153 research outputs found

    Framework For Efficient Cosimulation And Fast Prototyping on Multi-Components With AAA Methodology: LAR Codec Study Case

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    pp 1667 - 1671Real-time signal and image applications have significant time constraints involving the use of several powerful calculation units. Programmable multi-component architectures have proven to be a suitable solution combining flexibility and computation power. This paper presents a methodology for the fast design of signal and image processing applications. In a unified framework, application modeling, cosimulation and fast implementation onto parallel heterogeneous architectures are enabled and help to reduce time-to-market. Moreover, automatic code generation provides a high abstraction level for users. Finally, the worthwhile nature of Matlab/C language cosimulation is illustrated on a still image codec named LAR

    Virtual synchronization for fast distributed cosimulation of dataflow task graphs

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    An approach to design smart grids and their IT system by cosimulation

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    International audienceSmart grids are the oncoming generation of power grids, which rely on information and communication technologies to tackle decentralized and intermittent energy sources such as wind farms and photovoltaic plants. They integrate electronics, software information processing and telecommunications technical domains. Therefore the design of smart grids is complex because of the various technical domains and modeling tools at stake. In this article, we present an approach to their design, which relies on model driven engineering, executable models and FMI based cosimulation. This approach is illustrated on the use case of an insular power grid and allows to study the impact of power production decision

    Future Perspectives of Co-Simulation in the Smart Grid Domain

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    The recent attention towards research and development in cyber-physical energy systems has introduced the necessity of emerging multi-domain co-simulation tools. Different educational, research and industrial efforts have been set to tackle the co-simulation topic from several perspectives. The majority of previous works has addressed the standardization of models and interfaces for data exchange, automation of simulation, as well as improving performance and accuracy of co-simulation setups. Furthermore, the domains of interest so far have involved communication, control, markets and the environment in addition to physical energy systems. However, the current characteristics and state of co-simulation testbeds need to be re-evaluated for future research demands. These demands vary from new domains of interest, such as human and social behavior models, to new applications of co-simulation, such as holistic prognosis and system planning. This paper aims to formulate these research demands that can then be used as a road map and guideline for future development of co-simulation in cyber-physical energy systems

    How to avoid a local epidemic becoming a global pandemic

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    Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel

    A Markov Chain Random Field Cosimulation-Based Approach for Land Cover Post-classification and Urban Growth Detection

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    The recently proposed Markov chain random field (MCRF) approach has great potential to significantly improve land cover classification accuracy when used as a post-classification method by taking advantage of expert-interpreted data and pre-classified image data. This doctoral dissertation explores the effectiveness of the MCRF cosimulation (coMCRF) model in land cover post-classification and further improves it for land cover post-classification and urban growth detection. The intellectual merits of this research include the following aspects: First, by examining the coMCRF method in different conditions, this study provides land cover classification researchers with a solid reference regarding the performance of the coMCRF method for land cover post-classification. Second, this study provides a creative idea to reduce the smoothing effect in land cover post-classification by incorporating spectral similarity into the coMCRF method, which should be also applicable to other geostatistical models. Third, developing an integrated framework by integrating multisource data, spatial statistical models, and morphological operator reasoning for large area urban vertical and horizontal growth detection from medium resolution remotely sensed images enables us to detect and study the footprint of vertical and horizontal urbanization so that we can understand global urbanization from a new angle. Such a new technology can be transformative to urban growth study. The broader impacts of this research are concentrated on several points: The first point is that the coMCRF method and the integrated approach will be turned into open access user-friendly software with a graphical user interface (GUI) and an ArcGIS tool. Researchers and other users will be able to use them to produce high-quality land cover maps or improve the quality of existing land cover maps. The second point is that these research results will lead to a better insight of urban growth in terms of horizontal and vertical dimensions, as well as the spatial and temporal relationships between urban horizontal and vertical growth and changes in socioeconomic variables. The third point is that all products will be archived and shared on the Internet

    Improvements in groundwater flow modeling through the integration of resistivity logs and hydraulic conductivity and the use of variogram uncertainty

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    This study developed a coregionalized model to estimate hydraulic conductivity using spatial cross correlation between hydraulic conductivity and borehole geophysical data (a transform of the formation factor). An experimental pseudocross variogram is used instead of a cross variogram because data are not collocated. Experimental variogram uncertainty is investigated using confidence intervals for the experimental variogram calculated assuming variogram sills are lognormally distributed. These intervals are used for sensitivity modeling using kriging, cokriging, simulation and cosimulation. The hydraulic conductivity fields generated by kriging, cokriging, simulation, and cosimulation are then used in a high-resolution groundwater model created using telescopic mesh refinement (TMR) from a regional flow model of the Chicot Aquifer system in southwestern Louisiana. Results are analyzed to assess the significance of adding additional information (i.e., transform of formation factor), the process (i.e., kriging versus simulation and cokriging versus cosimulation) and variogram uncertainty on the groundwater flow model. Spatial images and flow predictions using regionalized models based on sparse conductivity data only are compared with coregionalized models using both conductivity and resistivity data, and the effects on model accuracy and robustness are discussed. Coregionalized model (i.e., cokriging) and simulation process (i.e., cosimulation) significantly affect groundwater flow model prediction. A new approach examines sensitivity of a capture zone groundwater model for the Chicot aquifer parameter uncertainty. Sensitivities to spatial variability of hydraulic conductivity, porosity, and aquifer thickness were investigated. The method calibrated aquifer properties to flow and geophysical data using cosimulation of hydraulic conductivity and formation factor, simulation for porosity, and kriging for aquifer thickness. Geostatistical model uncertainty was analyzed with a Bayesian method. Aquifer property models were scored using integral range to preserve correlation among variogram parameters. Variogram and pseudocrossvariogram models were selected from a lower bound, median, and upper bound of the posterior probability distribution of integral range. A steady-state two-dimensional groundwater flow model of the Chicot aquifer beneath Acadia Parish in Southwestern Louisiana examined capture zone sensitivity to spatial structure of aquifer properties. The capture zone model was insensitive to porosity variability and sensitive to hydraulic conductivity and aquifer thickness. The proposed method demonstrates the importance of model uncertainty compared with fluctuations of a fixed geostatistical model

    New architecture for heterogeneous real-time simulation

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    This thesis investigates a new architecture for modeling and simulating complex distributed real-time systems. Modeling adequately a large distributed real time system may involve, due to its complexity, several different theoretical vehicles such as queuing theory, finite state machines, and others. Currently there are no software tools, which would offer combining such heterogeneous features into a single comprehensive simulation environment. This study involves integrating 3 tools, SES/workbench, an offline simulator using queuing theory as its modeling discipline, ObjecTime as a real-time simulator based on finite state machines as its modeling discipline, and VxWorks real-time kernel used for free modeling in the VMEbus environment. We developed an architecture, which connects all 3 simulators into an integrated system, in which parameters and simulation results can be freely exchanged between tools. In addition, the system is enhanced by a web-based interface, which can be used to provide input and obtain output of the entire system and help in distributing the simulation over the Internet. The new architecture was extensively tested and applied to a large-scale distributed embedded simulation in a military environment
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