An Echo State Network (ESN) based predictive control method for aircraft system is proposed to increase the flight quality. Aerocraft is a nonlinear and time-varying system and its controllers are usually designed by ...
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The analytical algorithm of program quaternion is studied, aiming at the problem of the arbitrary spacecraft attitude-adjusting control. It also provides the analytical constructor method of the program quaternion for...
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This paper proposes an approximated power method to track the signal subspace. Using rank-one update model, the signal subspace can be obtained by finding the least square solutions of two unconstrained weight functio...
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In order to resolve the problem of skew phenomenon in the handwritten document image during the scanning process, a new skew angle detection algorithm based on maximum gradient difference as well as Hough transform wa...
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Linear regression for Hidden Markov Model (HMM) parameters is widely used for the adaptive training of time series pattern analysis especially for speech processing. This paper realizes a fully Bayesian treatment of l...
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Linear regression for Hidden Markov Model (HMM) parameters is widely used for the adaptive training of time series pattern analysis especially for speech processing. This paper realizes a fully Bayesian treatment of linear regression for HMMs by using variational techniques. This paper analytically derives the variational lower bound of the marginalized log-likelihood of the linear regression. By using the variational lower bound as an objective function, we can optimize the model topology and hyper-parameters of the linear regression without controlling them as tuning parameters; thus, we realize linear regression for HMM parameters in a non-parametric Bayes manner. Experiments on large vocabulary continuous speech recognition confirm the generalizability of the proposed approach, especially for small quantities of adaptation data.
Solving the optimal control problem with a free final time, such as suborbital launch vehicle (SLV) trajectory optimization with two control variables and multi-constraints ones based on particle swarm optimization (P...
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Recently, Gutierrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices -- spiking neural P systems (for short, SN P systems). However, the binary enc...
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Recently, Gutierrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices -- spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information are encoded as the time interval between spikes). In this work, three SN P systems are constructed as adder, subtracter and multiplier, respectively. In these devices, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes.
CAMSHIFT algorithm and Comaniciu/Meer algorithm are two fundamental frameworks of mean shift procedure for video target *** paper generalizes the two well-known mean shift tracking algorithms,originally due to Bradski...
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CAMSHIFT algorithm and Comaniciu/Meer algorithm are two fundamental frameworks of mean shift procedure for video target *** paper generalizes the two well-known mean shift tracking algorithms,originally due to Bradski and Comaniciu/Meer.A new general similarity function which defines the distance between the target model and target candidate is employed to calculate the pixel weights and the target *** target size is iteratively estimated and updated based on the zeroth order moment of the pixel *** we prove that both the CAMSHIFT algorithm and the Comaniciu/Meer algorithm can be included in the generalized mean shift tracking *** tracking performances of three mean shift algorithms in the unified framework are shown and compared in the experimental results.
Based on the current development of Model Driven Architecture (MDA) in Enterprise Information System (EIS), the paper proposes a DMDA, a new development architecture to improve EIS development speed and quality. The b...
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Based on the current development of Model Driven Architecture (MDA) in Enterprise Information System (EIS), the paper proposes a DMDA, a new development architecture to improve EIS development speed and quality. The basic idea of the DMDA is to take advantage of formal description, decomposition and re-use on EIS development process to let computer understand development demands of developers and then consequently generate some business models having nothing to do with specific technical realization, combine with specific technical basic platform to finally generate the EIS meeting actual demands based on data model. The practice proves the architecture is capable of simplifying EIS development difficulty, expediting application and development speed, improve development quality and achieve the rapid development purpose.
Compression schemes for EEG signals are developed based on matrix and tensor decomposition. Various ways to arrange EEG signals into matrices and tensors are explored, and several matrix and tensor decomposition schem...
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Compression schemes for EEG signals are developed based on matrix and tensor decomposition. Various ways to arrange EEG signals into matrices and tensors are explored, and several matrix and tensor decomposition schemes are applied, including SVD, CUR, PARAFAC, the Tucker decomposition, and recent random fiber selection approaches. Rate-distortion curves for the proposed matrix and tensor-based EEG compression schemes are computed. It shown that PARAFAC has the best compression performance in this context.
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