scholarly journals Lie Group Methods in Blind Signal Processing

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 440 ◽  
Author(s):  
Dariusz Mika ◽  
Jerzy Jozwik
Keyword(s):  
Signal Processing ◽  
Lie Groups ◽  
Lie Group ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Search Space ◽  
Optimization Techniques ◽  
Blind Signal Processing ◽  
Blind Signal ◽  
Group Methods

This paper deals with the use of Lie group methods to solve optimization problems in blind signal processing (BSP), including Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). The paper presents the theoretical fundamentals of Lie groups and Lie algebra, the geometry of problems in BSP as well as the basic ideas of optimization techniques based on Lie groups. Optimization algorithms based on the properties of Lie groups are characterized by the fact that during optimization motion, they ensure permanent bonding with a search space. This property is extremely significant in terms of the stability and dynamics of optimization algorithms. The specific geometry of problems such as ICA and ISA along with the search space homogeneity enable the use of optimization techniques based on the properties of the Lie groups O ( n ) and S O ( n ) . An interesting idea is that of optimization motion in one-parameter commutative subalgebras and toral subalgebras that ensure low computational complexity and high-speed algorithms.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

Application of Blind Signal Processing in Image Watermarking Systems

2020 ◽  
Author(s):  
Chuen-Yau Chen ◽  
Cheng-Yuan Lin ◽  
Yi-Ze Zou ◽  
Hung-Ming Hsiao ◽  
Yen-Ting Chen
Keyword(s):  
Signal Processing ◽  
Image Watermarking ◽  
Blind Signal Processing ◽  
Blind Signal

scholarly journals Comparative Study of Optimization Algorithms for The Optimal Reservoir Operation

2018 ◽  
Vol 246 ◽  
pp. 01003
Author(s):  
Xinyuan Liu ◽  
Yonghui Zhu ◽  
Lingyun Li ◽  
Lu Chen
Keyword(s):  
Reservoir Operation ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Complex Problem ◽  
Optimization Techniques ◽  
Decision Variables ◽  
Handling Methods ◽  
Optimality Algorithm ◽  
Parameter Values

Apart from traditional optimization techniques, e.g. progressive optimality algorithm (POA), modern intelligence algorithms, like genetic algorithms, differential evolution have been widely used to solve optimization problems. This paper deals with comparative analysis of POA, GA and DE and their applications in a reservoir operation problem. The results show that both GA and DES are feasible to reservoir operation optimization, but they display different features. GA and DE have many parameters and are difficult in determination of these parameter values. For simple problems with mall number of decision variables, GA and DE are better than POA when adopting appropriate parameter values and constraint handling methods. But for complex problem with large number of variables, POA combined with simplex method are much superior to GA and DE in time-assuming and quality of optimal solutions. This study helps to select proper optimization algorithms and parameter values in reservoir operation.

W1066 Detection of Small Bowel Slow Wave Frequencies with Noninvasive Biomagnetic Recording and Semi-Blind Signal Processing

2009 ◽  
Vol 136 (5) ◽  
pp. A-646
Author(s):  
Jonathan Erickson ◽  
Chike B. Obioha ◽  
Leonard A. Bradshaw ◽  
William O. Richards
Keyword(s):  
Signal Processing ◽  
Small Bowel ◽  
Slow Wave ◽  
Blind Signal Processing ◽  
Blind Signal

An Agent Based Optimization Approach to Manufacturing Process Planning

2001 ◽  
Author(s):  
Saurabh Deshpande ◽  
Jonathan Cagan
Keyword(s):  
Stochastic Optimization ◽  
Process Planning ◽  
Manufacturing Process ◽  
Optimization Problems ◽  
Search Space ◽  
Optimization Techniques ◽  
Process Models ◽  
Optimization Approach ◽  
Agent Based ◽  
Manufacturing Process Planning

Abstract Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.

Study on the Mechanical Noise Sources Identification Without Source Signal Inputs Based on Blind Signal Processing

2007 ◽  
Author(s):  
Yong Jiang ◽  
Lin He ◽  
Lin-Ke Zhang
Keyword(s):  
Signal Processing ◽  
Blind Separation ◽  
Noise Sources ◽  
Blind Signal Processing ◽  
Sources Identification ◽  
Blind Signal ◽  
Mechanical Noise ◽  
Input Signals ◽  
Tank Experiment ◽  
Source Signal

The mechanical noise sources identification without source signal inputs was mainly studied in this paper with the theory of blind signal processing (BSP). In traditional noise sources identification methods, the preknowledge of noise source input signals and transmission paths was required in advance. In order to overcome this shortage, a blind sources separation/deconvolution model of mechanical noise sources identification was suggested, based on the analysis of the characteristics of vibration and acoustic signals’ production, transmission and mixing. And a natural gradient method of convolutive blind separation (CBS) was carried out based on minimal mutual information (MMI). Accordingly the validity of this method was confirmed by tank experiment.

Sign in / Sign up

Export Citation Format

Share Document