34 research outputs found

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023

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    Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida

    Evaluation of optimal solutions in multicriteria models for intelligent decision support

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    La memoria se enmarca dentro de la optimización y su uso para la toma de decisiones. La secuencia lógica ha sido la modelación, implementación, resolución y validación que conducen a una decisión. Para esto, hemos utilizado herramientas del análisis multicrerio, optimización multiobjetivo y técnicas de inteligencia artificial. El trabajo se ha estructurado en dos partes (divididas en tres capítulos cada una) que se corresponden con la parte teórica y con la parte experimental. En la primera parte se analiza el contexto del campo de estudio con un análisis del marco histórico y posteriormente se dedica un capítulo a la optimización multicriterio en el se recogen modelos conocidos, junto con aportaciones originales de este trabajo. En el tercer capítulo, dedicado a la inteligencia artificial, se presentan los fundamentos del aprendizaje estadístico , las técnicas de aprendizaje automático y de aprendizaje profundo necesarias para las aportaciones en la segunda parte. La segunda parte contiene siete casos reales a los que se han aplicado las técnicas descritas. En el primer capítulo se estudian dos casos: el rendimiento académico de los estudiantes de la Universidad Industrial de Santander (Colombia) y un sistema objetivo para la asignación del premio MVP en la NBA. En el siguiente capítulo se utilizan técnicas de inteligencia artificial a la similitud musical (detección de plagios en Youtube), la predicción del precio de cierre de una empresa en el mercado bursátil de Nueva York y la clasificación automática de señales espaciales acústicas en entornos envolventes. En el último capítulo a la potencia de la inteligencia artificial se le incorporan técnicas de análisis multicriterio para detectar el fracaso escolar universitario de manera precoz (en la Universidad Industrial de Santander) y, para establecer un ranking de modelos de inteligencia artificial de se recurre a métodos multicriterio. Para acabar la memoria, a pesar de que cada capítulo contiene una conclusión parcial, en el capítulo 8 se recogen las principales conclusiones de toda la memoria y una bibliografía bastante exhaustiva de los temas tratados. Además, el trabajo concluye con tres apéndices que contienen los programas y herramientas, que a pesar de ser útiles para la comprensión de la memoria, se ha preferido poner por separado para que los capítulos resulten más fluidos

    OPTIMUM DESIGN AND OPERATION OF COMBINED COOLING HEATING AND POWER SYSTEM WITH UNCERTAINTY

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    Combined cooling, heating, and power (CCHP) systems utilize renewable energy sources, waste heat energy, and thermally driven cooling technology to simultaneously provide energy in three forms. They are reliable by virtue of main grid independence and ultra-efficient because of cascade energy utilization. These merits make CCHP systems potential candidates as energy suppliers for commercial buildings. Due to the complexity of CCHP systems and environmental uncertainty, conventional design and operation strategies that depend on expertise or experience might lose effectiveness and protract the prototyping process. Automation-oriented approaches, including machine learning and optimization, can be utilized at both design and operation stages to accelerate decision-making without losing energy efficiency for CCHP systems. As the premise of design and operation for the combined system, information about building energy consumption should be determined initially. Therefore, this thesis first constructs deep learning (DL) models to forecast energy demands for a large-scale dataset. The building types and multiple energy demands are embedded in the DL model for the first time to make it versatile for prediction. The long short-term memory (LSTM) model forecasts 50.7% of the tasks with a coefficient of variation of root mean square error (CVRMSE) lower than 20%. Moreover, 60% of the tasks predicted by LSTM satisfy ASHRAE Guideline 14 with a CVRMSE under 30%. Thermal conversion systems, including power generation subsystems and waste heat recovery units, play a vital role in the overall performance of CCHP systems. Whereas a wide choice of components, nonlinear characteristics of these components challenge the automation process of system design. Therefore, this thesis second designs a configuration optimization framework consisting of thermodynamic cycle representation, evaluation, and optimizer to accelerate the system design process and maximize thermal efficiency. The framework is the first one to implement graphic knowledge and thermodynamic laws to generate new CO2 power generation (S-CO2) system configurations. The framework is then validated by optimizing the S-CO2 system's configurations under simple and complex component number limitations. The optimized S-CO2 system reaches 49.8% thermal efficiency. This efficiency is 2.3% higher than the state of the art. Third, operation strategy with uncertainty for CCHP systems is proposed in this thesis for a hospital with a floor area of 22,422 m2 at College Park, Maryland. The hospital energy demands are forecasted from the DL model. And the S-CO2 power subsystem is implemented in CCHP after optimizing from the configuration optimizer. A stochastic approximation is combined with an autoregression model to extract uncertain energy demands for the hospital. Load-following strategies, stochastic dynamic programming (SDP), and approximation approaches are implemented for CCHP system operation without and with uncertainties. As a case study, the optimization-based operation overperforms the best load-following strategy by 14% of the annual cost. Approximation-based operation strategy highly improves the computational efficiency of SDP. The daily operating cost with uncertain cooling, heating, and electricity demands is about 0.061 /m2,andapotentialannualcostisabout22.33/m2, and a potential annual cost is about 22.33 /m2. This thesis fills the gap in multiple energy types forecast for multiple building types via DL models, prompts the design automation of S-CO2 systems by configuration optimization, and accelerates operation optimization of a CCHP system with uncertainty by an approximation approach. In-depth data-driven methods and diversified optimization techniques should be investigated further to boost the system efficiency and advance the automation process of the CCHP system

    Power Losses Estimation in Low Voltage Smart Grids

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    Mención Internacional en el título de doctorOne of the European Union Targets was to replace at least 80% of all traditional energy meters with electronic smart meters by 2020. However, by the end of 2020, the European region (EU 27 including the UK) had installed no more than 150 million smart electricity meters, representing a penetration rate of 50% for smart meters. By 2026, It is expected that there will be more than 227 million smart meters in households due to the updated planning and target numbers, which will affect many European markets, including western and northern Europe. This scenario would contribute to the general purpose of building a more sustainable distribution system for the future. This thesis contributes to the field of power losses estimation and optimization in low-voltage (LV) smart grids in large-scale distribution areas. To contextualize the importance of the research, it has been necessary to explain the unbalanced nature of low voltage distribution networks where there is a huge deployment of smart meter rollout, and there is also uncertainty related to renewable energy generation. Main results of the thesis have been applied in two smart grid research projects: the national project OSIRIS (Optimizaci´on de la Supervisi´on Inteligente de la Red de Distribuci´on) and the European project IDE4L (Ideal Grid For All ). Smart metering infrastructure allows distributor system operators (DSOs) to have detailed information about the customers energy consumption or generation. Smart meters measure the active and reactive energy consumption/generation of customers using different discrete time resolutions which range from 15-60 min. A large-scale smart meter rollout allows service providers to gain information about the energy consumed and produced by each customer in near-real time. This knowledge can be used to compute the aggregated network power losses at any given time. In this case, network power losses are calculated by means of customers’ smart meters measurements, in terms of both active and reactive energy consumption, and by the energy measured by the smart meter supervisor located at the secondary substation (SS). The problem of network losses estimation becomes more challenging as a results of the existence of not-technical losses due to electricity fraud or smart meter measurements anomalous (null or extremely high) or even because there are customers’ smart meters that can be out of service. One of the differential keys of LV smart grids is the presence of single-phase loads and unbalanced operation, which makes it necessary to adopt a complete three-phase model of the LV distribution network to calculate the real value of the power losses. This scenario makes the process of power loss estimation a computationally intensive problem. The challenge is even greater when estimating the power losses of large-scale distribution networks, composed of thousands of SSs. In recent years, environmental concerns have led to the increasing integration of a considerable number of distributed energy resources (DERs) into LV smart grids. This fact prompts DSOs and regulators to provide the maximum energy efficiency in their networks (i.e., the smallest power loss values) and maximum sustainable energy consumption. Detailed understanding of the network’s behavior in terms of power losses and the use of electricity is necessary to achieve this energy efficiency. However, the above scenario presents some drawbacks. The integration of DERs units, such as photovoltaic (PV) panels, into distribution networks can produce an increment of network power losses if the DERs units are not optimally located, coordinated, or controlled. Additionally, the network can experience technical contingencies such as cable’s overloads and nodal over-voltages or can lead to an inefficient system operation due to high energy losses or cables that exceed thermal limits. Moreover, there is a great uncertainty associated with the distributed power generation from PVs because its energy generation depend on weather conditions, including ambient temperature and solar irradiance, which are highly intermittent and fluctuating. Uncertainty is also present in some loads with stochastic behavior, such as plug-in electric vehicles (PEV), which adds an uncertainty layer and makes their optimal integration more complex. Therefore, DSOs require advanced methods to estimate power losses in unbalanced large-scale LV smart grids under uncertain situations. Such estimations would facilitate the deployment of policies and practices that lead to a safe and efficient integration of DERs in the form of flexibility mechanisms. In this context, flexibility mechanisms are essential to achieve optimal operation conditions under extreme uncertainty. Flexibility mechanisms can be deployed to tackle the imbalance between generation and demand that results from the uncertainty that is latent in LV smart grids. These flexibility mechanisms are based on modifying the normal power consumption (for the demand side) or power generation (for the generation side), according to a flexibility scheduling at the request of the network operator. In summary, DSOs face the challenge of managing network losses over large geographical areas where there are hundreds of secondary substations and thousands of feeders, with multiple customers and an ever-increasing presence of renewable DERs. Power losses estimation is thus paramount to improve network energy efficiency in the context of the European Union energy policies. This situation is complicated by the unbalanced operation of those networks and the presence of uncertainty. To address these challenges, this thesis focuses on the following objectives: 1. Power losses estimation in unbalanced LV smart grids under uncertainty. 2. Power losses estimation in unbalanced LV smart grids in large areas with a presence of DERs. 3. Flexibility scheduling for power losses minimization in unbalanced smart grids under uncertainty. The mentioned objectives are achieved by taking advantage of smart metering infrastructures, machine and deep learning models and mathematical programming techniques which allows DSOs to reduce their total power losses within the distribution network. This approach entails using flexibility mechanisms to operate the distribution network optimally and enhance the load management and DG expansion planning. According to the objectives identified earlier, the main contributions of this thesis are the following: 1. Power losses estimation in unbalanced LV smart grids under uncertainty conditions. An optimization-based procedure to estimate load consumption of non-telemetered customers. A Markov chain-based process to estimate intra-hour load demand for data having a low resolution and for non-telemetered customers or customers which smart meters provide incorrect measurements. 2. Power losses estimation in unbalanced LV smart grids in large-scale areas with a presence of DERs. A data mining approach to reduce a high-dimensionality dataset in smart grids to yield a reduced set of relevant features. A clustering process to obtain representative feeders within a large-scale distribution area of smart grids. A deep learning-based power losses estimator for large-scale LV smart grids. The method is formulated as a deep neural network that uses as input features the power load demand and power generation of a set of representative feeders. The model gives, as output, the power losses of the whole area. 3. Flexibility scheduling for power losses minimization in unbalanced smart grids under uncertainty. A robust optimization model for the flexibility scheduling optimization model for unbalanced smart grids with distributed resources, such as PV panels and PEV devices.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Natalia Alguacil Conde.- Secretario: Pablo Ledesma Larrea.- Vocal: Samuele Grill

    Advanced Computational Methods for Oncological Image Analysis

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    [Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

    Numerical and Evolutionary Optimization 2020

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    This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

    New models and methods for classification and feature selection. a mathematical optimization perspective

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    The objective of this PhD dissertation is the development of new models for Supervised Classification and Benchmarking, making use of Mathematical Optimization and Statistical tools. Particularly, we address the fusion of instruments from both disciplines, with the aim of extracting knowledge from data. In such a way, we obtain innovative methodologies that overcome to those existing ones, bridging theoretical Mathematics with real-life problems. The developed works along this thesis have focused on two fundamental methodologies in Data Science: support vector machines (SVM) and Benchmarking. Regarding the first one, the SVM classifier is based on the search for the separating hyperplane of maximum margin and it is written as a quadratic convex problem. In the Benchmarking context, the goal is to calculate the different efficiencies through a non-parametric deterministic approach. In this thesis we will focus on Data Envelopment Analysis (DEA), which consists on a Linear Programming formulation. This dissertation is structured as follows. In Chapter 1 we briefly present the different challenges this thesis faces on, as well as their state-of-the-art. In the same vein, the different formulations used as base models are exposed, together with the notation used along the chapters in this thesis. In Chapter 2, we tackle the problem of the construction of a version of the SVM that considers misclassification errors. To do this, we incorporate new performance constraints in the SVM formulation, imposing upper bounds on the misclassification errors. The resulting formulation is a quadratic convex problem with linear constraints. Chapter 3 continues with the SVM as the basis, and sets out the problem of providing not only a hard-labeling for each of the individuals belonging to the dataset, but a class probability estimation. Furthermore, confidence intervals for both the score values and the posterior class probabilities will be provided. In addition, as in the previous chapter, we will carry the obtained results to the field in which misclassified errors are considered. With such a purpose, we have to solve either a quadratic convex problem or a quadratic convex problem with linear constraints and integer variables, and always taking advantage of the parameter tuning of the SVM, that is usually wasted. Based on the results in Chapter 2, in Chapter 4 we handle the problem of feature selection, taking again into account the misclassification errors. In order to build this technique, the feature selection is embedded in the classifier model. Such a process is divided in two different steps. In the first step, feature selection is performed while at the same time data is separated via an hyperplane or linear classifier, considering the performance constraints. In the second step, we build the maximum margin classifier (SVM) using the selected features from the first step, and again taking into account the same performance constraints. In Chapter 5, we move to the problem of Benchmarking, where the practices of different entities are compared through the products or services they provide. This is done with the aim of make some changes or improvements in each of them. Concretely, in this chapter we propose a Mixed Integer Linear Programming formulation based in Data Envelopment Analysis (DEA), with the aim of perform feature selection, improving the interpretability and comprehension of the obtained model and efficiencies. Finally, in Chapter 6 we collect the conclusions of this thesis as well as future lines of research
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