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Control of Pollutant Emissions from a Boiler Through the Percentage of Oxygen
(International Information and Engineering Technology Association, 2021-06-25)
CORDOVES GARCIA, ALEXIS
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Carlos A. Pérez Alban
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Jorge R. Terán Benalcázar
;
Alexis Cordovés Rodríguez
;
Leandro L. Lorente-Leyva
<jats:p>The goal of this paper is to obtain an automated system for the control of the quality of the combustion gases of a boiler. The concentration of oxygen present in the combustion gases is detected by means of a lambda sensor, and a proportional PID controller automatically regulates, through an actuator, the air inlet to the boiler, thus ensuring the emission of CO2 and other polluting gases into the atmosphere within the parameters established by the current environmental regulations. The control system has an HMI display and a modular PLC. The results achieved also ensure the reduction of fuel consumption of the boiler.</jats:p>
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A Novel Linear-Model-Based Methodology for Predicting the Directional Movement of the Euro-Dollar Exchange Rate
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Mauricio Argotty-Erazo
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Antonio Blázquez-Zaballos
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Carlos A. Argoty-Eraso
;
Leandro L. Lorente-Leyva
;
Nadia N. Sánchez-Pozo
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Pineapple Drying Analysis in Packing Plants Through Computational Fluid Dynamics
(International Information and Engineering Technology Association, 2022-02-28)
CORDOVES GARCIA, ALEXIS
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Alexis Cordovés Rodríguez
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Karla Tatiana Núñez Buñay
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Arlys M. Lastre Aleaga
;
Leandro L. Lorente-Leyva
The aim of this research is to determine the average fluid velocity values and the average total pressure at the outlet of the ventilation system conduit for the drying, by air impulsion, of pineapples in packing plants, in order to ensure the required humidity level of the crown for its proper packaging. The CAD model of the air conduction was obtained in Solidworks and Computational Fluid Dynamics (CFD) techniques were applied to simulate the performance of the ventilation system. Air was indicated as the type of fluid, and an internal analysis of the pipe with roughness Rz = 3.2 μm. As a computational domain, the pipeline was established, from the connection to the fan to the outlet diffuser. As boundary conditions were established; a W and Z Manufacturing regenerative fan at the inlet of the flow pipe 618,439 m3/h, and at the outlet of the pipe the value of atmospheric pressure. As a result of the simulation, an average velocity value at the diffuser outlet of 127.294 m/s was obtained, higher than the required 96.06 m/s. An average total pressure value of 111718.73 Pa was obtained in the conduit suitable for the proposed purposes. For the conveyor belt, it is recommended to use, in each row of pineapples, the simulated fan with a simple diffuser with a rectangular section of 1 x 20 cm, or a regenerative fan with a flow of 1248.77 m3/h and a diffuser with two-outlet bifurcation.</jats:p>
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Design and Implementation of an IoT Control and Monitoring System for the Optimization of Shrimp Pools using LoRa Technology
(The Science and Information Organization, 2023)
José M. Pereira Pontón
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Verónica Ojeda
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Víctor Asanza
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Leandro L. Lorente-Leyva
;
Diego H. Peluffo-Ordóñez
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Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals
(International Information and Engineering Technology Association, 2023-08-31)
Karen Torres
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Jhon Espinoza
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Víctor Asanza
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Leandro L. Lorente-Leyva
;
Diego H. Peluffo-Ordóñez
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A 3D Printed, Bionic Hand Powered by EMG Signals and Controlled by an Online Neural Network
(MDPI AG, 2023-06-14)
Karla Avilés-Mendoza
;
Neil George Gaibor-León
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Víctor Asanza
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Leandro L. Lorente-Leyva
;
Diego H. Peluffo-Ordóñez
<jats:p>About 8% of the Ecuadorian population suffers some type of amputation of upper or lower limbs. Due to the high cost of a prosthesis and the fact that the salary of an average worker in the country reached 248 USD in August 2021, they experience a great labor disadvantage and only 17% of them are employed. Thanks to advances in 3D printing and the accessibility of bioelectric sensors, it is now possible to create economically accessible proposals. This work proposes the design of a hand prosthesis that uses electromyography (EMG) signals and neural networks for real-time control. The integrated system has a mechanical and electronic design, and the latter integrates artificial intelligence for control. To train the algorithm, an experimental methodology was developed to record muscle activity in upper extremities associated with specific tasks, using three EMG surface sensors. These data were used to train a five-layer neural network. the trained model was compressed and exported using TensorflowLite. The prosthesis consisted of a gripper and a pivot base, which were designed in Fusion 360 considering the movement restrictions and the maximum loads. It was actuated in real time thanks to the design of an electronic circuit that used an ESP32 development board, which was responsible for recording, processing and classifying the EMG signals associated with a motor intention, and to actuate the hand prosthesis. As a result of this work, a database with 60 electromyographic activity records from three tasks was released. The classification algorithm was able to detect the three muscle tasks with an accuracy of 78.67% and a response time of 80 ms. Finally, the 3D printed prosthesis was able to support a weight of 500 g with a safety factor equal to 15.</jats:p>
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Condition Monitoring of Wind Turbines: A Case Study of the Gibara II Wind Farm
(International Information and Engineering Technology Association, 2023-04-30)
Yorley Arbella-Feliciano
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Carlos A. Trinchet-Varela
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Leandro L. Lorente-Leyva
;
Diego H. Peluffo-Ordóñez
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Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor
(MDPI AG, 2023-09-19)
Ulbio Alejandro-Sanjines
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Anthony Maisincho-Jivaja
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Victor Asanza
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Leandro L. Lorente-Leyva
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Diego H. Peluffo-Ordóñez
<jats:p>Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor–critic agent, where its objective is to optimize the actor’s policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent’s learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function.</jats:p>
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MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks
(Elsevier BV, 2023-10)
Víctor Asanza
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Leandro L. Lorente-Leyva
;
Diego H. Peluffo-Ordóñez
;
Daniel Montoya
;
Kleber Gonzalez

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