Automated identification of changes in electrode contact properties for long-term permanent ERT monitoring experiments

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. E79-E94 ◽  
Author(s):  
John Deceuster ◽  
Olivier Kaufmann ◽  
Michel Van Camp
Keyword(s):  
Repeated Measurements ◽  
Cumulative Distribution ◽  
Time Slice ◽  
Data Sets ◽  
Automated Identification ◽  
Weighting Method ◽  
Data Set ◽  
Electrode Contact ◽  
The Given

Electrical resistivity tomography (ERT) monitoring experiments are being conducted more often to image spatiotemporal changes in soil properties. When conducting long-term ERT monitoring, the identification of suspicious electrodes in a permanent spread is of major importance because changes in electrode contact properties of a single electrode may affect the quality of many measurements on each time-slice. An automated methodology was developed to detect these temporal changes in electrode contact properties, based on a Bayesian approach called “weights of evidence.” Contrasts [Formula: see text] and studentized contrasts [Formula: see text] are estimators of the influence of each electrode in the global data quality. A consolidated studentized contrast [Formula: see text] is introduced to consider the proportion of rejected quadripoles which contain a single electrode. These estimators are computed for each time-slice using [Formula: see text]-factor (coefficient of variation of repeated measurements) threshold values, from 0 to 10%, to discriminate between selected and rejected quadripoles. An automated detection strategy is proposed to identify suspicious electrodes by comparing the [Formula: see text] to the [Formula: see text] (maximum expected [Formula: see text] values when every electrode is good for the given data set). These [Formula: see text] are computed using Monte-Carlo simulations of a hundred random draws where the distribution of [Formula: see text]-factor values follows a Weibull cumulative distribution, with [Formula: see text] and [Formula: see text], fitted on a background data set filtered using a 5% threshold on absolute reciprocal errors. The efficiency of the methodology and its sensitivity to the selected reciprocal error threshold are assessed on synthetic and field data. Our approach is suitable to detect suspicious electrodes and slowly changing conditions affecting the galvanic contact resistances where classical approaches are shown to be inadequate except when the faulty electrode is disconnected. A data-weighting method is finally proposed to ensure that only good data will be used in the inversion of ERT monitoring data sets.

scholarly journals Do galactic bars depend on environment?: an information theoretic analysis of Galaxy Zoo 2

2020 ◽  
Vol 501 (1) ◽  
pp. 994-1001
Author(s):  
Suman Sarkar ◽  
Biswajit Pandey ◽  
Snehasish Bhattacharjee
Keyword(s):  
Spatial Distribution ◽  
Mutual Information ◽  
Local Density ◽  
Statistical Significance ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Host Galaxy ◽  
Data Sets ◽  
Data Set ◽  
Information Theoretic

ABSTRACT We use an information theoretic framework to analyse data from the Galaxy Zoo 2 project and study if there are any statistically significant correlations between the presence of bars in spiral galaxies and their environment. We measure the mutual information between the barredness of galaxies and their environments in a volume limited sample (Mr ≤ −21) and compare it with the same in data sets where (i) the bar/unbar classifications are randomized and (ii) the spatial distribution of galaxies are shuffled on different length scales. We assess the statistical significance of the differences in the mutual information using a t-test and find that both randomization of morphological classifications and shuffling of spatial distribution do not alter the mutual information in a statistically significant way. The non-zero mutual information between the barredness and environment arises due to the finite and discrete nature of the data set that can be entirely explained by mock Poisson distributions. We also separately compare the cumulative distribution functions of the barred and unbarred galaxies as a function of their local density. Using a Kolmogorov–Smirnov test, we find that the null hypothesis cannot be rejected even at $75{{\ \rm per\ cent}}$ confidence level. Our analysis indicates that environments do not play a significant role in the formation of a bar, which is largely determined by the internal processes of the host galaxy.

Trophic Level Relationships in Pelagic Food Webs: Comparisons Derived from Long-Term Data Sets for Oneida Lake, New York (USA), and Lake St. George, Ontario (Canada)

1992 ◽  
Vol 49 (8) ◽  
pp. 1588-1596 ◽  
Author(s):  
Donald J. McQueen ◽  
Edward L. Mills ◽  
John L. Forney ◽  
Mark R. S. Johannes ◽  
John R. Post
Keyword(s):  
New York ◽  
Total Phosphorus ◽  
Trophic Level ◽  
Data Sets ◽  
Phytoplankton Abundance ◽  
Data Set ◽  
Oneida Lake ◽  
Pelagic Food Webs ◽  
Term Data

We used standardized methods to analyze a 14-yr data set from Oneida Lake and a 10-yr data set from Lake St. George. We estimated mean summer concentrations of several trophic level indicators including piscivores, planktivores, zooplankton, phytoplankton, and total phosphorus, and we then investigated the relationships between these variables. Both data sets yielded similar long-term and short-term trends. The long-term mean annual trends were that (1) the relationships between concentrations of planktivores and zooplankton (including daphnids) were always negative, (2) the relationships between concentrations of zooplankton and various measures of phytoplankton abundance were unpredictable and never statistically significant, and (3) the relationships between total phosphorus and various measures of phytoplankton abundance were always positive. Over short periods, the data from both lakes showed periodic, strong top-down relationships between concentrations of zooplankton (especially large Daphnia) and chlorophyll a, but these events were unpredictable and were seldom related to piscivore abundance.

scholarly journals Intention, Principle, Outputs and Aims of the Experimental Pavilion Research of Building Envelopes Including Windows for Wooden Buildings

2017 ◽  
Vol 13 (1) ◽  
pp. 42-51 ◽  
Author(s):  
Daniela Štaffenová ◽  
Ján Rybárik ◽  
Miroslav Jakubčík
Keyword(s):  
Experimental Research ◽  
Climatic Conditions ◽  
Point Of View ◽  
Physical Parameters ◽  
Data Sets ◽  
Data Set ◽  
Building Envelopes ◽  
Climate Conditions ◽  
Time Period

AbstractThe aim of experimental research in the area of exterior walls and windows suitable for wooden buildings was to build special pavilion laboratories. These laboratories are ideally isolated from the surrounding environment, airtight and controlled by the constant internal climate. The principle of experimental research is measuring and recording of required physical parameters (e.g. temperature or relative humidity). This is done in layers of experimental fragment sections in the direction from exterior to interior, as well as in critical places by stable interior and real exterior climatic conditions. The outputs are evaluations of experimental structures behaviour during the specified time period, possibly during the whole year by stable interior and real exterior boundary conditions. The main aim of this experimental research is processing of long-term measurements of experimental structures and the subsequent analysis. The next part of the research consists of collecting measurements obtained with assistance of the experimental detached weather station, analysis, evaluation for later setting up of reference data set for the research locality, from the point of view of its comparison to the data sets from Slovak Hydrometeorological Institute (SHMU) and to localities with similar climate conditions. Later on, the data sets could lead to recommendations for design of wooden buildings.

scholarly journals Prediction of Progestin Affinity for the Human Progesterone Receptor Based on Corrected RBA Data

2018 ◽  
Vol 1 (4) ◽  
pp. e00080
Author(s):  
A.V. Mikurova ◽  
V.S. Skvortsov
Keyword(s):  
Progesterone Receptor ◽  
Prediction Equation ◽  
Binding Activity ◽  
Data Sets ◽  
Data Set ◽  
The Third ◽  
Relative Binding ◽  
Human Progesterone Receptor ◽  
Nuclear Progesterone Receptor

The modeling of complexes of 3 sets of steroid and nonsteroidal progestins with the ligand-binding domain of the nuclear progesterone receptor was performed. Molecular docking procedure, long-term simulation of molecular dynamics and subsequent analysis by MM-PBSA (MM-GBSA) were used to model the complexes. Using the characteristics obtained by the MM-PBSA method two data sets of steroid compounds obtained in different scientific groups a prediction equation for the value of relative binding activity (RBA) was constructed. The RBA value was adjusted so that in all samples the actual activity was compared with the progesterone activity. The third data set of nonsteroidal compounds was used as a test. The resulted equation showed that the prediction results could be applied to both steroid molecules and nonsteroidal progestins.

Long-Term Rainfall Forecast Model Based on The TabNet and LightGbm Algorithm

2020 ◽  
Author(s):  
Tianyu Xu ◽  
Yongchuan Yu ◽  
Jianzhuo Yan ◽  
Hongxia Xu
Keyword(s):  
Prediction Model ◽  
Feature Fusion ◽  
Forecast Model ◽  
Data Sets ◽  
Good Prediction ◽  
Data Set ◽  
Rainfall Prediction ◽  
Improve Model ◽  
Probability Prediction

Abstract Due to the problems of unbalanced data sets and distribution differences in long-term rainfall prediction, the current rainfall prediction model had poor generalization performance and could not achieve good prediction results in real scenarios. This study uses multiple atmospheric parameters (such as temperature, humidity, atmospheric pressure, etc.) to establish a TabNet-LightGbm rainfall probability prediction model. This research uses feature engineering (such as generating descriptive statistical features, feature fusion) to improve model accuracy, Borderline Smote algorithm to improve data set imbalance, and confrontation verification to improve distribution differences. The experiment uses 5 years of precipitation data from 26 stations in the Beijing-Tianjin-Hebei region of China to verify the proposed rainfall prediction model. The test set is to predict the rainfall of each station in one month. The experimental results shows that the model has good performance with AUC larger than 92%. The method proposed in this study further improves the accuracy of rainfall prediction, and provides a reference for data mining tasks.

scholarly journals Extended Odd Fréchet-G Family of Distributions

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Suleman Nasiru
Keyword(s):  
Maximum Likelihood Method ◽  
Real Data ◽  
Likelihood Method ◽  
Data Sets ◽  
Data Set ◽  
New Class ◽  
New Family ◽  
Rate Functions ◽  
The Given ◽  
Family Of Distributions

The need to develop generalizations of existing statistical distributions to make them more flexible in modeling real data sets is vital in parametric statistical modeling and inference. Thus, this study develops a new class of distributions called the extended odd Fréchet family of distributions for modifying existing standard distributions. Two special models named the extended odd Fréchet Nadarajah-Haghighi and extended odd Fréchet Weibull distributions are proposed using the developed family. The densities and the hazard rate functions of the two special distributions exhibit different kinds of monotonic and nonmonotonic shapes. The maximum likelihood method is used to develop estimators for the parameters of the new class of distributions. The application of the special distributions is illustrated by means of a real data set. The results revealed that the special distributions developed from the new family can provide reasonable parametric fit to the given data set compared to other existing distributions.

scholarly journals Calibration methods for rotating shadowband irradiometers and optimizing the calibration duration

2016 ◽  
Vol 9 (4) ◽  
pp. 1601-1612 ◽  
Author(s):  
Wilko Jessen ◽  
Stefan Wilbert ◽  
Bijan Nouri ◽  
Norbert Geuder ◽  
Holger Fritz
Keyword(s):  
Spectral Response ◽  
Atmospheric Condition ◽  
Resource Assessment ◽  
Data Sets ◽  
Data Set ◽  
Direct Normal Irradiance ◽  
Calibration Methods ◽  
Long Term Result ◽  
Time Of Year

Abstract. Resource assessment for concentrated solar power (CSP) needs accurate direct normal irradiance (DNI) measurements. An option for such measurement campaigns is the use of thoroughly calibrated rotating shadowband irradiometers (RSIs). Calibration of RSIs and Si-sensors is complex because of the inhomogeneous spectral response of these sensors and incorporates the use of several correction functions. One calibration for a given atmospheric condition and air mass might not be suitable under different conditions. This paper covers procedures and requirements of two calibration methods for the calibration of rotating shadowband irradiometers. The necessary duration of acquisition of test measurements is examined with regard to the site-specific conditions at Plataforma Solar de Almería (PSA) in Spain. Seven data sets of long-term test measurements were collected. For each data set, calibration results of varying durations were compared to its respective long-term result. Our findings show that seasonal changes of environmental conditions are causing small but noticeable fluctuation of calibration results. Calibration results within certain periods (i.e. November to January and April to May) show a higher likelihood of deviation. These effects can partially be attenuated by including more measurements from outside these periods. Consequently, the duration of calibrations at PSA can now be selected depending on the time of year in which measurements commence.

scholarly journals High resolution atmospheric reconstruction for Europe 1948–2012: coastDat2

2013 ◽  
Vol 6 (2) ◽  
pp. 779-809 ◽  
Author(s):  
B. Geyer
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Data Set ◽  
Horizontal Grid ◽  
Global Reanalysis ◽  
Marine Climate

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and long-term changes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges etc.) over many decades. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013, doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for entire Europe, including Baltic and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with a regional climate model and a horizontal grid size of 0.22° in rotated coordinates. Global reanalysis data were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

scholarly journals Relationships between body reserve dynamics and rearing performances in meat ewes1

2019 ◽  
Vol 97 (10) ◽  
pp. 4076-4084
Author(s):  
Tiphaine Macé ◽  
Dominique Hazard ◽  
Fabien Carrière ◽  
Sebastien Douls ◽  
Didier Foulquié ◽  
...  
Keyword(s):  
Genetic Relationships ◽  
Genetic Correlations ◽  
Repeated Measurements ◽  
Production Cycle ◽  
Data Sets ◽  
Data Set ◽  
Litter Weight ◽  
Significant Relationships ◽  
Physiological Stage ◽  
Changes Over Time

Abstract The main objective of this work was to study the relationships between body reserve (BR) dynamics and rearing performance (PERF) traits in ewes from a Romane meat sheep flock managed extensively on “Causse” rangelands in the south of France. Flock records were used to generate data sets covering 14 lambing years (YR). The data set included 1,146 ewes with 2 ages of first lambing (AGE), 3 parities (PAR), and 4 litter sizes (LS). Repeated measurements of the BW and BCS were used as indicators of BR. The ewe PERF traits recorded were indirect measurements for maternal abilities and included prolificacy, litter weight and lamb BW at lambing and weaning, ADG at 1, 2, and 3 mo after lambing, and litter survival from lambing to weaning. The effects of different BW and BCS trajectories (e.g., changes in BW and BCS across the production cycle), previously been characterized in the same animals, on PERF traits were investigated. Such trajectories reflected different profiles at the intraflock level in the dynamics of BR mobilization–accretion cycles. Genetic relationships between BR and PERF traits were assessed. All the fixed variables considered (i.e., YR, AGE, PAR, LS, and SEX ratio of the litter) have significant effects on the PERF traits. Similarly, BW trajectories had an effect on the PERF traits across the 3 PARs studied, particularly during the first cycle (PAR 1). The BCS trajectories only affected prolificacy, lamb BW at birth, and litter survival. Most of the PERF traits considered here showed moderate heritabilities (0.17–0.23) except for prolificacy, the lamb growth rate during the third month and litter survival which showed very low heritabilities. With exception of litter survival and prolificacy, ewe PERF traits were genetically, strongly, and positively correlated with BW whatever the physiological stage. A few weak genetic correlations were found between BCS and PERF traits. As illustrated by BW and BCS changes over time, favorable genetic correlations were found, even if few and moderate, between BR accretion or mobilization and PERF traits, particularly for prolificacy and litter weight at birth. In conclusion, our results show significant relationships between BR dynamics and PERF traits in ewes, which could be considered in future sheep selection programs aiming to improve robustness.

Exemplar-Based Learning Classifier System with Dynamic Matching Range for Imbalanced Data

2017 ◽  
Vol 21 (5) ◽  
pp. 868-875
Author(s):  
Hiroyasu Matsushima ◽  
Keiki Takadama ◽  
◽  
Keyword(s):  
Imbalanced Data ◽  
Data Sets ◽  
Sigmoid Function ◽  
Learning Classifier System ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Dynamic Matching ◽  
Learning Classifier ◽  
Stable Performance ◽  
The Given

In this paper, we propose a method to improve ECS-DMR which enables appropriate output for imbalanced data sets. In order to control generalization of LCS in imbalanced data set, we propose a method of applying imbalance ratio of data set to a sigmoid function, and then, appropriately update the matching range. In comparison with our previous work (ECS-DMR), the proposed method can control the generalization of the appropriate matching range automatically to extract the exemplars that cover the given problem space, wchich consists of imbalanced data set. From the experimental results, it is suggested that the proposed method provides stable performance to imbalanced data set. The effect of the proposed method using the sigmoid function considering the data balance is shown.

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