.publisher.spa.fl_str_mv Corporación Universidad de la Costa
dc.source.spa.fl_str_mv Procedia Computer Science
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spelling amelec, viloriaPineda Lezama, Omar BonergeCabrera, Danelys2021-01-07T14:25:32Z2021-01-07T14:25:32Z20201877-0509https://hdl.handle.net/11323/7664https://doi.org/10.1016/j.procs.2020.07.095Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This paper presents a hybrid methodology based on a type 1 fuzzy model in singleton version using a 2k factorial design that optimizes the model of the expert system and serves to perform in-line inspection. The factorial design method provides the required database for the creation of the rule base for the fuzzy model and also generates the database to train the expert system. The proposed method was validated in the process of verifying dimensional parameters by means of images compared with the ANFIS and RBFN models which show greater margins of error in the approximation of the function represented by the system compared with the proposed model. The results obtained show that the model has an excellent performance in the prediction and quality control of the industrial process studied when compared with similar expert system techniques as ANFIS and RBFN.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Pineda Lezama, Omar BonergeCabrera, Danelys-will be generated-orcid-0000-0002-9486-9764-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920317956Neural networksIrrigation controlInstrumentation and image analysisMicro-greenhouseInspection process for dimensioning through images and fuzzy logicArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Zhang, C., Craine, W. A., McGee, R. J., Vandemark, G. J., Davis, J. B., Brown, J., ... & Sankaran, S. (2020). Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops. Sensors, 20(5), 1450.[2] Zhang, F., Zhang, X.: Classification and Quality Evaluation of Tobacco leaves Based in Image Processing and Fuzzy Comprehensive Evaluation. Sensors. 11(3), pp. 2369–2384 (2011)[3] Mittal, P., Saini, R. K., & Jain, N. K. (2019). Image enhancement using fuzzy logic techniques. In Soft Computing: Theories and Applications (pp. 537-546). Springer, Singapore.[4] Ramya, H. R., & Sujatha, B. K. (2016, October). A novel approach for medical image fusion using fuzzy logic type-2. In 2016 International Conference on Circuits, Controls, Communications and Computing (I4C) (pp. 1-5). IEEE.[5] Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018, October). Noise parameter estimation for non-singleton fuzzy logic systems. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2960-2965). IEEE.[6] Izquierdo, N.V., Lezama, O.B.P., Dorta, R.G., Viloria, A., Deras, I., Hernández-Fernández, L.: Fuzzy logic applied to the performance evaluation. Honduran coffee sector case. In: Tan, Y., Shi, Y., Tang, Q. (eds.) Advances in Swarm Intelligence, ICSI 2018. Lecture Notes in Computer Science, vol 10942. Springer, Cham (2018)[7] Bora, D. J., & Thakur, R. S. (2018). An Efficient Technique for Medical Image Enhancement Based on Interval Type-2 Fuzzy Set Logic. In Progress in Computing, Analytics and Networking (pp. 667-678). Springer, Singapore.[8] Gonzalez, C. I., Melin, P., Castro, J. R., Castillo, O., & Mendoza, O. (2016). Optimization of interval type-2 fuzzy systems for image edge detection. Applied Soft Computing, 47, 631-643.[9] Reyes, D., Álvarez, A., Rincón, E. J., Valderrama, J., Noradino, P., & Méndez, G. M. (2017, October). A PID using a non-singleton fuzzy logic system type 1 to control a second-order system. In North American Fuzzy Information Processing Society Annual Conference (pp. 264- 269). Springer, Cham.[10] Sv, A. K., & Srivatsa, S. K. (2018). An image fusion technique based on sparse wavelet transform and non-singleton type-2 FNN techniques. TAGA J, 14, 76-86.[11] Bengochea-Guevara, J. M., Andújar, D., Cantuña, K., Garijo-Del-Río, C., & Ribeiro, A. (2019, November). An Autonomous Guided Field Inspection Vehicle for 3D Woody Crops Monitoring. In Iberian Robotics conference (pp. 164-175). Springer, Cham.[12] Myna, A. N., & Prakash, J. (2018). Medical Image Fusion using Interval Type 2 Fuzzy Logic. International Journal of Applied Engineering Research, 13(14), 11410-11416.[13] Mohammadzadeh, A., & Kayacan, E. (2019). A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications. Neurocomputing, 338, 63-71.[14] Desta, H. (2017). Development of Automatic Sesame Grain Classification and Grading System Using Image Processing Techniques (Doctoral dissertation, Addis Ababa University).[15] Davila, I., Lopez-Juarez, I., Mendez, G. M., Osorio-Comparan, R., Lefranc, G., & Cubillos, C. (2017). A singleton type-1 fuzzy logic controller for on-line error compensation during robotic welding. International Journal of Computers Communications & Control, 12(2), 201- 216.[16] Varela, N., Silva, J., Pineda, O. B., & Cabrera, D. (2020). Prediction of the Corn Grains Yield through Artificial Intelligence. Procedia Computer Science, 170, 1017-1022.[17] Viloria A., Varela N., Pérez D.M., Lezama O.B.P. (2020) Data Processing for Direct Marketing Through Big Data. In: Smys S., Tavares J., Balas V., Iliyasu A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. 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