https://uvadoc.uva.es/handle/10324/1304 Dpto. Ingeniería de Sistemas y Automática - Comunicaciones a congresos, conferencias, etc. 2026-04-16T12:43:54Z https://uvadoc.uva.es/handle/10324/78416 Integrating emerging Artificial Intelligence (AI) tools into the teaching of Industrial Engineering courses has become a crucial aspect of the university environment. This requires both the continuous training of faculty members and the integration of new AI-based tools into teaching, as well as careful consideration of the significant ethical and social impact of these technologies. To address these objectives, the University of Valladolid (Spain) has funded an Innovative Educational Project at the Industrial Engineering School, with the participation of five different technological departments involved in its engineering degrees. This paper presents the main results and conclusions obtained during the initial phases of this project, which have primarily focused on training lecturers in the use of AI tools to effectively integrate them into their teaching methodologies and to develop new educational materials. Specifically, AI tools are being used to generate questionnaires for student self-assessment, based on the content covered in each session. A key finding is the necessity of encouraging students to develop a strong critical mindset when using AI tools, particularly in analysing the reasoning behind AI-generated solutions. It has been observed that AI-driven self-assessment is particularly beneficial for theoretical knowledge, although students are guided to critically evaluate the AI's output, especially in problem-solving, where errors in intermediate steps can occur. The paper also presents specific examples related to systems and automation engineering using different AI tools and analysing their responses collaboratively with students to identify their strengths and weaknesses, highlighting both the potential and the current limitations of AI tools in these practical domains. To summarize the main conclusions derived from this study, it is essential to encourage students to develop a strong critical mindset when evaluating responses provided by AI-based tools. To achieve this, it is important to allow and encourage the use of such tools in the classroom, guiding students in identifying inconsistencies in AI-generated texts or results, and comparing them with other sources of knowledge. Moreover, it is a priority for lecturers to stay continuously updated on advancements in AI-based tools. Anticipating the impact of these tools on teaching is crucial, as their development and applications are constantly evolving and improving. Furthermore, with the gradual integration of AI into teaching activities, future engineers will be well-positioned to adapt to and lead technological changes in their industrial careers. Initiatives and experiences like this study will help both lecturers and students in their daily activities, allowing them to adapt to constant technological changes and helping them become better professionals in the not-so-distant future. 2025-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/75071 Fuzzy modeling is one of the best known techniques to model systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high accuracy, but show poor performance in complexity or interpretability, which are key aspects of Fuzzy Logic. There are several approaches in the literature to deal with the complexity and interpretability challenges for fuzzy rule based systems (FRBSs). In this paper, a post-processing approach is proposed via a genetic rule selection based on the relevance of each rule (using Orthogonal Transformations (OTs), in this case P-QR) and the well-known accuracy-interpretability trade-off. The main objective is to check the true significance, drawbacks and advantages of the rule selection based on OTs to manage the accuracy-interpretability trade-off. In order to achieve this aim, a neuro-fuzzy system (FasArtFuzzy Adaptive System ART based) and several case studies from the KEEL Project Repository are used to tune and check this selection of rules based on rule relevance by OTs, genetic selection and accuracy-interpretability trade-off. This neuro-fuzzy system generates Mamdani FRBSs, in an approximate way. SPEA2 is the multi-objective evolutionary algorithm (MOEA) tool used to tune the proposed rule selection, and different interpretability measures have been considered. 2013-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/74723 This paper addresses the challenging problem of fuzzy modeling in high-dimensional and large scale regression datasets. To this end, we propose a scalable two-stage method for obtaining accurate fuzzy models in high-dimensional regression problems using approximate Takagi-Sugeno-Kang Fuzzy Rule-Based Systems. In the first stage, we propose an effective Multi-Objective Evolutionary Algorithm, based on an embedded genetic Data Base learning (involved variables, granularities and a slight lateral displacement of fuzzy partitions) together with an inductive rule base learning within the same process. The second stage is a post-processing process based on a second MOEA to perform a rule selection and a fine scatter-based tuning of the Membership Functions. Moreover, it incorporates an efficient Kalman filter to estimate the coefficients of the consequent polynomial functions in the Takagi-Sugeno-Kang rules. In both stages, we include mechanisms in order to significantly improve the accuracy of the model and to ensure a fast convergence in high-dimensional regression problems. The proposed method is compared to the classical ANFIS method and to a well-known evolutionary learning algorithm for obtaining accurate TSK systems in 8 datasets with different sizes and dimensions, obtaining better results. 2013-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/74718 Fuzzy modeling is one of the most known and used techniques in different areas to emulate the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, the models can present a poor performance. Several approaches are found in the specialized literature to reduce the complexity and improve the interpretability of the fuzzy models. Here, a post-processing approach is taken into account via the definition of the rules selection criterion that aims to choose the most relevant rules according to the well-known accuracy-interpretability trade-off. This criterion is based on Orthogonal Transformations, here the QRP transformation is taking into consideration, and its parameters are tuned genetically. The main objective is to check the true significance, drawbacks and advantages the firing matrix of the rules, that is the foundation of the most usual approaches based on orthogonal transformations for the complexity reduction of the fuzzy models. A neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART based), and several case studies, data sets from the KEEL Project Repository, are used to tune and check this approach. This neuro-fuzzy system generates Mamdani fuzzy rule based systems (FRBSs), each with its own particularities and complexities from the point of view of fuzzy sets and rule generation. NSGA-II is the MOEA tool used to tune the criterion parameters based on accuracy-interpretability ideas. 2011-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/66106 This paper focuses on solving the optimization of crude oil operations scheduling carried out in a real system composed of a refinery and a marine terminal, over a monthly horizon. In the present article, we introduce a large-scale mixed-integer non-linear programming (MINLP) model that faithfully represents the operation and characteristics of the system. Considering the model’s complexity and its non-linear and non-convex nature, the challenge lies in solving the model in a time frame that meets the user’s needs. 2023-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/58413 En este artículo se presentan las experiencias con Copito, un robot social que durante más de dos años ha realizado labores de terapia, ocio y entretenimiento en la residencia Centro Gerontológico Lacort de Viana de Cega (Valladolid). Los principales objetivos planteados en este proyecto han sido: i. Introducir un robot social en un entorno operacional de un centro gerontológico por largos periodos de tiempo. ii. Crear una serie de contenidos adaptados a personas mayores que sean ofrecidos por el robot, motivando la interacción y que les permitan mantenerse activos física y mentalmente. iii. Realizar un seguimiento pormenorizado de las interacciones, que sirva como elemento de realimentación y mejora del sistema. iv. Incorporar el robot como un elemento más de los protocolos de seguimiento y terapia que se realizan a los residentes del Centro. El artículo presenta un estudio sobre la aceptabilidad del robot por los residentes a lo largo del tiempo basado en el modelo Almere y algunas lecciones aprendidas de interacción y sobre cómo acotar las limitaciones existentes. 2022-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/58412 En este artículo se presenta el desarrollo de un robot de cables para la limpieza de ventanas. El sistema está compuesto por una cesta con rodillo escamoteable, sistema pulverizador de agua y escurridor. La cesta está suspendida mediante dos grúas motorizadas que permiten posicionar la cesta en cualquier ubicación de una fachada. El sistema está programado sobre tres controladores Esp32 que se comunican de forma inalámbrica mediante protocolo ESPNOW. El manejo se realiza mediante un mando a distancia que permite el movimiento y accionamiento de la cesta de forma manual y programar trayectorias mediante comandos GCODE. 2022-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/48814 This paper considers the problem of designing an H∞ observer-based controllers for continuous nonlinear systems presented by Takagi–Sugeno (T–S) model with the presence of parameter uncertainties and external disturbance. Some change of variables has been developed to linearize the bilinear terms. As a consequence, the bilinear problem conditions are converted into a set of Linear Matrix Inequalities (LMIs). Sufficient conditions f or design t he observer and controller gains are deduced in terms of LMIs conditions which can be practically solved in single step. The four-tank process application is used to show the effectiveness of the proposed method, revealing a better compromise between the simplicity and the conservatism of design method, outperforming in respect to previous approaches. 2019-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/48813 A model of a fiberwood panel manufacturing process is being developed. This model is developed to re-produce the evolution of unmeasurable variables within the mat (pressure, temperatures), in order to re-produce the operation of the process. For this, the main process (pressing) is modeled in detail to evaluate how energy and matter is transported through the process. Preliminary results demonstrate the possibility of simulating reproduce internal variables throughout the pressing process. 2021-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/45604 Monitoring large-scale processes is a crucial task to ensure the safety and reliability of the plants. This paper proposes an approach for decentralized fault detection in largescale processes. The measured variables of the plant are divided into multiple and possibly overlapping blocks using different techniques based on data. Local monitoring methods are applied in each block using DPCA (Dynamic Principal Component Analysis) model. The local results are then fused by the Bayesian inference strategy. This paper also compares different techniques to decompose the plant looking for the best strategy from the point of view of the fault detection results. The proposed method was applied to the widely used benchmark Tennessee Eastman Process, showing its effectiveness when compared with a centralized method and another decentralized technique. 2019-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/45603 The construction of tunnels has serious geomechan-ical uncertainties involving matters of both safety and budget.Nowadays, modern machinery gathers very useful informationabout the drilling process: the so-called Monitor While Drilling(MWD) data. So, one challenge is to provide support for thetunnel construction based on thison-sitedata .Here, an MWD based methodology to support tunnel con-struction is introduced: a Rock Mass Rating (RMR) estimationis provided by an MWD rocky based characterization of theexcavation front and expert knowledge [1].Well-known machine learning (ML) and computational intel-ligence (CI) techniques are used. In addition, a collectible and“interpretable”base of knowledge is obtained, linking MWDcharacterized excavation fronts and RMR.The results from a real tunnel case show a good and serviceableperformance: the accuracy of the RMR estimations is high,Errortest∼=3%, using a generated knowledge base of 15 fuzzyrules, 3 linguistic variables and 3 linguistic terms.This proposal is, however, is open to new algorithms toreinforce its performance 2018-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/45602 Los Sistemas Basados en Reglas Difusas (SBRD) permiten modelar problemas reales, manejando no solo la no precisión en lo referente al conocimiento a manejar, sino también la precisión con la que modelan el problema y la capacidad para “interpretar” su comportamiento. Por otro lado aparece el concepto de relevancia de las reglas difusas del SBRD, parece que lo idóneo es que sus reglas sean relevantes, o lo más relevantes posible. Relevancia, precisión e interpretabilidad son las tres métricas que se consideran, y analizan, en este trabajo para conseguir SBRDs que presenten buenas prestaciones respecto a estos tres objetivos, centrándonos finalmente en cómo es la relevancia de las reglas de los SBRD en el equilibrio precisi´oninterpretabilidad. Basándose en Transformaciones Ortogonales (SVD, PQR, OLS) es posible estimar la Relevancia de una regla difusa, y analizar como influye dicho valor en la búsqueda del equilibrio precisión-interpretabilidad en un SBRD. Usando nueve conjuntos de datos del repositorio KEEL, dos algoritmos de modelado: uno aproximativo (FasArt) y otro lingüístico (NefProx), y siguiendo una estrategia de optimización multi-objetivo (SPEA2), se presentan a continuación los resultados obtenidos que muestran el concepto de relevancia como un factor importante, y contradictorio, a tener en cuenta a la hora de generar SBRD. 2018-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/45601 En este trabajo se afronta el problema de la detección e identificación de fallos en plantas industriales complejas. Dicho problema se aborda como una toma de decisiones basada en operadores lingüísticos OWA, lo cual permite fusionar diversos métodos de identificación de fallos (FI) alternativos. De esta manera la diagnosis de fallos resulta más robusta, y por otro lado el aspecto lingüístico de los operadores manejados encaja fácilmente en el contexto de la detección e identificación de fallos. La identificación se lleva a cabo usando varios métodos de FI muy utilizados, la solución de cada método se agrega usando operadores del tipo Ordered Weighed Average (OWA), basados en cuantificadores Regular Increasing Monotone (RIM). En este artículo se ha hecho una comparativa de los términos lingüísticos más conocidos para implementar estos operadores OWA-RIM en el contexto de la identificación de fallos. Esto se ha aplicado a un benchmak de plantas depuradoras de aguas residuales. 2018-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/45600 Este artículo propone un método de detección de fallos dinámico y descentralizado. Para hacer la detección de los fallos descentralizada, la planta se divide en bloques de variables que compartan algún tipo de correlación usando métodos de regresión. En cada grupo se incorpora un método de detección de fallos dinámico, en concreto el método DPCA: Análisis de componentes principales dinámico, cuyos resultados son fusionados por un procesador central, utilizando el Criterio de Inferencia Bayesiano (BIC), devolviendo un resultado global. Esta propuesta ha sido aplicada sobre un modelo de planta industrial ampliamente utilizado y comparado con el DPCA centralizado para verificar su efectividad. 2018-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/45599 This paper proposes a dynamic and decentralized fault detection method. The plant is divided in groups whose members are selected using linear and non-linear modelling techniques. In each group a Principal Component Analysis model does the fault detection, including delayed data to get a dynamic method. Then, a central node fuses the results of each group, using Bayesian Index Criterion (BIC), to get a global detection outcome. The method was tested on a widely used benchmark and compared with other proposal to check its effectiveness. 2018-01-01T00:00:00Z https://uvadoc.uva.es/handle/10324/45598 This paper addresses the problem of leak localization in water distribution networks (WDN) using Fisher Discriminant Analysis (FDA). First, the paper introduces how FDA can be used for leak localization using the information of pressure measurements from the sensors available in the WDN. Then, the problem of sensor placement is considered when the proposed leak localization based on FDA is used. The proposed leak localization and sensor placement approaches based on FDA will be used using a well-known WDN case study. 2018-01-01T00:00:00Z