An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter

Azami, Hamed; Mohammadi, Karim; Bozorgtabar, Behzad

doi:10.4236/jsip.2012.31006

Cited by 123 publications

(57 citation statements)

References 14 publications

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“…This method is based on mathematical technique published by Savitzky andGolay in 1964 (Luo et al 2005;Savitzky and Golay 1964). A remarkable advantage of this filter is that it tends to keep features of the distribution such as extrema which are often flattened by other smoothing methods such as SMA (Azami et al 2012).…”

Section: Regression-based Smoothing Methodsmentioning

confidence: 99%

A comparison of methods for denoising of well test pressure data

Moosavi

Qajar

Riazi

2018

J Petrol Explor Prod Technol

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Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS-ARMA to real field data shows promising results.

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Section: Regression-based Smoothing Methodsmentioning

confidence: 99%

A comparison of methods for denoising of well test pressure data

Moosavi

Qajar

Riazi

2018

J Petrol Explor Prod Technol

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“…We apply the Savitzky–Golay smoothing filter [22] to eliminate short-term variations and wave artifacts without affecting the ‘sharp’ signal change points. This filter is not influenced by shifting effect which is important for detecting the true boundaries of segments [23]. We then adapted the Modified Varri method [24] to detect the amplitude-shift points of the RD signal that defines the boundary of primary segments.…”

Section: Methodsmentioning

confidence: 99%

Hierarchical Discovery of Large-scale and Focal Copy Number Alterations in Low-coverage Cancer Genomes

Khalil

Khyriem

Chattopadhyay

et al. 2019

Preprint

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AbstractMotivationDetection of copy number alterations (CNA) is critical to understand genetic diversity, genome evolution and pathological conditions such as cancer. Cancer genomes are plagued with widespread multi-level structural aberrations of chromosomes that pose challenges to discover CNAs of different length scales with distinct biological origin and function. Although several tools are available to identify CNAs using read depth (RD) of coverage, they fail to distinguish between large-scale and focal alterations due to inaccurate modeling of the RD signal of cancer genomes. These tools are also affected by RD signal variations, pronounced in low-coverage data, which significantly inflate false detection of change points and inaccurate CNA calling.ResultsWe have developed CNAtra to hierarchically discover and classify ‘large-scale’ and ‘focal’ copy number gain/loss from whole-genome sequencing (WGS) data. CNAtra provides an analytical and visualization framework for CNV profiling using single sequencing sample. CNAtra first utilizes multimodal distribution to estimate the copy number (CN) reference from the complex RD profile of the cancer genome. We utilized Savitzy-Golay filter and Modified Varri segmentation to capture the change points. We then developed a CN state-driven merging algorithm to identify the large segments with distinct copy number. Next, focal alterations were identified in each large segment using coverage-based thresholding to mitigate the adverse effects of signal variations. We tested CNAtra calls using experimentally verified segmental aneuploidies and focal alterations which confirmed CNAtra’s ability to detect and distinguish the two alteration phenomena. We used realistic simulated data for benchmarking the performance of CNAtra against other detection tools where we artificially spiked-in CNAs in the original cancer profiles. We found that CNAtra is superior in terms of precision, recall, and f-measure. CNAtra shows the highest sensitivity of 93% and 97% for detecting focal and large-scale alterations respectively. Visual inspection of CNAs showed that CNAtra is the most robust detection tool for low-coverage cancer data.Availability and implementationCNAtra is an open source software implemented in MATLAB, and is available at https://github.com/AISKhalil/CNAtra

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“…In spite of its simplicity, the MA filter is optimal for a common task such as reducing random noise [23]. The MV filtering is triggered after the expected startup spike.…”

Section: A Low-pass Filtering Of Raw Datamentioning

confidence: 99%

Energy Consumption Data Based Machine Anomaly Detection

Chen¹,

Fei²,

Wang³

et al. 2014

2014 Second International Conference on Advanced Cloud and Big Data

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Abstract-The ever increasing of product development and the scarcity of the energy resources that those manufacturing activities heavily rely on have made it of great significance the study on how to improve the energy efficiency in manufacturing environment. Energy consumption sensing and collection enables the development of effective solutions to higher energy efficiency. Further, it is found that the data on energy consumption of manufacturing machines also contains the information on the conditions of these machines. In this paper, methods of machine anomaly detection based on energy consumption information are developed and applied to cases on our Syil X4 computer numerical control (CNC) milling machine. Further, given massive amount of energy consumption data from large amount machining tasks, the proposed algorithms are being implemented on a Storm and Hadoop based framework aiming at online realtime machine anomaly detection.

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An Improved Signal Segmentation Using Moving Average and Savitzky-Golay Filter

Cited by 123 publications

References 14 publications

A comparison of methods for denoising of well test pressure data

A comparison of methods for denoising of well test pressure data

Hierarchical Discovery of Large-scale and Focal Copy Number Alterations in Low-coverage Cancer Genomes

Energy Consumption Data Based Machine Anomaly Detection

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