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Article
Publication date: 4 August 2020

Alaa Tharwat

Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box…

Abstract

Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details. Therefore, in this paper, the basics of ICA are provided to show how it works to serve as a comprehensive source for researchers who are interested in this field. This paper starts by introducing the definition and underlying principles of ICA. Additionally, different numerical examples in a step-by-step approach are demonstrated to explain the preprocessing steps of ICA and the mixing and unmixing processes in ICA. Moreover, different ICA algorithms, challenges, and applications are presented.

Details

Applied Computing and Informatics, vol. 17 no. 2
Type: Research Article
ISSN: 2634-1964

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Article
Publication date: 18 July 2008

F.H. Bellamine and A. Elkamel

This paper seeks to present a novel computational intelligence technique to generate concise neural network models for distributed dynamic systems.

Abstract

Purpose

This paper seeks to present a novel computational intelligence technique to generate concise neural network models for distributed dynamic systems.

Design/methodology/approach

The approach used in this paper is based on artificial neural network architectures that incorporate linear and nonlinear principal component analysis, combined with generalized dimensional analysis.

Findings

Neural network principal component analysis coupled with generalized dimensional analysis reduces input variable space by about 90 percent in the modeling of oil reservoirs. Once trained, the computation time is negligible and orders of magnitude faster than any traditional discretisation schemes such as fine‐mesh finite difference.

Practical implications

Finding the minimum number of input independent variables needed to characterize a system helps in extracting general rules about its behavior, and allows for quick setting of design guidelines, and particularly when evaluating changes in the physical properties of systems.

Originality/value

The methodology can be used to simulate dynamical systems characterized by differential equations, in an interactive CAD and optimization providing faster on‐line solutions and speeding up design guidelines.

Details

Engineering Computations, vol. 25 no. 5
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 1 April 2003

Tadej Kosel, Igor Grabec and Franc Kosel

In Part I, an intelligent acoustic emission (AE) locator is described while the Part II discusses a blind source separation, time delay estimation and location of two…

Abstract

In Part I, an intelligent acoustic emission (AE) locator is described while the Part II discusses a blind source separation, time delay estimation and location of two continuous AE sources. AE analysis is used for characterization and location of developing defects in materials. AE sources often generate a mixture of various statistically independent signals. A difficult problem of AE analysis is a separation and characterization of signal components when the signals from various sources and the mode of mixing are unknown. Recently, blind source separation (BSS) by independent component analysis (ICA) has been used to solve these problems. The purpose of this paper is to demonstrate the applicability of ICA to locate two independent simultaneously active AE sources on an aluminum band specimen. The method is promising for non‐destructive testing of aircraft frame structures by AE analysis.

Details

Aircraft Engineering and Aerospace Technology, vol. 75 no. 2
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 1 February 2002

Tadej Kosel and Igor Grabec

Acoustic emission analysis (AE) is used for characterization and location of developing defects in materials. AE sources often generate a mixture of various statistically…

Abstract

Acoustic emission analysis (AE) is used for characterization and location of developing defects in materials. AE sources often generate a mixture of various statistically independent signals. One difficult problem of AE analysis is the separation and characterization of signal components when the signals from various sources and the way in which the signals were mixed are unknown. Recently, blind source separation (BSS) by independent component analysis (ICA) has been used to solve these problems. The main purpose of this paper is to demonstrate the applicability of ICA to time‐delay estimation of two independent continuous AE sources on an aluminum beam. It is shown that it is possible to estimate time delays by ICA, and thus to locate two independent simultaneously emitted sources.

Details

Aircraft Engineering and Aerospace Technology, vol. 74 no. 1
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 1 October 2005

D. Roy Mahapatra, S. Suresh, S.N. Omkar and S. Gopalakrishnan

To develop a new method for estimation of damage configuration in composite laminate structure using acoustic wave propagation signal and a reduction‐prediction neural…

Abstract

Purpose

To develop a new method for estimation of damage configuration in composite laminate structure using acoustic wave propagation signal and a reduction‐prediction neural network to deal with high dimensional spectral data.

Design/methodology/approach

A reduction‐prediction network, which is a combination of an independent component analysis (ICA) and a multi‐layer perceptron (MLP) neural network, is proposed to quantify the damage state related to transverse matrix cracking in composite laminates using acoustic wave propagation model. Given the Fourier spectral response of the damaged structure under frequency band‐selective excitation, the problem is posed as a parameter estimation problem. The parameters are the stiffness degradation factors, location and approximate size of the stiffness‐degraded zone. A micro‐mechanics model based on damage evolution criteria is incorporated in a spectral finite element model (SFEM) for beam type structure to study the effect of transverse matrix crack density on the acoustic wave response. Spectral data generated by using this model is used in training and testing the network. The ICA network called as the reduction network, reduces the dimensionality of the broad‐band spectral data for training and testing and sends its output as input to the MLP network. The MLP network, in turn, predicts the damage parameters.

Findings

Numerical demonstration shows that the developed network can efficiently handle high dimensional spectral data and estimate the damage state, damage location and size accurately.

Research limitations/implications

Only numerical validation based on a damage model is reported in absence of experimental data. Uncertainties during actual online health monitoring may produce errors in the network output. Fault‐tolerance issues are not attempted. The method needs to be tested using measured spectral data using multiple sensors and wide variety of damages.

Practical implications

The developed network and estimation methodology can be employed in practical structural monitoring system, such as for monitoring critical composite structure components in aircrafts, spacecrafts and marine vehicles.

Originality/value

A new method is reported in the paper, which employs the previous works of the authors on SFEM and neural network. The paper addresses the important problem of high data dimensionality, which is of significant importance from practical engineering application viewpoint.

Details

Engineering Computations, vol. 22 no. 7
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 5 June 2009

Anas N. Al‐Rabadi

New approaches for non‐classical neural‐based computing are introduced. The developed approaches utilize new concepts in three‐dimensionality, invertibility and…

Abstract

Purpose

New approaches for non‐classical neural‐based computing are introduced. The developed approaches utilize new concepts in three‐dimensionality, invertibility and reversibility to perform the required neural computing. The various implementations of the new neural circuits using the introduced paradigms and architectures are presented, several applications are shown, and the extension for the utilization in neural‐systolic computing is also introduced.

Design/methodology/approach

The new neural paradigms utilize new findings in computational intelligence and advanced logic synthesis to perform the functionality of the basic neural network (NN). This includes the techniques of three‐dimensionality, invertibility and reversibility. The extension of implementation to neural‐systolic computing using the introduced reversible neural‐systolic architecture is also presented.

Findings

Novel NN paradigms are introduced in this paper. New 3D paradigm of NL circuits called three‐dimensional inverted neural logic (3DINL) circuits is introduced. The new 3D architecture inverts the inputs and weights in the standard neural architecture: inputs become bases on internal interconnects, and weights become leaves of the network. New reversible neural network (RevNN) architecture is also introduced, and a RevNN paradigm using supervised learning is presented. The applications of RevNN to multiple‐output feedforward discrete plant control and to reversible neural‐systolic computing are also shown. Reversible neural paradigm that includes reversible neural architecture utilizing the extended mapping technique with an application to the reversible solution of the maze problem using the reversible counterpropagation NN is introduced, and new neural paradigm of reversibility in both architecture and training using reversibility in independent component analysis is also presented.

Originality/value

Since the new 3D NNs can be useful as a possible optimal design choice for compacting a learning (trainable) circuit in 3D space, and because reversibility is essential in the minimal‐power computing as the reduction of power consumption is a main requirement for the circuit synthesis of several emerging technologies, the introduced methods for non‐classical neural computation are new and interesting for the design of several future technologies that require optimal design specifications such as three‐dimensionality, regularity, super‐high speed, minimum power consumption and minimum size such as in low‐power control, adiabatic signal processing, quantum computing, and nanotechnology.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 2 no. 2
Type: Research Article
ISSN: 1756-378X

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Content available
Article
Publication date: 1 March 2002

Alex M. Andrew

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Abstract

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Kybernetes, vol. 31 no. 2
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 1 May 2005

Shuxue Ding, Andrzej Cichocki, Jie Huang and Daming Wei

We present an approach for blind separation of acoustic sources produced from multiple speakers mixed in realistic room environments. We first transform recorded signals…

Abstract

We present an approach for blind separation of acoustic sources produced from multiple speakers mixed in realistic room environments. We first transform recorded signals into the time‐frequency domain to make mixing become instantaneous. We then separate the sources in each frequency bin based on an independent component analysis (ICA) algorithm. For the present paper, we choose the complex version of fixedpoint iteration (CFPI), i.e. the complex version of FastICA, as the algorithm. From the separated signals in the time‐frequency domain, we reconstruct output‐separated signals in the time domain. To solve the so‐called permutation problem due to the indeterminacy of permutation in the standard ICA, we propose a method that applies a special property of the CFPI cost function. Generally, the cost function has several optimal points that correspond to the different permutations of the outputs. These optimal points are isolated by some non‐optimal regions of the cost function. In different but neighboring bins, optimal points with the same permutation are at almost the same position in the space of separation parameters. Based on this property, if an initial separation matrix for a learning process in a frequency bin is chosen equal to the final separation matrix of the learning process in the neighboring frequency bin, the learning process automatically leads us to separated signals with the same permutation as that of the neighbor frequency bin. In each bin, but except the starting one, by chosen the initial separation matrix in such a way, the permutation problem in the time domain reconstruction can be avoided. We present the results of some simulations and experiments on both artificially synthesized speech data and real‐world speech data, which show the effectiveness of our approach.

Details

International Journal of Pervasive Computing and Communications, vol. 1 no. 2
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 28 October 2014

Ryszard Szupiluk and Tomasz Ząbkowski

The purpose of this paper is to propose a noise identification method for data without temporal structure, in which application of typical mathematical white or colored…

Abstract

Purpose

The purpose of this paper is to propose a noise identification method for data without temporal structure, in which application of typical mathematical white or colored noise models is very limited due to observation order requirements. The method is used to identify the destructive elements and to eliminate them what finally brings prediction improvement.

Design/methodology/approach

The paper concerns noise detection problem presented in the framework of ensemble methods via blind signals separation. The authors utilize the Extended Generalized Lambda Distribution (EGLD) model to compare the signals with the target.

Findings

The authors proposed novel signals similarity measure which is based on the EGLD system. The authors showed that it can be applied for data with or without time structure, as well as for data which are mutually uncorrelated. It turned out that method is effective for noise identification and can be an alternative, in many cases, to correlation approach, particularly for noise identification problems.

Originality/value

In this method the improvement of prediction results is associated with elimination of the real physical factors rather than mathematical averaging in terms of arbitrary assumed distributions. In this approach, it does not matter what is the structure of aggregated models, what significantly distinct this approach from such techniques as boosting or bagging, in which the aggregation process applies to the models of similar structure. For this reason the methodology is focussed on physical noises elimination from predictions and it is complementary to the other ensemble approaches.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 33 no. 6
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 13 June 2008

Chih‐Fong Tsai and David C. Yen

Image classification or more specifically, annotating images with keywords is one of the important steps during image database indexing. However, the problem with current…

Abstract

Purpose

Image classification or more specifically, annotating images with keywords is one of the important steps during image database indexing. However, the problem with current research in terms of image retrieval is more concentrated on how conceptual categories can be well represented by extracted, low level features for an effective classification. Consequently, image features representation including segmentation and low‐level feature extraction schemes must be genuinely effective to facilitate the process of classification. The purpose of this paper is to examine the effect on annotation effectiveness of using different (local) feature representation methods to map into conceptual categories.

Design/methodology/approach

This paper compares tiling (five and nine tiles) and regioning (five and nine regions) segmentation schemes and the extraction of combinations of color, texture, and edge features in terms of the effectiveness of a particular benchmark, automatic image annotation set up. Differences between effectiveness on concrete or abstract conceptual categories or keywords are further investigated, and progress towards establishing a particular benchmark approach is also reported.

Findings

In the context of local feature representation, the paper concludes that the combined color and texture features are the best to use for the five tiling and regioning schemes, and this evidence would form a good benchmark for future studies. Another interesting finding (but perhaps not surprising) is that when the number of concrete and abstract keywords increases or it is large (e.g. 100), abstract keywords are more difficult to assign correctly than the concrete ones.

Research limitations/implications

Future work could consider: conduct user‐centered evaluation instead of evaluation only by some chosen ground truth dataset, such as Corel, since this might impact effectiveness results; use of different numbers of categories for scalability analysis of image annotation as well as larger numbers of training and testing examples; use of Principle Component Analysis or Independent Component Analysis, or indeed machine learning techniques for low‐level feature selection; use of other segmentation schemes, especially more complex tiling schemes and other regioning schemes; use of different datasets, use of other low‐level features and/or combination of them; use of other machine learning techniques.

Originality/value

This paper is a good start for analyzing the mapping between some feature representation methods and various conceptual categories for future image annotation research.

Details

Library Hi Tech, vol. 26 no. 2
Type: Research Article
ISSN: 0737-8831

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