Pre‐Filter Design for Iterative Controller Parameter Tuning Using Data‐Driven Minimum Variance Regulatory Controllers

2021 ◽  
Author(s):  
Shotaro Shiroi ◽  
Shiro Masuda
Keyword(s):  
Filter Design ◽  
Parameter Tuning ◽  
Minimum Variance ◽  
Data Driven ◽  
Controller Parameter ◽  
Using Data

scholarly journals Enhancing action recognition of construction workers using data-driven scene parsing

2018 ◽  
Vol 24 (7) ◽  
pp. 568-580 ◽  
Author(s):  
Jun Yang
Keyword(s):  
Object Detection ◽  
Action Recognition ◽  
Semantic Information ◽  
Parameter Tuning ◽  
Construction Workers ◽  
Data Driven ◽  
Local Context ◽  
Construction Sites ◽  
Scene Parsing ◽  
Using Data

Vision-based action recognition of construction workers has attracted increasing attention for its diverse applications. Though state-of-the-art performances have been achieved using spatial-temporal features in previous studies, considerable challenges remain in the context of cluttered and dynamic construction sites. Considering that workers actions are closely related to various construction entities, this paper proposes a novel system on enhancing action recognition using semantic information. A data-driven scene parsing method, named label transfer, is adopted to recognize construction entities in the entire scene. A probabilistic model of actions with context is established. Worker actions are first classified using dense trajectories, and then improved by construction object recognition. The experimental results on a comprehensive dataset show that the proposed system outperforms the baseline algorithm by 10.5%. The paper provides a new solution to integrate semantic information globally, other than conventional object detection, which can only depict local context. The proposed system is especially suitable for construction sites, where semantic information is rich from local objects to global surroundings. As compared to other methods using object detection to integrate context information, it is easy to implement, requiring no tedious training or parameter tuning, and is scalable to the number of recognizable objects.

Optimal 3-D traveltime tomography

Geophysics ◽  
1998 ◽  
Vol 63 (3) ◽  
pp. 1053-1061 ◽  
Author(s):  
Margaret J. Eppstein ◽  
David E. Dougherty
Keyword(s):  
A Priori Estimates ◽  
Filter Design ◽  
A Priori ◽  
Covariance Structure ◽  
Minimum Variance ◽  
Data Driven ◽  
Spatial Covariance ◽  
Traveltime Tomography ◽  
Parameter Field ◽  
Conditional Estimates

We propose a practical new method for 3-D traveltime tomography. The method combines an efficient approximation to the extended Kalman filter for rapid, accurate, nonlinear tomography, with the concept of data‐driven zonation, in which the dimensionality and geometry of the parameterization are dynamically determined using cluster analysis and region merging by random field union. The Bayesian filter uses geostatistics as it recursively incorporates measurements in an optimal (minimum‐variance) manner. Geologic knowledge is introduced through a priori estimates of the parameter field and its spatial covariance. Conditional estimates of the parameter number, geometry, value, and covariance are evolved. An initial decomposition of the 3-D domain into 2-D slices, the simplified filter design, and the data‐driven reduction in parameter dimensionality, all contribute to make the method computationally feasible for large 3-D domains. The method is verified by the inversion of crosswell seismic traveltimes to 3-D estimates of seismic slowness in four synthetic heterogeneous domains. Starting with homogeneous, fully distributed slowness fields, and no knowledge of the true covariance structure, the method is able to accurately and efficiently resolve the structure and values of markedly different domains.

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

Enhanced Resilient State Estimation Using Data-Driven Auxiliary Models

2020 ◽  
Vol 16 (1) ◽  
pp. 639-647 ◽  
Author(s):  
Olugbenga Moses Anubi ◽  
Charalambos Konstantinou
Keyword(s):  
State Estimation ◽  
Data Driven ◽  
Using Data
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