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Fast Non-Linear Methods for Dynamic Texture Prediction

Published on Oct 1, 2018 in ICIP (International Conference on Image Processing)
· DOI :10.1109/icip.2018.8451479
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
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Abstract
This paper aims to develop a fast dynamic-texture prediction method, using tools from non-linear dynamical modeling, and fast approaches for approximate regression. We consider dynamic textures to be described by patch-level non-linear processes, thus requiring tools such as delay-embedding to uncover a phase-space where dynamical evolution can be more easily modeled. After mapping the observed time-series from a dynamic texture video to its recovered phase-space, a time-efficient approximate prediction method is presented which utilizes locality-sensitive hashing approaches to predict possible phase-space vectors, given the current phase-space vector. Our experiments show the favorable performance of the proposed approach, both in terms of prediction fidelity, and computational time. The proposed algorithm is applied to shading prediction in utility scale solar arrays.
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Newest
Published on 2018in IEEE Transactions on Neural Networks11.68
Huan Song1
Estimated H-index: 1
(ASU: Arizona State University),
Jayaraman J. Thiagarajan10
Estimated H-index: 10
(LLNL: Lawrence Livermore National Laboratory)
+ 1 AuthorsAndreas Spanias26
Estimated H-index: 26
(ASU: Arizona State University)
Building highly nonlinear and nonparametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that induce a reproducing kernel Hilbert space (RKHS) for inferring non-linear models through the construction of similarity functions from data. These methods are particularly preferred in cases where the training data sizes are limited and when prior knowledge of the data similarities is available. Despite their usefulness, the...
Published on May 1, 2018
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University),
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University)
+ 5 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
This paper describes three methods used in the development of a utility-scale solar cyber-physical system. The study describes remote fault detection using machine learning approaches, power output optimization using cloud movement prediction and consensus-based solar array parameter estimation. Dynamic cloud movement, shading and soiling, lead to fluctuations in power output and loss of efficiency. For optimization of output power, a cloud movement prediction algorithm is proposed. Integrated f...
Published on Sep 1, 2017 in ICIP (International Conference on Image Processing)
Olena Chubach2
Estimated H-index: 2
(RWTH Aachen University),
Patrick Garus1
Estimated H-index: 1
(RWTH Aachen University)
+ 1 AuthorsJens-Rainer Ohm27
Estimated H-index: 27
(RWTH Aachen University)
This paper presents improvements to a dynamic texture synthesis approach which is based on motion distribution statistics, able to produce high visual quality of synthesised dynamic textures. The aim is to recreate synthetically highly textured regions like water, leaves and smoke, instead of processing them with a conventional codec such as HEVC. The method involves two steps: analysis, where motion distribution statistics are computed, and synthesis, where the texture region is synthesized. De...
Published on Aug 1, 2017
Uday Shankar Shanthamallu2
Estimated H-index: 2
(ASU: Arizona State University),
Andreas Spanias26
Estimated H-index: 26
(ASU: Arizona State University)
+ 1 AuthorsMike Stanley1
Estimated H-index: 1
(NXP Semiconductors)
This paper provides a brief survey of the basic concepts and algorithms used for Machine Learning and its applications. We begin with a broader definition of machine learning and then introduce various learning modalities including supervised and unsupervised methods and deep learning paradigms. In the rest of the paper, we discuss applications of machine learning algorithms in various fields including pattern recognition, sensor networks, anomaly detection, Internet of Things (IoT) and health m...
Published on Aug 1, 2017
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University),
Sameeksha Katoch1
Estimated H-index: 1
(ASU: Arizona State University)
+ 8 AuthorsMahesh K. Banavar12
Estimated H-index: 12
(Clarkson University)
In this paper, we describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled senso...
Published on Jul 1, 2017
Gowtham Muniraju1
Estimated H-index: 1
(ASU: Arizona State University),
Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 5 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
Matthew Tesfaldet1
Estimated H-index: 1
,
Marcus A. Brubaker16
Estimated H-index: 16
,
Konstantinos G. Derpanis22
Estimated H-index: 22
We introduce a two-stream model for dynamic texture synthesis. Our model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow prediction. Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the optical flow ConvNet model its dynamics. To generate a novel texture, a ...
Christina Funke2
Estimated H-index: 2
,
Leon A. Gatys14
Estimated H-index: 14
+ 1 AuthorsMatthias Bethge39
Estimated H-index: 39
Here we present a parametric model for dynamic textures. The model is based on spatiotemporal summary statistics computed from the feature representations of a Convolutional Neural Network (CNN) trained on object recognition. We demonstrate how the model can be used to synthesise new samples of dynamic textures and to predict motion in simple movies.
Published on Jun 1, 2016in Sustainable Energy, Grids and Networks
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Santoshi T. Buddha3
Estimated H-index: 3
(ASU: Arizona State University)
+ 4 AuthorsDevarajan Srinivasan2
Estimated H-index: 2
(ASU: Arizona State University)
As more utility scale photovoltaic (PV) power plants are installed, there is a need to improve monitoring and management of PV arrays. A procedure is presented here for optimizing the electrical configuration of a PV array under a variety of operating conditions. Computer simulations and analysis with synthetic and real data are presented in this paper. The performance of the optimization system is evaluated for a variety of partial shading conditions using a SPICE circuit simulator. In general,...
Published on Sep 1, 2013 in ICIP (International Conference on Image Processing)
Xing Yan2
Estimated H-index: 2
,
Hong Chang22
Estimated H-index: 22
,
Xilin Chen50
Estimated H-index: 50
Real-world nonlinear dynamic textures (DTs) usually consist of temporally multiple linear DTs which cannot be correctly modeled by previous works. In this paper, we propose piecewise linear dynamic systems (PLDS) to model temporally multiple DTs. PLDS simultaneously decides the temporal segmentation, models each DT segment with an LDS and the whole DT by switching between the LDS'. Experimental results verify that PLDS can capture the stochastic and dynamic nature of temporally multiple DTs and ...
Cited By1
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Published on May 2, 2019in Synthesis Lectures on Signal Processing
Henry Braun4
Estimated H-index: 4
(ASU: Arizona State University),
Pavan K. Turaga21
Estimated H-index: 21
(ASU: Arizona State University)
+ 3 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
Abstract Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors includi...
Published on Jul 1, 2018
Farib Khondoker (ASU: Arizona State University), Sunil Rao2
Estimated H-index: 2
(ASU: Arizona State University)
+ 1 AuthorsCihan Tepedelenlioglu26
Estimated H-index: 26
(ASU: Arizona State University)
When collecting solar energy via photovoltaic (PV) panel arrays, one common issue is the potential occurrence of faults. Faults arise from panel short-circuit, soiling, shading, ground leakage and other sources. Machine learning algorithms have enabled data-based classification of faults. In this paper, we present an Internet-based PV array fault monitoring simulation using the Java-Dsp(j-Dsp)simulation environment. We first develop a solar array simulation in J-DSP and then form appropriate gra...
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