scholarly journals On Time Scales of Intrinsic Oscillations in the Climate System

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 459
Author(s):  
Anastasios A. Tsonis ◽  
Geli Wang ◽  
Wenxu Lu ◽  
Sergey Kravtsov ◽  
Christopher Essex ◽  
...  
Keyword(s):  
Low Frequency ◽  
Milankovitch Cycles ◽  
Data Sets ◽  
External Forcing ◽  
Natural Oscillations ◽  
Climatic Oscillations ◽  
Slow Feature Analysis ◽  
Limited Length ◽  
Past Data ◽  
Cumulative Evidence

Proxy temperature data records featuring local time series, regional averages from areas all around the globe, as well as global averages, are analyzed using the Slow Feature Analysis (SFA) method. As explained in the paper, SFA is much more effective than the traditional Fourier analysis in identifying slow-varying (low-frequency) signals in data sets of a limited length. We find the existence of a striking gap from ~1000 to about ~20,000 years, which separates intrinsic climatic oscillations with periods ranging from ~ 60 years to ~1000 years, from the longer time-scale periodicities (20,000 yr +) involving external forcing associated with Milankovitch cycles. The absence of natural oscillations with periods within the gap is consistent with cumulative evidence based on past data analyses, as well as with earlier theoretical and modeling studies.

scholarly journals The Sampling Distribution of Disease-Associated Alleles

Genetics ◽  
1997 ◽  
Vol 147 (4) ◽  
pp. 1855-1861 ◽  
Author(s):  
Montgomery Slatkin ◽  
Bruce Rannala
Keyword(s):  
Low Frequency ◽  
Null Model ◽  
Sampling Distribution ◽  
Death Process ◽  
Published Data ◽  
Data Sets ◽  
Likelihood Functions ◽  
Alternative Hypotheses ◽  
Size Standard ◽  
Birth Death

Abstract A theory is developed that provides the sampling distribution of low frequency alleles at a single locus under the assumption that each allele is the result of a unique mutation. The numbers of copies of each allele is assumed to follow a linear birth-death process with sampling. If the population is of constant size, standard results from theory of birth-death processes show that the distribution of numbers of copies of each allele is logarithmic and that the joint distribution of numbers of copies of k alleles found in a sample of size n follows the Ewens sampling distribution. If the population from which the sample was obtained was increasing in size, if there are different selective classes of alleles, or if there are differences in penetrance among alleles, the Ewens distribution no longer applies. Likelihood functions for a given set of observations are obtained under different alternative hypotheses. These results are applied to published data from the BRCA1 locus (associated with early onset breast cancer) and the factor VIII locus (associated with hemophilia A) in humans. In both cases, the sampling distribution of alleles allows rejection of the null hypothesis, but relatively small deviations from the null model can account for the data. In particular, roughly the same population growth rate appears consistent with both data sets.

scholarly journals Historical drought patterns over Canada and their teleconnections with large-scale climate signals

2018 ◽  
Vol 22 (6) ◽  
pp. 3105-3124 ◽  
Author(s):  
Zilefac Elvis Asong ◽  
Howard Simon Wheater ◽  
Barrie Bonsal ◽  
Saman Razavi ◽  
Sopan Kurkute
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Kendall Test ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Data Sets ◽  
Canadian Prairies ◽  
Agriculture Industry ◽  
Spatio Temporal

Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems, and health. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann–Kendall test, rotated empirical orthogonal function, continuous wavelet transform, and wavelet coherence analyses are used, respectively, to investigate the trend, spatio-temporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified – the Canadian Prairies and northern central Canada. The analyses also revealed the presence of a dominant periodicity of between 8 and 32 months in the Prairie region and between 8 and 40 months in the northern central region. These cycles of low-frequency variability are found to be associated principally with the Pacific–North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, their duration, and how often they occur.

Assessment of Damage Progression in Automotive Electronics Assemblies Subjected to Temperature and Vibration

2018 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas
Keyword(s):  
Instantaneous Frequency ◽  
Low Frequency ◽  
Principal Component ◽  
Large Data ◽  
Data Sets ◽  
Automotive Electronics ◽  
Damage Progression ◽  
Mode Decomposition ◽  
Electronic Assemblies ◽  
Strain Signal

Electronics in automotive underhood environments is used for a number of safety critical functions. Reliable continued operation of electronic safety systems without catastrophic failure is important for safe operation of the vehicle. There is need for prognostication methods, which can be integrated, with on-board sensors for assessment of accrued damage and impending failure. In this paper, leadfree electronic assemblies consisting of daisy-chained parts have been subjected to high temperature vibration at 5g and 155°C. Spectrogram has been used to identify the emergence of new low frequency components with damage progression in electronic assemblies. Principal component analysis has been used to reduce the dimensionality of large data-sets and identify patterns without the loss of features that signify damage progression and impending failure. Variance of the principal components of the instantaneous frequency has been shown to exhibit an increasing trend during the initial damage progression, attaining a maximum value and decreasing prior to failure. The unique behavior of the instantaneous frequency over the period of vibration can be used as a health-monitoring feature for identifying the impending failures in automotive electronics. Further, damage progression has been studied using Empirical Mode Decomposition (EMD) technique in order to decompose the signals into Independent Mode Functions (IMF). The IMF’s were investigated based on their kurtosis values and a reconstructed strain signal was formulated with all IMF’s greater than a kurtosis value of three. PCA analysis on the reconstructed strain signal gave better patterns that can be used for prognostication of the life of the components.

scholarly journals CLUSTER–STAFF search coil magnetometer calibration – comparisons with FGM

2014 ◽  
Vol 3 (2) ◽  
pp. 153-177 ◽  
Author(s):  
P. Robert ◽  
N. Cornilleau-Wehrlin ◽  
R. Piberne ◽  
Y. de Conchy ◽  
C. Lacombe ◽  
...  
Keyword(s):  
Transfer Functions ◽  
Low Frequency ◽  
Temporal Analysis ◽  
Fluxgate Magnetometer ◽  
Data Sets ◽  
Spectral Matrix ◽  
Spatio Temporal ◽  
Cross Calibration ◽  
The Waves ◽  
The Way

Abstract. The main part of the Cluster Spatio-Temporal Analysis of Field Fluctuations (STAFF) experiment consists of triaxial search coils allowing the measurements of the three magnetic components of the waves from 0.1 Hz up to 4 kHz. Two sets of data are produced, one by a module to filter and transmit the corresponding waveform up to either 10 or 180 Hz (STAFF-SC), and the second by the onboard Spectrum Analyser (STAFF-SA) to compute the elements of the spectral matrix for five components of the waves, 3 × B and 2 × E (from the EFW experiment), in the frequency range 8 Hz to 4 kHz. In order to understand the way the output signals of the search coils are calibrated, the transfer functions of the different parts of the instrument are described as well as the way to transform telemetry data into physical units across various coordinate systems from the spinning sensors to a fixed and known frame. The instrument sensitivity is discussed. Cross-calibration inside STAFF (SC and SA) is presented. Results of cross-calibration between the STAFF search coils and the Cluster Fluxgate Magnetometer (FGM) data are discussed. It is shown that these cross-calibrations lead to an agreement between both data sets at low frequency within a 2% error. By means of statistics done over 10 yr, it is shown that the functionalities and characteristics of both instruments have not changed during this period.

Seiche Excitation in Port San Juan, British Columbia

1979 ◽  
Vol 36 (10) ◽  
pp. 1223-1227
Author(s):  
D. D. Lemon ◽  
P. H. LeBlond ◽  
T. R. Osborn
Keyword(s):  
British Columbia ◽  
Water Waves ◽  
Pressure Gauge ◽  
Low Frequency ◽  
San Juan ◽  
External Forcing ◽  
Edge Waves ◽  
Rectangular Basin ◽  
Juan De Fuca ◽  
Juan De Fuca Strait

Seiche motions observed in San Juan Harbour with a bottom-mounted pressure gauge have been Fourier-analyzed and interpreted in terms of a theoretical model of oscillations in a rectangular basin with an exponential depth profile. Two of the observed periods (at 14.6 and 38.5 min) are identified with resonances of the basin; two other significant low frequency peaks (at 21 and 55 min) do not coincide with resonant periods of the basin and must be due to strong external forcing. Higher frequency fluctuations (20–160 s) are attributed to swell and to its subharmonic interactions with edge waves. Key words: water waves, seiches, mathematical model, Juan de Fuca Strait, British Columbia

Periodic Streaming Data Reduction Using Flexible Adjustment of Time Section Size

2008 ◽  
pp. 1231-1249
Author(s):  
Jaehoon Kim ◽  
Seong Park
Keyword(s):  
Data Stream ◽  
Estimation Error ◽  
Real Life ◽  
Streaming Data ◽  
Data Sets ◽  
Storage Allocation ◽  
Time Section ◽  
Proper Size ◽  
Real Life Data ◽  
Past Data

Much of the research regarding streaming data has focused only on real time querying and analysis of recent data stream allowable in memory. However, as data stream mining, or tracking of past data streams, is often required, it becomes necessary to store large volumes of streaming data in stable storage. Moreover, as stable storage has restricted capacity, past data stream must be summarized. The summarization must be performed periodically because streaming data flows continuously, quickly, and endlessly. Therefore, in this paper, we propose an efficient periodic summarization method with a flexible storage allocation. It improves the overall estimation error by flexibly adjusting the size of the summarized data of each local time section. Additionally, as the processing overhead of compression and the disk I/O cost of decompression can be an important factor for quick summarization, we also consider setting the proper size of data stream to be summarized at a time. Some experimental results with artificial data sets as well as real life data show that our flexible approach is more efficient than the existing fixed approach.

Machine learning-based fracture-hit detection algorithm using LFDAS signal

2019 ◽  
Vol 38 (7) ◽  
pp. 520-524 ◽  
Author(s):  
Ge Jin ◽  
Kevin Mendoza ◽  
Baishali Roy ◽  
Darryl G. Buswell
Keyword(s):  
Machine Learning ◽  
Low Frequency ◽  
Detection Algorithm ◽  
Data Sets ◽  
Fracture Zones ◽  
Learning Techniques ◽  
Distributed Acoustic Sensing ◽  
Probability Of Fracture ◽  
Simple Neural Network

Low-frequency distributed acoustic sensing (LFDAS) signal has been used to detect fracture hits at offset monitor wells during hydraulic fracturing operations. Typically, fracture hits are manually identified, which can be subjective and inefficient. We implemented machine learning-based models using supervised learning techniques in order to identify fracture zones, which demonstrate a high probability of fracture hits automatically. Several features are designed and calculated from LFDAS data to highlight fracture-hit characterizations. A simple neural network model is trained to fit the manually picked fracture hits. The fracture-hit probability, as predicted by the model, agrees well with the manual picks in training, validation, and test data sets. The algorithm was used in a case study of an unconventional reservoir. The results indicate that smaller cluster spacing design creates denser fractures.

scholarly journals A European lightning density analysis using 5 years of ATDnet data

2014 ◽  
Vol 14 (4) ◽  
pp. 815-829 ◽  
Author(s):  
G. Anderson ◽  
D. Klugmann
Keyword(s):  
Detailed Analysis ◽  
Arrival Time ◽  
Detection Efficiency ◽  
Low Frequency ◽  
Continuous Data ◽  
Data Sets ◽  
Current Form ◽  
Very Low Frequency ◽  
Density Analysis ◽  
Using Data

Abstract. The Met Office has operated a very low frequency (VLF) lightning location network since 1987. The long-range capabilities of this network, referred to in its current form as ATDnet, allow for relatively continuous detection efficiency across Europe with only a limited number of sensors. The wide coverage and continuous data obtained by Arrival Time Differing NETwork (ATDnet) are here used to create data sets of lightning density across Europe. Results of annual and monthly detected lightning density using data from 2008–2012 are presented, along with more detailed analysis of statistics and features of interest. No adjustment has been made to the data for regional variations in detection efficiency.

scholarly journals Pansharpening with a Gradient Domain GIF Based on NSST

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 229
Author(s):  
Jiao Jiao ◽  
Lingda Wu
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Fusion Rule ◽  
Objective Evaluation ◽  
Data Sets ◽  
Multi Scale ◽  
Guided Image Filter ◽  
Gradient Domain ◽  
Spatial Quality

In order to improve the fusion quality of multispectral (MS) and panchromatic (PAN) images, a pansharpening method with a gradient domain guided image filter (GIF) that is based on non-subsampled shearlet transform (NSST) is proposed. First, multi-scale decomposition of MS and PAN images is performed by NSST. Second, different fusion rules are designed for high- and low-frequency coefficients. A fusion rule that is based on morphological filter-based intensity modulation (MFIM) technology is proposed for the low-frequency coefficients, and the edge refinement is carried out based on a gradient domain GIF to obtain the fused low-frequency coefficients. For the high-frequency coefficients, a fusion rule based on an improved pulse coupled neural network (PCNN) is adopted. The gradient domain GIF optimizes the firing map of the PCNN model, and then the fusion decision map is calculated to guide the fusion of the high-frequency coefficients. Finally, the fused high- and low-frequency coefficients are reconstructed with inverse NSST to obtain the fusion image. The proposed method was tested using the WorldView-2 and QuickBird data sets; the subjective visual effects and objective evaluation demonstrate that the proposed method is superior to the state-of-the-art pansharpening methods, and it can efficiently improve the spatial quality and spectral maintenance.

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