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Found 21 papers, 1 papers with code
Differentially Private Communication of Measurement Anomalies in the Smart Grid
In this paper, we present a framework based on differential privacy (DP) for querying electric power measurements to detect system anomalies or bad data.
MALCOM-PSGD: Inexact Proximal Stochastic Gradient Descent for Communication-Efficient Decentralized Machine Learning
Recent research indicates that frequent model communication stands as a major bottleneck to the efficiency of decentralized machine learning (ML), particularly for large-scale and over-parameterized neural networks (NNs).
Blind Graph Matching Using Graph Signals
Our analysis shows that the blind matching outcome converges to the result obtained with known graph topologies when the signal sampling size is large and the signal noise is small.
Differential Privacy for Class-based Data: A Practical Gaussian Mechanism
In this paper, we present a notion of differential privacy (DP) for data that comes from different classes.
Solar Photovoltaic Systems Metadata Inference and Differentially Private Publication
Stakeholders in electricity delivery infrastructure are amassing data about their system demand, use, and operations.
Constrained Reinforcement Learning for Predictive Control in Real-Time Stochastic Dynamic Optimal Power Flow
This paper presents a novel primal-dual approach for learning optimal constrained DRL policies for dynamic optimal power flow problems, with the aim of controlling power generations and battery outputs.
Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI
The effective representation, precessing, analysis, and visualization of large-scale structured data over graphs are gaining a lot of attention.
Spatio-Temporal Graph Convolutional Neural Networks for Physics-Aware Grid Learning Algorithms
This paper proposes a model-free Volt-VAR control (VVC) algorithm via the spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL) framework, whose goal is to control smart inverters in an unbalanced distribution system.
Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation
Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltages in distribution systems.
Differentially Private $K$-means Clustering Applied to Meter Data Analysis and Synthesis
It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers.
Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid
The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework.
A User Guide to Low-Pass Graph Signal Processing and its Applications
The notion of graph filters can be used to define generative models for graph data.
Blind Community Detection from Low-rank Excitations of a Graph Filter
The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals.
Accelerating Incremental Gradient Optimization with Curvature Information
This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems.
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization
We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems.
Curvature-aided Incremental Aggregated Gradient Method
We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method.
RIDS: Robust Identification of Sparse Gene Regulatory Networks from Perturbation Experiments
To overcome this challenge, we develop the Robust IDentification of Sparse networks (RIDS) method that reconstructs the GRN from a small number of perturbation experiments.
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems
The convergence of the proposed algorithm is studied by viewing the decentralized algorithm as an inexact FW algorithm.
Distributed Estimation of the Operating State of a Single-Bus DC MicroGrid without an External Communication Interface
We propose a decentralized Maximum Likelihood solution for estimating the stochastic renewable power generation and demand in single bus Direct Current (DC) MicroGrids (MGs), with high penetration of droop controlled power electronic converters.
Modeling Group Dynamics Using Probabilistic Tensor Decompositions
We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources.
Active Sensing of Social Networks
The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i. e., agents whose opinions are not influenced by their neighbors.
