Blind Source Separation Research Based on the Feature Distance Using Evolutionary Algorithms

Yang, Yang; Wang, Xiuqin; Zhang, Di

doi:10.20855/ijav.2014.19.4360

Cited by 3 publications

(7 citation statements)

References 15 publications

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“…AVCC for the PSO and the proposed algorithms This work has been compared to the previous studies that treat the problem of BSS separation. It has been clearly noticed that the proposed method HEPSO outperforms the GA+ kurtosis [21] in terms of SDR (shown in HEPSO achieves an excellent performance (100%). Table 5 presents more details about the comparison accuracy of the proposed work with GA+ kurtosis [21] Table 5.…”

Section: Methodsmentioning

confidence: 89%

“…It has been clearly noticed that the proposed method HEPSO outperforms the GA+ kurtosis [21] in terms of SDR (shown in HEPSO achieves an excellent performance (100%). Table 5 presents more details about the comparison accuracy of the proposed work with GA+ kurtosis [21] Table 5. AVCC for the GA+ kurtosisand the proposed algorithms…”

Section: Methodsmentioning

confidence: 89%

“…In order to examine the performance of the proposed technique, we evaluate it using three performance indexes: Source to Distortion Ratio (SDR) [21], Absolute Value of Correlation Coefficients (AVCC)) [39] and Mean Square Error (MSE) [36]. They are, respectively defined as follows:…”

Section: Performance Measurementioning

confidence: 99%

“…Also, evolutionary algorithms are successfully applied to solve several optimization problems in diverse fields. Genetic algorithms (GA) [21]- [24], the Particle Swarm Optimization (PSO) algorithm [25]- [27] and the Artificial Bee Colony (ABC) algorithm [28] have been applied to find the optimal separation matrix. In this paper, the High Exploration PSO (HEPSO) algorithm [29] is used to optimize the objective function in order to estimate the coefficients of the separating matrix, to avoid local optima and to find the efficient global optimum.…”

Section: Introductionmentioning

confidence: 99%

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Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

2019

KSII TIIS

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Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

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Section: Methodsmentioning

confidence: 89%

Section: Methodsmentioning

confidence: 89%

Section: Performance Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

2019

KSII TIIS

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“…In order to overcome the drawbacks of ICA approach, many optimization methods have been developed, based on evolutionary algorithms. These algorithms have been extensively used for tackling BSS problems [5][6][7][8]. By using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), a blind separation method based on reducing mutual information has been introduced in [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm

Khalfa

Sahed

Kenane

et al. 2022

Eng. Technol. Appl. Sci. Res.

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Signal and image separation are extensively used in numerous imaging applications and communication systems. In this paper, a novel Blind Source Separation (BSS) approach, based on the Hybrid Firefly Particle Swarm Optimization (HFPSO), is proposed for separating mixed images. This approach processes the observed source without any prior knowledge about the model and the statistics of the source signal. The proposed method presents high robustness against local minima and converges quickly to the global minimum. Via numerical simulations, the proposed approach is tested and validated in comparison with standard Particle Swarm Optimization (PSO), Robust Independent Component Analysis (RobustICA), and Artificial Bee Colony (ABC) algorithms. The obtained results show that the presented technique outperforms the existing ones in terms of quality of image separation, the Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Moreover, the obtained results demonstrate that our approach provides also promising results in image separation from noisy mixtures.

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Blind Image Separation Using the JADE Method

Saeed¹,

Zohaib²

2022

The 1st International Conference on Computational Engineering and Intelligent Systems

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In this paper, we characterized and reviewed the emergence of fundamental and extended losses that limit the efficiency of a photovoltaic (PV) system. Although there is an upper theoretical bound to the power conversion efficiency of solar cells, i.e., the Shockley Queisser limit, in a practical environment, the consideration of inevitable losses in a whole PV system is imperative to optimally harvest solar energy. In this regard, this study quantifies the losses from a PV cell level to the whole PV system. It was perceived that reported losses on the PV cell level included the low energy bandgap, thermalization, recombination (surface and bulk recombination), optical absorption, space charge region, finite thickness, and metal contact loss, and it was determined that cutting techniques mainly constrained the power conversion efficiency of the solar cell. Furthermore, the detailed PV array losses were classified as mismatch power losses, dust accumulation losses, temperature effects, material quality losses, and ohmic wiring losses. The unavoidable system losses were quantified as inverter losses, maximum power point tracking losses, battery losses, and polarization losses. The study also provides insights into potential approaches to combat these losses and can become a useful guide to better visualize the overall phenomenology of a PV System.

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Blind Source Separation Research Based on the Feature Distance Using Evolutionary Algorithms

Cited by 3 publications

References 15 publications

Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

Blind Audio Source Separation Based On High Exploration Particle Swarm Optimization

A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm

Blind Image Separation Using the JADE Method

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