Quantitative Independent Component Selection Using Attractor Analysis for Noise Reduction in Magnetocardiogram Signals

Kobayashi, Kei; Iwai, Morio

doi:10.1109/tmag.2018.2845903

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…As room temperature detection technology for MCG is still being developed, there is no open data source. Thus, different MCG measurement methods have been reported based on different detection conditions, and even based on different evaluation criteria, [32][33][34] making it difficult to compare such methods without complete reproduction of the other systems.…”

Section: = -+mentioning

confidence: 99%

Coherent detection stochastic resonance assisted biomagnetometer for measuring magnetocardiography at room temperature

Liao

Jin

Kuwahata

et al. 2021

Appl. Phys. Express

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A method of magnetocardiography (MCG) measurement using an overdamped bistable model based stochastic resonance (SR) technique for advancing biomagnetic sensors is proposed. To determine the system parameters for SR, an evolutionary algorithm combined with the coherent detection (CD) method is applied. We acquire and process an animal MCG at room temperature, detected by a commercially available optically pumped magnetometer. A comparison with a lowpass filter and the traditional CD method verified the ability of the proposed method to obtain a higher signal-to-noise ratio and peak value of the MCG, whilst retaining sufficient dynamic information.

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Section: = -+mentioning

confidence: 99%

Coherent detection stochastic resonance assisted biomagnetometer for measuring magnetocardiography at room temperature

Liao

Jin

Kuwahata

et al. 2021

Appl. Phys. Express

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“…Addressing this limitation, this study investigates the simultaneous correlation between source and load power in a microgrid and weather features, conducting research on the joint ultra-short-term prediction of source and load power in a microgrid. Additionally, commonly used dimensionality reduction algorithms include Principal Component Analysis (PCA) (Wang et al, 2023), Independent Component Analysis (ICA) (Kobayashi and Iwai, 2018), Factor Analysis (FA) (Ramirez et al, 2019;Wu et al, 2024), etc. FA merges numerous features into several representative common factors to extract latent factors among features, accurately capturing the relevant information in the data (Zhou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Ultra-short-term prediction of microgrid source load power considering weather characteristics and multivariate correlation

Huang,

Sun,

Shao

et al. 2024

Front. Energy Res.

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Multiple microgrids interconnect to form a microgrid cluster. To fully exploit the comprehensive benefits of the microgrid cluster, it is imperative to optimize dispatch based on the matching degree between the sources and loads of each microgrid. The power of distributed energy sources such as wind and photovoltaic systems and the sensitive loads in microgrids is related to the regional weather characteristics. Given the relatively small geographical scope of microgrid areas and the fact that distributed energy sources and loads within the grid share the same weather characteristics, simultaneous ultra-short-term forecasting of power for both sources and loads is essential in the same environmental context. Firstly, the introduction of the multi-variable uniform information coefficient (MV-UIC) is proposed for extracting the correlation between weather characteristics and the sequences of source and load power. Subsequently, the application of factor analysis (FA) is introduced to reduce the dimensionality of input feature variables. Drawing inspiration from the concept of combination forecasting models, a combined forecasting model based on Error Back Propagation Training (BP), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory Neural Network (BiLSTM) is constructed. This model is established on the MV-UIC-FA foundation for the joint ultra-short-term forecasting of source and load power in microgrids. Simulation is conducted using the DTU 7K 47-bus system as an example to analyze the accuracy, applicability, and effectiveness of the proposed joint forecasting method for sources and loads.

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Machine Learning-Based Automated Method for Effective De-noising of Magnetocardiography Signals Using Independent Component Analysis

Kesavaraja,

Sengottuvel,

Patel

et al. 2024

Circuits Syst Signal Process

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Quantitative Independent Component Selection Using Attractor Analysis for Noise Reduction in Magnetocardiogram Signals

Cited by 4 publications

References 6 publications

Coherent detection stochastic resonance assisted biomagnetometer for measuring magnetocardiography at room temperature

Coherent detection stochastic resonance assisted biomagnetometer for measuring magnetocardiography at room temperature

Ultra-short-term prediction of microgrid source load power considering weather characteristics and multivariate correlation

Machine Learning-Based Automated Method for Effective De-noising of Magnetocardiography Signals Using Independent Component Analysis

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