Norm Optimization Algorithm Research Articles

Fabric defect detection based on deep-handcrafted feature and weighted low-rank matrix representation

In the process of textile production, automatic defect detection plays a key role in controlling product quality. Due to the complex texture features of fabric image, the traditional detection methods have poor adaptability, and low detection accuracy. The low rank representation model can divide the image into the low rank background and sparse object, and has proven suitable for fabric defect detection. However, how to further effectively characterize the fabric texture is still problematic in this kind of method. Moreover, most of them adopt nuclear norm optimization algorithm to solve the low rank model, which treat every singular value in the matrix equally. However, in the task of fabric defect detection, different singular values of feature matrix represent different information. In this paper, we proposed a novel fabric defect detection method based on the deep-handcrafted feature and weighted low-rank matrix representation. The feature characterization ability is effectively improved by fusing the global deep feature extracted by VGG network and the handcrafted low-level feature. Moreover, a weighted low-rank representation model is constructed to treat the matrix singular values differently by different weights, thus the most distinguishing feature of fabric texture can be preserved, which can efficiently outstand the defect and suppress the background. Qualitative and quantitative experiments on two public datasets show that our proposed method outperforms the state-of-the-art methods.

Collaborative Compressive Radar Imaging With Saliency Priors

Although several works have been done on high-resolution inverse synthetic aperture radar (ISAR) imaging via compressive sampling technology, they only explore sparse priors of targets in the scene and the image is recovered cell by cell separately. In order to potentially enhance targets and suppress clutters for fast and higher quality imaging, in this paper, we advance a collaborative compressive ISAR (CC-ISAR) imaging approach, by exploring both sparse priors and saliency priors of targets. First, a geometric saliency map is derived by performing pulse contourlet transform on a preliminary image. Then, a graph Laplacian is constructed to regularize a multiple measurement vector problem for collaborative compressive radar imaging. Third, targets are approximately separated from the background in the saliency map, and salient weights are defined for the target and background, respectively, to derive a saliency weighted $l_{1}$ -norm optimization algorithm. Some experiments are taken on real ISAR data to evaluate the performance of the proposed method, and both visual results and numerical guidelines prove that CC-ISAR imaging method can obtain more accurate targets and outperform its counterparts.

Blind Impulse Estimation and Removal Using Sparse Signal Decomposition Framework for OFDM Systems

Orthogonal frequency-division multiplexing (OFDM) is a multi-carrier modulation scheme that has been employed in many communication standards. The performance of OFDM is severely degraded by the presence of impulsive noise caused due to different nonlinear devices and power amplifiers. In this paper, we propose a novel automated framework to detect and remove impulse noise in OFDM system. The proposed method is based on sparse decomposition and $$l_1$$ -norm optimization algorithm of the received signal over an over-complete matrix composed of both sine and cosine waveforms and time-shifted impulse waveforms. By proper construction of the over-complete matrix, the impulse removal and symbol decoding have been performed simultaneously. Thus, it can reduce the computational complexity. Our method does not require any assumption about the location and magnitude of the impulse and does not demand pilot symbols or null subcarriers unlike other existing methods. Thus, the proposed method is blind in nature. The method is evaluated using different levels of impulse noise and signal-to-noise ratios varying from 0 to 20 dB. Preliminary evaluation results demonstrate the effectiveness of the sparse representation with the proposed dictionaries in effectively removing the impulse noise and improving the bit error rate under different noise levels.

Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension

Reducing the acquisition time for two-dimensional nuclear magnetic resonance (2D NMR) spectra is important. One way to achieve this goal is reducing the acquired data. In this paper, within the framework of compressed sensing, we proposed to undersample the data in the indirect dimension for a type of self-sparse 2D NMR spectra, that is, only a few meaningful spectral peaks occupy partial locations, while the rest of locations have very small or even no peaks. The spectrum is reconstructed by enforcing its sparsity in an identity matrix domain with ℓp (p = 0.5) norm optimization algorithm. Both theoretical analysis and simulation results show that the proposed method can reduce the reconstruction errors compared with the wavelet-based ℓ1 norm optimization.

Genetic algorithms in norm-optimal linear and non-linear iterative learning control

In this paper it is analysed whether or not it is possible to apply the norm-optimal iterative learning control algorithm to non-linear plant models. As a new theoretical result it is shown that if the non-linear plant meets a certain technical invertibility condition, the sequence of tracking errors generated by the norm-optimal algorithm will converge geometrically to zero. However, due to the non-linear nature of the plant, it is typically impossible to calculate analytically the sequence of input functions produced by the norm-optimal algorithm. Therefore it is proposed that genetic algorithms can be used as a computational tool to calculate the sequence of norm-optimal inputs. The proposed approach benefits from the design of a low-pass FIR filter. This filter successfully removes unwanted high frequency components of the input signal, which are generated by the genetic algorithm method due to the random nature of the genetic algorithm search. Simulations are used to illustrate the performance of this new approach, and they demonstrate good results in terms of convergence speed and tracking of the reference signal regardless of the nature of the plant.

Design of disturbance attenuating controllers: A Riccati-based FH norm optimization algorithm

We develop the FH norm approach for disturbance rejection in discrete-time systems using controllers of bounded order. We propose a new computational algorithm to determine the optimal controller based on the use of algebraic Riccati equations, which in the limit reduce to Lyapunov equations that appear in the necessary conditions. An example is provided to demonstrate the usefulness of this approach.