MGARD+: Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction

Multilevel dimensionality-reduction methods

When data sets are multilevel (group nesting or repeated measures), different sources of variations must be identified. In the framework of unsupervised analyses, multilevel simultaneous component analysis (MSCA) has recently been proposed as the most satisfactory option for analyzing multilevel data. MSCA estimates submodels for the different levels in data and thereby separates the “within”-s...

Multilevel Modeling Methods for E-Collaboration Data

A data-driven approach to optimizing spectral speech enhancement methods for various error criteria

Gain functions for spectral noise suppression have been derived in literature for some error criteria and statistical models. These gain functions are only optimal when the statistical model is correct and the speech and noise spectral variances are known. Unfortunately, the speech distributions are unknown and can at best be determined conditionally on the estimated spectral variance. We show ...

islanding detection methods for microgrids

امروزه استفاده از منابع انرژی پراکنده کاربرد وسیعی یافته است . اگر چه این منابع بسیاری از مشکلات شبکه را حل می کنند اما زیاد شدن آنها مسائل فراوانی برای سیستم قدرت به همراه دارد . استفاده از میکروشبکه راه حلی است که علاوه بر استفاده از مزایای منابع انرژی پراکنده برخی از مشکلات ایجاد شده توسط آنها را نیز منتفی می کند . همچنین میکروشبکه ها کیفیت برق و قابلیت اطمینان تامین انرژی مشترکان را افزایش ...

Data Reduction Techniques for Scientific Visualization and Data Analysis

The classic paradigm for scientific visualization and data analysis is post-hoc, where simulation codes write results on the file system and visualization routines read them back to operate. This paradigm sees file I/O as an increasing bottleneck, in terms of both transfer rates and storage capacity. As a result, simulation scientists, as data producers, often sample available time slices to sa...