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To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources required to apply sophisticated machine learning models. Further, for economic reasons,...
Clinical electroencephalographic (EEG) data varies significantly depending on a number of operational conditions (e.g., the type and placement of electrodes, the type of electrical grounding used). This investigation explores the statistical differences present in two different referential montages: Linked Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately 45% of the data...
Cross-language transfer speech recognition aims to transform phoneme models for a source language to recognize a target language lacking labeled data and other linguistic resources. In this paper, sparse auto-encoder, a deep learning method, is introduced to derive shared speech features between source and target language using semi-supervised learning. It can extract the shared representation of...
This paper describes WECC's (formerly WSCC) experience in generator testing and model validation. WSCC introduced a generator testing requirement following failure of simulations to reproduce events that occurred in the summer of 1996 in the Western Interconnection. The benefits of generator testing have been indisputable. The generator model data has been improved significantly in many cases. It...
This paper provides an update on a composite load model development in Western Electricity Coordinating Council (WECC). A composite load model structure is described. The two salient features of the new load model are: (a) the model recognizes electrical distance between the transmission bus and the end-uses and (b) the model represents the diversity in composition and dynamic characteristics of various...
Evenly-spaced data with missing values was processed using power spectral density and autoregressive moving-average methods. Auto-Regressive Moving Average (ARMA) models were developed to represent field tillage patterns for a drill, a drag/harrow, chisel or NH3 applicator, and disk tools.
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