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Dissertations / Theses on the topic 'Critical infrastructure Machine Learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

GAGGERO, GIOVANNI BATTISTA. "Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1068918.

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Managing critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches th

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2

Samstad, Anna. "A simulation and machine learning approach to critical infrastructure resilience appraisal : Case study on payment disruptions." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33745.

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This study uses a simulation to gather data regarding a payment disruption. The simulation is part of a project called CCRAAAFFFTING, which examines what happens to a society when a payment disruption occurs. The purpose of this study is to develop a measure for resilience in the simulation and use machine learning to analyse the attributes in the simulation to see how they affect the resilience in the society. The resilience is defined as “the ability to bounce back to a previous state”, and the resilience measure is developed according to this definition. Two resilience measurements are defi

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Otoum, Safa. "Machine Learning-driven Intrusion Detection Techniques in Critical Infrastructures Monitored by Sensor Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39090.

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In most of critical infrastructures, Wireless Sensor Networks (WSNs) are deployed due to their low-cost, flexibility and efficiency as well as their wide usage in several infrastructures. Regardless of these advantages, WSNs introduce various security vulnerabilities such as different types of attacks and intruders due to the open nature of sensor nodes and unreliable wireless links. Therefore, the implementation of an efficient Intrusion Detection System (IDS) that achieves an acceptable security level is a stimulating issue that gained vital importance. In this thesis, we investigate the

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Lahza, Hassan Fareed M. "Designing a feature construction and selection approach for machine learning-based intrusion detection in industrial control system networks." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132657/1/Hassan%20Fareed%20M_Lahza_Thesis.pdf.

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This thesis presents an approach for constructing new accurate and efficient advanced features to improve the accuracy of detecting cyber-attacks using machine learning on the critical infrastructure networks. The empirical results indicate that our feature construction approach not only outperforms other methods in term of detection rate and performance but also provides automation to the entire construction processes. This thesis also proposes a framework for constructing advanced features for various critical infrastructure communication protocols by adopting and improving the window-based

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Malik, Muhammad Imran. "BEWARE: A methodical approach to develop BEnign and MalWARE datasets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2024. https://ro.ecu.edu.au/theses/2790.

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Cyber-Physical Systems (CPSes) are continually advancing in many Critical Infrastructure (CI) sectors, such as energy, healthcare, the military, and telecommunication. These systems are persistently targeted by nation-sponsored cyber criminal groups, who continuously refine their techniques and attack methods. Malicious software (malware) attacks are the most common of these tactics and can have severe consequences, as observed in the Stuxnet, BlackEnergy, Industroyer, and Triton attacks. A dataset consisting of validated benign and malware samples is a fundamental ingredient in developing sol

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Abdelaty, Maged Fathy Youssef. "Robust Anomaly Detection in Critical Infrastructure." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/352463.

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Critical Infrastructures (CIs) such as water treatment plants, power grids and telecommunication networks are critical to the daily activities and well-being of our society. Disruption of such CIs would have catastrophic consequences for public safety and the national economy. Hence, these infrastructures have become major targets in the upsurge of cyberattacks. Defending against such attacks often depends on an arsenal of cyber-defence tools, including Machine Learning (ML)-based Anomaly Detection Systems (ADSs). These detection systems use ML models to learn the profile of the normal behavio

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Arce, Munoz Samuel. "Optimized 3D Reconstruction for Infrastructure Inspection with Automated Structure from Motion and Machine Learning Methods." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8469.

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Infrastructure monitoring is being transformed by the advancements on remote sensing, unmanned vehicles and information technology. The wide interaction among these fields and the availability of reliable commercial technology are helping pioneer intelligent inspection methods based on digital 3D models. Commercially available Unmanned Aerial Vehicles (UAVs) have been used to create 3D photogrammetric models of industrial equipment. However, the level of automation of these missions remains low. Limited flight time, wireless transfer of large files and the lack of algorithms to guide a UAV thr

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Chong, Linsen. "Modeling Naturalistic Driver Behavior in Traffic Using Machine Learning." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/76834.

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This research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied to simulate driver naturalistic driving behavior including risk-taking behavior during an incident and lateral evasive behavior which have not yet been captured in existing literature. Two special machi

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Nizampuram, Pranay. "Prediction of re-admissions for critical health conditions : A Machine Learning Approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10821.

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Context. Re-admission is the return hospitalization within 30 days from the date of original admission or discharge from hospital. Thecosts of the unplanned re-admissions were estimated to $25 billion per year alone in the U.S. Re-admission rate also has a huge impact onquality of care provided to the patients, cost of health care, and utilization of hospital resources and the image of the care provider. Studies indicate huge potential of savings that can be achieved with incremental performance improvements in detecting cases of preventable re-admissions. Objectives. In this study we ﬁnd the

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Xu, Jin. "Machine Learning – Based Dynamic Response Prediction of High – Speed Railway Bridges." Thesis, KTH, Bro- och stålbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278538.

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Targeting heavier freights and transporting passengers with higher speeds became the strategic railway development during the past decades significantly increasing interests on railway networks. Among different components of a railway network, bridges constitute a major portion imposing considerable construction and maintenance costs. On the other hand, heavier axle loads and higher trains speeds may cause resonance occurrence on bridges; which consequently limits operational train speed and lines. Therefore, satisfaction of new expectations requires conducting a large number of dynamic assess

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Johnson, Alistair E. W. "Mortality prediction and acuity assessment in critical care." Thesis, University of Oxford, 2014. https://ora.ox.ac.uk/objects/uuid:2486465e-8fda-47a9-b82e-c0a93f4f1fc4.

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Accurate mortality prediction in intensive care units (ICUs) allows for the risk adjustment of study populations, aids in patient care and provides a method for benchmarking overall hospital and ICU performance. ICU risk-adjustment models are primarily comprised of an integer severity of illness score which increases with increasing patient risk of mortality. First published in the 1980s, the improvements to these scores primarily consisted of increasing the dimensionality of the model, and hence also increasing their complexity. This thesis aims to improve upon these models. First, the field

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Purkayastha, Pratik. "Diagnostics and Prognostics of safety critical systems using machine learning, time and frequency domain analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17603.

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The prime focus of this thesis was to develop a robust Prognostic and Diagnostic Health Management module (PDHM), capable of detecting faults, classifying faults, fault progression tracking and estimating time to failure. Priority was to obtain as much accuracy as possible with the bare minimum amount of sensors as possible. Algorithms like k-Nearest Neighbors (k-NN), Linear and Non- Linear regression and development of rule engine to identify safe operating limits were deployed. The entire solution was developed using R (v 3.5.0). The accuracy of around 98% was obtained in diagnostics. For Pr

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Stojnić, Robert. "Critical assessment and further development of statistical modelling and machine learning methods in computational biology." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607955.

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Smith, Evan McLean. "A Collection of Computer Vision Algorithms Capable of Detecting Linear Infrastructure for the Purpose of UAV Control." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/81448.

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One of the major application areas for UAVs is the automated traversing and inspection of infrastructure. Much of this infrastructure is linear, such as roads, pipelines, rivers, and railroads. Rather than hard coding all of the GPS coordinates along these linear components into a flight plan for the UAV to follow, one could take advantage of computer vision and machine learning techniques to detect and travel along them. With regards to roads and railroads, two separate algorithms were developed to detect the angle and distance offset of the UAV from these linear infrastructure components to

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15

LANGE, THOMAS. "New Reliable Operation Infrastructure for Dynamic, High-dependability Applications." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2935598.

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Neves, Cláudia. "Structural Health Monitoring of Bridges : Model-free damage detection method using Machine Learning." Licentiate thesis, KTH, Bro- och stålbyggnad, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205616.

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This is probably the most appropriate time for the development of robust and reliable structural damage detection systems as aging civil engineering structures, such as bridges, are being used past their life expectancy and beyond their original design loads. Often, when a significant damage to the structure is discovered, the deterioration has already progressed far and required repair is substantial. This is both expensive and has negative impact on the environment and traffic during replacement. For the exposed reasons the demand for efficient Structural Health Monitoring techniques is curr

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Nääs, Starberg Filip, and Axel Rooth. "Predicting a business application's cloud server CPU utilization using the machine learning model LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301247.

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Cloud Computing sees increased adoption as companies seek to increase flexibility and reduce cost. Although the large cloud service providers employ a pay-as-you-go pricing model and enable customers to scale up and down quickly, there is still room for improvement. Workload in the form of CPU utilization often fluctuates which leads to unnecessary cost and environmental impact for companies. To help mitigate this issue, the aim of this paper is to predict future CPU utilization using a long short-term memory (LSTM) machine learning model. By predicting utilization up to 30 minutes into the fu

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Jahangiri, Arash. "Investigating Violation Behavior at Intersections using Intelligent Transportation Systems: A Feasibility Analysis on Vehicle/Bicycle-to-Infrastructure Communications as a Potential Countermeasure." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76729.

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The focus of this dissertation is on safety improvement at intersections and presenting how Vehicle/Bicycle-to-Infrastructure Communications can be a potential countermeasure for crashes resulting from drivers' and cyclists' violations at intersections. The characteristics (e.g., acceleration capabilities, etc.) of transportation modes affect the violation behavior. Therefore, the first building block is to identify the users' transportation mode. Consequently, having the mode information, the second building block is to predict whether or not the user is going to violate. This step focuses on

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SACCO, ALESSIO. "Towards Autonomous Computer Networks in Support of Critical Systems." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2968454.

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Blakely, Logan. "Spectral Clustering for Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series." Thesis, Portland State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10980011.

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<p> The increasing demand for and prevalence of distributed energy resources (DER) such as solar power, electric vehicles, and energy storage, present a unique set of challenges for integration into a legacy power grid, and accurate models of the low-voltage distribution systems are critical for accurate simulations of DER. Accurate labeling of the phase connections for each customer in a utility model is one area of grid topology that is known to have errors and has implications for the safety, efficiency, and hosting capacity of a distribution system. This research presents a methodology for

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Avverahalli, Ravi Darshan. "Identifying and Prioritizing Critical Information in Military IoT: Video Game Demonstration." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104070.

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Current communication and network systems are not built for delay-sensitive applications. The most obvious fact is that the communication capacity is only achievable in theory with infinitely long codes, which means infinitely long delays. One remedy for this is to use shorter codes. Conceptually, there is a deeper reason for the difficulties in such solutions: in Shannon's original 1948 paper, he started out by stating that the "semantic aspects" of information is "irrelevant" to communications. Hence, in Shannon's communication system, as well as every network built after him, we put all inf

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gao, shenjian, and Yanwen Tan. "Paving the Way for Self-driving Cars - Software Testing for Safety-critical Systems Based on Machine Learning : A Systematic Mapping Study and a Survey." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15681.

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Context: With the development of artificial intelligence, autonomous vehicles are becoming more and more feasible and the safety of Automated Driving (AD) system should be assured. This creates a need to analyze the feasibility of verification and validation approaches when testing safety-critical system that contains machine learning (ML) elements. There are many studies published in the context of verification and validation (V&V) research area related to safety-critical components. However, there are still blind spots of research to identify which test methods can be used to test compon

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Collin, Sofie. "Synthetic Data for Training and Evaluation of Critical Traffic Scenarios." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177779.

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Modern camera-based vehicle safety systems heavily rely on machine learning and consequently require large amounts of training data to perform reliably. However, collecting and annotating the needed data is an extremely expensive and time-consuming process. In addition, it is exceptionally difficult to collect data that covers critical scenarios. This thesis investigates to what extent synthetic data can replace real-world data for these scenarios. Since only a limited amount of data consisting of such real-world scenarios is available, this thesis instead makes use of proxy scenarios, e.g. si

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Javvaji, Pavan Kumar. "STATISTICAL METHODS FOR CRITICAL PATHS SELECTION AND FAULT COVERAGE IN INTEGRATED CIRCUITS." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1664.

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With advances in technology, modern integrated circuits have higher complexities and reduced transistor sizing. In deep sub-micron, the parameter variation-control is difficult and component delays vary from one manufactured chip to another. Therefore, the delays are not discrete values but are a statistical quantity, and statistical evaluation methods have gained traction. Furthermore, fault injection based gate-level fault coverage is non-scalable and statistical estimation methods are preferred. This dissertation focuses on scalable statistical methods to select critical paths in the presen

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Sommerlot, Andrew Richard. "Coupling Physical and Machine Learning Models with High Resolution Information Transfer and Rapid Update Frameworks for Environmental Applications." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/89893.

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Few current modeling tools are designed to predict short-term, high-risk runoff from critical source areas (CSAs) in watersheds which are significant sources of non point source (NPS) pollution. This study couples the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model with the Climate Forecast System Reanalysis (CFSR) model and the Global Forecast System (GFS) model short-term weather forecast, to develop a CSA prediction tool designed to assist producers, landowners, and planners in identifying high-risk areas generating storm runoff and pollution. Short-term predictions for

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Pérennou, Loïc. "Virtual machine experience design : a predictive resource allocation approach for cloud infrastructures." Thesis, Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1246/document.

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L’un des principaux défis des fournisseurs de services cloud est d’offrir aux utilisateurs une performance acceptable, tout en minimisant les besoins en matériel et énergie. Dans cette thèse CIFRE menée avec Outscale, un fournisseur de cloud, nous visons à optimiser l’allocation des ressources en utilisant de nouvelles sources d’information. Nous caractérisons la charge de travail pour comprendre le stress résultant sur l’orchestrateur, et la compétition pour les ressources disponibles qui dégrade la qualité de service. Nous proposons un modèle pour prédire la durée d’exécution des VMs à parti

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Rezvani, Arany Roushan. "Gaussian Process Model Predictive Control for Autonomous Driving in Safety-Critical Scenarios." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-161430.

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This thesis is concerned with model predictive control (MPC) within the field of autonomous driving. MPC requires a model of the system to be controlled. Since a vehicle is expected to handle a wide range of driving conditions, it is crucial that the model of the vehicle dynamics is able to account for this. Differences in road grip caused by snowy, icy or muddy roads change the driving dynamics and relying on a single model, based on ideal conditions, could possibly lead to dangerous behaviour. This work investigates the use of Gaussian processes for learning a model that can account for vary

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Redolfi, Alberto. "E-infrastructure, segmentation du cortex, environnement de contrôle qualité : un fil rouge pour les neuroscientifiques." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS191/document.

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Les neurosciences sont entrées dans l'ère des « big data ». Les ordinateurs de bureau individuels ne sont plus adaptés à l'analyse des téraoctets et potentiellement des pétaoctets qu'impliquent les images cérébrales. Pour combler le gouffre qui existe entre la taille des données et les possibilités standard d'extraction des informations, on développe actuellement des infrastructures virtuelles en Amérique du Nord, au Canada et également en Europe. Ces infrastructures dématérialisées permettent d'effectuer des expériences en imagerie médicale à l'aide de ressources informatiques dédiées telles

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Ruffels, Aaron. "Model-Free Damage Detection for a Small-Scale Steel Bridge." Thesis, KTH, Bro- och stålbyggnad, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232363.

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Around the world bridges are ageing. In Europe approximately two thirds of all railway bridges are over 50 years old. As these structures age, it becomes increasingly important that they are properly maintained. If damage remains undetected this can lead to premature replacement which can have major financial and environmental costs. It is also imperative that bridges are kept safe for the people using them. Thus, it is necessary for damage to be detected as early as possible. This research investigates an unsupervised, model-free damage detection method which could be implemented for continuo

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Ngouakang, Ive Marcial. "Using the MQTT to implement an IoT infrastructure." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21039/.

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In this thesis we investigated the MQTT protocol, which results in being a valid solution for data transmission in an IoT solution. After having explored the main communication protocols, we focused on the main features of the MQTT that has been selected for our application. To this purpose, a MQTT broker has been created on Node-RED, while the remaining elements of the application have been developed on MATLAB. In particular, we exploited the MQTT and the Database toolboxes: the former is used to exchange data between two PCs through the MQTT protocol, while the latter is necessary to insert

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Pérennou, Loïc. "Virtual machine experience design : a predictive resource allocation approach for cloud infrastructures." Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1246.

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L’un des principaux défis des fournisseurs de services cloud est d’offrir aux utilisateurs une performance acceptable, tout en minimisant les besoins en matériel et énergie. Dans cette thèse CIFRE menée avec Outscale, un fournisseur de cloud, nous visons à optimiser l’allocation des ressources en utilisant de nouvelles sources d’information. Nous caractérisons la charge de travail pour comprendre le stress résultant sur l’orchestrateur, et la compétition pour les ressources disponibles qui dégrade la qualité de service. Nous proposons un modèle pour prédire la durée d’exécution des VMs à parti

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Auernhammer, Katja [Verfasser], Felix [Akademischer Betreuer] Freiling, Kolagari Ramin [Akademischer Betreuer] Tavakoli, Felix [Gutachter] Freiling, Kolagari Ramin [Gutachter] Tavakoli, and Dominique [Gutachter] Schröder. "Mask-based Black-box Attacks on Safety-Critical Systems that Use Machine Learning / Katja Auernhammer ; Gutachter: Felix Freiling, Ramin Tavakoli Kolagari, Dominique Schröder ; Felix Freiling, Ramin Tavakoli Kolagari." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2021. http://d-nb.info/1238358292/34.

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Galbincea, Nicholas D. "Critical Analysis of Dimensionality Reduction Techniques and Statistical Microstructural Descriptors for Mesoscale Variability Quantification." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500642043518197.

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Amarasinghe, Kasun. "Explainable Neural Networks based Anomaly Detection for Cyber-Physical Systems." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6091.

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Cyber-Physical Systems (CPSs) are the core of modern critical infrastructure (e.g. power-grids) and securing them is of paramount importance. Anomaly detection in data is crucial for CPS security. While Artificial Neural Networks (ANNs) are strong candidates for the task, they are seldom deployed in safety-critical domains due to the perception that ANNs are black-boxes. Therefore, to leverage ANNs in CPSs, cracking open the black box through explanation is essential. The main objective of this dissertation is developing explainable ANN-based Anomaly Detection Systems for Cyber-Physical System

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Alagöz, Ibrahim [Verfasser], Reinhard [Akademischer Betreuer] German, Reinhard [Gutachter] German, Detlef [Gutachter] Kips, and Robert [Gutachter] Schober. "Development of a Methodology for the efficient Validation of Safety-Critical Systems using Machine Learning Optimization Techniques / Ibrahim Alagöz ; Gutachter: Reinhard German, Detlef Kips, Robert Schober ; Betreuer: Reinhard German." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2020. http://d-nb.info/1206416807/34.

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Lagerkvist, Love. "Computation as Strange Material : Excursions into Critical Accidents." Thesis, Malmö universitet, Institutionen för konst, kultur och kommunikation (K3), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43639.

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Waking up in a world where everyone carries a miniature supercomputer, interaction designers find themselves in their forerunners dreams. Faced with the reality of planetary-scale we have to confront the task of articulating approaches responsive this accidental ubiquity of computation. This thesis attempts such a formulation by defining computation as a strange material, a plasticity shaped equally by its technical properties and the mode of production by which is its continuously re-produced. The definition is applied through a methodology of excursions — participatory explorations into two

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Topcu, Taylan Gunes. "Management of Complex Sociotechnical Systems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97844.

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Sociotechnical systems (STSs) rely on the collaboration between humans and autonomous decision-making units to fulfill their objectives. Highly intertwined social and technical contextual factors influence the collaboration between these human and engineered elements, and consequently the performance characteristics of the STS. In the next two decades, the role allocated to STSs in our society will drastically increase. Thus, the effective design of STSs requires an improved understanding of the human-autonomy interdependency. This dissertation brings together management science along with sy

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Mosqueiro, Thiago Schiavo. "Processamento de informação em redes neurais sensoriais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/76/76131/tde-29102015-113036/.

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Com os avanços em eletrônica analógica e digital dos últimos 50 anos, a neurociência ganhou grande momentum e nasceu uma de suas áreas que atualmente mais recebe financiamento: neurociência computacional. Estudos nessa área, ainda considerada recente, vão desde estudos moleculares de trocas iônicas por canais iônicos (escala nanométrica), até influências de populações neurais no comportamento de grandes mamíferos (escala de até metros). O coração da neurociência computacional compreende técnicas inter- e multidisciplinares, envolvendo biologia de sistemas, bioquímica, modelagem matemática, est

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Dahmouni, Nor El Islam. "Caractérisation multidimensionnelle du canal de propagation et contribution de l'intelligence artificielle à la classification des scénarios de propagation pour les communications véhiculaires." Electronic Thesis or Diss., Université de Lille (2022-....), 2024. http://www.theses.fr/2024ULILN014.

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Au cours des dernières années, les réseaux mobiles ont considérablementamélioré leurs performances, notamment avec l'avènement de la 5G offrant desdébits bien plus élevés que ceux de la 4G. Ces avancées sont attribuables aux progrèstechnologiques dans divers domaines tels que les systèmes antennaires intégrés et lescomposants électroniques alliant rapidité et faible consommation. La 5G inaugureune ère de services diversifiés incluant l'Internet des Objets (IoT) et lescommunications véhiculaires, de Véhicule à Infrastructure (V2I) et de Véhicule àVéhicule (V2V). Dans ce contexte, la 5G propose

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Jansson, David, and Viktoria Sjöbohm. "En maskininlärningsanalys av ursprunget till bostadsområdens attraktionskraft : En kvantitativ studie kring Points of Interest inverkan på bostadsområdens attraktionskraft." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281309.

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Efterfrågan på bostadsrätter i Stockholm har under de senaste åren varit hög och priserna svänger ständigt. Idag är många överens om att ‘läget’ är en av de viktigaste parametrarna i värderingen av en bostadsrätt men vad ‘läget’ egentligen innebär är inte lika självklart. Denna studie har syftet att undersöka potentiella samband mellan ett läges attraktivitet och antal Points of Interest i en bostadsrätts närområde. Studien avser att besvara vilka parametrar som har stärst inverkan på ett bostadsområdes attraktionskraft. Points of Interest utgörs av bland annat postnummer, antal restauranger o

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Cherifa-Luron, Ményssa. "Prédiction des épisodes d'hypotension à partir de données longitudinales à haute fréquence recueillies auprès de patients en soins intensifs." Electronic Thesis or Diss., Université Paris Cité, 2021. https://wo.app.u-paris.fr/cgi-bin/WebObjects/TheseWeb.woa/wa/show?t=8076&f=67992.

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La révolution numérique en santé, traduite à la fois par la centralisation et l'accès à de grandes bases de données médicales et par des avancées considérables de l'intelligence artificielle (IA), a permis de créer de nouvelles opportunités pour la Science des données appliquée à la médecine. Remettant le patient au cœur du système de soin, l’essor de ses nouvelles technologies garanti une médecine plus personnalisée capable d'identifier plus précocement des facteurs prédictifs et pronostics individuels. Ce travail de thèse s'inscrit dans le concept de la médecine personnalisée. Plus exactemen

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BELCORE, ELENA. "Generation of a Land Cover Atlas of environmental critic zones using unconventional tools." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2907028.

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Attal, Ferhat. "Classification de situations de conduite et détection des événements critiques d'un deux roues motorisé." Thesis, Paris Est, 2015. http://www.theses.fr/2015PEST1003/document.

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L'objectif de cette thèse est de développer des outils d'analyse de données recueillies sur les deux roues motorisés (2RMs). Dans ce cadre, des expérimentations sont menées sur des motos instrumentés dans un contexte de conduite réelle incluant à la fois des conduites normales dites naturelles et des conduites à risques (presque chute et chute). Dans la première partie de la thèse, des méthodes d'apprentissage supervisé ont été utilisées pour la classification de situations de conduite d'un 2RM. Les approches développées dans ce contexte ont montré l'intérêt de prendre en compte l'aspect tempo

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Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.

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S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a pr

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Chikhladze, Dimitri. "Infrastructure for machine learning and computer vision." Master's thesis, 2019. http://hdl.handle.net/10362/72833.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>The infrastructure surrounding machine learning projects is of utmost importance: Machine learning projects require data acquisition mechanisms, software for data processing, as well as a benchmarking platform for evaluating performance of machine learning algorithms over time. In this report we describe our work aimed at developing such infrastructure for a Europe based computer vision startup specializing in human behaviour tracking. We discuss three projects comp

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Chang, Chai-Hao, and 張家豪. "Link Prediction using Supervised Machine Learning in Advanced Metering Infrastructure." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/jp8gn2.

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碩士<br>國立清華大學<br>資訊工程學系所<br>105<br>Advanced Metering Infrastructure (AMI) wireless communications network is an important part of smart grid architecture. How to effectively deploy fewer concentrators to collect data from smart meters in time will be a challenging problem. In the actual deployment, all we know are the meter and candidate concentrator position. We don’t know the link between meter and candidate is connectable or not before deployment or actual measurement. As the increases of number of candidate concentrator position, the measurement cost also raise. Therefore, this thesis will

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Bernarda, Mariana Serrano Lopes da. "Portfolio optimization: from markowitz to machine learning." Master's thesis, 2022. http://hdl.handle.net/10362/134510.

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Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management<br>In the past few decades, substantial progress has been made in portfolio optimization, especially with the emergence of machine learning. Therefore, it is essential to find the models that not only achieve the best results but also simplify the process. This project aims to demonstrate that to achieve optimal portfolios cannot be based only on traditional statistical methods. Therefore the Random Forest regression mode

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Chang, Ying-Cheng, and 張英城. "Power System Critical Clearing Time Prediction using Extreme Learning Machine." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/91148506216809252583.

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碩士<br>義守大學<br>電機工程學系<br>103<br>This thesis uses extreme learning machine (ELM) to predict critical clearing time (CCT). CCT is a measurement for measuring power system transient stability. A larger CCT suggests this power system stability is stronger. However, it wastes a lot of time to obtain CCT by using the conventional time-domain method. In order to accelerate the CCT computation, many researchers have considered the usage of neural networks in the past three decades. Recently, ELM is a refined product of neural networks in less than ten years. It is the offspring of single layer feedforw

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Chuang, Wen-Tze, and 莊文澤. "Detecting Critical Timing Paths Caused by Dynamic Voltage Drop Using Machine Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/j9nhr6.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>107<br>Timing constrain will reduce operational frequency of large integrated circuits or system-on-a-chip, and it is often caused by setup timing violation which would be influenced by dynamic voltage drop, can be referred to as maximum timing pushout. This problem is exacerbated in the FinFET designs. This thesis proposes a method using machine learning techniques to predict critical scenarios quickly for analyzing dynamic voltage drop and critical timing paths predictor for accurate timing analysis. First, we use a classification model to predict critical level o

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Xiao, Wei. "Optimal control and learning for safety-critical autonomous systems." Thesis, 2021. https://hdl.handle.net/2144/43103.

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Optimal control of autonomous systems is a fundamental and challenging problem, especially when many stringent safety constraints and tight control limitations are involved such that solutions are hard to determine. It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). Although computationally efficient, this method is limited by several factors which ar

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