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Found 14 papers, 0 papers with code
Maximal electric power generation from varying ocean waves with LC-tuned reactive PTO force
The reactive Power Take Off (PTO) force is the key to maximizing mechanical power absorption and electric power generation of Wave Energy Converters (WECs) from ocean waves with variable frequency, but its study is limited due to its difficulty in physical realization.
Experiment-based deep learning approach for power allocation with a programmable metasurface
Enabled by the tunability of a programmable metasurface, large sets of experimental data in various configurations can be collected for DNN training.
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
The comprehensive results demonstrate that the proposed SCCAM method can achieve better performance compared with the state-of-the-art methods on fault classification and root cause analysis.
Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring
In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring.
Structure Parameter Optimized Kernel Based Online Prediction with a Generalized Optimization Strategy for Nonstationary Time Series
For structure parameters, the kernel dictionary is selected by some sparsification techniques with online selective modeling criteria, and moreover the kernel covariance matrix is intermittently optimized in the light of the covariance matrix adaptation evolution strategy (CMA-ES).
Self-learning sparse PCA for multimode process monitoring
This paper proposes a novel sparse principal component analysis algorithm with self-learning ability for successive modes, where synaptic intelligence is employed to measure the importance of variables and a regularization term is added to preserve the learned knowledge of previous modes.
PET Image Reconstruction with Multiple Kernels and Multiple Kernel Space Regularizers
To reduce the reconstruction error and the sensitivity to iteration number, we present a general class of multi-kernel matrices and two regularizers consisting of kernel image dictionary and kernel image Laplacian quatradic, and use them to derive the single-kernel regularized EM and multi-kernel regularized EM algorithms for PET image reconstruction.
Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation
In this paper, recursive cointegration analysis (RCA) is first proposed to distinguish the real faults from normal systems changes, where the model is updated once a new normal sample arrives and can adapt to slow change of cointegration relationship.
Monitoring multimode processes: a modified PCA algorithm with continual learning ability
The computational complexity and key parameters are discussed to further understand the relationship between PCA and the proposed algorithm.
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation
By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring.
An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
To realize this purpose, the technique of a mixture of probabilistic principal component analysers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach.
Bayesian Hypothesis Testing for Block Sparse Signal Recovery
The Block-BHTA comprises the detection and recovery of the supports, and the estimation of the amplitudes of the block sparse signal.
Iterative Bayesian Reconstruction of Non-IID Block-Sparse Signals
Unlike the existing algorithms for block sparse signal recovery which assume the cluster structure of the nonzero elements of the unknown signal to be independent and identically distributed (i. i. d.
Multichannel Compressive Sensing MRI Using Noiselet Encoding
In CS-MRI, the randomly under-sampled Fourier matrix is used as the measurement matrix and the wavelet transform is usually used as sparsifying transform matrix.
