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Finding Faults in PV Systems: Supervised and Unsupervised Dictionary Learning With SSTDR

IEEE SENSORS JOURNAL 2021年第4期21卷 4855-4865页

作者： Edun, Ayobami S. LaFlamme, Cody Kingston, Samuel R. Tetali, Harsha Vardhan Benoit, Evan J. Scarpulla, Michael Furse, Cynthia M. Harley, Joel B. Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Univ Utah Dept Elect & Comp Engn Salt Lake City UT 84112 USA LiveWire Test Labs Inc Salt Lake City UT 84117 USA

This article explains the use of supervised and unsupervised dictionary learning approaches on spread spectrum time domain (SSTDR) data to detect and locate disconnections in a PV array consisting of five panels. The aim is to decompose an SSTDR reflection signature into different components where each component has a physical interpretation, such as noise, environmental effects, and faults. In the unsupervised dictionary learning approach, the decomposed components are inspected to detect and localize faults. The maximum difference between actual and predicted location of the fault is 0.44 m on a system with five panels connected to an SSTDR box with a leader cable of 59.13 m and total length of 67.36 m including the effective length of the panels. In the supervised dictionary learning approach, the dictionary components are used to classify the SSTDR data to their respective fault types. Our results show a 97% accuracy using the supervised learning approach.

关键词： Dictionaries Circuit faults Machine learning Matching pursuit algorithms Electronic mail Encoding Sensors Dictionary learning faults K-SVD solar panels sparse coding SSTDR

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SUPERVISED TRAINING OF SIAMESE SPIKING NEURAL NETWORKS WITH EARTH MOVER'S DISTANCE 47

SUPERVISED TRAINING OF SIAMESE SPIKING NEURAL NETWORKS WITH ...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Pabian, Mateusz Rzepka, Dominik Pawlak, Miroslaw AGH Univ Sci & Technol Dept Measurement & Elect Krakow Poland

ISBN: (纸本)9781665405409

This study adapts the highly-versatile siamese neural network model to the event data domain. We introduce a supervised training framework for optimizing Earth Mover's Distance (EMD) between spike trains with spiking neural networks (SNN). We train this model on images of the MNIST dataset converted into spiking domain with novel conversion schemes. The quality of the siamese embeddings of input images was evaluated by measuring the classifier performance for different dataset coding types. The models achieved performance similar to existing SNN-based approaches (F1-score of up to 0.9386) while using only about 15% of hidden layer neurons to classify each example. Furthermore, models which did not employ a sparse neural code were about 45% slower than their sparse counterparts. These properties make the model suitable for low energy consumption and low prediction latency applications.

关键词： spiking neural networks siamese neural networks event-based computing sparse coding

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Inverse sparse Object Tracking via Adaptive Representation 18th

Inverse Sparse Object Tracking via Adaptive Representation

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18th International Conference on Intelligent Computing (ICIC)

作者： Mi, Jian-Xun Gao, Yun Li, Renjie Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China

ISBN: (纸本)9783031138324;9783031138317

sparse representation has been widely applied in object tracking, which is challenging due to various factors, such as occlusion, illumination variation, and background clutter. In this paper, we propose an inverse sparse tracking algorithm based on adaptive representation. Unlike the conventional tracking methods, we explore the low-rank property from the group structure of the candidate particles and calculate their similarity between foreground and background, which makes our representations more discriminative and robust. Specifically, we employed the candidate particles as the dictionary atoms to sparse encode the templates, which makes less cost on optimization. The adaptive regularization based on the rank of each group of candidate sub-matrix is employed in our method to adapt to appearance changes. Moreover, foreground templates and background templates are collected as positive and negative dictionaries, respectively, to make the tracker more discriminate from the complex environment. The experiment is conducted on a public dataset OTB100, and the quantitative results are reported, which demonstrate the favorable performance of the proposed method over some state-of-the-art trackers.

关键词： Object tracking sparse representation sparse coding

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HIGH-DIMENSIONAL sparse BAYESIAN LEARNING WITHOUT COVARIANCE MATRICES 47

HIGH-DIMENSIONAL SPARSE BAYESIAN LEARNING WITHOUT COVARIANCE...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Lin, Alexander Song, Andrew H. Bilgic, Berkin Ba, Demba Harvard Univ Sch Engn & Appl Sci Boston MA 02115 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA Athinoula A Martinos Ctr Biomed Imaging Charlestown MA USA Harvard Med Sch Dept Radiol Boston MA 02115 USA

ISBN: (纸本)9781665405409

sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem. However, the most popular inference algorithms for SBL become too expensive for high-dimensional settings, due to the need to store and compute a large covariance matrix. We introduce a new inference scheme that avoids explicit construction of the covariance matrix by solving multiple linear systems in parallel to obtain the posterior moments for SBL. Our approach couples a little-known diagonal estimation result from numerical linear algebra with the conjugate gradient algorithm. On several simulations, our method scales better than existing approaches in computation time and memory, especially for structured dictionaries capable of fast matrix-vector multiplication.

关键词： sparse Bayesian learning compressed sensing sparse coding

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DICTIONARY-BASED FUSION OF CONTACT AND ACOUSTIC MICROPHONES FOR WIND NOISE REDUCTION 17

DICTIONARY-BASED FUSION OF CONTACT AND ACOUSTIC MICROPHONES ...

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17th International Workshop on Acoustic Signal Enhancement (IWAENC)

作者： Tammen, Marvin Li, Xilin Doclo, Simon Theverapperuma, Lalin Meta Real Labs Res Burlingame CA 94010 USA Carl von Ossietzky Univ Oldenburg Oldenburg Germany Cluster Excellence Hearing4all Oldenburg Germany

ISBN: (数字)9781665468671

ISBN: (纸本)9781665468671

In mobile speech communication applications, wind noise can lead to a severe reduction of speech quality and intelligibility. Since the performance of speech enhancement algorithms using acoustic microphones tends to substantially degrade in extremely challenging scenarios, auxiliary sensors such as contact microphones can be used. Although contact microphones offer a much lower recorded wind noise level, they come at the cost of speech distortion and additional noise components. Aiming at exploiting the advantages of acoustic and contact microphones for wind noise reduction, in this paper we propose to extend conventional single-microphone dictionary-based speech enhancement approaches by simultaneously modeling the acoustic and contact microphone signals. We propose to train a single speech dictionary and two noise dictionaries and use a relative transfer function to model the relationship between the speech components at the microphones. Simulation results show that the proposed approach yields improvements in both speech quality and intelligibility compared to several baseline approaches, most notably approaches using only the contact microphones or only the acoustic microphone.

关键词： wind noise reduction sparse coding sensor fusion

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An Image Quality Assessment Method based on sparse Neighbor Significance

An Image Quality Assessment Method based on Sparse Neighbor ...

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International Conference on Image Processing and Vision Engineering (IMPROVE)

作者： Aydi, Selcuk Ilhan Gebze Tech Univ Fac Comp Engn Inst Sci Darica Turkey

ISBN: (纸本)9789897585630

In this paper, the image quality assessment problem is tackled from a sparse coding perspective, and a new automated image quality assessment algorithm is presented. Specifically, the input image is first divided into non-overlapping blocks and sparse coding is used to reconstruct a central sub-block using the neighboring sub-blocks as dictionaries. The resulting 2D sparse vectors from each neighboring sub-block, are devised as significance maps that are then used in similarity measures between the reference and distorted images. The proposed method is compared against various recently introduced shallow and deep methods across four datasets and multiple distortion types. The experimental results that have been obtained show that it possesses a strong correlation with the Human Visual System and outperforms its counterparts.

关键词： Image Quality Assessment sparse coding Human Visual System

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Group Invariant Dictionary Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 3612-3626页

作者： Soh, Yong Sheng Natl Univ Singapore Dept Math Singapore 119076 Singapore Inst High Performance Comp Singapore 138632 Singapore

The dictionary learning problem concerns the task of representing data as sparse linear sums drawn from a smaller collection of basic building blocks. In application domains where such techniques are deployed, we frequently encounter datasets where some form of symmetry or invariance is present. Motivated by this observation, we develop a framework for learning dictionaries for data under the constraint that the collection of basic building blocks remains invariant under such symmetries. Our procedure for learning such dictionaries relies on representing the symmetry as the action of a matrix group acting on the data, and subsequently introducing a convex penalty function so as to induce sparsity with respect to the collection of matrix group elements. Our framework specializes to the convolutional dictionary learning problem when we consider integer shifts. Using properties of positive semidefinite Hermitian Toeplitz matrices, we develop an extension that learns dictionaries that are invariant under continuous shifts. Our numerical experiments on synthetic data and ECG data show that the incorporation of such symmetries as priors are most valuable when the dataset has few data-points, or when the full range of symmetries is inadequately expressed in the dataset.

关键词： Dictionaries Machine learning Task analysis Convolution Time series analysis Signal processing algorithms Neural networks sparse coding equivariance atomic norms circulant matrices orbitopes

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A robust image representation method against illumination and occlusion variations

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IMAGE AND VISION COMPUTING 2021年 112卷 104212-104212页

作者： Tan, Jin Zhang, Taiping Zhao, Linchang Luo, Xiaoliu Tang, Yuan Yan Chongqing Univ Coll Comp Sci Chongqing Peoples R China Chongqing Jiaotong Univ Sch Informat Sci & Engn Chongqing Peoples R China Zhuhai UM Sci & Technol Res Inst Zhuhai Peoples R China

Matrix data has arised in many field, especially in the field of image processing and computer vision. In traditional approaches, the original images need to be vectorized to one-dimension vectors, which may destroy the inherent structure of images. A novel geometrical sparse representation (GSR) model with single image is introduced in this paper that solves a model to measure the similarity between the input image and the single dictionary image. Unlike the traditional sparse representation model, the proposed model does not need to vectorize the image, so as to preserve the inherent geometrical structure of the image. We further introduce a binary coding method to preserve the local patterns of the image and enhance the sparsity of the GSR coefficients. Our method is used for face images with variations of structural noise (occlusion, illumination, etc.), extensive experiments show that our method can be competitive with or even superior to the baseline methods. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Geometrical structure sparse coding Illumination analysis Occlusion

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Direct Delta Mush Skinning Compression with Continuous Examples

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ACM TRANSACTIONS ON GRAPHICS 2021年第4期40卷 1-13页

作者： Binh Huy Le Villeneuve, Keven Gonzalez-Ochoa, Carlos SEED Elect Arts Redwood City CA 94065 USA SEED Elect Arts Montreal PQ Canada

Direct Delta Mush (DDM) is a high-quality, direct skinning method with a low setup cost. However, its storage and run-time computing cost are relatively high for two reasons: its skinning weights are 4 x 4 matrices instead of scalars like other direct skinning methods, and its computation requires one 3 x 3 Singular Value Decomposition per vertex. In this paper, we introduce a compression method that takes a DDM model and splits it into two layers: the first layer is a smaller DDM model that computes a set of virtual bone transformations and the second layer is a Linear Blend Skinning model that computes per-vertex transformations from the output of the first layer. The two-layer model can approximate the deformation of the original DDM model with significantly lower costs. Our main contribution is a novel problem formulation for the DDM compression based on a continuous example-based technique, in which we minimize the compression error on an uncountable set of example poses. This formulation provides an elegant metric for the compression error and simplifies the problem to the common linear matrix factorization. Our formulation also takes into account the skeleton hierarchy of the model, the bind pose, and the range of motions. In addition, we propose a new update rule to optimize DDM weights of the first layer and a modification to resolve the floating-point cancellation issue of DDM.

关键词： deformation skeletal animation delta mush linear blend skinning data driven example poses sparse coding least squares

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Dictionary Learning with Accumulator Neurons 22

Dictionary Learning with Accumulator Neurons

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International Conference on Neuromorphic Systems (ICONS)

作者： Parpart, Gavin Watkins, Yijing Gonzalez, Carlos Stewart, Terrence C. Kim, Edward Rego, Jocelyn O'Brien, Andrew Nesbit, Steven C. Kenyon, Garrett T. Pacific Northwest Natl Lab Richland WA 99354 USA Natl Res Council Canada Ottawa ON Canada Drexel Univ Philadelphia PA USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781450397896

The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using dictionaries of spatiotemporal features optimized in an unsupervised manner for sparse reconstruction. Non-spiking LCA has previously been used to achieve unsupervised learning of spatiotemporal dictionaries composed of convolutional kernels from raw, unlabeled video. We demonstrate how unsupervised dictionary learning with spiking LCA (S-LCA) can be efficiently implemented using accumulator neurons, which combine a conventional leaky-integrate-and-fire (LIF) spike generator with an additional state variable that is used to minimize the difference between the integrated input and the spiking output. We demonstrate dictionary learning across a wide range of dynamical regimes, from graded to intermittent spiking, for inferring sparse representations of both static images drawn from the CIFAR database as well as video frames captured from a DVS camera. On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the LCA model used to infer sparse spatiotemporal representations migrates from graded to spiking. We conclude that accumulator neurons are likely to provide a powerful enabling component of future neuromorphic hardware for implementing online unsupervised learning of spatiotemporal dictionaries optimized for sparse reconstruction of streaming video from event based DVS cameras.

关键词： sparse coding dynamic vision sensors local competitive algorithms spiking neural networks dictionary learning accumulator neurons leaky integrator neurons

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