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David Balderas Silva
Tecnologico de Monterrey National Department of Research, Puente 222, Del. Tlalpan, Mexico City 14380, Mexico

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Short Biography

Balderas studied Mechatronics Engineering and graduated in 2005 from Universidad Panamericana. Subsequently, he completed his graduate studies and obtained an M.Sc. in Biomedical Engineering in 2009 from Delft University of Technology. He finished his Ph.D. in Engineering Sciences, with a specialization in Artificial Intelligence and Brain–Computer Interfaces in Monterrey Institute of Technology and Higher Education.

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Review
Published: 03 August 2021 in Future Internet
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Education 4.0 is looking to prepare future scientists and engineers not only by granting them with knowledge and skills but also by giving them the ability to apply them to solve real life problems through the implementation of disruptive technologies. As a consequence, there is a growing demand for educational material that introduces science and engineering students to technologies, such as Artificial Intelligence (AI) and Brain–Computer Interfaces (BCI). Thus, our contribution towards the development of this material is to create a test bench for BCI given the basis and analysis on how they can be discriminated against. This is shown using different AI methods: Fisher Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Restricted Boltzmann Machines (RBM) and Self-Organizing Maps (SOM), allowing students to see how input changes alter their performance. These tests were done against a two-class Motor Image database. First, using a large frequency band and no filtering eye movement. Secondly, the band was reduced and the eye movement was filtered. The accuracy was analyzed obtaining values around 70∼80% for all methods, excluding SVM and SOM mapping. Accuracy and mapping differentiability increased for some subjects for the second scenario 70∼85%, meaning either their band with the most significant information is on that limited space or the contamination because of eye movement was better mitigated by the regression method. This can be translated to saying that these methods work better under limited spaces. The outcome of this work is useful to show future scientists and engineers how BCI experiments are conducted while teaching them the basics of some AI techniques that can be used in this and other several experiments that can be carried on the framework of Education 4.0.

ACS Style

David Balderas; Pedro Ponce; Diego Lopez-Bernal; Arturo Molina. Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces. Future Internet 2021, 13, 202 .

AMA Style

David Balderas, Pedro Ponce, Diego Lopez-Bernal, Arturo Molina. Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces. Future Internet. 2021; 13 (8):202.

Chicago/Turabian Style

David Balderas; Pedro Ponce; Diego Lopez-Bernal; Arturo Molina. 2021. "Education 4.0: Teaching the Basis of Motor Imagery Classification Algorithms for Brain-Computer Interfaces." Future Internet 13, no. 8: 202.

Journal article
Published: 27 July 2021 in Future Internet
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One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technologies, such as Machine Learning (ML), as well as the skills to implement this knowledge to solve real-life problems. Therefore, both students and professors require teaching and learning tools that facilitate the introduction to such topics. Consequently, this study looks forward to contributing to the development of those tools by introducing the basic theory behind three machine learning classifying algorithms: K-Nearest-Neighbor (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron; as well as discussing the diverse advantages and disadvantages of each method. Moreover, it is proposed to analyze how these methods work on different conditions through their implementation over a test bench. Thus, in addition to the description of each algorithm, we discuss their application to solving three different binary classification problems using three different datasets, as well as comparing their performances in these specific case studies. The findings of this study can be used by teachers to provide students the basic knowledge of KNN, LDA, and perceptron algorithms, and, at the same time, it can be used as a guide to learn how to apply them to solve real-life problems that are not limited to the presented datasets.

ACS Style

Diego Lopez-Bernal; David Balderas; Pedro Ponce; Arturo Molina. Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems. Future Internet 2021, 13, 193 .

AMA Style

Diego Lopez-Bernal, David Balderas, Pedro Ponce, Arturo Molina. Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems. Future Internet. 2021; 13 (8):193.

Chicago/Turabian Style

Diego Lopez-Bernal; David Balderas; Pedro Ponce; Arturo Molina. 2021. "Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems." Future Internet 13, no. 8: 193.

Preprint
Published: 01 June 2021
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Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub (e.g. airports) or to evacuate a disaster zone. Many methods have been used to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have increasingly become more popular in this kind of applications, given their robustness and scalability. In this paper, we have implemented an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be able to solve the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we present the integration and functionality of these methods applied in a pair of NAO robots.

ACS Style

Daniela Magallán-Ramirez; Areli Rodriguez-Tirado; Jorge David Martínez-Aguilar; Carlos Francisco Moreno-García; David Balderas; Edgar Omar López-Caudana. Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning. 2021, 1 .

AMA Style

Daniela Magallán-Ramirez, Areli Rodriguez-Tirado, Jorge David Martínez-Aguilar, Carlos Francisco Moreno-García, David Balderas, Edgar Omar López-Caudana. Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning. . 2021; ():1.

Chicago/Turabian Style

Daniela Magallán-Ramirez; Areli Rodriguez-Tirado; Jorge David Martínez-Aguilar; Carlos Francisco Moreno-García; David Balderas; Edgar Omar López-Caudana. 2021. "Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning." , no. : 1.

Original article
Published: 03 February 2021 in The International Journal of Advanced Manufacturing Technology
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The fourth industrial revolution, Industry 4.0, has been characterized by novel concepts introduction in manufacturing systems that enable smart factories with vertically and horizontally communication to improve their performance. Many virtual systems allow to predict foul conditions, save energy, study special cases, and so on, yet they need to implement new digital tools that allow developing manufacturing process in a better manner. As a result, Digital-Twin platforms are a good alternative since they are virtual models that could receive online and offline data. Thus, programmed algorithms can be evaluated to know the performance of the manufacturing process. These virtualizations and interconnections between elements of the manufacturing process become important components with an increasing role in dealing with supply, production times, and delivery chains as they run in parallel and find optimal performance before implementing these conditions into the real system. This study focuses on the use of a Digital-Twin that integrates a metaheuristic optimization and a direct Simulink model for printed circuit boards (PCB) design and processing focused on the drilling process. The results show that metaheuristic optimization can be integrated into the Digital-Twin concept as part of the production system into the drilling process. In the first part, it shows that depending on the penalization the optimization focuses on the lower path and forgets on changing the tools, yet as the penalization raises it focuses on finishing drilling with one tool before changing. Second, it is important where on the PCB it starts the drilling, with less time depending on each plaque. Third, it can be observed that using optimization can triple the amount of PCBs that can be manufactured. Finally, on an 8-hr run the Digital-Twin that didn’t use optimization can only work with three different designs, differently with optimization it can have 7-8 changes in the PCB design.

ACS Style

David Balderas; Alexandro Ortiz; Efraín Méndez; Pedro Ponce; Arturo Molina. Empowering Digital Twin for Industry 4.0 using metaheuristic optimization algorithms: case study PCB drilling optimization. The International Journal of Advanced Manufacturing Technology 2021, 113, 1295 -1306.

AMA Style

David Balderas, Alexandro Ortiz, Efraín Méndez, Pedro Ponce, Arturo Molina. Empowering Digital Twin for Industry 4.0 using metaheuristic optimization algorithms: case study PCB drilling optimization. The International Journal of Advanced Manufacturing Technology. 2021; 113 (5-6):1295-1306.

Chicago/Turabian Style

David Balderas; Alexandro Ortiz; Efraín Méndez; Pedro Ponce; Arturo Molina. 2021. "Empowering Digital Twin for Industry 4.0 using metaheuristic optimization algorithms: case study PCB drilling optimization." The International Journal of Advanced Manufacturing Technology 113, no. 5-6: 1295-1306.

Technical paper
Published: 10 September 2020 in International Journal on Interactive Design and Manufacturing (IJIDeM)
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Most socio-technological Systems are complex and have many variables interconnected, which makes them hard to predict and more important to control. Moreover, in many cases it is required to choose a plan of action that would bring some consequences and/or rewards. Under those circumstances it is important to make a decision based on the system’s variables, requirements, munificence and complexity and that they are evaluated using feasibility, viability, key performance indicators and time. In particular, the time required to make a decision. Hence, the correct use of decision-making tools would increase the chance of making a correct choice. For this reason, this work focuses on the list of requirements for a facilitation room, facilitation staff and tools, that would help the stakeholders to make a choice in a timely manner. The work ends with some suggestions that are likely to favor the final decision.

ACS Style

David Balderas; Jose Martin Molina Espinosa; Sergio Ruiz Loza. Decision-making laboratory for socio-technological systems. International Journal on Interactive Design and Manufacturing (IJIDeM) 2020, 14, 1557 -1568.

AMA Style

David Balderas, Jose Martin Molina Espinosa, Sergio Ruiz Loza. Decision-making laboratory for socio-technological systems. International Journal on Interactive Design and Manufacturing (IJIDeM). 2020; 14 (4):1557-1568.

Chicago/Turabian Style

David Balderas; Jose Martin Molina Espinosa; Sergio Ruiz Loza. 2020. "Decision-making laboratory for socio-technological systems." International Journal on Interactive Design and Manufacturing (IJIDeM) 14, no. 4: 1557-1568.

Journal article
Published: 12 June 2020 in Energies
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Nowadays, owing to the growing interest in renewable energy, Photovoltaic systems (PV) are responsible of supplying more than 500,000 GW of the electrical energy consumed around the world. Therefore, different converters topologies, control algorithms, and techniques have been studied and developed in order to maximize the energy harvested by PV sources. Maximum Power Point Tracking (MPPT) methods are usually employed with DC/DC converters, which together are responsible for varying the impedance at the output of photovoltaic arrays, leading to a change in the current and voltage supplied in order to achieve a dynamic optimization of the transferred energy. MPPT algorithms such as, Perturb and Observe (P&O) guarantee correct tracking behavior with low calibration parameter dependence, but with a compromised relation between the settling time and steady-state oscillations, leading to a trade off between them. Nevertheless, proposed methods like Particle Swarm Optimization- (PSO) based techniques have improved the settling time with the addition of lower steady-state oscillations. Yet, such a proposal performance is highly susceptible and dependent to correct and precise parameter calibration, which may not always ensure the expected behavior. Therefore, this work presents a novel alternative for MPPT, based on the Earthquake Optimization Algorithm (EA) that enables a solution with an easy parameters calibration and an improved dynamic behavior. Hence, a boost converter case study is proposed to verify the suitability of the proposed technique through Simscape Power Systems™ simulations, regarding the dynamic model fidelity capabilities of the software. Results show that the proposed structure can easily be suited into different power applications. The proposed solution, reduced between 12% and 36% the energy wasted in the simulation compared to the P&O and PSO based proposals.

ACS Style

Efrain Mendez; Alexandro Ortiz; Pedro Ponce; Israel Macias; David Balderas; Arturo Molina. Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm. Energies 2020, 13, 3047 .

AMA Style

Efrain Mendez, Alexandro Ortiz, Pedro Ponce, Israel Macias, David Balderas, Arturo Molina. Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm. Energies. 2020; 13 (12):3047.

Chicago/Turabian Style

Efrain Mendez; Alexandro Ortiz; Pedro Ponce; Israel Macias; David Balderas; Arturo Molina. 2020. "Improved MPPT Algorithm for Photovoltaic Systems Based on the Earthquake Optimization Algorithm." Energies 13, no. 12: 3047.

Journal article
Published: 31 December 2018 in Expert Systems with Applications
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Although done nearly effortlessly by humans, digital systems cannot easily recognize images or predictions from recent observations. Tackling these limitations by proposing novel algorithms to improve the performance of image processing would have widespread implications in a variety of fields, including robotics, manufacturing, biomedicine, and automation. To provide a computer with this combined ability and transform it into an intelligent system, an algorithm must combine memory with an image decomposition procedure. Artificial neural networks (ANNs) are algorithms that aim to solve tasks such as classification, clustering, pattern recognition, and prediction by resembling brain connections. Specifically, three ANNs have excelled in specific areas: deep neural networks (DNNs), which use intrinsic connections to create prediction maps; long short-term memory neural networks (LSTMs), which use recurrent connections to emulate a type of memory; and convolutional neural networks (CNNs), which can decompose complex data through layers for simpler analysis. Although these algorithms can solve certain tasks of image sequence prediction, they cannot easily solve entire problems on their own. Nevertheless, combining these networks may enable solving such problems with ease. Thus, this article evaluates the combination of ANNs into two novel algorithms developed with the aim of improving image sequence prediction: (i) a combination of CNNs and LSTMs to form a CLNN and (ii) a combination of CNNs, LSTMs, and DNNs to form a CLDNN. Although the developed algorithms require a longer training time, they require less training epochs to have better accuracy than their predecessors. Furthermore, both developed methods were capable of accurately performing the image sequence prediction task, outperforming each individual method, as well as predicting longer and greater numbers of sequences correctly. Overall, the developed algorithms were able to better decompose inputs, remember previous inputs, and more accurately predict sequences of images. This allows the prediction of the next step in the sequence, which can be used as part of an intelligent system to make an analysis and an informed decision on the next course of action.

ACS Style

David Balderas; Pedro Ponce; Arturo Molina. Convolutional long short term memory deep neural networks for image sequence prediction. Expert Systems with Applications 2018, 122, 152 -162.

AMA Style

David Balderas, Pedro Ponce, Arturo Molina. Convolutional long short term memory deep neural networks for image sequence prediction. Expert Systems with Applications. 2018; 122 ():152-162.

Chicago/Turabian Style

David Balderas; Pedro Ponce; Arturo Molina. 2018. "Convolutional long short term memory deep neural networks for image sequence prediction." Expert Systems with Applications 122, no. : 152-162.

Journal article
Published: 01 June 2018 in ICT Express
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In general, it is not a simple task to predict sequences or classify images, and it is even more problematic when both are combined. Nevertheless, biological systems can easily predict sequences and are good at image recognition. For these reason Long-Short Term Memory and Convolutional Neural Networks where created and are based on the memory and visual systems. These algorithms have shown great properties and show certain resemblance, yet they are still not the same as their biological counterpart. This article review the biological bases and compares them.

ACS Style

David Balderas Silva; Pedro Ponce Cruz; Arturo Molina Gutierrez. Are the long–short term memory and convolution neural networks really based on biological systems? ICT Express 2018, 4, 100 -106.

AMA Style

David Balderas Silva, Pedro Ponce Cruz, Arturo Molina Gutierrez. Are the long–short term memory and convolution neural networks really based on biological systems? ICT Express. 2018; 4 (2):100-106.

Chicago/Turabian Style

David Balderas Silva; Pedro Ponce Cruz; Arturo Molina Gutierrez. 2018. "Are the long–short term memory and convolution neural networks really based on biological systems?" ICT Express 4, no. 2: 100-106.