Hardware architecture design for complementary ensemble empirical mode decomposition algorithm

Das, Kaushik; Pradhan, Sambhu Nath

doi:10.1016/j.vlsi.2023.03.011

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Based on the EEMD method, the complementary EEMD (CEEMD) method is proposed to reduce the residual noise by adding opposing white noise pairs to the signal. The CEEMD method is further extended to the multidimensional CEEMD (MCEEMD) method for multi-dimensional signal analysis [41,42]. In this work, the MCEEMD method is employed to decompose thermal image sequences into a series of IMFs.…”

Section: Jinst 18 T11002mentioning

confidence: 99%

Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

Zhang,

Li,

Wang

et al. 2023

J. Inst.

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Due to high-temperature resistance, high strength, and excellent fatigue resistance, composite materials are widely used in automotive manufacturing, aerospace, infrastructure and other fields. Consequently, the demand for defect detection of composite materials is also increasing. As a non-destructive testing technique, the active infrared thermography, which can achieve full-field defect detection, is suitable for defect detection of composite materials. However, this method is susceptible to noises caused by the environment and heating sources. In order to solve the problem of the defect signal being submerged by these noises, a multi-dimensional complementary ensemble empirical mode decomposition (MCEEMD) algorithm is introduced in this paper. This method can decompose the signal into the low-frequency background noise, the high-frequency heating noise, and useful defect signals, and these noises can be easily removed to improve the contrast to noise ratio (CNR) of defect images. Based on this proposed method, a defect detection experiment on the carbon fiber reinforced plastic (CFRP) is performed in this paper, and experimental results show that the method can effectively remove environmental noise and heating noise, and it can detect 11 out of 12 defects on the CFRP sample with an average CNR of 9.107. Compared with the traditional differential absolute contrast method, this method can detect one additional small defect with the aspect ratio of 1.67 and one deep defect with a depth of 2 mm.

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Section: Jinst 18 T11002mentioning

confidence: 99%

Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

Zhang,

Li,

Wang

et al. 2023

J. Inst.

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Hardware implementation of bearing fault diagnosis using empirical mode decomposition

Ninawe,

Deshmukh

2024

Nondestructive Testing and Evaluation

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Artificial Intelligence Towards Enhancing the Risk Management Practices During the Design Process

Algheetany,

Othman,

Alamoudy

2024

IOP Conf. Ser.: Earth Environ. Sci.

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The design process across various industries involves intricate decision-making, problem-solving, and consideration of multiple factors. However, risks during the design process can jeopardize project success and lead to costly errors. Traditional risk management approaches in design often rely on manual & computerized analysis, which is time-consuming, limited in scope, and prone to human biases. The emergence of artificial intelligence (AI) presents an opportunity to revolutionize risk management in design. AI algorithms can process vast amounts of data, identify patterns, and provide predictive insights, enhancing risk identification and mitigation. This research aims to investigate the relationship between design risks and AI towards enhancing the risk management process. Using a mixed methodology approach, a qualitative method was used through investigating previous literature to identify traditional risk management limitations, design risks and AI tools and methods. Secondly, relationship between identified risks and AI tools was proposed. This was followed by quantitative method through case studies analysis that assess the validity of the proposed relationship. The goal is to establish a new paradigm where AI and risk management converge to create a future where risks that occur during the design process can effectively be identified and managed, fostering innovation and improving project outcomes.

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Hardware architecture design for complementary ensemble empirical mode decomposition algorithm

Cited by 5 publications

References 29 publications

Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

Hardware implementation of bearing fault diagnosis using empirical mode decomposition

Artificial Intelligence Towards Enhancing the Risk Management Practices During the Design Process

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