Control and communication systems for automated vehicles cooperation and coordination

Mención Internacional en el título de doctorThe technological advances in the Intelligent Transportation Systems (ITS) are exponentially improving over the last century. The objective is to provide intelligent and innovative services for the different modes of transportation, towards a better, safer, coordinated and smarter transport networks. The Intelligent Transportation Systems (ITS) focus is divided into two main categories; the first is to improve existing components of the transport networks, while the second is to develop intelligent vehicles which facilitate the transportation process. Different research efforts have been exerted to tackle various aspects in the fields of the automated vehicles. Accordingly, this thesis is addressing the problem of multiple automated vehicles cooperation and coordination. At first, 3DCoAutoSim driving simulator was developed in Unity game engine and connected to Robot Operating System (ROS) framework and Simulation of Urban Mobility (SUMO). 3DCoAutoSim is an abbreviation for "3D Simulator for Cooperative Advanced Driver Assistance Systems (ADAS) and Automated Vehicles Simulator". 3DCoAutoSim was tested under different circumstances and conditions, afterward, it was validated through carrying-out several controlled experiments and compare the results against their counter reality experiments. The obtained results showed the efficiency of the simulator to handle different situations, emulating real world vehicles. Next is the development of the iCab platforms, which is an abbreviation for "Intelligent Campus Automobile". The platforms are two electric golf-carts that were modified mechanically, electronically and electrically towards the goal of automated driving. Each iCab was equipped with several on-board embedded computers, perception sensors and auxiliary devices, in order to execute the necessary actions for self-driving. Moreover, the platforms are capable of several Vehicle-to-Everything (V2X) communication schemes, applying three layers of control, utilizing cooperation architecture for platooning, executing localization systems, mapping systems, perception systems, and finally several planning systems. Hundreds of experiments were carried-out for the validation of each system in the iCab platform. Results proved the functionality of the platform to self-drive from one point to another with minimal human intervention.Los avances tecnológicos en Sistemas Inteligentes de Transporte (ITS) han crecido de forma exponencial durante el último siglo. El objetivo de estos avances es el de proveer de sistemas innovadores e inteligentes para ser aplicados a los diferentes medios de transporte, con el fin de conseguir un transporte mas eficiente, seguro, coordinado e inteligente. El foco de los ITS se divide principalmente en dos categorías; la primera es la mejora de los componentes ya existentes en las redes de transporte, mientras que la segunda es la de desarrollar vehículos inteligentes que hagan más fácil y eficiente el transporte. Diferentes esfuerzos de investigación se han llevado a cabo con el fin de solucionar los numerosos aspectos asociados con la conducción autónoma. Esta tesis propone una solución para la cooperación y coordinación de múltiples vehículos. Para ello, en primer lugar se desarrolló un simulador (3DCoAutoSim) de conducción basado en el motor de juegos Unity, conectado al framework Robot Operating System (ROS) y al simulador Simulation of Urban Mobility (SUMO). 3DCoAutoSim ha sido probado en diferentes condiciones y circunstancias, para posteriormente validarlo con resultados a través de varios experimentos reales controlados. Los resultados obtenidos mostraron la eficiencia del simulador para manejar diferentes situaciones, emulando los vehículos en el mundo real. En segundo lugar, se desarrolló la plataforma de investigación Intelligent Campus Automobile (iCab), que consiste en dos carritos eléctricos de golf, que fueron modificados eléctrica, mecánica y electrónicamente para darle capacidades autónomas. Cada iCab se equipó con diferentes computadoras embebidas, sensores de percepción y unidades auxiliares, con la finalidad de transformarlos en vehículos autónomos. Además, se les han dado capacidad de comunicación multimodal (V2X), se les han aplicado tres capas de control, incorporando una arquitectura de cooperación para operación en modo tren, diferentes esquemas de localización, mapeado, percepción y planificación de rutas. Innumerables experimentos han sido realizados para validar cada uno de los diferentes sistemas incorporados. Los resultados prueban la funcionalidad de esta plataforma para realizar conducción autónoma y cooperativa con mínima intervención humana.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Francisco Javier Otamendi Fernández de la Puebla.- Secretario: Hanno Hildmann.- Vocal: Pietro Cerr

Production Optimization Indexed to the Market Demand Through Neural Networks

Connectivity, mobility and real-time data analytics are the prerequisites for a new model of intelligent production management that facilitates communication between machines, people and processes and uses technology as the main driver. Many works in the literature treat maintenance and production management in separate approaches, but there is a link between these areas, with maintenance and its actions aimed at ensuring the smooth operation of equipment to avoid unnecessary downtime in production. With the advent of technology, companies are rushing to solve their problems by resorting to technologies in order to fit into the most advanced technological concepts, such as industries 4.0 and 5.0, which are based on the principle of process automation. This approach brings together database technologies, making it possible to monitor the operation of equipment and have the opportunity to study patterns of data behavior that can alert us to possible failures. The present thesis intends to forecast the pulp production indexed to the stock market value.The forecast will be made by means of the pulp production variables of the presses and the stock exchange variables supported by artificial intelligence (AI) technologies, aiming to achieve an effective planning. To support the decision of efficient production management, in this thesis algorithms were developed and validated with from five pulp presses, as well as data from other sources, such as steel production and stock exchange, which were relevant to validate the robustness of the model. This thesis demonstrated the importance of data processing methods and that they have great relevance in the model input since they facilitate the process of training and testing the models. The chosen technologies demonstrated good efficiency and versatility in performing the prediction of the values of the variables of the equipment, also demonstrating robustness and optimization in computational processing. The thesis also presents proposals for future developments, namely in further exploration of these technologies, so that there are market variables that can calibrate production through forecasts supported on these same variables.Conectividade, mobilidade e análise de dados em tempo real são pré-requisitos para um novo modelo de gestão inteligente da produção que facilita a comunicação entre máquinas, pessoas e processos, e usa a tecnologia como motor principal. Muitos trabalhos na literatura tratam a manutenção e a gestão da produção em abordagens separadas, mas existe uma correlação entre estas áreas, sendo que a manutenção e as suas políticas têm como premissa garantir o bom funcionamento dos equipamentos de modo a evitar paragens desnecessárias na linha de produção. Com o advento da tecnologia há uma corrida das empresas para solucionar os seus problemas recorrendo às tecnologias, visando a sua inserção nos conceitos tecnológicos, mais avançados, tais como as indústrias 4.0 e 5.0, as quais têm como princípio a automatização dos processos. Esta abordagem junta as tecnologias de sistema de informação, sendo possível fazer o acompanhamento do funcionamento dos equipamentos e ter a possibilidade de realizar o estudo de padrões de comportamento dos dados que nos possam alertar para possíveis falhas. A presente tese pretende prever a produção da pasta de papel indexada às bolsas de valores. A previsão será feita por via das variáveis da produção da pasta de papel das prensas e das variáveis da bolsa de valores suportadas em tecnologias de artificial intelligence (IA), tendo como objectivo conseguir um planeamento eficaz. Para suportar a decisão de uma gestão da produção eficiente, na presente tese foram desenvolvidos algoritmos, validados em dados de cinco prensas de pasta de papel, bem como dados de outras fontes, tais como, de Produção de Aço e de Bolsas de Valores, os quais se mostraram relevantes para a validação da robustez dos modelos. A presente tese demonstrou a importância dos métodos de tratamento de dados e que os mesmos têm uma grande relevância na entrada do modelo, visto que facilita o processo de treino e testes dos modelos. As tecnologias escolhidas demonstraram uma boa eficiência e versatilidade na realização da previsão dos valores das variáveis dos equipamentos, demonstrando ainda robustez e otimização no processamento computacional. A tese apresenta ainda propostas para futuros desenvolvimentos, designadamente na exploração mais aprofundada destas tecnologias, de modo a que haja variáveis de mercado que possam calibrar a produção através de previsões suportadas nestas mesmas variáveis

Multilayer Network Modeling and Analysis, with Applications in Neuroscience and Social Work, with an Emphasis on Community Detection

Networks provide a language and framework for exploring connection and interrelation. As our understanding of the world as a complex system made up of parts that are fundamentally connected and interrelated grows, so does our need for this language and framework. Much of the structure and function of a network arises from mesoscale structure, from the ways that the constituent parts group together. Community detection has arisen as a tool for measuring this mesoscale structure. In this thesis, we applied the tools of network analysis to two datasets. In the first chapter, we provided background on the network analysis tools used in this thesis, including the definition of various types of networks, a suite of measures on networks, and an overview of the community detection methods relevant to this thesis. In the second chapter, we explored the social network of agencies who provide services to probationers with mental health concerns. Due to the voluntary response to the questionnaire survey underlying the network, the network was incomplete, which drove our analytical decisions. Specifically, it drove the measures that we chose to use to analyze the networks; we selected edge-weight, in-degree, and average in-weight. Examining the correlations of the measures between the various question-layers provided insight into the structure and connections between the questions on the surveys, and the underlying social connections that they measure. This work could be furthered by applying this same analysis to further data of a similar type, or to a recollection of data from the same agencies, in order to see which observed patterns are common among multiple similar data, and which are unique to this specific data set. The difference in the correlations observed between the edge weights and the correlations observed between the in-degrees led to constructing theoretical models of pairs of network layers, with increasing complexity to more closely capture the structure of the data we have. While we ended with a model that rather closely matched the observed data, integrating edge-weight inhomogeneity across nodes, rather than simply having inhomogeneity arise from the incomplete nature of the network, would allow us to further explore whether the results we previously observed are primarily driven by the selective structure of the responses or represent actual structure in the complete network. We use regression analyses to predict information on the Expert, Shared, or Refer question-layers from information on the Trust, Contact, Duration, and Benefit question-layers. Using a logistic regression on the edge-weights, and linear regression on the in-degrees and average in-weights, we find a high degree of accuracy of prediction for the former and high correlation coefficient for the latter, and explore the statistical significance of the resulting regression coefficients. We also used these measures to explore the differences between criminal justice agencies and human service agencies. We found strong differences in the ways that these agencies are connected to, specifically by CJ agencies, who tend to more strongly connect, communicate, and trust each other than they do HS agencies. More data would need to be collected in order to make a more definitive claim, in order to examine the possibility that this is a byproduct of the structure of this particular dataset. In the third chapter, we attempted to discriminate between networks of EEG waveform correlations of brains undergoing different tasks. We built feature vectors from various network measures, including flexibility, which relied on community detection. We explored cutting edge tools for selecting the two resolution parameters necessary to run GenLouvain community detection on temporal networks. We lightly adapted the existing tools for use on a population of temporal networks. While these methods selected parameter values for the L=301 dataset, when we moved to the L=31 dataset, it failed to. This work could be furthered by continuing to explore why these tools failed on the new dataset, and adapting the tools in such a way that they select meaningful parameters on the reduced dataset. We then constructed our network measures on the L=31 dataset, and ran various machine learning discrimination algorithms on the different class pairs. We obtained high discrimination between networks associated with the GNG task and the nB task on the basis of local efficiency, resulting in an average accuracy of discrimination of 89%, 73%, and 77% (for scans with positive valence, negative valence, and neutral valence respectively). The significance of these results were verified with a large permutation test. We then explored the results, finding that global efficiency results in 87% accuracy, reducing our feature space from 1984-dimensional to 31-dimensional. Applying PCA to the feature vectors to attempt further feature reduction, we were able to acheive 82% accuracy with only a 2-dimensional feature vector. From this, we were able to make the preliminary conclusion that alpha networks (networks derived from alpha brain wave activity) associated with GNG exhibit a spike of higher global efficiency than nB networks during the period that the stimuli is displayed, and that GNG networks exhibit lower deviance in theta global efficiency than nB networks at several points in the three second test window. In the fourth chapter, we began an exploration into alterations to an existing generative model for temporal community structure in order to better capture features observed in community structure derived from real world networks. The sizes of communities in the existing model simply diffuse towards a uniform distribution as time progressed, so our first alteration adapted the model to drive towards a predetermined time sequence of community sizes. More work is needed on several stages of this model in order to allow it to keep up with faster community changes. Our second alteration adds a Markov chain model for communities that nodes start in and the communities that they end up in, in order to better capture non-uniform flow between communities. In order to more accurately determine the strengths and weaknesses of these models, application to a wider variety of temporal community structures derived from relevant networks would be necessary. Additionally, using the proposed new models to attempt to recreate existing theoretical work done using the original generative model would provide evidence that these generative models not only capture more relevant community features than the original model, but also that these new models are still mathematically tractable.Doctor of Philosoph

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia