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Journal articles on the topic 'Detection quantique'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Tsai, Du-Ming, and Cheng-Hsiang Yang. "A quantile–quantile plot based pattern matching for defect detection." Pattern Recognition Letters 26, no. 13 (2005): 1948–62. http://dx.doi.org/10.1016/j.patrec.2005.02.002.

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Tee, Kong Fah, Yuzhi Cai, and Hua-Peng Chen. "Structural damage detection using quantile regression." Journal of Civil Structural Health Monitoring 3, no. 1 (2012): 19–31. http://dx.doi.org/10.1007/s13349-012-0030-3.

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Moriguchi, Hiroyuki, Ichiro Takeuchi, Masayuki Karasuyama, et al. "Adaptive Kernel Quantile Regression for Anomaly Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (2009): 230–36. http://dx.doi.org/10.20965/jaciii.2009.p0230.

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In this paper, we study a problem of anomaly detection from time series-data. We use kernel quantile regression (KQR) to predict the extreme (such as 0.01 or 0.99) quantiles of the future time-series data distribution. It enables us to tell whether the probability of observing a certain time-series sequence is larger than, say, 1 percent or not. In this paper, we develop an efficient update algorithm of KQR in order to adapt the KQR in on-line manner. We propose a new algorithm that allows us to compute the optimal solution of the KQR when a new training pattern is inserted or deleted. We demo

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Li, Zhaoyuan, and Maozai Tian. "Detecting Change-Point via Saddlepoint Approximations." Journal of Systems Science and Information 5, no. 1 (2017): 48–73. http://dx.doi.org/10.21078/jssi-2017-048-26.

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AbstractIt’s well-known that change-point problem is an important part of model statistical analysis. Most of the existing methods are not robust to criteria of the evaluation of change-point problem. In this article, we consider “mean-shift” problem in change-point studies. A quantile test of single quantile is proposed based on saddlepoint approximation method. In order to utilize the information at different quantile of the sequence, we further construct a “composite quantile test” to calculate the probability of every location of the sequence to be a change-point. The location of change-po

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Xia, Wentao, Wei Xiong, and Maozai Tian. "Heteroscedasticity Detection and Estimation with Quantile Difference Method." Journal of Systems Science and Complexity 29, no. 2 (2015): 511–30. http://dx.doi.org/10.1007/s11424-015-3161-x.

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Manekiya, Mohammedhusen, Massimo Donelli, Abhinav Kumar, and Sreedevi Menon. "A Novel Detection Technique for a Chipless RFID System Using Quantile Regression." Electronics 7, no. 12 (2018): 409. http://dx.doi.org/10.3390/electronics7120409.

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This work presents a novel approach for improving the detection capabilities of a chipless Radio Frequency Identification (RFID) system based on quantile regression. The main drawback of chipless RFID systems is the limited response of the tags due to the low-quality factor of the resonators, used to encode the information in the tag. The detection becomes very challenging especially for real-time data when noise is present. This work proposes the use of quantile regression to enhance the system performance. A chipless RFID system prototype has been fabricated (as a proof of concept) and exper

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Syed Musa, Syed Mohamad Sadiq, Mohd Salmi Md Noorani, Fatimah Abdul Razak, Munira Ismail, Mohd Almie Alias, and Saiful Izzuan Hussain. "An Early Warning System for Flood Detection Using Critical Slowing Down." International Journal of Environmental Research and Public Health 17, no. 17 (2020): 6131. http://dx.doi.org/10.3390/ijerph17176131.

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The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall’s tau rank correlation, which we found is inconsistent for our data se

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Zhou, Mi, Huixia Judy Wang, and Yanlin Tang. "Sequential change point detection in linear quantile regression models." Statistics & Probability Letters 100 (May 2015): 98–103. http://dx.doi.org/10.1016/j.spl.2015.01.031.

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9

Seo, Han Son, and Min Yoon. "Least quantile squares method for the detection of outliers." Communications for Statistical Applications and Methods 28, no. 1 (2021): 81–88. http://dx.doi.org/10.29220/csam.2021.28.1.081.

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Hong, Duan, and Yang Luo. "A Gesture Trace Detection Method Using DTW." Applied Mechanics and Materials 380-384 (August 2013): 3874–77. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3874.

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This paper puts forward a gestures trajectory recognition method based on DTW. Through the direction characteristic to calculate trajectory characteristics, and through the coding to quantize direction characteristic, and at the same time, considering the direction of the cyclical, proposed a new distance equation for calculating distance. The experimental results prove that the method realized the dynamic gesture recognition in the complex static background.

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Schalge, Bernd, Richard Blender, and Klaus Fraedrich. "Blocking Detection Based on Synoptic Filters." Advances in Meteorology 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/717812.

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The Tibaldi-Molteni blocking index is supplemented by additional filter criteria to eliminate cut-off lows and subsynoptic structures. We introduce three blocking filters and analyse their sensitivities: (i) a quantile filter requiring a minimum geopotential height anomaly to reject cut-off lows, (ii) an extent filter to extract scales above a minimum zonal width, and (iii) a persistence filter to extract events with a minimum duration. Practical filter application is analysed in two case studies and the blocking climatologies for the Northern and the Southern Hemisphere.

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Zhou, Zhili, Meimin Wang, Yi Cao, and Yuecheng Su. "CNN Feature-Based Image Copy Detection with Contextual Hash Embedding." Mathematics 8, no. 7 (2020): 1172. http://dx.doi.org/10.3390/math8071172.

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As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applicatio

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Ciuperca, Gabriela. "Real time change-point detection in a nonlinear quantile model." Sequential Analysis 36, no. 1 (2017): 87–110. http://dx.doi.org/10.1080/07474946.2016.1275482.

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Ye, Wuyi, Yangguang Zhu, Yuehua Wu, and Baiqi Miao. "Markov regime-switching quantile regression models and financial contagion detection." Insurance: Mathematics and Economics 67 (March 2016): 21–26. http://dx.doi.org/10.1016/j.insmatheco.2015.11.002.

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15

Nortey, Ezekiel N. N., Reuben Pometsey, Louis Asiedu, Samuel Iddi, and Felix O. Mettle. "Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression." International Journal of Mathematics and Mathematical Sciences 2021 (February 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6667671.

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Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or

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Parente, Paulo M. D. C., and João M. C. Santos Silva. "Quantile Regression with Clustered Data." Journal of Econometric Methods 5, no. 1 (2016): 1–15. http://dx.doi.org/10.1515/jem-2014-0011.

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AbstractWe study the properties of the quantile regression estimator when data are sampled from independent and identically distributed clusters, and show that the estimator is consistent and asymptotically normal even when there is intra-cluster correlation. A consistent estimator of the covariance matrix of the asymptotic distribution is provided, and we propose a specification test capable of detecting the presence of intra-cluster correlation. A small simulation study illustrates the finite sample performance of the test and of the covariance matrix estimator.

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Ekin, Tahir, and Paul Damien. "Analysis of Health Care Billing via Quantile Variable Selection Models." Healthcare 9, no. 10 (2021): 1274. http://dx.doi.org/10.3390/healthcare9101274.

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Fraudulent billing of health care insurance programs such as Medicare is in the billions of dollars. The extent of such overpayments remains an issue despite the emerging use of analytical methods for fraud detection. This motivates policy makers to also be interested in the provider billing characteristics and understand the common factors that drive conservative and/or aggressive behavior. Statistical approaches to tackling this problem are confronted by the asymmetric and/or leptokurtic distributions of billing data. This paper is a first attempt at using a quantile regression framework and

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Rotejanaprasert, Chawarat, and Andrew Lawson. "A Bayesian Quantile Modeling for Spatiotemporal Relative Risk: An Application to Adverse Risk Detection of Respiratory Diseases in South Carolina, USA." International Journal of Environmental Research and Public Health 15, no. 9 (2018): 2042. http://dx.doi.org/10.3390/ijerph15092042.

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Quantile modeling has been seen as an alternative and useful complement to ordinary regression mainly focusing on the mean. To directly apply quantile modeling to areal data the discrete conditional quantile function of the data can be an issue. Although jittering by adding a small number from a uniform distribution to impose pseudo-continuity has been proposed, the approach can have a great influence on responses with small values. Thus we proposed an alternative to model the quantiles of relative risk for spatiotemporal areal health data within a Bayesian framework using the log-Laplace dist

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19

Genter, Franziska, Sara J. Marks, Guillaume Clair-Caliot, et al. "Evaluation of the novel substrate RUG™ for the detection ofEscherichia coliin water from temperate (Zurich, Switzerland) and tropical (Bushenyi, Uganda) field sites." Environmental Science: Water Research & Technology 5, no. 6 (2019): 1082–91. http://dx.doi.org/10.1039/c9ew00138g.

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20

Teng, Wei, Rui Jiang, Xian Ding, Yibing Liu, and Zhiyong Ma. "Detection and Quantization of Bearing Fault in Direct Drive Wind Turbine via Comparative Analysis." Shock and Vibration 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2378435.

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Bearing fault is usually buried by intensive noise because of the low speed and heavy load in direct drive wind turbine (DDWT). Furthermore, varying wind speed and alternating loads make it difficult to quantize bearing fault feature that indicates the degree of deterioration. This paper presents the application of multiscale enveloping spectrogram (MuSEnS) and cepstrum to detect and quantize bearing fault in DDWT. MuSEnS can manifest fault modulation information adaptively based on the capacity of complex wavelet transform, which enables the weak bearing fault in DDWT to be detected. Cepstrum

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21

Sarra, Annalina, Lara Fontanella, Pasquale Valentini, and Sergio Palermi. "Quantile regression and Bayesian cluster detection to identify radon prone areas." Journal of Environmental Radioactivity 164 (November 2016): 354–64. http://dx.doi.org/10.1016/j.jenvrad.2016.06.014.

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22

Cannon, Alex J. "Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure." Journal of Climate 29, no. 19 (2016): 7045–64. http://dx.doi.org/10.1175/jcli-d-15-0679.1.

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Abstract Univariate bias correction algorithms, such as quantile mapping, are used to address systematic biases in climate model output. Intervariable dependence structure (e.g., between different quantities like temperature and precipitation or between sites) is typically ignored, which can have an impact on subsequent calculations that depend on multiple climate variables. A novel multivariate bias correction (MBC) algorithm is introduced as a multidimensional analog of univariate quantile mapping. Two variants are presented. MBCp and MBCr respectively correct Pearson correlation and Spearma

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23

Ho, Chiu Man, and Nobuchika Okada. "Off-shell supersymmetry." International Journal of Modern Physics A 30, no. 33 (2015): 1550194. http://dx.doi.org/10.1142/s0217751x15501948.

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Supersymmetry does not dictate the way we should quantize the fields in the supermultiplets, and so we have the freedom to quantize the Standard Model (SM) particles and their superpartners differently. We propose a generalized quantization scheme under which a particle can only appear off-shell, while its contributions to quantum corrections are exactly the same as those in the usual quantum field theory. We apply this quantization scheme solely to the sparticles in the R-parity preserving Minimal Supersymmetric Standard Model (MSSM). Thus, sparticles can only appear off-shell. They could be

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24

Wosniok, Werner, and Rainer Haeckel. "A new indirect estimation of reference intervals: truncated minimum chi-square (TMC) approach." Clinical Chemistry and Laboratory Medicine (CCLM) 57, no. 12 (2019): 1933–47. http://dx.doi.org/10.1515/cclm-2018-1341.

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Abstract All known direct and indirect approaches for the estimation of reference intervals (RIs) have difficulties in processing very skewed data with a high percentage of values at or below the detection limit. A new model for the indirect estimation of RIs is proposed, which can be applied even to extremely skewed data distributions with a relatively high percentage of data at or below the detection limit. Furthermore, it fits better to some simulated data files than other indirect methods. The approach starts with a quantile-quantile plot providing preliminary estimates for the parameters

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25

Sun, Bo, and Liang Chen. "Quantile regression for challenging cases of eQTL mapping." Briefings in Bioinformatics 21, no. 5 (2019): 1756–65. http://dx.doi.org/10.1093/bib/bbz097.

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Abstract Mapping of expression quantitative trait loci (eQTLs) facilitates interpretation of the regulatory path from genetic variants to their associated disease or traits. High-throughput sequencing of RNA (RNA-seq) has expedited the exploration of these regulatory variants. However, eQTL mapping is usually confronted with the analysis challenges caused by overdispersion and excessive dropouts in RNA-seq. The heavy-tailed distribution of gene expression violates the assumption of Gaussian distributed errors in linear regression for eQTL detection, which results in increased Type I or Type II

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26

Bali, S., B. Hazra, and V. Pakrashi. "Quantile autoregressive modeling for non-linear change detection in vibrating structural systems." Mechanics Research Communications 100 (September 2019): 103397. http://dx.doi.org/10.1016/j.mechrescom.2019.103397.

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27

Ciuperca, Gabriela, and Matúš Maciak. "Change‐point detection in a linear model by adaptive fused quantile method." Scandinavian Journal of Statistics 47, no. 2 (2020): 425–63. http://dx.doi.org/10.1111/sjos.12412.

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28

Chevapatrakul, Thanaset, and Danilo V. Mascia. "Detecting overreaction in the Bitcoin market: A quantile autoregression approach." Finance Research Letters 30 (September 2019): 371–77. http://dx.doi.org/10.1016/j.frl.2018.11.004.

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29

Reich, Brian J. "Spatiotemporal quantile regression for detecting distributional changes in environmental processes." Journal of the Royal Statistical Society: Series C (Applied Statistics) 61, no. 4 (2012): 535–53. http://dx.doi.org/10.1111/j.1467-9876.2011.01025.x.

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Wang, Wenjing, Wei Liu, Tiantian Qi, et al. "Dust discrimination in dynamic light scattering based on a quantile outliers detection method." Powder Technology 366 (April 2020): 546–51. http://dx.doi.org/10.1016/j.powtec.2020.02.078.

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31

Eo, Soo-Heang, Daewoo Pak, Jeea Choi, and HyungJun Cho. "Outlier Detection using Projection Quantile Regression for Mass Spectrometry Data with Low Replication." BMC Research Notes 5, no. 1 (2012): 236. http://dx.doi.org/10.1186/1756-0500-5-236.

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32

Dehling, H., R. Fried, and M. Wendler. "A robust method for shift detection in time series." Biometrika 107, no. 3 (2020): 647–60. http://dx.doi.org/10.1093/biomet/asaa004.

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Summary We present a robust and nonparametric test for the presence of a changepoint in a time series, based on the two-sample Hodges–Lehmann estimator. We develop new limit theory for a class of statistics based on two-sample U-quantile processes in the case of short-range dependent observations. Using this theory, we derive the asymptotic distribution of our test statistic under the null hypothesis of a constant level. The proposed test shows better overall performance under normal, heavy-tailed and skewed distributions than several other modifications of the popular cumulative sums test bas

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Wilcox, Rand R., and H. J. Keselman. "Detecting heteroscedasticity in a simple regression model via quantile regression slopes." Journal of Statistical Computation and Simulation 76, no. 8 (2006): 705–12. http://dx.doi.org/10.1080/10629360500107923.

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Zhu, Ya Dong, Jing Wang Zhang, Chao Wang, and Zhen Jun Liu. "Fragmentation Degree Research Based on File’s Layout." Applied Mechanics and Materials 80-81 (July 2011): 1217–25. http://dx.doi.org/10.4028/www.scientific.net/amm.80-81.1217.

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Numerous resource allocating and releasing will cause fragmentation which will seriously affect the I/O performance of file systems. One method to solve this problem is file defragmentation. This paper focuses on fragmentation detection, the key part of the file defragmentation. We estimate file fragmentation situation through file’s layout, and then analyze the detailed effects of the situation on performance measurement. Then we quantize this by the file’s “fragmentation degree” through theoretical and experimental analyses. Finally we use it to estimate the file’s fragmentation and decide w

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Jung, Chunggil, Yonggwan Lee, Jiwan Lee, and Seongjoon Kim. "Performance Evaluation of the Multiple Quantile Regression Model for Estimating Spatial Soil Moisture after Filtering Soil Moisture Outliers." Remote Sensing 12, no. 10 (2020): 1678. http://dx.doi.org/10.3390/rs12101678.

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The spatial distribution of soil moisture (SM) was estimated by a multiple quantile regression (MQR) model with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and filtered SM data from 2013 to 2015 in South Korea. For input data, observed precipitation and SM data were collected from the Korea Meteorological Administration and various institutions monitoring SM. To improve the work of a previous study, prior to the estimation of SM, outlier detection using the isolation forest (IF) algorithm was applied to the observed SM data. The original observed SM data resulted in IF_SM data

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Lim, Daeyoung, Beomjo Park, David Nott, Xueou Wang, and Taeryon Choi. "Sparse signal shrinkage and outlier detection in high-dimensional quantile regression with variational Bayes." Statistics and Its Interface 13, no. 2 (2020): 237–49. http://dx.doi.org/10.4310/sii.2020.v13.n2.a8.

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Cho, HyungJun, Yang-jin Kim, Hee Jung Jung, Sang-Won Lee, and Jae Won Lee. "OutlierD: an R package for outlier detection using quantile regression on mass spectrometry data." Bioinformatics 24, no. 6 (2008): 882–84. http://dx.doi.org/10.1093/bioinformatics/btn012.

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Ciuperca, Gabriela. "Test by adaptive LASSO quantile method for real-time detection of a change-point." Metrika 81, no. 6 (2018): 689–720. http://dx.doi.org/10.1007/s00184-018-0676-x.

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Guo, Jun, and Chang Ren Zhu. "A Novel Method of Ship Detection from Spaceborne Optical Image Based on Spatial Pyramid Matching." Applied Mechanics and Materials 190-191 (July 2012): 1099–103. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.1099.

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In this paper we propose an automatic ship detection method in High Resolution optical satellite images based on neighbor context information. First, a pre-detection of targets gives us candidates. For each candidate, we choose an extended region called candidate with neighborhood which comprises candidate and its neighbor area. Second, the patches of candidate with neighborhood are got by a regular grid, and their SIFT(Scale Invariant Feature Transform) features are extracted. Then the SIFT features of training images are clustered with the K-means algorithm to form a codebook of the patches.

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Benoumechiara, Nazih, Nicolas Bousquet, Bertrand Michel, and Philippe Saint-Pierre. "Detecting and modeling critical dependence structures between random inputs of computer models." Dependence Modeling 8, no. 1 (2020): 263–97. http://dx.doi.org/10.1515/demo-2020-0016.

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AbstractUncertain information on input parameters of computer models is usually modeled by considering these parameters as random, and described by marginal distributions and a dependence structure of these variables. In numerous real-world applications, while information is mainly provided by marginal distributions, typically from samples, little is really known on the dependence structure itself. Faced with this problem of incomplete or missing information, risk studies that make use of these computer models are often conducted by considering independence of input variables, at the risk of i

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Zhang, Hanze, Yangxin Huang, Wei Wang, Henian Chen, and Barbara Langland-Orban. "Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features." Statistical Methods in Medical Research 28, no. 2 (2017): 569–88. http://dx.doi.org/10.1177/0962280217730852.

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In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4

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Shiau, Jenq-Tzong, and Wen-Hong Huang. "Detecting distributional changes of annual rainfall indices in Taiwan using quantile regression." Journal of Hydro-environment Research 9, no. 3 (2015): 368–80. http://dx.doi.org/10.1016/j.jher.2014.07.006.

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Wang, W., X. Chen, P. Shi, and P. H. A. J. M. van Gelder. "Detecting changes in extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China." Hydrology and Earth System Sciences 12, no. 1 (2008): 207–21. http://dx.doi.org/10.5194/hess-12-207-2008.

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Abstract. Extreme hydro-meteorological events have become the focus of more and more studies in the last decade. Due to the complexity of the spatial pattern of changes in precipitation processes, it is still hard to establish a clear view of how precipitation has changed and how it will change in the future. In the present study, changes in extreme precipitation and streamflow processes in the Dongjiang River Basin in southern China are investigated with several nonparametric methods, including one method (Mann-Kendall test) for detecting trend, and three methods (Kolmogorov–Smirnov test, Lev

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Kim, S., C. Hwang, J. W. Lee, J. Shim, and I. Sohn. "Support Vector Machine Quantile Regression for Detecting Differentially Expressed Genes in Microarray Analysis." Methods of Information in Medicine 47, no. 05 (2008): 459–67. http://dx.doi.org/10.3414/me0396.

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Summary Objectives: One of the main objectives of microarray analysis is to identify genes differentially expressed under two distinct experimental conditions. This task is complicated by the noisiness of data and the large number of genes that are examined. Fold change (FC) based gene selection often misleads because error variability for each gene is heterogeneous in different intensity ranges. Several statistical methods have been suggested, but some of them result in high false positive rates because they make very strong parametric assumptions. Methods: We present support vector quantile

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Ferrara, Maria, Sinan Guloksuz, Shadie Burke, et al. "F136. PARSING DUP TO REFINE EARLY DETECTION: QUANTILE REGRESSION OF RESULTS FROM THE SCANDINAVIAN TIPS STUDY." Schizophrenia Bulletin 44, suppl_1 (2018): S272—S273. http://dx.doi.org/10.1093/schbul/sby017.667.

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Lee, MinJae, Mohammad H. Rahbar, and Hooshang Talebi. "A nonparametric method for assessment of interactions in a median regression model for analyzing right censored data." Statistical Methods in Medical Research 28, no. 4 (2018): 1170–87. http://dx.doi.org/10.1177/0962280217751518.

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We propose a nonparametric test for interactions when we are concerned with investigation of the simultaneous effects of two or more factors in a median regression model with right censored survival data. Our approach is developed to detect interaction in special situations, when the covariates have a finite number of levels with a limited number of observations in each level, and it allows varying levels of variance and censorship at different levels of the covariates. Through simulation studies, we compare the power of detecting an interaction between the study group variable and a covariate

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VIAL, F., S. THOMMEN, and L. HELD. "A simulation study on the statistical monitoring of condemnation rates from slaughterhouses for syndromic surveillance: an evaluation based on Swiss data." Epidemiology and Infection 143, no. 16 (2015): 3423–33. http://dx.doi.org/10.1017/s0950268815000989.

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SUMMARYSyndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (e.g. economic). Several European SyS systems use data collected during meat inspection for syndromic surveillance of animal health, as some diseases may be more easily detected post-mortem than at their point of origin or during the ante-mortem inspection upon arrival at the slaughterhouse. In this paper we use simulation to evaluate the performance of a quasi-Poisson regression (also known as an improved Farrington) algorithm f

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Kaur, Inderpreet, Imke Hüser, Tianran Zhang, Berit Gehrke, and Johannes Kaiser. "Correcting Swath-Dependent Bias of MODIS FRP Observations With Quantile Mapping." Remote Sensing 11, no. 10 (2019): 1205. http://dx.doi.org/10.3390/rs11101205.

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Active fire observations with satellite instruments exhibit a well-documented increase of the detection threshold with increasing pixel footprint size, i.e., distance from the sub-satellite point. This results in a viewing angle-dependent, negative bias in gridded representations of the observed Fire Radiative Power (FRP), which in turn is frequently being used for climate monitoring of biomass burning and for pyrogenic emission inventories. We present a method based on quantile mapping to alleviate this bias and apply it to the gridded-FRP from the Moderate Resolution Imaging Spectroradiomete

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Tramblay, Yves, Louise Mimeau, Luc Neppel, Freddy Vinet, and Eric Sauquet. "Detection and attribution of flood trends in Mediterranean basins." Hydrology and Earth System Sciences 23, no. 11 (2019): 4419–31. http://dx.doi.org/10.5194/hess-23-4419-2019.

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Abstract:

Abstract. Floods have strong impacts in the Mediterranean region and there are concerns about a possible increase in their intensity due to climate change. In this study, a large database of 171 basins located in southern France with daily discharge data with a median record length of 45 years is considered to analyze flood trends and their drivers. In addition to discharge data, outputs of precipitation, temperature, evapotranspiration from the SAFRAN reanalysis and soil moisture computed with the ISBA land surface model are also analyzed. The evolution of land cover in these basins is analyz

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Xu, Qifa, Zhenhua Fan, Weiyin Jia, and Cuixia Jiang. "Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing." Wind Energy 22, no. 10 (2019): 1390–401. http://dx.doi.org/10.1002/we.2375.

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