1,517 research outputs found
Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification
To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples
Tight Guarantees for Multi-unit Prophet Inequalities and Online Stochastic Knapsack
Prophet inequalities are a useful tool for designing online allocation
procedures and comparing their performance to the optimal offline allocation.
In the basic setting of -unit prophet inequalities, the magical procedure of
Alaei (2011) with its celebrated performance guarantee of
has found widespread adoption in mechanism design and
other online allocation problems in online advertising, healthcare scheduling,
and revenue management. Despite being commonly used for implementing online
allocation, the tightness of Alaei's procedure for a given has remained
unknown. In this paper we resolve this question, characterizing the tight bound
by identifying the structure of the optimal online implementation, and
consequently improving the best-known guarantee for -unit prophet
inequalities for all . We also consider a more general online stochastic
knapsack problem where each individual allocation can consume an arbitrary
fraction of the initial capacity. We introduce a new "best-fit" procedure for
implementing a fractionally-feasible knapsack solution online, with a
performance guarantee of , which we also show
is tight. This improves the previously best-known guarantee of 0.2 for online
knapsack. Our analysis differs from existing ones by eschewing the need to
split items into "large" or "small" based on capacity consumption, using
instead an invariant for the overall utilization on different sample paths.
Finally, we refine our technique for the unit-density special case of knapsack,
and improve the guarantee from 0.321 to 0.3557 in the multi-resource
appointment scheduling application of Stein et al. (2020). All in all, our
results imply \textit{tight} Online Contention Resolution Schemes for
-uniform matroids and the knapsack polytope, respectively, which has further
implications in mechanism design
A THz Video SAR Imaging Algorithm Based on Chirp Scaling
In video synthetic aperture radar (SAR) imaging mode, the polar format
algorithm (PFA) is more computational effective than the backprojection
algorithm (BPA). However, the two-dimensional (2-D) interpolation in PFA
greatly affects its computational speed, which is detrimental to the real-time
imaging of video SAR. In this paper, a terahertz (THz) video SAR imaging
algorithm based on chirp scaling is proposed, which utilizes the small
synthetic angular feature of THz SAR and the inherent property of linear
frequency modulation. Then, two-step chirp scaling is used to replace the 2-D
interpolation in the PFA to obtain a similar focusing effect, but with a faster
operation. Point target simulation is used to verify the effectiveness of the
proposed method.Comment: 5 pages, 7 figure
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