6,231 research outputs found

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Optimization of a robust reinforcement learning policy

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    A major challenge for the integration of unmanned air vehicle (UAV) in the current civil applications is the sense-and-avoid (SAA) capability and the consequent possibility of mid-air collision avoidance. Although UAS have been shown to be efficient under different and varied conditions, their safety, reliability, and compliance with aviation regulations remain to be proven. In autonomous collision avoidance, UAS sense hazards with the sensors equipped on them and make decisions on manoeuvres autonomously for collision avoidance at the minimum safe time before impact. Thus, it is required for each individual UAS to have capabilities to recognize urgent threats and undertake the evasive manoeuvres immediately. Most of the current sense and avoid algorithms are composed of separated obstacle detection and tracking algorithm and decision-making algorithm on avoidance manoeuvre. Implementing artificial intelligence (AI), reinforcement learning (RL) algorithm combines both sense and avoid functions through state and action space. An autonomous agent learns to perform complex tasks by maximizing reward signals while interacting with its environment. It may be infeasible to test a policy in all contexts since it is difficult to ensure it works as broadly as intended. In these cases, it is important to trade-off between performance and robustness while learning a policy. This work develops an optimization method for a robust reinforcement learning policy for a nonlinear small unmanned air systems (sUAS), in AirSim using a model-free architecture. Using an on-line trained reinforcement learning agent, the difference of an optimized robust reinforcement learning (RRL) policy together with a conventional RL and RRL algorithm will be reproduced.Engineering and Physical Sciences Research Council (EPSRC): 2454266 Thales U

    Vegetation responses to variations in climate: A combined ordinary differential equation and sequential Monte Carlo estimation approach

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    Vegetation responses to variation in climate are a current research priority in the context of accelerated shifts generated by climate change. However, the interactions between environmental and biological factors still represent one of the largest uncertainties in projections of future scenarios, since the relationship between drivers and ecosystem responses has a complex and nonlinear nature. We aimed to develop a model to study the vegetation’s primary productivity dynamic response to temporal variations in climatic conditions as measured by rainfall, temperature and radiation. Thus, we propose a new way to estimate the vegetation response to climate via a non-autonomous version of a classical growth curve, with a time-varying growth rate and carrying capacity parameters according to climate variables. With a Sequential Monte Carlo Estimation to account for complexities in the climate-vegetation relationship to minimize the number of parameters. The model was applied to six key sites identified in a previous study, consisting of different arid and semiarid rangelands from North Patagonia, Argentina. For each site, we selected the time series of MODIS NDVI, and climate data from ERA5 Copernicus hourly reanalysis from 2000 to 2021. After calculating the time series of the a posteriori distribution of parameters, we analyzed the explained capacity of the model in terms of the linear coefficient of determination and the parameters distribution variation. Results showed that most rangelands recorded changes in their sensitivity over time to climatic factors, but vegetation responses were heterogeneous and influenced by different drivers. Differences in this climate-vegetation relationship were recorded among different cases: (1) a marginal and decreasing sensitivity to temperature and radiation, respectively, but a high sensitivity to water availability; (2) high and increasing sensitivity to temperature and water availability, respectively; and (3) a case with an abrupt shift in vegetation dynamics driven by a progressively decreasing sensitivity to water availability, without any changes in the sensitivity either to temperature or radiation. Finally, we also found that the time scale, in which the ecosystem integrated the rainfall phenomenon in terms of the width of the window function used to convolve the rainfall series into a water availability variable, was also variable in time. This approach allows us to estimate the connection degree between ecosystem productivity and climatic variables. The capacity of the model to identify changes over time in the vegetation-climate relationship might inform decision-makers about ecological transitions and the differential impact of climatic drivers on ecosystems.Estación Experimental Agropecuaria BarilocheFil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Perri, Daiana Vanesa. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Perri, Daiana Vanesa. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Easdale, Marcos Horacio. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Easdale, Marcos Horacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentin

    Signals and Images in Sea Technologies

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    Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas

    Special Topics in Information Technology

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    This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

    Differential Models, Numerical Simulations and Applications

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    This Special Issue includes 12 high-quality articles containing original research findings in the fields of differential and integro-differential models, numerical methods and efficient algorithms for parameter estimation in inverse problems, with applications to biology, biomedicine, land degradation, traffic flows problems, and manufacturing systems

    CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

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    This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship

    Recent Advances in Single-Particle Tracking: Experiment and Analysis

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    This Special Issue of Entropy, titled “Recent Advances in Single-Particle Tracking: Experiment and Analysis”, contains a collection of 13 papers concerning different aspects of single-particle tracking, a popular experimental technique that has deeply penetrated molecular biology and statistical and chemical physics. Presenting original research, yet written in an accessible style, this collection will be useful for both newcomers to the field and more experienced researchers looking for some reference. Several papers are written by authorities in the field, and the topics cover aspects of experimental setups, analytical methods of tracking data analysis, a machine learning approach to data and, finally, some more general issues related to diffusion

    Dynamic Traffic Management of Highway Networks

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    Efficient operation of traffic networks via management strategies can guarantee overall societal benefits for both the humans and the environment. As the number of vehicles and the need for transportation grows, dynamic traffic management aims to increase the safety and efficiency of the traffic networks without the need to change the infrastructure of the existing roads. Since the highway networks are considered permanent investments that are expensive to build and maintain, the main scope of this dissertation is to propose traffic flow models and methods to improve the efficiency of the current highway systems without the need to change their infrastructure. When all vehicles in a network are \textit{Human-Driven Vehicles} (HDVs), and changing the infrastructure is either so expensive or impossible, then one reasonable approach to improve the efficiency of traffic networks is through the control of traffic signal lights specially because the behavior of the human drivers cannot be directly controlled. A literature review of highway traffic control demonstrate that \textit{Ramp Metering} (RM) is one of the most commonly used approaches as it improves the network performance in regards to travel time, travel distance, throughput, etc and cost-wise, it is a very economical approach. As such, in this research, the ultimate goal focus is to extend the current literature on traffic managements of highway networks by offering new models and algorithms to improve this field. To reach this goal, the first step is to focus on improving and extending the current traffic flow models. There are two categories of traffic flow models in the literature: First-order models, and Second-order models. Many different extensions of the famous first-order model called the Cell-Transmission Model (CTM) have been proposed throughout the past decades, each one proposed based on different criteria and the specific needs of different applications. In the first part of this dissertation, a performance assessment of the most important extensions of CTM will be performed. Then, based on this evaluation, an extended version of the CTM, called the Piece-Wise Affine Approximation-CTM (\textit{PWA-CTM}), will be offered which will be proven to have better performance regarding the evolution of traffic flow and computation time comparing to the previous versions of this model. In the next step, the focus will be shifted to second-order models as they have better capabilities of modeling the behavior of traffic flow comparing to the first-order models. However, any optimization scheme for highway traffic control based on these models is highly nonlinear and computationally intensive. As such, in this part of the research, a linearization of the famous second-order model called the \textit{METANET} will be offered which is based on PWA approximations and also synthetic data generation techniques. With extensive simulations, it will be shown that this linearized approximation can greatly impact the computational complexity of any optimization-based traffic control framework based on this second-order traffic flow model. Moreover, to have significant traffic management improvements, not only the underlying traffic models, but also the control strategies should be enhanced. The availability of increasing computational power and sensing and communication capabilities, as well as advances in the field of machine learning, has developed \textit{learning-based} control approaches which can address constraint satisfaction and closed-loop performance optimization. In this chapter, \textit{Reinforcement Learning} (RL) algorithms will be investigated to solve the optimal control problem of RM. In the case of RM, RL-based techniques offer a potentially appealing alternative method to solve the problem at hand, since they are data-based and make no assumptions on the underlying model parameters. Towards this direction, it is convenient to study the road model as a multi-agent system of non-homogeneous networked agents. In the following, a novel formulation of the RM problem as an optimal control problem based on a first-order multi-agent dynamical system will be offered. Then, applying policy gradient RL algorithms, a probabilistic policy will be found that solves the ramp-metering problem. The performance of the optimal policy learnt will be investigated under different scenarios to evaluate its efficiency
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