AutoSpec: Detecting Exiguous Frequency Changes in Time Series

Elimination of seasonality temperature trends in a structural health monitoring of bridges is considered in the article. On example of a beam bridge, eigenfrequencies are plotted against seasonal fluctuations of environmental temperature. Next, analysis of a time series, formed from a data of frequency changes in the bridge, was made. To describe the time series, two different methods were implemented: ARIMA model based on a statistical relationship between the data and LSTM model of a recurrent neural network.

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Flood Seasonality in the Congo River Basin

10.5194/egusphere-egu21-64 ◽

2021 ◽

Author(s):

Bola Bosongo Gode ◽

Neal Jeff ◽

Tshimanga Raphael ◽

Hawker Lauwrence ◽

Trigg Mark A ◽

...

Keyword(s):

Time Series ◽

Cluster Analysis ◽

Climate Models ◽

Flood Frequency ◽

Congo Basin ◽

Time Interval ◽

Flood Events ◽

Frequency Method ◽

North West ◽

Frequency Changes

Information on flood seasonality is required in many practical applications of hydrology and water resources management. However, an understanding of flood seasonality and how flood frequencies may have changed over time has not been established for the Congo Basin. The main objective of this study is therefore to identify flood seasonality and change in frequency the Congo Basin (CB). The analysis based on six major drainage areas of the CB, where we used a Peaks Over Threshold (POT) flood time-series with three peaks per year. The relative frequency method is applied to identify flood seasons, and then a cluster analysis is performed to classify flood into type based on monthly frequency. The directional statistics method is used to determine the mean day of flood and the flood variability measure. To identify flood frequency changes, the analysis of variance was applied to test the difference between two flood frequency time series blocks before and after the change point year. Results show that four gauging stations exhibit a unimodal flood seasonality distribution while two gauging stations have bimodal flood seasonality distribution, and two significant flood rich months are observed in all studied gauging stations. &#160;The cluster analysis results in four spatially flood types with distinct seasonality characteristics. Mean flood dates show that the time interval between adjacent flood events in the south and south-east is shorter compared to time interval between flood events in the north and north-west. It is observed that, in almost all gauging stations, there is strong flood seasonality, and the geographical location of a watershed is indicative of its flood pattern. Most of significant decreasing frequencies are found in the southern part of the Congo Basin. There are no significant changes in flood frequency identified in the northern and eastern part of the Basin. However, flood frequencies have been increasing in the centre and western part of the Basin. This study suggest that, exploring flood generating factors and the drivers of change can provide insights for understanding the influence of these factors on floods as climate models projected changes in extreme precipitation and aridity in the future. &#160;&#160;&#160;

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The LDE-Type Flare Occurrence (1969-1993)

International Astronomical Union Colloquium ◽

10.1017/s0252921100025458 ◽

1994 ◽

Vol 144 ◽

pp. 279-282

Author(s):

A. Antalová

Keyword(s):

Time Series ◽

Fourier Analysis ◽

Sunspot Cycle ◽

List Type ◽

Type Number ◽

Solar Cycles ◽

Type Iv ◽

Long Duration ◽

Cycle Maximum ◽

Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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SEM image sharpness analysis

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100163174 ◽

1996 ◽

Vol 54 ◽

pp. 142-143

Author(s):

M. T. Postek ◽

A. E. Vladar

Keyword(s):

High Frequency ◽

Low Frequency ◽

Quantitative Information ◽

Primary Electron ◽

Image Sharpness ◽

Critical Dimensions ◽

Sem Image ◽

Frequency Changes ◽

Electron Microscopes ◽

Scanning Electron

Fully automated or semi-automated scanning electron microscopes (SEM) are now commonly used in semiconductor production and other forms of manufacturing. The industry requires that an automated instrument must be routinely capable of 5 nm resolution (or better) at 1.0 kV accelerating voltage for the measurement of nominal 0.25-0.35 micrometer semiconductor critical dimensions. Testing and proving that the instrument is performing at this level on a day-by-day basis is an industry need and concern which has been the object of a study at NIST and the fundamentals and results are discussed in this paper.In scanning electron microscopy, two of the most important instrument parameters are the size and shape of the primary electron beam and any image taken in a scanning electron microscope is the result of the sample and electron probe interaction. The low frequency changes in the video signal, collected from the sample, contains information about the larger features and the high frequency changes carry information of finer details. The sharper the image, the larger the number of high frequency components making up that image. Fast Fourier Transform (FFT) analysis of an SEM image can be employed to provide qualitiative and ultimately quantitative information regarding the SEM image quality.

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