A two-phase preconditioning strategy based on a factored sparse approximate inverse is proposed for solving sparse indefinite matrices. In each phase, the strategy first makes the original matrix diagonally dominant t...
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ISBN:
(纸本)9781605581057
A two-phase preconditioning strategy based on a factored sparse approximate inverse is proposed for solving sparse indefinite matrices. In each phase, the strategy first makes the original matrix diagonally dominant to enhance the stability by a shifting method, and constructs an inverse approximation of the shifted matrix by utilizing a factored sparse approximate inverse preconditioner. The two inverse approximation matrices produced from each phase are then combined to be used as a preconditioner. Experimental results show that the presented strategy improves the accuracy and the stability of the preconditioner on solving indefinite sparse matrices. Furthermore, the strategy ensures that convergence rate of the preconditioned iterations of the two-phase preconditioning strategy is much better than that of the standard sparse approximate inverse ones for solving indefinite matrices. Copyright 2008 ACM.
Transport equations for particle, momentum, and energy densities in two conduction bands are applied to a self-consistent numerical simulation of heterojunction bipolar transistors. Simple formulas for the relaxation ...
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Transport equations for particle, momentum, and energy densities in two conduction bands are applied to a self-consistent numerical simulation of heterojunction bipolar transistors. Simple formulas for the relaxation frequencies, by which the variation of the conduction band energy and doping concentration in a heterojunction device are easily taken into account, are proposed. The electron transport in the AlGaAs/GaAs heterojunction bipolar transistor with two different collector structures is analyzed and discussed.< >
Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combi...
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Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.
In this paper, we introduce our experiments on SLIM and DRBL diskless PC clusters. We constructed them by using 16 machines and only one disk. We run the matrix multiplication and bioinformatics software to evaluate t...
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The laser self-mixing grating interferometer based on Cr atomlithography gratings hasbeen applied to the primarycalibration of accelerometers due to its compact structure, low cost,high accuracy, and directon-site tra...
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The laser self-mixing grating interferometer based on Cr atomlithography gratings hasbeen applied to the primarycalibration of accelerometers due to its compact structure, low cost,high accuracy, and directon-site traceability. However, the high line density of Cr gratings(4700 l/mm) introduces dense outliers in interferometricsignals, complicating displacement demodulation via conventionalderivative-based methods and causing frequent phase jumps. To addressthis challenge, we propose a hybrid algorithm integrating thecontinuous wavelet transform and the Hilbert transform, which enablesrobust displacement demodulation under high-noise *** validation on a commercial MEMS accelerometerdemonstrated exceptional accuracy: 100 consecutive data segments weresuccessfully demodulated with a displacement fitting goodness of $R^2 = 0.9964$ ( $\sigma_{R_2} =0.0027$ ), and the derived sensitivitydeviated by only 0.1% from the nominal value. This algorithmestablishes a paradigm for the high-accuracy dynamic calibration ofinertial sensors in field applications.
A doubly clamped microbeam actuated by electrostatic force with squeezed gas film damping is a well-known and standard micro-device in microelectromechanical system (MEMS) for many researchers to demonstrate how reduc...
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A doubly clamped microbeam actuated by electrostatic force with squeezed gas film damping is a well-known and standard micro-device in microelectromechanical system (MEMS) for many researchers to demonstrate how reduced-order dynamic macromodel is an effective way to faithfully capture the device behaviors. However it still takes time to directly recompute the time-dependant nonlinear terms in macromodels which are generated by a proper orthogonal decomposition (POD) method with Galerkin procedure at every time step during the macromodel simulation. This paper proposes two methods for speeding up the computation of macromodel simulations. In the first method, the computation speedup is achieved based on the concept of precomputation upon the basis functions are available. In the second method, cubic splines approximation is used to interpolate the basis functions and their first and second derivatives, and spatial integration is performed by application of the Gaussian quadrature. Numerical results show both methods could enhance the efficiency of the macromodel simulation compared with our previous computation results.
In recent years, e-Learning has become a popular method of learning. Generally, an e-Learning Platform which provided multi-media content required a high capacity storage device such as NAS (Network Attached Storage) ...
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Since the beginning of the 21st century, knowledge has been accumulating at a tremendous speed. Access to global books, literature, and data are continually increasing in large quantities. With the development of Inte...
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This research investigates the development of a linguistics for artificial intelligence (AI) to demystify the ”black box” of AI. At its core, the language of AI is Embedding—a novel high-dimensional, intelligent la...
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This research investigates the development of a linguistics for artificial intelligence (AI) to demystify the ”black box” of AI. At its core, the language of AI is Embedding—a novel high-dimensional, intelligent language. Embedding exhibits dual characteristics: it operates both as a semantic domain and as a mathematical point. This duality enables Embedding to maintain the discrete, symbolic nature of human languages while facilitating continuous operations in high-dimensional spaces, unlocking significant potential for advanced intelligence. A series of specialized experiments were designed to explore Embedding’s intrinsic properties, including its behavior as a semantic cloud in high-dimensional space, its degrees of freedom, and spatial transformations. Key findings include the discovery of substantial redundant dimensions in embeddings, confirmation that embeddings lack critical dimensions, and the measurement of engineering dimensions in natural language. This research also establishes the linguistic foundations and application limits of techniques such as dropout strategies, AI model distillation, and scaling laws among others. Building on these insights, we propose innovative solutions across several fields, including AI architecture design, AI reasoning, domain-based embedding search, and the construction of a multi-intelligence spectrum for embeddings. Ultimately, we introduce a foundational methodology for embedding everything from real-world into the AI world, providing a comprehensive reference framework for the evolution of artificial general intelligence (AGI) and artificial superintelligence (ASI). Additionally, this research explores linguistic approaches to the co-evolution of human intelligence and artificial intelligence.
With the rapid growth of e-Learning, a tremendous amount of learning content has been developed by numerous providers. Recently, the Sharable Content Object Reference Model (SCORM) has been widely accepted as a standa...
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