Sentence examples for robust fusion from inspiring English sources

Most nations involved in ITER have robust fusion research programs that are essential to tackle other key scientific and technical issues.

This paper is concerned with the event-triggered robust fusion estimation problem for uncertain multi-rate sampled-data systems with stochastic nonlinearities and the colored measurement noises.

A novel distributed fusion architecture is proposed, which makes full use of the information of different sensors to produce robust fusion estimation.

The results of our experiments demonstrate the importance of seamless, robust fusion for realistic simulation of water flow patterns using multiple high-resolution DEMs.

Our approach is based on a robust fusion of multiple significant descriptors (SF, HOG, Haar, and SURF) in order to construct an efficient feature vector representing head pose variations.

Therefore, we propose an efficient and robust fusion of the most pertinent head pose descriptors and we decide to use the SVM classifier since it is adapted to the real-world applications and it proves its efficiency in literature.

This paper addresses the design of robust centralized fusion (CF) and weighted measurement fusion (WMF) Kalman estimators for a class of uncertain multisensor systems with linearly correlated white noises.

But in a little more than two hours, Mr. Wright and the screenwriter, Deborah Moggach, have created as satisfyingly rich and robust a fusion of romance, historical detail and genial social satire as the time allows.

Here, you should simplify the message (e.g., "Heck of a Class Project: An 'Affordable, Robust, Compact' Fusion Reactor Design, Buildable in a Decade") and present much less evidence (e.g. stronger magnetic fields).

Based on M-estimate, the problem of robust estimation fusion in decentralized architecture when the sensor noises are contaminated by outliers is considered.

This paper addresses the design of robust weighted fusion Kalman filters for multisensor time-varying systems with uncertainties of noise variances.