Retrieving images to match with a hand-drawn sketch query is a highly desired feature, especially with the popularity of devices with touch screens. Although query-by-sketch has been extensively studied since 1990s, i...
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In order to solve the challenging problem of diagnosis for sensor bias and drift faults, a method of sensor fault diagnosis based on the least squares support vector machine (LS-SVM) online prediction is proposed. In ...
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Chemical industry is complex and continuous process industry, and the control and management of the long-term safe operation involves a great deal of information and data on the staffs, management, equipment and techn...
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Fault diagnosis based on the wavelet packet decomposition, one-against-one support vector machine (SVM) and genetic algorithm (GA) is proposed in order to realize the real-time sensor fault diagnosis accurately. The i...
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It is important for a chemical plant to find a suitable performance appraisal method. In this paper, based on the ACP (artificial system, computational experiment, and parallel execution) theory and the PageRank algor...
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Traditional temporal logics such as LTL (Linear Temporal Logic) and CTL (Computation Tree Logic) have shown tremendous success in specifying and verifying hardware and software systems. However, this kind of logic can...
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It's an important need for a large chemical plant to roundly and deeply evaluate the design prototype of plant human machine interaction (HMI) using in the control room. To meet this need, we propose an evaluation...
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When Multi-DSP parallel architecture transfers to distributed memory way from shared memory way, its parallelism with fine-grained become weak, and it's difficult to offer SIFT's complexcomputing and satisfy ...
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With the fast development of the economy, the urban traffic demands increases rapidly, Bus rapid transit (BRT) system, a new type and high efficient bus operator system and a comprehensive mass transit system between ...
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The standard compressive sensing (CS) aims to recover sparse signal from single mea- surement vector which is known as SMV model. By contrast, recovery of sparse signals from multiple measurement vectors is called MMV...
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The standard compressive sensing (CS) aims to recover sparse signal from single mea- surement vector which is known as SMV model. By contrast, recovery of sparse signals from multiple measurement vectors is called MMV model. In this paper, we consider the recovery of jointly sparse signals in the MMV model where multiple signal measurements are represented as a matrix and the sparsity of signal occurs in common locations. The sparse MMV model can be formulated as a matrix (2;1)-norm minimization problem, which is much more difficult to solve than the l1-norm minimization in standard CS. In this paper, we propose a very fast algorithm, called MMV-ADM, to solve the jointly sparse signal recovery problem in MMV settings based on the alternating direction method (ADM). The MMV- ADM alternately updates the recovered signal matrix, the Lagrangian multiplier and the residue, and all update rules only involve matrix or vector multiplications and summations, so it is simple, easy to implement and much faster than the state-of-the-art method MMVprox. Numerical simulations show that MMV-ADM is at least dozens of times faster than MMVprox with comparable recovery accuracy. Copyright 2011 by the authors.
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