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000905813 0247_ $$2doi$$a10.1109/AMPS50177.2021.9586042
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000905813 037__ $$aFZJ-2022-01033
000905813 1001_ $$0P:(DE-Juel1)186779$$aCarta, Daniele$$b0$$eCorresponding author
000905813 1112_ $$a2021 IEEE 11th International Workshop on Applied Measurements for Power Systems (AMPS)$$cCagliari$$d2021-09-29 - 2021-10-01$$wItaly
000905813 245__ $$aPerformance Evaluation of a Missing Data Recovery Approach Based on Compressive Sensing
000905813 260__ $$c2021
000905813 300__ $$a1-6
000905813 3367_ $$2ORCID$$aCONFERENCE_PAPER
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000905813 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1643098175_6536
000905813 520__ $$aIn this paper, a Compressive Sensing-based approach is proposed to recover missing data in time series signals. The presented technique is based on the combined application of two mathematical techniques: the Discrete Cosine Transform (DCT) and a $\ell_1$-minimization algorithm. The former allows representing the system under test with reference to a new sparse base, while the latter is one of the possible approaches to solve a Compressive Sensing problem, well-known for the capability of recovering undersampled sparse signals.After presenting the state of the art and the steps characterizing the proposed approach, the recovery performances are tested on real voltage and current Root Mean Square (rms) signals, stored on a database. In particular, the different impact on the recovery of random discontinuous values and wide missing signals is evaluated by means of the Mean Absolute Percentage Error (MAPE).
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000905813 7001_ $$0P:(DE-Juel1)179029$$aBenigni, Andrea$$b1$$ufzj
000905813 773__ $$a10.1109/AMPS50177.2021.9586042
000905813 8564_ $$uhttps://ieeexplore.ieee.org/abstract/document/9586042
000905813 8564_ $$uhttps://juser.fz-juelich.de/record/905813/files/Performance_Evaluation_of_a_Missing_Data_Recovery_Approach_Based_on_Compressive_Sensing.pdf$$yRestricted
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000905813 9141_ $$y2021
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