Geometrical Methods for the Analysis of Simulation Bundles

Efficiently analyzing large amounts of high dimensional data derived from the simulation of industrial products is a challenge that is confronted in this thesis. For this purpose, simulations are considered as abstract objects and assumed to be living in lower dimensional space. The aim of this thesis is to characterize and analyze these simulations, this is done by examining two different approaches. Firstly, from the perspective of manifold learning using diffusion maps and demonstrating its application and merits; the inherent assumption of manifold learning is that high dimensional data can be considered to be located on a low dimensional abstract manifold. Unfortunately, this can not be verified in practical applications as it would require the existence of several thousand datasets, where in reality only a few hundred are available due to computational costs. To overcome these restrictions, a new way of characterizing the set of simulations is proposed where it is assumed that transformations send simulations to other simulations. Under this assumption, the theoretical framework of shape spaces can be applied wherein a quotient space of a pre-shape space (the space of simulations shapes) modulo a transformation group is used. It is propound to add into this setting, the construction of positive definite operators that are assumed invariant to specific transformations. They are built using only one simulation and as a consequence all other simulations can be projected to the eigen-basis of these operators. A new representation of all simulations is thus obtained based on the projection coefficients in a very much analogous way to the use of the Fourier transformation. The new representation is shown to be significantly reduced, depending on the smoothness of the data. Several industrial applications for time dependent datasets from engineering simulations are provided to demonstrate the usefulness of the method and put forward several research directions and possible new applications

Temporospatial Context-Aware Vehicular Crash Risk Prediction

With the demand for more vehicles increasing, road safety is becoming a growing concern. Traffic collisions take many lives and cost billions of dollars in losses. This explains the growing interest of governments, academic institutions and companies in road safety. The vastness and availability of road accident data has provided new opportunities for gaining a better understanding of accident risk factors and for developing more effective accident prediction and prevention regimes. Much of the empirical research on road safety and accident analysis utilizes statistical models which capture limited aspects of crashes. On the other hand, data mining has recently gained interest as a reliable approach for investigating road-accident data and for providing predictive insights. While some risk factors contribute more frequently in the occurrence of a road accident, the importance of driver behavior, temporospatial factors, and real-time traffic dynamics have been underestimated. This study proposes a framework for predicting crash risk based on historical accident data. The proposed framework incorporates machine learning and data analytics techniques to identify driving patterns and other risk factors associated with potential vehicle crashes. These techniques include clustering, association rule mining, information fusion, and Bayesian networks. Swarm intelligence based association rule mining is employed to uncover the underlying relationships and dependencies in collision databases. Data segmentation methods are employed to eliminate the effect of dependent variables. Extracted rules can be used along with real-time mobility to predict crashes and their severity in real-time. The national collision database of Canada (NCDB) is used in this research to generate association rules with crash risk oriented subsequents, and to compare the performance of the swarm intelligence based approach with that of other association rule miners. Many industry-demanding datasets, including road-accident datasets, are deficient in descriptive factors. This is a significant barrier for uncovering meaningful risk factor relationships. To resolve this issue, this study proposes a knwoledgebase approximation framework to enhance the crash risk analysis by integrating pieces of evidence discovered from disparate datasets capturing different aspects of mobility. Dempster-Shafer theory is utilized as a key element of this knowledgebase approximation. This method can integrate association rules with acceptable accuracy under certain circumstances that are discussed in this thesis. The proposed framework is tested on the lymphography dataset and the road-accident database of the Great Britain. The derived insights are then used as the basis for constructing a Bayesian network that can estimate crash likelihood and risk levels so as to warn drivers and prevent accidents in real-time. This Bayesian network approach offers a way to implement a naturalistic driving analysis process for predicting traffic collision risk based on the findings from the data-driven model. A traffic incident detection and localization method is also proposed as a component of the risk analysis model. Detecting and localizing traffic incidents enables timely response to accidents and facilitates effective and efficient traffic flow management. The results obtained from the experimental work conducted on this component is indicative of the capability of our Dempster-Shafer data-fusion-based incident detection method in overcoming the challenges arising from erroneous and noisy sensor readings

Data driven and physics based methods to assess the mechanical response of advanced materials: from experiments to efficient predictions

L'abstract è presente nell'allegato / the abstract is in the attachmen

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

A Data Mining Methodology for Vehicle Crashworthiness Design

This study develops a systematic design methodology based on data mining theory for decision-making in the development of crashworthy vehicles. The new data mining methodology allows the exploration of a large crash simulation dataset to discover the underlying relationships among vehicle crash responses and design variables at multiple levels and to derive design rules based on the whole-vehicle safety requirements to make decisions about component-level and subcomponent-level design. The method can resolve a major issue with existing design approaches related to vehicle crashworthiness: that is, limited abilities to explore information from large datasets, which may hamper decision-making in the design processes. At the component level, two structural design approaches were implemented for detailed component design with the data mining method: namely, a dimension-based approach and a node-based approach to handle structures with regular and irregular shapes, respectively. These two approaches were used to design a thin-walled vehicular structure, the S-shaped beam, against crash loading. A large number of design alternatives were created, and their responses under loading were evaluated by finite element simulations. The design variables and computed responses formed a large design dataset. This dataset was then mined to build a decision tree. Based on the decision tree, the interrelationships among the design parameters were revealed, and design rules were generated to produce a set of good designs. After the data mining, the critical design parameters were identified and the design space was reduced, which can simplify the design process. To partially replace the expensive finite element simulations, a surrogate model was used to model the relationships between design variables and response. Four machine learning algorithms, which can be used for surrogate model development, were compared. Based on the results, Gaussian process regression was determined to be the most suitable technique in the present scenario, and an optimization process was developed to tune the algorithm’s hyperparameters, which govern the model structure and training process. To account for engineering uncertainty in the data mining method, a new decision tree for uncertain data was proposed based on the joint probability in uncertain spaces, and it was implemented to again design the S-beam structure. The findings show that the new decision tree can produce effective decision-making rules for engineering design under uncertainty. To evaluate the new approaches developed in this work, a comprehensive case study was conducted by designing a vehicle system against the frontal crash. A publicly available vehicle model was simplified and validated. Using the newly developed approaches, new component designs in this vehicle were generated and integrated back into the vehicle model so their crash behavior could be simulated. Based on the simulation results, one can conclude that the designs with the new method can outperform the original design in terms of measures of mass, intrusion and peak acceleration. Therefore, the performance of the new design methodology has been confirmed. The current study demonstrates that the new data mining method can be used in vehicle crashworthiness design, and it has the potential to be applied to other complex engineering systems with a large amount of design data

Doctor of Philosophy

dissertationThe interest in multimodal transportation improvements is increasing in cities across the U.S. Investing in multimodal infrastructure benefits the portion of urban population that is unable to drive due to a variety of reasons such as personal preference, age, and affordability. It is also well known that active transportation such as walking, biking, and taking transit, can improve public health due to increased physical activity, and reduce traffic congestion by reducing the average person's delay. While improved multimodal infrastructure and accessibility attracts new users, it can possibly increase their exposure to risk from crashes. In urban areas where the "safety in numbers phenomenon" does not exist, nonmotorized user vulnerability becomes a predominant risk factor when they are involved in a crash, even at lower vehicle speeds. This dissertation aims to explore the factors that are associated with safety outcomes in urban multimodal transportation systems, and develop methods that can be used to estimate safety effects of multimodal infrastructure and accessibility improvements. Using Chicago as a case study, a comprehensive dataset is developed that significantly contributes to the existing literature by including socio-economic, land use, road network, travel demand, and crash data. Area-wide analysis on the census tract level provides a broader perspective about safety issues that multimodal users encounter in cities. The characteristics of a multimodal transportation system are expressed through the presence of multimodal infrastructure, street connectivity and network completeness, and accessibility to destinations for multimodal users. A set of statistical areal safety models (SASM) based on both frequentist and Bayesian statistical inference is applied to estimate the factors that are associated with total and severe vehicular, pedestrian, and bicyclist crashes in urban multimodal transportation systems. The results show that the current safety evaluation methods need to acknowledge the complexity of multimodal transportation systems through the inclusion of diverse factors that may influence safety outcomes, particularly for more vulnerable users. The methods developed in this research can further be used to expand the current practice of evaluating multimodal transportation safety, and planning for city-wide investments in multimodal infrastructure and improved accessibility, while being able to estimate the expected safety outcomes

Otimização eficiente global dirigida por metamodelos combinados : novos caminhos abertos pela aproximação por mínimos quadrados

Orientador: Alberto Luiz SerpaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: O presente trabalho representa a compilação dos resultados anteriores dessa pesquisa no campo de metamodelos combinados e otimização eficiente global (EGO), os quais foram sumetidos para publicação em periódicos especializados. Recentemente foi implementado nesse trabalho de doutorado o algoritmo LSEGO que é uma abordagem para conduzir algoritmos tipo EGO, baseando-se em metamodelos combinados através da aproximação por mínimos quadrados (metamodelos combinados LS). Através dos metamodelos combinados LS é possível estimar a incerteza da aproximação usando qualquer tipo de metamodelagem (e não somente do tipo kriging), permitindo estimar a função de expectativa de melhora para a função objetivo. Nos experimentos computacionais anteriores em problemas de otimização sem restrições, a abordagem LSEGO mostrou-se como uma alternativa viável para conduzir otimização eficiente global usando metamodelos combinados, sem se restringir a somente um ponto adicional por ciclo de otimização iterativa. Na presente tese o algoritmo LSEGO foi extendido de modo a tratar também problemas de otimização com restrições. Os resultados de testes numéricos com problemas analíticos e de referência e também em um estudo de caso de engenharia em escala industrial mostraram-se bastante promissores e competitivos em relação aos trabalhos similares encontrados na literaturaAbstract: In this work we review and compile the results of our previous research in the fields of ensemble of metamodels and efficient global optimization (EGO). Recently we implemented LSEGO that is an approach to drive EGO algorithms, based on LS (least squares) ensemble of metamodels. By means of LS ensemble of metamodels, it is possible to estimate the uncertainty of the prediction by using any kind of model (not only kriging) and provide an estimate for the expected improvement function. In previous numerical experiments with unconstrained optimization problems, LSEGO approach has shown to be a feasible alternative to drive efficient global optimization by using multiple or ensemble of metamodels, not restricted to kriging approximation or single infill point per optimization cycles. In the present work we extended the previous LSEGO algorithm to handle constrained optimization problems as well. Some numerical experiments were performed with analytical benchmark functions and also for industry scale engineering problems with competitive resultsDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic

Multilayer Cyberattacks Identification and Classification Using Machine Learning in Internet of Blockchain (IoBC)-Based Energy Networks

The world's need for energy is rising due to factors like population growth, economic expansion, and technological breakthroughs. However, there are major consequences when gas and coal are burnt to meet this surge in energy needs. Although these fossil fuels are still essential for meeting energy demands, their combustion releases a large amount of carbon dioxide and other pollutants into the atmosphere. This significantly jeopardizes community health in addition to exacerbating climate change, thus it is essential need to move swiftly to incorporate renewable energy sources by employing advanced information and communication technologies. However, this change brings up several security issues emphasizing the need for innovative cyber threats detection and prevention solutions. Consequently, this study presents bigdata sets obtained from the solar and wind powered distributed energy systems through the blockchain-based energy networks in the smart grid (SG). A hybrid machine learning (HML) model that combines both the Deep Learning (DL) and Long-Short-Term-Memory (LSTM) models characteristics is developed and applied to identify the unique patterns of Denial of Service (DoS) and Distributed Denial of Service (DDoS) cyberattacks in the power generation, transmission, and distribution processes. The presented big datasets are essential and significantly helps in identifying and classifying cyberattacks, leading to predicting the accurate energy systems behavior in the SG.© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)fi=vertaisarvioitu|en=peerReviewed

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems