Search results for "Data modeling"
showing 10 items of 112 documents
Joint Gaussian processes for inverse modeling
2017
Solving inverse problems is central in geosciences and remote sensing. Very often a mechanistic physical model of the system exists that solves the forward problem. Inverting the implied radiative transfer model (RTM) equations numerically implies, however, challenging and computationally demanding problems. Statistical models tackle the inverse problem and predict the biophysical parameter of interest from radiance data, exploiting either in situ data or simulated data from an RTM. We introduce a novel nonlinear and nonparametric statistical inversion model which incorporates both real observations and RTM-simulated data. The proposed Joint Gaussian Process (JGP) provides a solid framework…
Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
2018
Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…
Co-Designing Social Simulation Models For Policy Advise: Lessons Learned From the INFSO-SKIN Study
2019
This paper describes a participatory approach to co-designing social simulation models with policymakers using a case study of modeling European Commission policy. Managing the collaboration of a wide range of individuals or organizations is challenging but increasingly important as policy making becomes more complex. A framework for a co-design process based on a participatory approach is proposed. The framework suggests that the collaborative design should go through the following phases: Identifying user questions, data provision, model discussion for validation, visualization of results and discussing scope and limitations with stakeholders. Key findings are that the co-design process r…
A proposed mapping method for aligning machine execution data to numerical control code
2019
The visions of the digital thread and smart manufacturing have boosted the potential of relating downstream data to upstream decisions in design. However, to date, the tools and methods to robustly map across the related data representations is significantly lacking. In response, we propose a mapping technique for standard manufacturing data representations. Specifically, we focus on relating controller data from machining tools in the form of MTConnect, an emerging standard that defines the vocabulary and semantics as well as communications protocols for execution data, and G-Code, the most widely used standard for numerical control (NC) instructions. We evaluate the efficacy of our mappin…
DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization
2021
Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the underlying trade-offs among the conflicting objective functions in the problem and adjust preferences during the solution process. Incorporating preference information allows computing only solutions that are interesting to the decision maker, decreasing computation time significantly. Thus, interactive methods have many strengths making them viable for various applications. However, there is a lack of existing software frameworks to apply and experiment with interactive …
Discovering Differential Equations from Earth Observation Data
2020
Modeling and understanding the Earth system is a constant and challenging scientific endeavour. When a clear mechanistic model is unavailable, complex or uncertain, learning from data can be an alternative. While machine learning has provided excellent methods for detection and retrieval, understanding the governing equations of the system from observational data seems an elusive problem. In this paper we introduce sparse regression to uncover a set of governing equations in the form of a system of ordinary differential equations (ODEs). The presented method is used to explicitly describe variable relations by identifying the most expressive and simplest ODEs explaining data to model releva…
Automatic Identification System data - potential resource for marine vessels drift studies in the Baltic Sea
2020
Automatic Identification System (AIS) has been employed for increasing the safety at the sea. Nowadays general information about the marine traffic can be tracked in operational mode using www.marinetraffic.com web portal. Additional information can be purchased for commercial and research purposes. Our study highlights a possibility to study the drift using an additional information available at www.marinetraffic.com - selected data on vessel position during its status “not under command”, which corresponds to the drifting vessels. Trajectories of the drift from AIS have been compared with mathematically modeled point-wise drifter paths. Copernicus Marine Environmental Monitoring Service's…
First M87 Event Horizon Telescope Results. VII. Polarization of the Ring
2021
Full list of authors: Akiyama, Kazunori; Algaba, Juan Carlos; Alberdi, Antxon; Alef, Walter; Anantua, Richard; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Baloković, Mislav; Barrett, John; Benson, Bradford A.; Bintley, Dan; Blackburn, Lindy; Blundell, Raymond; Boland, Wilfred; Bouman, Katherine L.; Bower, Geoffrey C.; Boyce, Hope Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke; Broderick, Avery E.; Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chan, Chi-kwan; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Chesler, Paul M.; Cho, Ilje; Christian, Pierre; Conway, John E.…
A Graph-Grammar Approach to Represent Causal, Temporal and Other Contexts in an Oncological Patient Record
1996
AbstractThe data of a patient undergoing complex diagnostic and therapeutic procedures do not only form a simple chronology of events, but are closely related in many ways. Such data contexts include causal or temporal relationships, they express inconsistencies and revision processes, or describe patient-specific heuristics. The knowledge of data contexts supports the retrospective understanding of the medical decision-making process and is a valuable base for further treatment. Conventional data models usually neglect the problem of context knowledge, or simply use free text which is not processed by the program. In connection with the development of the knowledge-based system THEMPO (The…
Towards the definition of a sustainable Smart Model for the suburbs redevelopment
2020
Starting from the analysis of the problems that characterize the Italian suburbs, the application of a Smart Methodology to a real peripheral area is presented. In literature, several studies underline the urgent request of the city's periphery, enhancing local and national projects to increase the quality of life in the suburbs. In this framework, authors propose a multifunctional centre development, characterized by modern technologies (both structural and plant) to implement energy efficiency and social aggregation, in line with the citizen's needs. Once the simulation model of alternative solutions, such as construction type, energy system and social services, was elaborated in Matlab/S…