Papers by MARCO ANTONIO PEREZ CISNEROS
Artificial neural networks applied in the forecast of pollutants into the Rio Santiago, based on the sample of a pollutant, by data fusion
2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), 2016
Application of Artificial Neural Networks shows the results obtained by merging data from previou... more Application of Artificial Neural Networks shows the results obtained by merging data from previous shots show them, considering a contaminant based on the Santiago River to forecast relative to other contaminants in the water, without having to perform specific and independent testing of each pollutant. This article presents the results of recent analysis.
This chapter investigates service-oriented simulation frameworks from the ontological, epistemolo... more This chapter investigates service-oriented simulation frameworks from the ontological, epistemological, and teleological perspectives. First, we give an overview of various specific frameworks that imply particular referential ontological, epistemological, and teleological perspectives for real world systems. Then we combine the partial considerations derived from the review into a unifying framework. It inspects the crossover between the disciplines of M&S, service-orientation, and software/systems engineering. From a methodological perspective, we show its ontological, epistemological, and teleological implications for abstract approaches. The unifying framework can, in turn, facilitate the classification, evaluation, selection, description, and prescription of the known or proposed frameworks. Thus, the referential and methodological perspectives build a systematical philosophical foundation of the service-oriented simulation paradigm.
Probabilistic Model-Based Diagnosis
Lecture Notes in Computer Science, 2000
ABSTRACT Diagnosis, in artificial intelligence, has traditionally utilized heuristic rules which ... more ABSTRACT Diagnosis, in artificial intelligence, has traditionally utilized heuristic rules which in many domains are difficult to acquire. An alternative approach, model-based diagnosis, utilizes a model of the system and compares its predicted behavior against the actual behavior of the system for diagnosis. This paper presents a novel technique based on probabilistic models. Therefore, it is natural to include uncertainty in the model and in the measurements for diagnosis. This characteristic makes the proposed approach suitable for applications where reliable measurements are unlikely to occur or where a deterministic analytical model is difficult to obtain. The proposed approach can detect single or multiple faults through a vector of probabilities which reflects the degree of belief in the state of all the components of the system. A comparison against GDE, a classical approach for multiple fault diagnosis, is given.
Visual registration and tracking for traffic monitoring
2015 IEEE First International Smart Cities Conference (ISC2), 2015
This paper describes a work-in-progress system to process real-time information about conflictive... more This paper describes a work-in-progress system to process real-time information about conflictive traffic spots within a modern city. The system aims to integrate data about the status of particular street points through image sensors and several visual interpretation phases. This idea has been inspired by the increasing traffic problem in the Mexican city of Guadalajara and the recent advances in the field of telecommunications, networking and mobile phone technologies. The information is built over incoming data from traffic cameras whose images are processed by several visual computation algorithms in order to segment and track vehicles. The system calculates vehicle speed information that can be later observed through a virtual reality user interface. The visual algorithm includes visual extraction, motion detection and tracking modules which process color images that are captured through off-the-shelf cameras. The system represents one operational block of an integrated framework which can be later used by government agencies to overcome difficult events on the streets, such as accidents, abnormal people flow and such. The current status of the project and a quick review of its future contributions are presented at this paper.
Mathematical Problems in Engineering, 2015
This paper presents a predictive control strategy for an image-based visual servoing scheme that ... more This paper presents a predictive control strategy for an image-based visual servoing scheme that employs evolutionary optimization. The visual control task is approached as a nonlinear optimization problem that naturally handles relevant visual servoing constraints such as workspace limitations and visibility restrictions. As the predictive scheme requires a reliable model, this paper uses a local model that is based on the visual interaction matrix and a global model that employs 3D trajectory data extracted from a quaternion-based interpolator. The work assumes a free-flying camera with 6-DOF simulation whose results support the discussion on the constraint handling and the image prediction scheme.
Artificial neural networks vs regression techniques in the forecasting of contaminants in the Santiago River, based on the sample of a pollutant, through Data Fusion
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2017
The present article shows a comparison of the use of both Neuronal Networks and Regression method... more The present article shows a comparison of the use of both Neuronal Networks and Regression methods in the pollutant forecast in the Santiago River, analyzing data obtained from previous samples by data fusion and considering a base pollutant to predict the relationship with more pollutants present in the river, avoiding the detailed and particular tests of the other Contaminants. This paper determines how to choose the best algorithm of the both, based on the error forecast.
Computación y Sistemas, 2010
Resumen es: El diseno de algoritmos que operen sobre plantas con dinamicas no modeladas aun repre... more Resumen es: El diseno de algoritmos que operen sobre plantas con dinamicas no modeladas aun representa un reto en el area de control automatico. Una solucion podria ...
An accurate Cluster chaotic optimization approach for digital medical image segmentation
Neural Computing and Applications, 2021
Image segmentation is a crucial stage in digital image processing used to obtain a more straightf... more Image segmentation is a crucial stage in digital image processing used to obtain a more straightforward representation of images. Although classic bi-level segmentation is a relatively simple task, it only suffices to analyze rather simple images. More complex real-life scenarios such as medical imaging processing usually require multi-level segmentation to differentiate between the many regions of interest present in the original images. Traditional histogram-based approaches for multi-level segmentation tend to perform suboptimally, with the best performing being computationally expensive. This difficult compromise between performance and computational cost has led to the proposal of new approaches mixing a variety of optimization algorithms and statistical criteria. Despite the success of these new approaches, there is still room for improvement. It is under these circumstances that evolutionary algorithms like the cluster chaotic optimization (CCO) become relevant. The CCO takes advantage of the classification procedures of clustering techniques and the randomness of chaotic sequences for encouraging the search strategy. This paper proposes a novel method based on the CCO algorithm named minimum cross-entropy multi-level segmentation CCO (CEMS-CCO). The CEMS-CCO employs the cross-entropy as its fitness function and the CCO capabilities to deal with multimodal functions to search for the optimal solution to the multi-level segmentation problem. The CEMS-CCO shows competitive results for medical images multi-level segmentation regarding different quality metrics. Furthermore, its robustness and effectiveness are tested through the analysis of well-known benchmark images. Statistical analysis of the experimental results shows that the proposed CEMS-CCO technique outperforms state-of-the-art algorithms.
International Journal of Electrical Engineering & Education, 2010
This paper shows the potential of a Lego™-based low-cost commercial robotic platform for learning... more This paper shows the potential of a Lego™-based low-cost commercial robotic platform for learning and testing prototypes in higher education and research. The overall set-up aims to explain mobile robotic issues, including mechatronics, robotics and automatic control theory. The capabilities and limitations of Lego robots are studied within two experiments: the first shows how to eliminate a number of restrictions in Lego robots using some programming alternatives; the second addresses the complex problem of multi-position control. Algorithms and their additional tools have been fully designed, applied and documented, and the results are shown throughout the paper. The platform was found to be suitable for teaching and researching key issues related to the aforementioned fields.
Generation and Optimization of Fuzzy Controllers Using the NEFCON Model
Computacion Y Sistemas, Dec 1, 2010
The design of algorithms that operate on un- modeled dynamics plants still represents a challenge... more The design of algorithms that operate on un- modeled dynamics plants still represents a challenge in automatic control area. A solution could be the use of algorithms able to learn in real time by direct interaction with the plant. NEFCON, allows to build a Mamdani fuzzy controller able to learn rules and adapt the fuzzy sets. The main advantage of NEFCON compared with other learning approaches, is that its design express the current error state of the plant to be controlled. However, a disadvantage of NEFCON is its poor exploration of the states of the plant during the learning; disable its application on nonlinear dynamic systems. In this work the addition of Gaussian noise to the states of the plant is proposed with the objective to assure a wide exploration of the states, simplifying the convergence, when it is applied to nonlinear systems. In particular, the effectiveness of our proposal is shown in the control of the "ball and beam" dynamic system.
Control Lógico Programable
Análisis, diseño e implementación de un controlador de lazo cerrado basado en redes neuronales artificiales / M.A. Pérez Cisneros
ABSTRACT
Journal of Applied Research and Technology, 2013
How to cite Complete issue More information about this article Journal's homepage in redalyc.org ... more How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative
Expert Systems with Applications, 2015
Image segmentation plays an important role in image processing and computer vision. It is often u... more Image segmentation plays an important role in image processing and computer vision. It is often used to classify an image into separate regions, which ideally correspond to different real-world objects. Several segmentation methods have been proposed in the literature, being thresholding techniques the most popular. In such techniques, it is selected a set of proper threshold values that optimize a determined functional criterion, so that each pixel is assigned to a determined class according to its corresponding threshold points. One interesting functional criterion is the Tsallis entropy, which gives excellent results in bi-level thresholding. However, when it is applied to multilevel thresholding, its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. Therefore, in the process of finding the appropriate threshold values, it is desired to limit the number of evaluations of the objective function (Tsallis entropy). Under such circumstances, most of the optimization algorithms do not seem to be suited to face such problems as they usually require many evaluations before delivering an acceptable result. On the other hand, the Electromagnetism-Like algorithm is an evolutionary optimization approach which emulates the attraction-repulsion mechanism among charges for evolving the individuals of a population. This technique exhibits interesting search capabilities whereas maintains a low number of function evaluations. In this paper, a new algorithm for multilevel segmentation based on the Electromagnetism-Like algorithm is proposed. In the approach, the optimization algorithm based on the electromagnetism theory is used to find the optimal threshold values by maximizing the Tsallis entropy. Experimental results over several images demonstrate that the proposed approach is able to improve the convergence velocity, compared with similar methods such as Cuckoo search, and Particle Swarm Optimization.
Electromagnetism-like Optimization (EMO) is a global optimization algorithm, particularly well-su... more Electromagnetism-like Optimization (EMO) is a global optimization algorithm, particularly well-suited to solve problems featuring non-linear and multimodal cost functions. EMO employs searcher agents that emulate a population of charged particles which interact to each other according to electromagnetism's laws of attraction and repulsion. However, EMO usually requires a large number of iterations for a local search procedure; any reduction or cancelling over such number, critically perturb other issues such as convergence, exploration, population diversity and accuracy. This paper presents an enhanced EMO algorithm called OBEMO, which employs the Opposition-Based Learning (OBL) approach to accelerate the global convergence speed. OBL is a machine intelligence strategy which considers the current candidate solution and its opposite value at the same time, achieving a faster exploration of the search space. The proposed OBEMO method significantly reduces the required computational effort yet avoiding any detriment to the good search capabilities of the original EMO algorithm. Experiments are conducted over a comprehensive set of benchmark functions, showing that OBEMO obtains promising performance for most of the discussed test problems.
Discrete Time Nonlinear Identification via Recurrent High Order Neural Networks for a Three Phase Induction Motor
Lecture Notes in Computer Science, 2010
International Journal of Electrical Engineering Education, 2013
Fuzzy controllers have gained popularity in the past few decades with successful implementations ... more Fuzzy controllers have gained popularity in the past few decades with successful implementations in many fields that have enabled designers to control complex systems through linguistic-based rules in contrast to traditional methods. This paper presents an educational platform based on LEGO© NXT to assist the learning of fuzzy logic control principles at undergraduate level by providing a simple and easy-to-follow teaching setup. The proposed fuzzy control study aims to accustom students to the learning of fuzzy control fundamentals by building hands-on robotic experiments. The proposed educational platform has been successfully applied to several undergraduate courses within the Electronics Department in the University of Guadalajara. The description of robotic experiments and the evaluation of their impact on student performance are both provided in the paper.
Journal of Applied Mathematics, 2014
System identification is a complex optimization problem which has recently attracted the attentio... more System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: t...
International Journal of Electrical Engineering Education, 2011
In recent years, Artificial Intelligence (AI) techniques have emerged as useful non-traditional t... more In recent years, Artificial Intelligence (AI) techniques have emerged as useful non-traditional tools for solving various engineering problems. AI has thus become an important subject in the engineering curriculum. However, the design of a balanced AI course is not a trivial task as its concepts commonly overlap with many other disciplines relating a wide number of subjects and ranging from applied approaches to more formal mathematical issues. This paper presents the use of a simple robotic platform to assist the learning of basic AI concepts. The study is guided by simple experimentation on autonomous mobile robots. The presented material has been successfully tested as an AI teaching aid in the University of Guadalajara's robotics group, yielding motivation to students, increasing enrolment and retention on robotics-related courses and eventually contributing to the development of competent computer engineers.
Image Segmentation Using Artificial Bee Colony Optimization
Intelligent Systems Reference Library, 2013
ABSTRACT This chapter explores the use of the Artificial Bee Colony (ABC) algorithm to compute pi... more ABSTRACT This chapter explores the use of the Artificial Bee Colony (ABC) algorithm to compute pixel classification for image segmentation. ABC is a heuristic algorithm motivated by the intelligent behaviour of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. For the approximation scheme, each Gaussian function represents a pixel class and therefore a threshold. Unlike the Expectation-Maximization (EM) algorithm, the ABC-based method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental results demonstrate the algorithm’s ability to perform automatic multi-threshold selection yet showing interesting advantages by comparison to other well-known algorithms.
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Papers by MARCO ANTONIO PEREZ CISNEROS