On the Memory Cost of EMD Algorithm

Young, Hsu-Wen Vincent; Lin, Yu Chuan; Wang, Yung-Hung

doi:10.1109/access.2022.3218417

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…Empirical modal decomposition (EMD) 21,22 is commonly used to analyze non-stationary nonlinear sequences, but the number of intrinsic mode function (IMF) components of EMD cannot be set artificially, and modal aliasing easily occurs among the decomposed signals. In order to solve these problems, the work reported here used VMD to process the original signal.…”

Section: Extraction Of Discriminating Featuresmentioning

confidence: 99%

Accurate recognition of rice plants based on visual and tactile sensing

Chen,

Dang,

Zhang

et al. 2024

J Sci Food Agric

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BACKGROUNDCrop recognition is the basis of intelligent agricultural machine operations. Visual perception methods have achieved high recognition accuracy. However, the reliability of such methods is difficult to guarantee because of the complex environment of paddy fields. Tactile sensing methods are not affected by background or environmental interference, and have high reliability. However, in an ideal environment, the recognition accuracy is not as high as that of the visual method.RESULTSTo balance the accuracy and reliability of rice plant recognition, a combined visual–tactile method was proposed in this study. A rice plant recognition device was developed with a poly(vinylidene fluoride) sensor embedded inside the device as a tactile perceptron and a graphic designed as a visual perceptron. The primary role of the tactile perceptron is to initially recognize rice plants and provide a time point for image capture for visual perception. The main role of the visual perceptron is to extract features from the captured images and recognize rice plants again. The results of tactile and visual recognition were eventually fused to achieve accurate recognition of rice plants.CONCLUSIONThe contact speed between the recognition perceptron and rice–weed was selected for the field performance test based on the real situation of paddy field operation. The results showed that the accuracy and reliability of rice plant recognition decreased as the travelling speed of the paddy field operation machine increased. The results of this study provide a basis for intelligent farm machinery operations in rice fields. © 2024 Society of Chemical Industry.

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Section: Extraction Of Discriminating Featuresmentioning

confidence: 99%

Accurate recognition of rice plants based on visual and tactile sensing

Chen,

Dang,

Zhang

et al. 2024

J Sci Food Agric

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“…However, these methods are all based on fixed transform basis functions and cannot adaptively process structurally complex modulated echo data. The Empirical Mode Decomposition (EMD) [9] method based on signal decomposition theory can adaptively decompose the signal itself, dividing it into a series of Intrinsic Mode Functions (IMFs) that represent local energy anomalies within different frequency bands. However, this method is prone to mode mixing, boundary effects, and other issues.…”

Section: Introductionmentioning

confidence: 99%

Research on Modulation Signal Denoising Method Based on Improved Variational Mode Decomposition

Mo,

Lin,

Niu

et al. 2024

JERA

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In order to further analyze the micro-motion modulation signals generated by rotating components and extractmicro-motion features, a modulation signal denoising algorithm based on improved variational mode decomposition (VMD)is proposed. To improve the time-frequency performance, this method decomposes the data into narrowband signalsand analyzes the internal energy and frequency variations within the signal. Genetic algorithms are used to adaptivelyoptimize the mode number and bandwidth control parameters in the process of VMD. This approach aims to obtain theoptimal parameter combination and perform mode decomposition on the micro-motion modulation signal. The optimalmode number and quadratic penalty factor for VMD are determined. Based on the optimal values of the mode numberand quadratic penalty factor, the original signal is decomposed using VMD, resulting in optimal mode number intrinsicmode function (IMF) components. The effective modes are then reconstructed with the denoised modes, achieving signaldenoising. Through experimental data verification, the proposed algorithm demonstrates effective denoising of modulationsignals. In simulation data validation, the algorithm achieves the highest signal-to-noise ratio (SNR) and exhibits the bestperformance.

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“…While the Wavelet Packet Transform (WPT) [14] surpasses the limitation of the WT by decomposing signals beyond the low-frequency band, it remains powerless in addressing the inherently subjective process of basis function selection. In contrast to WT and WPT, which require the preselection of wavelet basis functions, EMD [15] is an adaptive signal analysis method. However, it is imperative to acknowledge the presence of mode mixing and endpoint effect [16].…”

Section: Introductionmentioning

confidence: 99%

A Novel Harmonic Detection Method for Microgrids Based on Variational Mode Decomposition and Improved Harris Hawks Optimization Algorithm

Wang,

Ren

et al. 2024

IEEE Access

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In the pursuit of enhancing harmonic detection precision within microgrids, this paper introduces a pioneering algorithm, VMD-DCHHO-HD, which amalgamates Variational Mode Decomposition (VMD) with an advanced Harris Hawk Optimization algorithm characterized by dynamic opposition-based learning and Cauchy mutation (DCHHO). This study establishes a fitness function based on Shannon entropy, thereby minimizing the Local Minimum Entropy (LME) as the optimization objective for DCHHO. Building upon this, the VMD crucial parameters are efficiently identified using the enhanced HHO algorithm (DCHHO), enabling precise decomposition of complex voltage signals. The proposed method effectively addresses issues commonly encountered in traditional Empirical Mode Decomposition (EMD) during harmonic analysis, such as mode mixing, endpoint effects, and significant errors. Notably, it adeptly captures harmonic components spanning diverse frequencies, offering a nuanced solution to common pitfalls in traditional methodologies. In simulation experiments, VMD-DCHHO-HD showcases remarkable proficiency in extracting microgrid voltage signals, excelling at discerning high-order, low-amplitude harmonic components amid noise. The algorithm's superior precision and heightened reliability, as affirmed by comparative analyses against existing methods, position it as an advanced tool for precise and robust harmonic analysis in microgrid systems.

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On the Memory Cost of EMD Algorithm

Cited by 6 publications

References 34 publications

Accurate recognition of rice plants based on visual and tactile sensing

Accurate recognition of rice plants based on visual and tactile sensing

Research on Modulation Signal Denoising Method Based on Improved Variational Mode Decomposition

A Novel Harmonic Detection Method for Microgrids Based on Variational Mode Decomposition and Improved Harris Hawks Optimization Algorithm

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