Linear regression analysis of regional mean speed of Athens city network using drone data: A multi-modal approach
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A hard challenge associated with infrastructure monitoring is to extract useful information from large amounts of measurement data in order to detect changes in structures. This paper presents a hybrid model-free approach that combines two model-free metho ...
This paper presents a new model-free data-interpretation approach for damage detection of bridges using long-term monitoring data. The approach combines two model-free methods: Moving Principal Component Analysis (MPCA) and Robust Regression Analysis (RRA) ...
We study the problem of distributed least-squares estimation over ad hoc adaptive networks, where the nodes have a common objective to estimate and track a parameter vector. We consider the case where there is stationary additive colored noise on both the ...
In order to achieve reliable information about deterioration, it is important to differentiate between changes in structural behaviour due to service loading (temperature, wind and traffic) and changes resulting from damage when interpreting measurement da ...
In ski jumping, take-off (TO) is considered as the most important phase. While spatio-temporal features have been identified during this phase, inter-segment coordination has never been investigated. This study proposed a new method using body worn inertia ...
To restore walking after transfemoral amputation, various actuated exoprostheses have been developed, which control the knee torque actively or via variable damping. In both cases, an important issue is to find the appropriate control that enables user-dom ...
To keep the freeway networks in a good condition, road works such as maintenance and reconstruction are carried out regularly. The resulting work zones including the related traffic management measures, give different traffic capacities of the infrastructu ...
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In principal component regression (PCR) and partial least-squares regression (PLSR), the use of unlabeled data, in addition to labeled data, helps stabilize the latent subspaces in the calibration step, typically leading to a lower prediction error. A non- ...
Productivity, quality, safety, and environmental concerns have driven major advancements in the development of process analyzers. Analyzers generate measurement data that are useful for characterizing product and process attributes (key variables), thereby ...
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, ...