English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We develop greedy algorithms to approximate the optimal solution to themulti-fidelity sensor selection problem, which is a cost constrainedoptimization problem prescribing the placement and number of cheap (lowsignal-to-noise) and expensive (high signal-to-noise) sensors in an environmentor state space. Specifically, we evaluate the composition of cheap andexpensive sensors, along with their placement, required to achieve accuratereconstruction of a high-dimensional state. We use the column-pivoted QRdecomposition to obtain preliminary sensor positions. How many of each type ofsensor to use is highly dependent upon the sensor noise levels, sensor costs,overall cost budget, and the singular value spectrum of the data measured. Suchnuances allow us to provide sensor selection recommendations based oncomputational results for asymptotic regions of parameter space. We alsopresent a systematic exploration of the effects of the number of modes andsensors on reconstruction error when using one type of sensor. Our extensiveexploration of multi-fidelity sensor composition as a function of datacharacteristics is the first of its kind to provide guidelines towards optimalmulti-fidelity sensor selection.

Multi-fidelity sensor selection: Greedy algorithms to place cheap and expensive sensors with cost constraints