DR. CHIDENTREE TREESATAYAPUN

@article{Lopez-Juarez2017,

title = {A hybrid non-invasive method for internal/external quality assessment of potatoes},

author = {Lopez-Juarez, Ismael and Rios-Cabrera, Reyes and Hsieh,S J and Howarth, M .},

url = {https://doi.org/10.1007/s00217-017-2936-9},

doi = {10.1007/s00217-017-2936-9},

issn = {1438-2385},

year  = {2017},

date = {2017-07-11},

journal = {European Food Research and Technology},

abstract = {Consumers purchase fruits and vegetables based on its quality, which can be defined as a degree of excellence which is the result of a combination of characteristics, attributes and properties that have significance for market acceptability. In this paper, a novel hybrid active imaging methodology for potato quality inspection that uses an optical colour camera and an infrared thermal camera is presented. The methodology employs an artificial neural network (ANN) that uses quality data composed by two descriptors as input. The ANN works as a feature classifier so that its output is the potato quality grade. The input vector contains information related to external characteristics, such as shape, weight, length and width. Internal characteristics are also accounted for in the input vector in the form of excessive sugar content. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The validation of the methodology was made through experimentation which consisted in fusing both, external and internal characteristics in the input vector to the ANN for an overall quality classification. Results using internal data as obtained from an infrared camera and fused with optical external parameters demonstrated the feasibility of the method since the prediction accuracy increased during potato grading.},

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}

@article{Ruelas-Santoyo2016,

title = {Neuro-Fuzzy System Applied to Recognize and Assessing the Damage In Carbon Steel Supported by Descriptive},

author = {Ruelas-Santoyo, E. and Vazquez-Lopez, J. A. and Y\'{a}\~{n}ez-Mendiola, J. and Lopez-Juarez, Ismael and Bravo-Barrera, C. F.},

year  = {2016},

date = {2016-06-06},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7437237,

title = {Dense Correspondence using Local Regions with Affine Transformations},

author = {Reyes, Alfredo and Lopez-Juarez, Ismael},

doi = {10.1109/TLA.2016.7437237},

issn = {1548-0992},

year  = {2016},

date = {2016-02-01},

journal = {IEEE Latin America Transactions},

volume = {14},

number = {2},

pages = {893-898},

abstract = {The development of a virtual environments is desirable in many engineering applications for several reasons. For example, it can reduce the number of dangerous operations during the virtual inspection of mines, pipes for gas, water and sewer to inspect its current condition and identify possible failures. The proposal in this paper is an alternative solution to this problem obtaining a virtual image through the reconstruction of the inner pipe for assessment and fault detection. The proposed method employs regions with affine transformations used as dense correspondence, which are calculated through local correspondences, such as the detector/descriptor SIFT (Scale-invariant feature transform). The SIFT is required to obtain the position and orientation of the camera in the captured positions to get the structure of the scene through correspondences and to create a 3D reconstruction.},

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pubstate = {published},

tppubtype = {article}

}

@article{Aviles-Vi\~{n}as2016b,

title = {On-line learning of welding bead geometry in industrial robots},

author = {Aviles-Vi\~{n}as, Jaime F and Rios-Cabrera, Reyes and Lopez-Juarez, Ismael },

url = {http://dx.doi.org/10.1007/s00170-015-7422-6},

doi = {10.1007/s00170-015-7422-6},

issn = {1433-3015},

year  = {2016},

date = {2016-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {83},

number = {1},

pages = {217--231},

abstract = {In this paper, we propose an architecture based on an artificial neural network (ANN), to learn welding skills automatically in industrial robots. With the aid of an optic camera and a laser-based sensor, the bead geometry (width and height) is measured. We propose a real-time computer vision algorithm to extract training patterns in order to acquire knowledge to later predict specific geometries. The proposal is implemented and tested in an industrial KUKA KR16 robot and a GMAW type machine within a manufacturing cell. Several data analysis are described as well as off-line and on-line training, learning strategies, and testing experimentation. It is demonstrated during our experiments that, after learning the skill, the robot is able to produce the requested bead geometry even without any knowledge about the welding parameters such as arc voltage and current. We implemented an on-line learning test, where the whole experiments and learning process take only about 4 min. Using this knowledge later, we obtained up to 95 % accuracy in prediction.},

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pubstate = {published},

tppubtype = {article}

}

@article{Rios-Cabrera2016,

title = {Robotic GMAW online learning: issues and experiments},

author = {Rios-Cabrera, Reyes and Morales-Diaz, America B. and Aviles-Vi\~{n}as, Jaime F and Lopez-Juarez, Ismael },

url = {http://dx.doi.org/10.1007/s00170-016-8618-0},

doi = {10.1007/s00170-016-8618-0},

issn = {1433-3015},

year  = {2016},

date = {2016-01-01},

journal = {The International Journal of Advanced Manufacturing Technology},

volume = {87},

number = {5},

pages = {2113--2134},

abstract = {This paper presents three main contributions: (i) an experimental analysis of variables, using well-defined statistical patterns applied to the main parameters of the welding process. (ii) An on-line/off-line learning and testing method, showing that robots can acquire a useful knowledge base without human intervention to learn and reproduce bead geometries. And finally, (iii) an on-line testing analysis including penetration of the bead, that is used to train an artificial neural network (ANN). For the experiments, an optic camera was used in order to measure bead geometry (width and height). Also real-time computer vision algorithms were implemented to extract training patterns. The proposal was carried out using an industrial KUKA robot and a GMAW type machine inside a manufacturing cell. We present expermental analysis that show different issues and solutions to build an industrial adaptive system for the robotics welding process.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7430073,

title = {Reconfiguration Distributed Objects in an Intelligent Manufacturing Cell},

author = {Benitez Perez, H. and Lopez-Juarez, Ismael and Garza-Alanis, P. C. and Rios-Cabrera, Reyes and Duran Chavesti, A.},

doi = {10.1109/TLA.2016.7430073},

issn = {1548-0992},

year  = {2016},

date = {2016-01-01},

journal = {IEEE Latin America Transactions},

volume = {14},

number = {1},

pages = {136-146},

abstract = {A manufacture system with the abilities of easy reconfiguration and highly scalability becomes flexible, dynamic and open to the use of software technologies. To give these abilities to a manufacture cell formed of three industrial robots and two conveyors, a middleware based on the programming standard Common Object Request Broker Architecture (CORA) was developed, thus creating a distributed manufacture cell, allowing us to have a real production with different final products. In order to optimize the production times of the different products to be manufactured, a product scheduler was developed using the algorithm Earlies Deadline First (EDF) and the support algorithm Deferrable Server (DS). Given that failures may occur on any of the specialized modules of the manufacture system, the self reconfiguration of the manufacture system is something very desirable. This article propose an algorithm to solve this problem, the algorithm identifies the failures in relation to the time it takes the system to make a product, then makes a modification on the working speed of the plant elements of the specialized modules.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7273794,

title = {Condition Estimation Of Carbon Steel Using A Neuro-Fuzzy System And Image Processing},

author = {Ruelas-Santoyo, E. A. and Vazquez-Lopez, J. A. and Yanez-Mendiola, J. and Lopez-Juarez, Ismael and Bravo-Barrera, C. F.},

url = {http://ieeexplore.ieee.org/document/7273794/},

doi = {10.1109/TLA.2015.7273794},

issn = {1548-0992},

year  = {2015},

date = {2015-07-01},

journal = {IEEE Latin America Transactions},

volume = {13},

number = {7},

pages = {2322-2328},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7273810,

title = {Surveillance System Mobile Object Using Segmentation Algorithms},

author = {Osorio-Comparan, Roman and Lopez-Juarez, Ismael and Pena-Cabrera, Mario and Lomas, V. and Lefranc, G. and Savage, J.},

url = {http://ieeexplore.ieee.org/document/7273810/?reload=true\&arnumber=7273810},

doi = {10.1109/TLA.2015.7273810},

issn = {1548-0992},

year  = {2015},

date = {2015-07-01},

journal = {IEEE Latin America Transactions},

volume = {13},

number = {7},

pages = {2441-2446},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7164216,

title = {Fuzzy Logic for Omni directional Mobile Platform Control Displacement using FPGA and Bluetooth},

author = {Pena-Cabrera, Mario and Gomez, J. A. and Osorio-Comparan, Roman and Lopez-Juarez, Ismael and Lomas, V. and Gomez, H. and Lefranc, G.},

url = {http://ieeexplore.ieee.org/document/7164216/},

doi = {10.1109/TLA.2015.7164216},

issn = {1548-0992},

year  = {2015},

date = {2015-06-01},

journal = {IEEE Latin America Transactions},

volume = {13},

number = {6},

pages = {1907-1914},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7164220,

title = {System Administration and Monitoring Moving Objects Using Mobile Devices},

author = {Osorio-Comparan, Roman and Vasquez, D. and Lopez-Juarez, Ismael and Pena-Cabrera, Mario and Savage, J. and Lefranc, G.},

url = {http://ieeexplore.ieee.org/document/7164220/},

doi = {10.1109/TLA.2015.7164220},

issn = {1548-0992},

year  = {2015},

date = {2015-06-01},

journal = {IEEE Latin America Transactions},

volume = {13},

number = {6},

pages = {1934-1939},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1108/IR-09-2014-0395,

title = {Acquisition of welding skills in industrial robots},

author = {Aviles-Vi\~{n}as, Jaime F and Lopez-Juarez, Ismael and Rios-Cabrera, Reyes },

url = {http://dx.doi.org/10.1108/IR-09-2014-0395},

doi = {10.1108/IR-09-2014-0395},

year  = {2015},

date = {2015-01-01},

journal = {Industrial Robot: An International Journal},

volume = {42},

number = {2},

pages = {156-166},

abstract = {Purpose \textendash The purpose of this paper was to propose a method based on an Artificial Neural Network and a real-time vision algorithm, to learn welding skills in industrial robotics. Design/methodology/approach \textendash By using an optic camera to measure the bead geometry (width and height), the authors propose a real-time computer vision algorithm to extract training patterns and to enable an industrial robot to acquire and learn autonomously the welding skill. To test the approach, an industrial KUKA robot and a welding gas metal arc welding machine were used in a manufacturing cell. Findings \textendash Several data analyses are described, showing empirically that industrial robots can acquire the skill even if the specific welding parameters are unknown. Research limitations/implications \textendash The approach considers only stringer beads. Weave bead and bead penetration are not considered. Practical implications \textendash With the proposed approach, it is possible to learn specific welding parameters despite of the material, type of robot or welding machine. This is due to the fact that the feedback system produces automatic measurements that are labelled prior to the learning process. Originality/value \textendash The main contribution is that the complex learning process is reduced into an input-process-output system, where the process part is learnt automatically without human supervision, by registering the patterns with an automatically calibrated vision system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Navarro-Gonzalez2015,

title = {On-line incremental learning for unknown conditions during assembly operations with industrial robots},

author = {Navarro-Gonzalez, Jose Luis and Lopez-Juarez, Ismael and Ordaz-Hernandez, Keny and Rios-Cabrera, Reyes },

url = {http://dx.doi.org/10.1007/s12530-014-9125-x},

doi = {10.1007/s12530-014-9125-x},

issn = {1868-6486},

year  = {2015},

date = {2015-01-01},

journal = {Evolving Systems},

volume = {6},

number = {2},

pages = {101--114},

abstract = {The assembly operation using industrial robots can be accomplished successfully in well-structured environments where the mating pair location is known in advance. However, in real-world scenarios there are uncertainties associated to sensing, control and modelling errors that make the assembly task very complex. In addition, there are also unmodeled uncertainties that have to be taken into account for an effective control algorithm to succeed. Among these uncertainties, it can be mentioned disturbances, backlash and aging of mechanisms. In this paper, a method to overcome the effect of those uncertainties based on the Fuzzy ARTMAP artificial neural network (ANN) to successfully accomplish the assembly task is proposed. Experimental work is reported using an industrial 6 DOF robot arm in conjunction with a vision system for part location and wrist force/torque sensing data for assembly. Force data is fed into an ANN evolving controller during a typical peg in hole (PIH) assembly operation. The controller uses an incremental learning mechanism that is solely guided by the sensed forces. In this article, two approaches are presented in order to compare the incremental learning capability of the manipulator. The first approach uses a primitive knowledge base (PKB) containing 16 primitive movements to learn online the first insertion. During assembly, the manipulator learns new patterns according to the learning criteria which turn the PKB into an enhanced knowledge base (EKB). During a second insertion the controller uses effectively the EKB and operation improves. The second approach employs minimum information (it contains only the assembly direction) and the process starts from scratch. After several operations, that knowledge base increases by including only the needed patterns to perform the insertion. Experimental results showed that the evolving controller is able to assemble the matting pairs enhancing its knowledge whenever it is needed depending on the part geometry and level of expertise. Our approach is demonstrated through several PIH operations with different tolerances and part geometry. As the robot's expertise evolves, the PIH operation is carried out faster with shorter assembly trajectories.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{delosAngelesHernandez2015,

title = {A hybrid learning method composed by the orthogonal least-squares and the back-propagation learning algorithms for interval A2-C1 type-1 non-singleton type-2 TSK fuzzy logic systems},

author = {de los Angeles Hernandez, Maria and Melin, Patricia and Mendez, Gerardo M. and Castillo, Oscar and Lopez-Juarez, Ismael},

url = {http://dx.doi.org/10.1007/s00500-014-1287-8},

doi = {10.1007/s00500-014-1287-8},

issn = {1433-7479},

year  = {2015},

date = {2015-01-01},

journal = {Soft Computing},

volume = {19},

number = {3},

pages = {661--678},

abstract = {The purpose of this paper is to present a hybrid learning method for interval A2-C1 type-1 non-singleton type-2 TSK fuzzy logic system that uses the recursive orthogonal least-squares algorithm to tune the type-1 consequent parameters, and the back-propagation algorithm to tune the interval type-2 antecedent parameters. Based on the combination of these two training algorithms the new hybrid learning method changes the interval type-2 fuzzy model parameters adaptively and minimizes the proposed error function as the new type-1 non-singleton input--output data pairs are processed. Its antecedent sets are interval type-2 fuzzy sets, its consequent sets are type-1 fuzzy sets, and its inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations. Comparison with the non-hybrid interval A2-C1 type-1 non-singleton type-2 Takagi--Sugeno--Kang fuzzy logic system that only uses the back-propagation algorithm for both antecedent and consequent parameter's adaptation demonstrates that the proposed hybrid algorithm is a well-performing nonlinear adaptation that enables the interval type-2 fuzzy model to optimally match the nonlinear behavior of the process. The application of the interval type-2 fuzzy logic as adaptable predictor using the proposed hybrid learning method was constructed for the modeling and prediction of the transfer bar surface temperature in an industrial hot strip mill facility. Experimental results demonstrated that this method improves the temperature prediction performance of the interval A2-C1 type-1 non-singleton type-2 Takagi--Sugeno--Kang fuzzy logic system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NavarroGonzalez201578b,

title = {On-line knowledge acquisition and enhancement in robotic assembly tasks},

author = {Navarro-Gonzalez, Jose Luis and Lopez-Juarez, Ismael and Rios-Cabrera, Reyes and Ordaz-Hernandez, Keny},

url = {http://www.sciencedirect.com/science/article/pii/S073658451400074X},

doi = {http://dx.doi.org/10.1016/j.rcim.2014.08.013},

issn = {0736-5845},

year  = {2015},

date = {2015-01-01},

journal = {Robotics and Computer-Integrated Manufacturing},

volume = {33},

pages = {78 - 89},

abstract = {Abstract Industrial robots are reliable machines for manufacturing tasks such as welding, panting, assembly, palletizing or kitting operations. They are traditionally programmed by an operator using a teach pendant in a point-to-point scheme with limited sensing capabilities such as industrial vision systems and force/torque sensing. The use of these sensing capabilities is associated to the particular robot controller, operative systems and programming language. Today, robots can react to environment changes specific to their task domain but are still unable to learn skills to effectively use their current knowledge. The need for such a skill in unstructured environments where knowledge can be acquired and enhanced is desirable so that robots can effectively interact in multimodal real-world scenarios. In this article we present a multimodal assembly controller (MAC) approach to embed and effectively enhance knowledge into industrial robots working in multimodal manufacturing scenarios such as assembly during kitting operations with varying shapes and tolerances. During learning, the robot uses its vision and force capabilities resembling a human operator carrying out the same operation. The approach consists of using a MAC based on the Fuzzy ARTMAP artificial neural network in conjunction with a knowledge base. The robot starts the operation having limited initial knowledge about what task it has to accomplish. During the operation, the robot learns the skill for recognising assembly parts and how to assemble them. The skill acquisition is evaluated by counting the steps to complete the assembly, length of the followed assembly path and compliant behaviour. The performance improves with time so that the robot becomes an expert demonstrated by the assembly of a kit with different part geometries. The kit is unknown by the robot at the beginning of the operation; therefore, the kit type, location and orientation are unknown as well as the parts to be assembled since they are randomly fed by a conveyor belt.},

note = {Special Issue on Knowledge Driven Robotics and Manufacturing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chinas2015,

title = {SVM and ANN Application to Multivariate Pattern Recognition Using Scatter Data},

author = {Chi\~{n}as-Sanchez, Pamela and Lopez-Juarez, Ismael and Vazquez, J. A. and Osorio-Comparan, Roman and Lefranc, Gaston},

url = {https://www.researchgate.net/publication/277145967_SVM_and_ANN_Application_to_Multivariate_Pattern_Recognition_Using_Scatter_Data},

doi = { 10.1109/TLA.2015.7112025},

issn = {1548-0992},

year  = {2015},

date = {2015-00-00},

journal = {IEEE Latin America Transactions},

volume = {13},

number = {5},

pages = {1633-2639},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PAMELA2014125,

title = {Reconocimiento de variables multivariantes empleando el estadistico T2 Hotelling y MEWMA mediante las RNA’s},

author = {Chi\~{n}as-Sanchez, Pamela and Lopez-Juarez, Ismael and Vazquez-Lopez, J. A.},

url = {http://www.sciencedirect.com/science/article/pii/S1405774315300111},

doi = {http://dx.doi.org/10.1016/S1405-7743(15)30011-1},

issn = {1405-7743},

year  = {2014},

date = {2014-01-01},

journal = {Ingeniera, Investigacion y Tecnologia},

volume = {15},

number = {1},

pages = {125 - 138},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LopezJuarez20135,

title = {Using Object’s Contour, Form and Depth to Embed Recognition Capability into Industrial Robots},

author = {Lopez-Juarez, Ismael and Castelan, Mario and Castro-Mart\^{i}nez, Francisco Javier and Pe\~{n}a-Cabrera, Mario and Osorio-Comparan, Roman},

url = {http://www.sciencedirect.com/science/article/pii/S1665642313715116},

doi = {http://dx.doi.org/10.1016/S1665-6423(13)71511-6},

issn = {1665-6423},

year  = {2013},

date = {2013-01-01},

journal = {Journal of Applied Research and Technology},

volume = {11},

number = {1},

pages = {5 - 17},

abstract = {Abstract Robot vision systems can differentiate parts by pattern matching irrespective of part orientation and location. Some manufacturers offer 3D guidance systems using robust vision and laser systems so that a 3D programmed point can be repeated even if the part is moved varying its location, rotation and orientation within the working space. Despite these developments, current industrial robots are still unable to recognize objects in a robust manner; that is, to distinguish an object among equally shaped objects taking into account not only the object’s contour but also its form and depth information, which is precisely the major contribution of this research. Our hypothesis establishes that it is possible to integrate a robust invariant object recognition capability into industrial robots by using image features from the object’s contour (boundary object information), its form (i.e., type of curvature or topographical surface information) and depth information (from stereo disparity maps). These features can be concatenated in order to form an invariant vector descriptor which is the input to an artificial neural network (ANN) for learning and recognition purposes. In this paper we present the recognition results under different working conditions using a KUKA KR16 industrial robot, which validated our approach.},

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pubstate = {published},

tppubtype = {article}

}