226 research outputs found
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery
In "extreme" computational imaging that collects extremely undersampled or
noisy measurements, obtaining an accurate image within a reasonable computing
time is challenging. Incorporating image mapping convolutional neural networks
(CNN) into iterative image recovery has great potential to resolve this issue.
This paper 1) incorporates image mapping CNN using identical convolutional
kernels in both encoders and decoders into a block coordinate descent (BCD)
signal recovery method and 2) applies alternating direction method of
multipliers to train the aforementioned image mapping CNN. We refer to the
proposed recurrent network as BCD-Net using identical encoding-decoding CNN
structures. Numerical experiments show that, for a) denoising low
signal-to-noise-ratio images and b) extremely undersampled magnetic resonance
imaging, the proposed BCD-Net achieves significantly more accurate image
recovery, compared to BCD-Net using distinct encoding-decoding structures
and/or the conventional image recovery model using both wavelets and total
variation.Comment: 5 pages, 3 figure
Vulnerability Assessment and Privacy-preserving Computations in Smart Grid
Modern advances in sensor, computing, and communication technologies enable various smart grid applications which highlight the vulnerability that requires novel approaches to the field of cybersecurity. While substantial numbers of technologies have been adopted to protect cyber attacks in smart grid, there lacks a comprehensive review of the implementations, impacts, and solutions of cyber attacks specific to the smart grid.In this dissertation, we are motivated to evaluate the security requirements for the smart grid which include three main properties: confidentiality, integrity, and availability. First, we review the cyber-physical security of the synchrophasor network, which highlights all three aspects of security issues. Taking the synchrophasor network as an example, we give an overview of how to attack a smart grid network. We test three types of attacks and show the impact of each attack consisting of denial-of-service attack, sniffing attack, and false data injection attack.Next, we discuss how to protect against each attack. For protecting availability, we examine possible defense strategies for the associated vulnerabilities.For protecting data integrity, a small-scale prototype of secure synchrophasor network is presented with different cryptosystems. Besides, a deep learning based time-series anomaly detector is proposed to detect injected measurement. Our approach observes both data measurements and network traffic features to jointly learn system states and can detect attacks when state vector estimator fails.For protecting data confidentiality, we propose privacy-preserving algorithms for two important smart grid applications. 1) A distributed privacy-preserving quadratic optimization algorithm to solve Security Constrained Optimal Power Flow (SCOPF) problem. The SCOPF problem is decomposed into small subproblems using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. 2) We use Paillier cryptosystem to secure the computation of the power system dynamic simulation. The IEEE 3-Machine 9-Bus System is used to implement and demonstrate the proposed scheme. The security and performance analysis of our implementations demonstrate that our algorithms can prevent chosen-ciphertext attacks at a reasonable cost
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
Subsequence clustering of multivariate time series is a useful tool for
discovering repeated patterns in temporal data. Once these patterns have been
discovered, seemingly complicated datasets can be interpreted as a temporal
sequence of only a small number of states, or clusters. For example, raw sensor
data from a fitness-tracking application can be expressed as a timeline of a
select few actions (i.e., walking, sitting, running). However, discovering
these patterns is challenging because it requires simultaneous segmentation and
clustering of the time series. Furthermore, interpreting the resulting clusters
is difficult, especially when the data is high-dimensional. Here we propose a
new method of model-based clustering, which we call Toeplitz Inverse
Covariance-based Clustering (TICC). Each cluster in the TICC method is defined
by a correlation network, or Markov random field (MRF), characterizing the
interdependencies between different observations in a typical subsequence of
that cluster. Based on this graphical representation, TICC simultaneously
segments and clusters the time series data. We solve the TICC problem through
alternating minimization, using a variation of the expectation maximization
(EM) algorithm. We derive closed-form solutions to efficiently solve the two
resulting subproblems in a scalable way, through dynamic programming and the
alternating direction method of multipliers (ADMM), respectively. We validate
our approach by comparing TICC to several state-of-the-art baselines in a
series of synthetic experiments, and we then demonstrate on an automobile
sensor dataset how TICC can be used to learn interpretable clusters in
real-world scenarios.Comment: This revised version fixes two small typos in the published versio
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