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Found 8 papers, 2 papers with code
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping
Elastic metrics such as dynamic time warping (DTW) offer a promising approach, but are limited by their computational complexity, non-differentiability and sensitivity to noise and outliers.
Closed-Form Diffeomorphic Transformations for Time Series Alignment
Gradient-based optimization frameworks containing diffeomorphic transformations require to calculate derivatives to the differential equation's solution with respect to the model parameters, i. e. sensitivity analysis.
A survey study of success factors in data science projects
In recent years, the data science community has pursued excellence and made significant research efforts to develop advanced analytics, focusing on solving technical problems at the expense of organizational and socio-technical challenges.
A novel method for error analysis in radiation thermometry with application to industrial furnaces
Accurate temperature measurements are essential for the proper monitoring and control of industrial furnaces.
ArchABM: an agent-based simulator of human interaction with the built environment. $CO_2$ and viral load analysis for indoor air quality
Recent evidence suggests that SARS-CoV-2, which is the virus causing a global pandemic in 2020, is predominantly transmitted via airborne aerosols in indoor environments.
Segmentation of cell-level anomalies in electroluminescence images of photovoltaic modules
In the operation & maintenance (O&M) of photovoltaic (PV) plants, the early identification of failures has become crucial to maintain productivity and prolong components' life.
Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance
This methodology will facilitate the creation of a drift database that will serve both as a training ground for concept drift detectors and as a valuable information to enhance the knowledge about maintenance of complex systems.
Data Science Methodologies: Current Challenges and Future Approaches
Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies.
