16 research outputs found
Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition
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De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets
The dynamic mode decomposition (DMD)—a popular method for performing data-driven Koopman spectral analysis—has gained increased popularity for extracting dynamically meaningful spatiotemporal descriptions of fluid flows from snapshot measurements. Often times, DMD descriptions can be used for predictive purposes as well, which enables informed decision-making based on DMD model forecasts. Despite its widespread use and utility, DMD can fail to yield accurate dynamical descriptions when the measured snapshot data are imprecise due to, e.g., sensor noise. Here, we express DMD as a two-stage algorithm in order to isolate a source of systematic error. We show that DMD’s first stage, a subspace projection step, systematically introduces bias errors by processing snapshots asymmetrically. To remove this systematic error, we propose utilizing an augmented snapshot matrix in a subspace projection step, as in problems of total least-squares, in order to account for the error present in all snapshots. The resulting unbiased and noise-aware total DMD (TDMD) formulation reduces to standard DMD in the absence of snapshot errors, while the two-stage perspective generalizes the de-biasing framework to other related methods as well. TDMD’s performance is demonstrated in numerical and experimental fluids examples. In particular, in the analysis of time-resolved particle image velocimetry data for a separated flow, TDMD outperforms standard DMD by providing dynamical interpretations that are consistent with alternative analysis techniques. Further, TDMD extracts modes that reveal detailed spatial structures missed by standard DMD
Quantitative evaluation of predictability of linear reduced-order model based on particle-image-velocimetry data of separated flow field around airfoil
The Relationship Between Selenium Levels and Breast Cancer: A Systematic Review and Meta-Analysis
The removal of amoxicillin from aquatic solutions using the TiO2/UV-C nanophotocatalytic method doped with trivalent iron
Application of clay-TiO2 nanocomposite packaging films on pears (Prunus communis L. cv. Williams) under cold storage
The Epilepsy Genetics Initiative: Systematic reanalysis of diagnostic exomes increases yield
OBJECTIVE: The Epilepsy Genetics Initiative (EGI) was formed in 2014 to create a centrally managed database of clinically generated exome sequence data. EGI performs systematic research-based reanalysis to identify new molecular diagnoses that were not possible at the time of initial sequencing and to aid in novel gene discovery. Herein we report on the efficacy of this approach 3 years after inception. METHODS: One hundred sixty-six individuals with epilepsy who underwent diagnostic whole exome sequencing (WES) were enrolled, including 139 who had not received a genetic diagnosis. Sequence data were transferred to the EGI and periodically reevaluated on a research basis. RESULTS: Eight new diagnoses were made as a result of updated annotations or the discovery of novel epilepsy genes after the initial diagnostic analysis was performed. In five additional cases, we provided new evidence to support or contradict the likelihood of variant pathogenicity reported by the laboratory. One novel epilepsy gene was discovered through dual interrogation of research and clinically generated WES. SIGNIFICANCE: EGI's diagnosis rate of 5.8% represents a considerable increase in diagnostic yield and demonstrates the value of periodic reinterrogation of whole exome data. The initiative's contributions to gene discovery underscore the importance of data sharing and the value of collaborative enterprises