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Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators
(MDPI, 2024)
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Marmolejo Saucedo, José Antonio
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Köse, Utku
The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration. ©The authors ©MDPI.
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Evolutionary Optimization of Entanglement Distillation Using Chialvo Maps
(Springer, 2023)
Ganesan, Timothy
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Marmolejo Saucedo, José Antonio
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Vasant, Pandian
In quantum information theory, entanglement distillation is a key component for designing quantum computer networks and quantum repeaters. In this work, the practical entanglement distillation problem is re-designed in a bilevel optimization framework. The primary goal of this work is to propose and test an effective optimization technique that combines evolutionary algorithms (differential evolution) and the Chialvo map - for solving the bilevel practical entanglement distillation problem. The primary idea is to leverage on the complex dynamical behavior of Chialvo maps to improve the optimization capabilities of the evolutionary algorithm. Analysis on the computational results and comparisons with a standard evolutionary algorithm implementation is presented. ©2023 springer, The authors.
Scopus© Citations 1 50
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A Strategy to Analyze the Metal Packaging Market in the Food Cans Industry Using Agent-Based Simulation
(Springer, 2024-01-01)
Chaverra Vargas, Luis F.
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Marmolejo Saucedo, José Antonio
Companies that manufacture metal packaging for canned jalapeño peppers require forecasts to determine the amounts of inventory needed to meet customer demand and anticipate sales opportunities. The nature of jalapeño pepper production depends on factors such as planting, harvesting, seasonality, pests, and climate change, among others, which generate a lot of uncertainty when estimating the needs of the client. The purpose of this research is to solve the problem of having a simple and more accurate forecast of jalapeño pepper availability in the market for the metal packaging business. Forecasts are currently based on historical data; the adjustment of these predictions does not always follow sudden changes in the data, and for this reason, there are other more complex methods, which use mathematics, where it seems that a mathematical expert is needed more than an expert in the business where the forecasting tool is used. Agent-based simulation allows the development of interaction rules between the different market agents, which, in a dynamic and flexible way, allows the development of simple models with complex behaviors where there are no formulas that govern them and allow interaction between them. The ABS model provides more details about the behavior of the jalapeño pepper crop, compared to the models evaluated in this chapter, which are based on equations.
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A State of the Art of Non-fungible Tokens: A Literature Review
(Springer, 2024-01-01)
Cattori Krähenbühl, Joan
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Marmolejo Saucedo, José Antonio
Non-fungible tokens, also known as NFTs, are based on the blockchain and are a type of proprietary digital asset registered on the blockchain, making them unique, irreplaceable, and indivisible assets. This main feature makes up the nascent and emerging phenomenon that has revolutionized the way digital assets are and will be handled in the future. Its success and importance has resonated particularly in the first and second quarter of 2022, and however, the full application and reach of NFTs through its technology in human life are still yet to be discovered. The main objective of this chapter is to inform the audience of developments in the research field, discuss the importance and significance of the issues investigated to analyze the relevance and suitability of the topic in order to inform the audience about its operations, and generate, through the NFTs, economical value and further knowledge for future applications. This is the reason why a systematic review of the literature on the subject of “non-fungible tokens” is carried out based on publications of Scopus indexed journals, with a collection of information from 2017 to 2022.
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Selecting the Distribution System using AHP and Fuzzy AHP Methods
(Springer Nature, 2024)
Saucedo-Martínez, Jania Astrid
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Salais-Fierro, Tomás Eloy
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Marmolejo Saucedo, José Antonio
In this research, we present a supporting tool for decision making by designing a distribution system for a trading company of supplies for the welding industry in Mexico. The case study encompasses a distribution system with shortage problems and poor fleet capacity. To address these problems, improvement options were grouped into three possible scenarios through a third-party logistics (3PL) service. Furthermore, for the evaluation and selection of one of the scenarios, the Analytic Hierarchy Process (AHP) methodology was proposed integrating fuzzy logic as a tool for decision making, including factors of uncertainty and subjectivity as well as a comparison with traditional AHP obtaining the best scenario, meeting the requirements of the company, and showing potential improvements in the desired service level for its distribution system. © 2024 Springer Nature
Scopus© Citations 3 8 1
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Genetic electro-search optimization for optimum energy consumption in edge computing-based internet of healthcare things
(Springer Nature, 2024)
Köse, Utku
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Marmolejo Saucedo, José Antonio
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Marmolejo-Saucedo, Liliana
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Rodriguez-Aguilar, Miriam
Energy consumption is a vital issue when optimum usage and carbon footprint are all considered in today’s Internet of Things (IoT) environments. Considering edge computing, that becomes too critical in terms of wireless devices with limited battery power. Especially in healthcare applications, the defined IoHT approach requires sustainability while future massive solutions may result negative outputs in terms of carbon footprint. So, optimum energy consumption seems positive in terms of multiple ways. In the literature, one trendy method is using clustering for lowering the energy consumption within the Internet of Healthcare Things (IoHT) environment on edge computing. In this study, optimization of energy consumption in IoHT was done via improved Genetic Electro-Search Optimization (GESO) algorithm. According to the obtained findings in the performed applications, GESO was effective enough in finding optimum conditions of energy consumption for an active IoHT setup. © 2024 Springer Nature
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Machine Learning for Digital Shadow Design in Health Insurance Sector
(Springer Nature, 2024)
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Marmolejo Saucedo, José Antonio
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Rodríguez-Aguilar, Miriam
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Marmolejo-Saucedo, Liliana
The digital transformation process in organizations has accelerated significantly in recent years; the COVID-19 pandemic was a catalyst that highlighted the need for digitalization in all sectors. In the case of the health sector, this process is complex due to the processes inherent in health care as well as the integration of multiple sectors that allow the provision of health services. A first approach towards the construction of a Digital Twin in health organizations is a Digital Shadow that allows an orderly transition towards digital operation in real time. This paper presents a first approach to the design of a Digital Shadow for the health insurance sector and specifically for the care of patients diagnosed with COVID-19 through the implementation of an analytical intelligence system based on machine learning models to forecast and monitor to patients who represent catastrophic cases for the insurer. © 2024 Springer Nature
Scopus© Citations 1 23 1
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A machine learning-based analytical intelligence system for forecasting demand of new products based on chlorophyll : a hybrid approach
(Springer, 2024)
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Marmolejo Saucedo, José Antonio
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Garcia-Llamas, Eduardo
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Rodríguez-Aguilar, Miriam
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Marmolejo-Saucedo, Liliana
This manuscript addresses the problem of forecasting the demand for innovative products with limited and inhomogeneous sales data over time. The main objective of the study is to use the information available from a group of innovative chlorophyll-based food products to build a coherent demand forecasting system. From a transactional database, time series were constructed for each group of products, analyzing the stationarity and seasonality of the time series through the Dickey–Fuller and Canova–Hansen tests. Likewise, an ARIMA model, a long short-term memory (LSTM) recurrent deep neural network, and a support vector machine (SVM) were trained to select the best model for each product based on a forecast performance metric. A comparison between classical forecasting techniques and machine learning models is shown. The LSTM neural network was the best model for most products because the internal architecture of the network allows not only to capture non-linear relationships between variables but is also capable of controlling the flow of information to preserve characteristics over time that are relevant for forecasts. The second-best model was the SVM, which allows capturing non-linear behaviors through kernel functions and uses a smaller amount of data for its estimation. Finally, the ARIMA model presented the lowest performance for all products. The objective of having various methodologies is that the system allows the best forecast to be selected according to the type of product, availability of information and methodology used, which will allow the company to integrate new products into the system over time. ©Springer
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Robust Optimization Model for Sustainable Supply Chain Design Integrating LCA
(2023)
Flores-Siguenza, Pablo
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Marmolejo Saucedo, José Antonio
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Supply chain management is the basis for the operations in an organization. The development of realistic supply chain designs that work effectively in the presence of disturbances in a stochastic environment and incorporate sustainability factors, is a complex challenge being investigated in recent years. However, the inclusion of a methodological structured framework to evaluate environmental impacts constitutes a knowledge gap in the literature on supply chain design. This study developed a model for sustainable supply chain design, integrating Life Cycle Assessment and based on a robust optimization approach. The study follows a 4-stage methodology beginning with data collection and the execution of a Life Cycle Assessment. Then, the deterministic modeling is proposed, concluding with a robust model. A bi-objective model is proposed to maximize utility and minimize environmental impact based on demand scenarios. The model was validated with real data from a medium-sized enterprise that produces antibacterial gel, generating as a result, different configuration alternatives for the supply chain to transport the products and raw materials between its elements. The conclusions of this work highlight the importance of including sustainability factors during supply chain design, the consequences and costs of its inclusion, as well as the priority actions that promote sustainable designs.
Scopus© Citations 2 7 1
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Intelligent Computing & Optimization: Proceedings of the 5th International Conference on Intelligent Computing and Optimization 2022 (ICO2022) : Preface
(Springer, 2023)
Weber, Gerhard-Wilhelm
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Marmolejo Saucedo, José Antonio
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Munapo, Elias
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González Morfín, Juan
The 5th edition of popular as well as prestigious International Conference on Intelligent Computing and Optimization (ICO) 2022, in short ICO’2022, will be held along an “online” platform, herewith respecting the care for everyone as necessitated by the pandemic COVID-19. The physical conference is foreseen to be celebrated at G Hua Hin Resort & Mall in Hua Hin, Thailand, once the COVID-19 will be jointly overcome. Indeed, the core objective of the international conference is to bring together in the spirit of community the global research leaders, distinguished experts and scholars from the scientific areas of Intelligent Computing and Optimization gathered all over the globe to share their knowledge and experiences on the current research achievements in diverse fields, to learn from their old and new friends and together create new research ideas and designs of collaboration. This conference creates and provides a “golden chance” for the international research community to interact and introduce their newest research advances, results, innovative discoveries and inventions in the midst of their scientific colleagues and their friends. The proceedings book of ICO’2022 is published by the renowned house of Springer Nature (Lecture Notes in Networks and Systems). ©Springer.
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