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Optimization of Textile SCs Design and Planning: A Bibliometric Analysis
(Springer Nature Switzerland, 2025)
Leandro L. Lorente-Leyva
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María del Mar Eva Alemany
;
Diego H. Peluffo-Ordóñez
This study provides a comprehensive overview of the current state of research in the design and operations planning optimization of textile supply chains. This paper includes a bibliometric analysis of 42 selected papers that reveals a focus on tactical decision-making, with an emphasis on the manufacturing stage. The bibliometric analysis exposes the influence of prominent authors and journals in this field. A co-occurrence map graphically represents the keywords used in the papers. The literature review results suggest that models are primarily developed in a deterministic context, with a focus on cost minimization and limited use of artificial intelligence techniques. The solution context exhibits a preference for static approaches and periodic replanning.
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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
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Leandro L. Lorente-Leyva
;
Nadia N. Sánchez-Pozo
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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
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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
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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
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Neil George Gaibor-León
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Víctor Asanza
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Leandro L. Lorente-Leyva
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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
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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
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Diego H. Peluffo-Ordóñez
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Daniel Montoya
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Kleber Gonzalez
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A conceptual framework for the operations planning of the textile supply chains: Insights for sustainable and smart planning in uncertain and dynamic contexts
(Elsevier BV, 2024-01)
Leandro L. Lorente-Leyva
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M.M.E. Alemany
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Diego H. Peluffo-Ordóñez
Recent practices in textile supply chains (SC) show a growing concern for sustainability not only in its economic dimension, but fundamentally in its environmental and social ones. One of the key management processes that affect sustainability is the SC operations planning since its fundamental role in achieving a balance between supply and demand in a sustainable manner. Moreover, in an uncertain and dynamic environment such as the textile sector, it is necessary to provide a certain learning capability to the operations planning techniques used to increase the speed and quality of response of the textile SC to unexpected situations. In this context, mathematical programming models, heuristics and artificial intelligence techniques have proven their validity to achieve sustainable, robust and smart supply chains. Despite their potential, neither a conceptual framework (CF) nor a literature review have been detected to support the development and study of such models in the textile supply chain operations planning. In view of these gaps, this paper proposes a CF for supporting the sustainable and smart operations planning of the textile supply chains in a dynamic and uncertain context based on a set of dimensions, categories and elements that reflect the specific characteristics of the textile sector. Firstly, a tentative CF is predefined based on other generic works on SC operations planning in uncertain context and the own authors’ knowledge. Secondly, a structured literature review based on this CF has been made resulting, at the same time, in the updating of some of its dimensions, categories and elements to reflect some textile specific characteristics. Consequently, the CF is not only predefined but also logically derived from the literature analysis. The results of the literature review show that there is a great opportunity to contribute to making textile supply chains more sustainable, smart, flexible, robust and resilient in dynamic and uncertain environments.

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