Optimal basis pursuit based on jaya optimization for adaptive fourier decomposition

Kirkbas, Ali; Kızılkaya, Aydın; Boğar, Eşref

doi:10.1109/tsp.2017.8076045

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…The most important advantage of the Jaya optimization method is that there are no parameters except the number of populations and the maximum number of iterations to be set. Thus, it can provide faster and more accurate results than other heuristic methods in solving the problems …”

Section: Jaya Algorithm Application For the Ems Of The Evmentioning

confidence: 99%

“…Thus, it can provide faster and more accurate results than other heuristic methods in solving the problems. 33 In the Jaya optimization method, fitness values are calculated for the solution candidates with respect to the determined object function in each step. Each solution candidate is moved at random speeds in order to approach the candidate who has achieved best fitness and move away from the candidate who has achieved the worst fitness.…”

Section: Jaya Algorithm Application For the Ems Of The Evmentioning

confidence: 99%

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Jaya algorithm‐based energy management system for battery‐ and ultracapacitor‐powered ultralight electric vehicle

Demircali

Köroğlu

2020

Int J Energy Res

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Summary To improve the driving performance of the electric vehicles, batteries or ultracapacitors (UCs) are frequently preferred in the energizing systems. In hybrid structures with multiple supply sources, an energy management system (EMS) is needed to improve the system efficiency, and to provide the optimum power sharing between a battery and a UC. The purpose of this study is to investigate the effectiveness of the Jaya optimization method for the urban use of the EMS of an ultralight electric vehicle powered by battery/UC. The performance of the proposed method is compared with dynamic programming (DP) that is one of the global optimization methods and particle swarm optimization (PSO) that is one of the other heuristic methods for real‐time applications. The simulation results show that Jaya‐EMS approached 3.1% to the DP, which yields the optimum result with respect to the total energy loss. In addition, the proposed method yields a loss of less than 1.9% from the PSO‐EMS. If all the above situations are considered, the proposed EMS method has less lossy alternative solution for the real‐time applications.

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Section: Jaya Algorithm Application For the Ems Of The Evmentioning

confidence: 99%

Section: Jaya Algorithm Application For the Ems Of The Evmentioning

confidence: 99%

Jaya algorithm‐based energy management system for battery‐ and ultracapacitor‐powered ultralight electric vehicle

Demircali

Köroğlu

2020

Int J Energy Res

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“…Apart from China and Asia, AFD has also achieved international influence. Interests, studies and applications of AFD are found in relevant literature, by Ph.D. thesis of F. D. Fulle at Michigan University on oxygenic photosynthesis; by A. Kirkbas et al on optimal basis pursuit based on jaya optimization for adaptive Fourier decomposition ( [44]); by V. Vatchev, on a class of intrinsic trigonometric mode polynomials ( [111]); by J. Mashreghi [17]); by L. Salomon on analysis of the anisotropy in image textures ( [98]); by F. Sakaguchi on the related integral-type method in higher order differential equations ( [99,100,101,102]); by P. León on instantaneous frequency estimation and representation of the audio signal through complex wavelet additive synthesis ( [46]); by F.E.…”

Section: Applicationsmentioning

confidence: 99%

A Novel Fourier Theory on Non-linear Phases and Applications

Qian

2018

Preprint

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Positive time varying frequency representation for transient signals has been a hearty desire of signal analysts due to its theoretical and practical importance. During approximately the last two decades there has formulated a signal decomposition and reconstruction method rooted in harmonic and complex analysis giving rise to the desired signal representation. The method decomposes any signal into a few basic signals that possess positive instantaneous frequencies. The theory has profound relations with classical mathematics and can be generalized to signals defined in higher dimensional manifolds with vector and matrix values, and in particular, promotes rational approximation in higher dimensions. This article mainly serves as a survey. It also gives a new proof for a general convergence result, as well as a proof for the necessity of multiple selection of the parameters.Expositorily, for a given real-valued signal f one can associate it with a Hardy space function F whose real part coincides with f. Such function F has the form F = f + iHf, where H stands for the Hilbert transformation of the context. We develop fast converging expansions of F in orthogonal terms of the formwhere B k 's are also Hardy space functions but with the additional propertiesThe original real-valued function f is accordingly expandedwhich, besides the properties of ρ k and θ k given above, also satisfiesReal-valued functions f (t) = ρ(t) cos θ(t) that satisfy the condition ρ ≥ 0, θ ′ (t) ≥ 0, H(ρ cos θ)(t) = ρ(t) sin θ(t),

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“…In the above method, it is necessary to rely on manual experience to set the estimated signal-to-noise ratio, percentage root-mean-square difference, and other thresholds as the basis for determining the selection of the number of decomposition levels, which may easily lead to overdecomposition or underdecomposition of the signal if it is not properly selected. Literature [20] improved the AFD algorithm in terms of the computational complexity of the algorithm and proposed a Jaya-based AFD method to reduce the computational complexity, but the adaptive criterion of the decomposition level is not clearly given. Therefore, a feature extraction method-based improved adaptive Fourier decomposition (IAFD) and Teager-Kaiser energy operator (TKEO) is applied in rolling bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

Guo,

Ma,

Xiong

et al. 2024

Journal of Sensors

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The study of bearing fault feature extraction using adaptive Fourier decomposition (AFD) holds significant practical importance. However, AFD is constrained by its reliance on prior knowledge for determining decomposition levels, which can result in either underdecomposition or overdecomposition based on a single indicator. Consequently, an improved adaptive Fourier decomposition (IAFD) is proposed. First, a combined weight index called SP is constructed, and the whale optimization algorithm is employed to optimize the SP weight parameter. Second, the IAFD decomposition levels can be adaptively determined using the optimized SP. Finally, a feature extraction method-based IAFD and Teager–Kaiser energy operator is applied in rolling bearing fault diagnosis. Case studies on the Case Western Reserve University and self-made KUST-SY datasets validate the effectiveness of the proposed method.

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Optimal basis pursuit based on jaya optimization for adaptive fourier decomposition

Cited by 7 publications

References 4 publications

Jaya algorithm‐based energy management system for battery‐ and ultracapacitor‐powered ultralight electric vehicle

Jaya algorithm‐based energy management system for battery‐ and ultracapacitor‐powered ultralight electric vehicle

A Novel Fourier Theory on Non-linear Phases and Applications

Fault Feature Extraction Method of Rolling Bearing Based on IAFD and TKEO

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