335 research outputs found

    Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework

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    The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation

    Contributions to time series analysis, modelling and forecasting to increase reliability in industrial environments.

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    356 p.La integración del Internet of Things en el sector industrial es clave para alcanzar la inteligencia empresarial. Este estudio se enfoca en mejorar o proponer nuevos enfoques para aumentar la confiabilidad de las soluciones de IA basadas en datos de series temporales en la industria. Se abordan tres fases: mejora de la calidad de los datos, modelos y errores. Se propone una definición estándar de métricas de calidad y se incluyen en el paquete dqts de R. Se exploran los pasos del modelado de series temporales, desde la extracción de características hasta la elección y aplicación del modelo de predicción más eficiente. El método KNPTS, basado en la búsqueda de patrones en el histórico, se presenta como un paquete de R para estimar datos futuros. Además, se sugiere el uso de medidas elásticas de similitud para evaluar modelos de regresión y la importancia de métricas adecuadas en problemas de clases desbalanceadas. Las contribuciones se validaron en casos de uso industrial de diferentes campos: calidad de producto, previsión de consumo eléctrico, detección de porosidad y diagnóstico de máquinas

    Selected Problems of High-Resolution Automotive Imaging Radar

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    This thesis aims at two selected problems in the development of high-resolution au- tomotive imaging radar: 1) The feasibility of using sub-THz for the next generation of automotive radar; 2) The development of the physics-based image segmentation approach on the automotive radar imagery. The wide range of feasibility studies on the use of sub-THz frequencies for auto- motive radar have been undertaken in the Microwave Integrated Systems Laboratory (MISL) at the University of Birmingham, and the candidate is in charge of the included study on the theoretical modelling and experimental verification of the attenuation through the vehicle infrastructures which is the first part of this thesis. The importance of this work is related to the fact that automotive radar is placed within the car infras- tructure. Therefore, it would be a potential show-stopper in the development of this innovation if attenuation within the car bumper or badge is prohibitively high. Both theoretical modelling and experimental measurement are conducted by considering the impact factors on the propagation properties of the sub-THz signal such as the incident angle, frequency, characteristic parameters of materials, and the thicknesses of infrastructure layers. The transmissivity of multilayered structure has been modelled and good agreement with the results of measurements was demonstrated, so that the developed approach can be used in further studies on propagation through car infrastruc- ture. The published results on transmissivity and complex permittivity of automotive paints are valuable for researchers in either field of THz technology or automotive radar. The image segmentation on automotive radar maps aims at identifying the passable and impassable areas for path planning in autonomous driving. Contrary to traditional radar, radar clutter is regarded as the physical meaningful information, which can deliver valuable feature information for surface characterization, and enable the full scene reconstruction of automotive radar maps. The proposed novel segmentation algorithm is a hybrid method composed of pre-segmentation based on image processing methods, and the region classification using the multivariate Gaussian distribution (MGD) classifier developed based on the statistical distribution feature parameters of radar returns of various areas. Moving target indication (MTI) is implemented for the first time based on frame-to-frame context association. The end-to-end segmentation framework is therefore achieved robustly with good segmentation performance, and automatically without human intervention

    Komponentenbasierte dynamische Modellierung von Energiesystemen und Energiemanagement-Strategien für ein intelligentes Stromnetz im Heimbereich

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    The motivation of this work is to present an energy cost reduction concept in a home area power network (HAPN) with intelligent generation and flexible load demands. This study endeavors to address the energy management system (EMS) and layout-design challenges faced by HAPN through a systematic design approach. The growing demand for electricity has become a significant burden on traditional power networks, prompting power engineers to seek ways to improve their efficiency. One such solution is to integrate dispersed generation sources, such as photovoltaic (PV) and storage systems, with an appropriate control mechanism at the distribution level. In recent years, there has been a significant increase in interest in the installation of PV-Battery systems, due to their potential to reduce carbon emissions and lower energy costs. This research proposes an optimal economic power dispatch strategy using Model Predictive Control (MPC) to enhance the overall performance of HAPN. A hybrid AC/DC microgrid concept is proposed to address the control choices made by the appliance scheduling and hybrid switching approaches based on a linear programming optimization framework. The suggested optimization criteria improve consumer satisfaction, minimize grid disconnections, and lower overall energy costs by deploying inexpensive clean energy generation and control. Various examples from actual case study demonstrate the use of the established EMS and design methodology.Die Motivation dieser Arbeit besteht darin, ein Konzept zur Senkung der Energiekosten in einem Heimnetzwerk (HAPN) mit intelligenter Erzeugung und exiblen Lastanforderungen vorzustellen. Im Rahmen dieser Forschungsarbeit wird ein Entwurf für ein HAPN entwickelt, indem das Energiemanagementsystem (EMS) und der Entwurf des Layouts auf der Grundlage des Systemmodells und der betrieblichen Anforderungen gelöst werden. Die steigende Nachfrage nach Elektrizität ist für traditionelle Stromnetze kostspielig und infrastrukturintensiv. Daher konzentrieren sich Energietechniker darauf, die Effizienz der derzeitigen Netze zu erhöhen. Dies kann durch die Integration verteilter Erzeugungsanlagen (z. B. Photovoltaik (PV), Speicher) mit einem geeigneten Kontrollmechanismus für das Energiemanagement auf der Verteilungsseite erreicht werden. Darüber hinaus hat das Interesse an der Installation von PV-Batterie-basierten Systemen aufgrund der Reduzierung der CO2-Emissionen und der Senkung der Energiekosten erheblich zugenommen. Es wird eine optimale wirtschaftliche Strategie für den Energieeinsatz unter Verwendung einer modellprädiktiven Steuerung (MPC) entwickelt. Es wird zudem ein hybrides AC/DC-Microgrid-Konzept vorgeschlagen, um die Steuerungsentscheidungen, die von den Ansätzen der Geräteplanung und der hybriden Umschaltung getroffen werden, auf der Grundlage eines linearen Programmierungsoptimierungsrahmens zu berücksichtigen. Die vorgeschlagenen Optimierungskriterien verbessern die Zufriedenheit der Verbraucher, minimieren Netzabschaltungen und senken die Gesamtenergiekosten durch den Einsatz von kostengünstiger und sauberer Energieerzeugung. Verschiedene Beispiele aus einer Fallstudie demonstrieren den Einsatz des entwickelten EMS und der Entwurfsmethodik

    Operational Research: methods and applications

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    This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    An automatic selection of optimal recurrent neural network architecture for processes dynamics modelling purposes

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    A problem related to the development of algorithms designed to find the structure of artificial neural network used for behavioural (black-box) modelling of selected dynamic processes has been addressed in this paper. The research has included four original proposals of algorithms dedicated to neural network architecture search. Algorithms have been based on well-known optimisation techniques such as evolutionary algorithms and gradient descent methods. In the presented research an artificial neural network of recurrent type has been used, whose architecture has been selected in an optimised way based on the above-mentioned algorithms. The optimality has been understood as achieving a trade-off between the size of the neural network and its accuracy in capturing the response of the mathematical model under which it has been learnt. During the optimisation, original specialised evolutionary operators have been proposed. The research involved an extended validation study based on data generated from a mathematical model of the fast processes occurring in a pressurised water nuclear reactor.Comment: 32 pages, 17 figures, code availabl
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