8,791 research outputs found
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging to the whole infrastructure (e.g., cables and related
insulation, transformers, breakers and so on). In real-world smart grid
systems, usually, additional information that are related to the operational
status of the grid itself are collected such as meteorological information.
Designing a suitable recognition (discrimination) model of faults in a
real-world smart grid system is hence a challenging task. This follows from the
heterogeneity of the information that actually determine a typical fault
condition. The second point is that, for synthesizing a recognition model, in
practice only the conditions of observed faults are usually meaningful.
Therefore, a suitable recognition model should be synthesized by making use of
the observed fault conditions only. In this paper, we deal with the problem of
modeling and recognizing faults in a real-world smart grid system, which
supplies the entire city of Rome, Italy. Recognition of faults is addressed by
following a combined approach of multiple dissimilarity measures customization
and one-class classification techniques. We provide here an in-depth study
related to the available data and to the models synthesized by the proposed
one-class classifier. We offer also a comprehensive analysis of the fault
recognition results by exploiting a fuzzy set based reliability decision rule
SMaSH: A Benchmarking Toolkit for Human Genome Variant Calling
Motivation: Computational methods are essential to extract actionable
information from raw sequencing data, and to thus fulfill the promise of
next-generation sequencing technology. Unfortunately, computational tools
developed to call variants from human sequencing data disagree on many of their
predictions, and current methods to evaluate accuracy and computational
performance are ad-hoc and incomplete. Agreement on benchmarking variant
calling methods would stimulate development of genomic processing tools and
facilitate communication among researchers.
Results: We propose SMaSH, a benchmarking methodology for evaluating human
genome variant calling algorithms. We generate synthetic datasets, organize and
interpret a wide range of existing benchmarking data for real genomes, and
propose a set of accuracy and computational performance metrics for evaluating
variant calling methods on this benchmarking data. Moreover, we illustrate the
utility of SMaSH to evaluate the performance of some leading single nucleotide
polymorphism (SNP), indel, and structural variant calling algorithms.
Availability: We provide free and open access online to the SMaSH toolkit,
along with detailed documentation, at smash.cs.berkeley.edu
CrossNorm: Normalization for Off-Policy TD Reinforcement Learning
Off-policy temporal difference (TD) methods are a powerful class of
reinforcement learning (RL) algorithms. Intriguingly, deep off-policy TD
algorithms are not commonly used in combination with feature normalization
techniques, despite positive effects of normalization in other domains. We show
that naive application of existing normalization techniques is indeed not
effective, but that well-designed normalization improves optimization stability
and removes the necessity of target networks. In particular, we introduce a
normalization based on a mixture of on- and off-policy transitions, which we
call cross-normalization. It can be regarded as an extension of batch
normalization that re-centers data for two different distributions, as present
in off-policy learning. Applied to DDPG and TD3, cross-normalization improves
over the state of the art across a range of MuJoCo benchmark tasks
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