Data Driven Design Optimization Methodology Development and Application

Zhao, Huimin; Knight, Doyle; Taskinoglu, Ezgi S.; Jovanovic, V.

doi:10.1007/978-3-540-24688-6_97

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…Popular choices are polynomial and rational functions [34], Kriging models [35], neural networks [36], and radial basis function (RBF) models [37]. These can be used to perform optimization and sensitivity analysis once the model is constructed [38].…”

Section: Broadcasting Network and Hurricane Effectsmentioning

confidence: 99%

Tool for Recovering after Meteorological Events Using a Real-Time REM and IoT Management Platform

Santana

David

Alonso

et al. 2019

Wireless Communications and Mobile Computing

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This paper is the design of a Radio Environment Map (REM) with a real-time tool to sense the radiofrequency spectrum and optimally places with Surrogate Modelling and Sequential Experimental Design tools a total of 72 SDR sensors in the selected area, using LoRa and/or NB-IoT technologies for networking. It permits the regulatory body to check the correct use of the assigned spectrum and constitutes a communication alternative in case of a catastrophic event, such as a hurricane or an earthquake, where radio and TV broadcasting play an important role in keeping people informed after such meteorological event. The radiobroadcast services use large antennas and high towers, making them vulnerable to such events. Regardless of the chosen technology, the IoT monitoring network will be more robust, since it uses small antennas and lower towers, and often a given area is covered by multiple base stations. The tool can be used to deploy new services in the nonserved area (e.g., 4G in the 700 MHz band at a lower cost or using TVWS techniques to provide communications and internet connection) and optimal interference management.

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Section: Broadcasting Network and Hurricane Effectsmentioning

confidence: 99%

Tool for Recovering after Meteorological Events Using a Real-Time REM and IoT Management Platform

Santana

David

Alonso

et al. 2019

Wireless Communications and Mobile Computing

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“…Since the National Natural Science Foundation of United States first put forward DDDAS, increasing applications have been made in variety of engineering and science practices, e.g. pollutant migration (Douglas, Efendiev 2006;Douglas et al 2004), fire simulation (Douglas et al 2004;Mandel et al 2005), engineering system design and control (Kim, Heller 2006;Zhao et al 2004), ecosystem prediction (Muttil, Lee 2005), emergency medical treatment decisions (Gaylor et al 2005), and natural disaster prediction (Parashar et al 2004), etc. Considering the difficulty in predicting accident developing during emergency management, a DDDAbased simulation model for oil spill emergency decision is developed to improve the accuracy of simulation and prediction of oil spill. Problems such as the method to map real-time data to simulation model, the method to recovery initial data of simulation model and the algorithms to simulate behavior of oil spill are designed.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Data-Driven Application Simulation Model for Oil Spill Emergency Decision in Port Water Area

2013

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In order to solve the difficulty in simulation and prediction the evolution of oil spill incidents in port water area caused by system complexity and environment variety, a simulation model based on Dynamic Data-Driven Application System (DDDAS) was developed. By assimilating real-time data, simulation model can update its initial condition and solutions, and modify its parameters. Firstly, the framework for DDDAS-based simulation model was designed. Then the method for mapping of real-time data to simulation model, the method for recovery of initial data for DDDAS, and the algorithms to simulate the behavior of oil spill were studied. Finally, numerical tests were provided to illustrate the validity of the proposed model. Results indicate that the DDDAS-based simulation method can improve the prediction accuracy of oil spill incidents.

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Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements

Darema

2004

Lecture Notes in Computer Science

314

145

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Abstract. Dynamic Data Driven Application Systems (DDDAS) entails the ability to incorporate additional data into an executing application -these data can be archival or collected on-line; and in reverse, the ability of applications to dynamically steer the measurement process. The paradigm offers the promise of improving modeling methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. This presents the potential to transform the way science and engineering are done, and induce a major impact in the way many functions in our society are conducted, such as manufacturing, commerce, hazard management, medicine. Enabling this synergistic feedback and control-loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms tolerant to perturbations from dynamic data injection and steering, and systems software to support the dynamic environments of concern here. Recent advances in complex applications, the advent of grid computing and of sensor systems, are some of the technologies that make it timely to embark in developing DDDAS capabilities. Research and development of such technologies requires synergistic multidisciplinary collaboration in the applications, algorithms, software systems, and measurements systems areas, and involving researchers in basic sciences, engineering, and computer sciences. The rest of the papers in the proceedings of this workshop provide examples of ongoing research developing DDDAS technologies within the context of specific and important application areas.

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Data Driven Design Optimization Methodology Development and Application

Cited by 6 publications

References 3 publications

Tool for Recovering after Meteorological Events Using a Real-Time REM and IoT Management Platform

Tool for Recovering after Meteorological Events Using a Real-Time REM and IoT Management Platform

A Dynamic Data-Driven Application Simulation Model for Oil Spill Emergency Decision in Port Water Area

Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements

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