Individual alpha frequency (IAF) is a promising electrophysiological marker of interindividual differences in cognitive function. IAF has been linked with trait‐like differences in information ...processing and general intelligence, and provides an empirical basis for the definition of individualized frequency bands. Despite its widespread application, however, there is little consensus on the optimal method for estimating IAF, and many common approaches are prone to bias and inconsistency. Here, we describe an automated strategy for deriving two of the most prevalent IAF estimators in the literature: peak alpha frequency (PAF) and center of gravity (CoG). These indices are calculated from resting‐state power spectra that have been smoothed using a Savitzky‐Golay filter (SGF). We evaluate the performance characteristics of this analysis procedure in both empirical and simulated EEG data sets. Applying the SGF technique to resting‐state data from n = 63 healthy adults furnished 61 PAF and 62 CoG estimates. The statistical properties of these estimates were consistent with previous reports. Simulation analyses revealed that the SGF routine was able to reliably extract target alpha components, even under relatively noisy spectral conditions. The routine consistently outperformed a simpler method of automated peak detection that did not involve spectral smoothing. The SGF technique is fast, open source, and available in two popular programming languages (MATLAB, Python), and thus can easily be integrated within the most popular M/EEG toolsets (EEGLAB, FieldTrip, MNE‐Python). As such, it affords a convenient tool for improving the reliability and replicability of future IAF‐related research.
Cloud computing providers face several challenges in precisely forecasting large-scale workload and resource time series. Such prediction can help them to achieve intelligent resource allocation for ...guaranteeing that users’ performance needs are strictly met with no waste of computing, network and storage resources. This work applies a logarithmic operation to reduce the standard deviation before smoothing workload and resource sequences. Then, noise interference and extreme points are removed via a powerful filter. A Min–Max scaler is adopted to standardize the data. An integrated method of deep learning for prediction of time series is designed. It incorporates network models including both bi-directional and grid long short-term memory network to achieve high-quality prediction of workload and resource time series. The experimental comparison demonstrates that the prediction accuracy of the proposed method is better than several widely adopted approaches by using datasets of Google cluster trace.
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•An adaptive Savitzky-Golay (S-G) filtering algorithm was developed.•The self-adjusting and follow-up actions of deep learning network can effectively solve the blindness of selecting ...the input filter parameters.•A spectroscopic sensor was bulit for nitrogen oxide (NO2).•CW-QCLs are promising spectroscopic sources for high-resolution laser absorption spectroscopy.
An improved Savitzky–Golay (S–G) filtering algorithm was developed to denoise the absorption spectroscopy of nitrogen oxide (NO2). A deep learning (DL) network was introduced to the traditional S–G filtering algorithm to adjust the window size and polynomial order in real time. The self-adjusting and follow-up actions of DL network can effectively solve the blindness of selecting the input filter parameters in digital signal processing. The developed adaptive S–G filter algorithm is compared with the multi-signal averaging filtering (MAF) algorithm to demonstrate its performance. The optimized S–G filtering algorithm is used to detect NO2 in a mid-quantum-cascade-laser (QCL) based gas sensor system. A sensitivity enhancement factor of 5 is obtained, indicating that the newly developed algorithm can generate a high-quality gas absorption spectrum for applications such as atmospheric environmental monitoring and exhaled breath detection.
Raman spectroscopy can be effectively used for detection and analysis of chemical agents that are serious threats in modern warfare, but the detection and analysis performance is prone to ...deterioration due to noise. The existing denoising technique has limitations that there is no criterion for selecting the window length and that the filtering distorts the peaks, key features for Raman spectral data analysis. To overcome such limitations, in this paper, we propose the peak‐aware adaptive denoising for Raman spectroscopy based on machine learning approach. The proposed technique utilizes the information of detected peaks to eliminate noise effectively using different window values optimal for each region in the Raman spectrum while preserving the shape of peaks. We conducted the various analyses and experiments, and the proposed technique showed a 28% lower Euclidean distance and a 48% lower Fréchet inception distance compared to the existing technique, meaning the proposed technique outperformed the existing one.
In this study, we propose the novel peak‐aware adaptive denoising technique tailored for Raman spectroscopy, aiming to overcome the limitations inherent in current methodologies that lack predefined criteria for selecting window length and may distort spectral peaks. Our proposed technique leverages machine learning and harnesses information derived from detected peaks to systematically eliminate noise while applying the optimal window length for each spectrum region, effectively reducing noise and adeptly preserving peak shapes. Comparative analyses show that our technique demonstrates a noteworthy 28% reduction in Euclidean distance and a substantial 48% decrease in Fréchet inception distance when contrasted with existing denoising techniques.
This article introduces a novel reference current generation technique based on the Savitzky-Golay filter (SGF) for shunt active power filter (SAPF). Although the existing and conventional SAPF can ...mitigate harmonics under grid disturbances and nonlinear load environments, they have numerous performance deficiencies including computational complexity, average filtration ability, and slow dynamic response. The proposed SAPF based on SGF can generate a harmonic-free fundamental reference current that can eventually improve the overall harmonic profile and performance compared to existing SAPFs. In addition, an SGF-based phase-locked loop (PLL) is introduced to generate reference phases directly from the distorted grid voltages by retaining the fundamental information of the signal. Thus, it does not induce any phase delay or distortions in the output, unlike the conventional PLLs utilized in the SAPFs. Comparative analysis of performance evaluation is carried out with other SAPFs, and the findings represent the principal achievements of this article. This includes a substantial improvement in performance and harmonic profile under transient conditions. The results are further validated through simulation analysis and hardware implementation.
Prediction of state of health (SOH) and remaining useful life (RUL) of lithium batteries (LIBs) are of great significance to the safety management of new energy systems. In this paper, time series ...features highly related to the RUL are mined from easily available battery parameters of voltage, current and temperature. By combining Savitzky-Golay (SG) filter with gated recurrent unit (GRU) neural networks, we developed a prediction model for the SOH and RUL of LIBs. The SG filter is used to denoise the aging features and the GRU model is used to predict RUL of LIBs with different charging strategies. Experiments and verification show that the proposed SG-GRU prediction model is an effective method for different applications, which could give out accurate prediction results under various charging strategies and different batteries with fast prediction response. The prediction model can accurately track the nonlinear degradation trend of capacity during the whole cycle life, and the root mean square error of prediction can be controlled within 1%.
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•Accurate prediction of SOH and RUL of lithium batteries is a challenging problem.•Time series characteristics related to the RUL are mined from battery parameters.•A prediction model for the SOH and RUL of LIBs is established.•The prediction model can accurately track the nonlinear degradation trend of capacity.•The root mean square error of prediction is controlled within 0.01.
Water environment time series prediction is important to efficient water resource management. Traditional water quality prediction is mainly based on linear models. However, owing to complex ...conditions of the water environment, there is a lot of noise in the water quality time series, which will seriously affect the accuracy of water quality prediction. In addition, linear models are difficult to deal with the nonlinear relations of data of time series. To address this challenge, this work proposes a hybrid model based on a long short-term memory-based encoder-decoder neural network and a Savitzky-Golay filter. Among them, the filter of Savitzky-Golay can eliminate the potential noise in the time series of water quality, and the long short-term memory can investigate nonlinear characteristics in a complicated water environment. In this way, an integrated model is proposed and effectively obtains statistical characteristics. Realistic data-based experiments prove that its prediction performance is better than its several state-of-the-art peers.
Coronary artery disease is a common chronic disease, also known as ischemic heart disease, which is a cardiac dysfunction caused by the insufficient blood supply to the heart and kills countless ...people every year. In recent years, coronary artery disease ranks first among the world’s top ten causes of death. Cardiac auscultation is still an important examination for diagnosing heart diseases. Many heart diseases can be diagnosed effectively by auscultation. However, cardiac auscultation relies on the subjective experience of physicians. To provide an objective diagnostic means and assist physicians in the diagnosis of heart sounds at a clinic, this study uses phonocardiograms to build an automatic classification model. This study proposes an automatic classification approach for phonocardiograms using deep learning and ensemble learning with a Savitzky–Golay filter. The experimental results showed that the proposed method is very competitive, and showed that the performance of the phonocardiogram classification model in hold out testing was 86.04% MAcc (86.46% sensitivity, 85.63% specificity), and in ten-fold cross validation it was 89.81% MAcc (91.73% sensitivity, 87.91% specificity). These two experimental results are all better than two state-of-art algorithms and show the potential to apply in real clinic situation.
•This paper focus on to construct a phonocardiogram automatic classification model.•An ensemble convolutional neural network with Savitzky–Golay filter is utilized.•The experimental results showed that the proposed method is very competitive.•This method will assist physicians in the diagnosis of heart sounds.
A phase-locked loop (PLL) based on synchronous reference frame (SRF) is a standard PLL that has a simple construction and performs well under undisrupted grid conditions. However, in imbalanced and ...harmonically disturbed environments, the fundamental grid voltage characteristics deteriorate greatly. Integrating a variety of filters in the control algorithm has been suggested as a solution to address this issue. An optimal low-pass filter characteristic can be achieved with the Savitzky-Golay filter (SGF), which is a zero-phase smoothing filter. The primary objective of this article is to offer a new small-signal design of SGF-based filtration in the SRF-PLL. Using this model, a researcher can easily evaluate the SGF-PLL's behavior and stability condition. A comparative analysis on performance evaluation is carried out with other PLLs, and the findings represent the principal achievements of this article. This includes a substantial improvement in dynamic response and harmonic profile under transient conditions because of SGF's superior filtration ability, which is obtained through the convolution process. Moreover, the results are also free of any unnecessary phase delay or distortions due to SGF's zero-phase filtering characteristics. The results are further validated through mathematical analysis and hardware implementation.
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