scholarly journals Impact of duration and missing data on the long-term photovoltaic degradation rate estimation

2022 ◽  
Vol 181 ◽  
pp. 738-748
Author(s):  
Irene Romero-Fiances ◽  
Andreas Livera ◽  
Marios Theristis ◽  
George Makrides ◽  
Joshua S. Stein ◽  
...  
Keyword(s):  
Missing Data ◽  
Degradation Rate ◽  
Rate Estimation

Evaluation of the effect of tamping on the track geometry condition: A case study

2016 ◽  
Vol 232 (2) ◽  
pp. 408-420 ◽  
Author(s):  
Iman Soleimanmeigouni ◽  
Alireza Ahmadi ◽  
Iman Arasteh Khouy ◽  
Christophe Letot
Keyword(s):  
Degradation Rate ◽  
Probabilistic Approach ◽  
Dominant Factor ◽  
Model Parameters ◽  
Track Geometry ◽  
Degradation Rates ◽  
Railway Maintenance ◽  
Long Term Prediction

Tamping is one of the major activities undertaken by railway maintenance managers to recover the track geometry condition. Modelling the effectiveness of tamping along with track geometry degradation is essential for long-term prediction of track geometry behaviour. The aim of this study is to analyse the effect of tamping on the different track geometry measurements, i.e. longitudinal level, alignment and cant, based on inspection car records from a part of the Main Western Line in Sweden. To model recovery after tamping, a probabilistic approach is applied. The track geometry condition before tamping was considered as the dominant factor for modelling the model parameters. Correlation analysis was performed to measure the linear relation between the recoveries of the different geometry measures. The results show a moderate correlation between the recovery of the longitudinal level and that of the cant, and a weak correlation between the recovery of the longitudinal level and that of the alignment. Linear regression and Wiener process were also applied to model track geometry degradation and to obtain degradation rates. The effect of tamping on degradation rate was analysed. It was observed that degradation rate increased after tamping.

scholarly journals Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

2018 ◽  
Author(s):  
Seyed Mahmood Taghavi-Shahri ◽  
Alessandro Fassò ◽  
Behzad Mahaki ◽  
Heresh Amini
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Missing Data ◽  
Exposure Assessment ◽  
Expectation Maximization ◽  
Space Time ◽  
Data Imputation ◽  
Land Use Regression ◽  
Missing Data Imputation

AbstractGraphical AbstractLand use regression (LUR) has been widely applied in epidemiologic research for exposure assessment. In this study, for the first time, we aimed to develop a spatiotemporal LUR model using Distributed Space Time Expectation Maximization (D-STEM). This spatiotemporal LUR model examined with daily particulate matter ≤ 2.5 μm (PM2.5) within the megacity of Tehran, capital of Iran. Moreover, D-STEM missing data imputation was compared with mean substitution in each monitoring station, as it is equivalent to ignoring of missing data, which is common in LUR studies that employ regulatory monitoring stations’ data. The amount of missing data was 28% of the total number of observations, in Tehran in 2015. The annual mean of PM2.5 concentrations was 33 μg/m3. Spatiotemporal R-squared of the D-STEM final daily LUR model was 78%, and leave-one-out cross-validation (LOOCV) R-squared was 66%. Spatial R-squared and LOOCV R-squared were 89% and 72%, respectively. Temporal R-squared and LOOCV R-squared were 99.5% and 99.3%, respectively. Mean absolute error decreased 26% in imputation of missing data by using the D-STEM final LUR model instead of mean substitution. This study reveals competence of the D-STEM software in spatiotemporal missing data imputation, estimation of temporal trend, and mapping of small scale (20 × 20 meters) within-city spatial variations, in the LUR context. The estimated PM2.5 concentrations maps could be used in future studies on short- and/or long-term health effects. Overall, we suggest using D-STEM capabilities in increasing LUR studies that employ data of regulatory network monitoring stations.Highlights-First Land Use Regression using D-STEM, a recently introduced statistical software-Assess D-STEM in spatiotemporal modeling, mapping, and missing data imputation-Estimate high resolution (20×20 m) daily maps for exposure assessment in a megacity-Provide both short- and long-term exposure assessment for epidemiological studies

scholarly journals LONG-TERM FSO/FPSO CHARTER RATE ESTIMATION

Kapal ◽  
2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Hesty A Kurniawati ◽  
Wasis D Aryawan ◽  
Achmad Baidowi
Keyword(s):  
Rate Estimation

Missing Data in Long-term Follow-up of Patients with Eating Disorders Using the Body Attitude Test

2012 ◽  
Vol 21 (3) ◽  
pp. 224-229 ◽  
Author(s):  
Candida Geerdens ◽  
Johan Vanderlinden ◽  
Guido Pieters ◽  
Amber De Herdt ◽  
Michel Probst
Keyword(s):  
Eating Disorders ◽  
Missing Data ◽  
The Body ◽  
Body Attitude ◽  
Attitude Test ◽  
Long Term Follow Up ◽  
Body Attitude Test

A General Likelihood Framework for Characterizing the Time Course of Neural Activity

2011 ◽  
Vol 23 (10) ◽  
pp. 2537-2566 ◽  
Author(s):  
Michael J. Prerau ◽  
Uri T. Eden
Keyword(s):  
Missing Data ◽  
Firing Rate ◽  
General Framework ◽  
Time Course ◽  
Estimation Accuracy ◽  
General Point ◽  
Neural Firing ◽  
Conditional Intensity ◽  
Intensity Estimation ◽  
Rate Estimation

We develop a general likelihood-based framework for use in the estimation of neural firing rates, which is designed to choose the temporal smoothing parameters that maximize the likelihood of missing data. This general framework is algorithm-independent and thus can be applied to a multitude of established methods for firing rate or conditional intensity estimation. As a simple example of the use of the general framework, we apply it to the peristimulus time histogram and kernel smoother, the methods most widely used for firing rate estimation in the electrophysiological literature and practice. In doing so, we illustrate how the use of the framework can employ the general point process likelihood as a principled cost function and can provide substantial improvements in estimation accuracy for even the most basic of rate estimation algorithms. In particular, the resultant kernel smoother is simple to implement, efficient to compute, and can accurately determine the bandwidth of a given rate process from individual spike trains. We perform a simulation study to illustrate how the likelihood framework enables the kernel smoother to pick the bandwidth parameter that best predicts missing data, and we show applications to real experimental spike train data. Additionally, we discuss how the general likelihood framework may be used in conjunction with more sophisticated methods for firing rate and conditional intensity estimation and suggest possible applications.

scholarly journals Railway track design & degradation

2018 ◽  
Vol 211 ◽  
pp. 11006
Author(s):  
Mehran Sadri ◽  
Tao Lu ◽  
Arjen Zoeteman ◽  
Michaël Steenbergen
Keyword(s):  
Degradation Rate ◽  
Early Stage ◽  
Mechanical Energy ◽  
Measurement Data ◽  
Empirical Models ◽  
Railway Track ◽  
Structural Performance ◽  
Design Parameters ◽  
Railway Tracks

The long-term behaviour of railway track has attracted increasing attention in recent years. Improvements in long-term structural performance reduce demands for maintenance and increase the continuous availability of railway lines. The focus of this paper is on the prediction of the sensitivity of a track design to long-term deterioration in terms of track geometry. According to the state of the art literature, degradation is often investigated using empirical models based on field measurement data. Although a rough maintenance forecast may be made employing empirical models, the predictions are not generic, and the physical processes which govern track degradation under train operation remain unclear. The first aim of this study is to present a mathematical model to elucidate the underlying physics of long-term degradation of railway tracks. The model consists of an infinitely long beam which is periodically supported by equidistantly discrete sleepers and a moving unsprung mass which represents a travelling train. The mechanical energy dissipated in the substructure is proposed to serve as a measure of the track degradation rate. Secondly, parametric studies on energy dissipation are conducted to identify effects of various track design parameters on the susceptibility of the track to degradation, as well as the effect of the train speed. It has been shown that the track/subgrade stiffness is the most influential parameter on degradation whereas other system parameters do influence the degradation rate but at lower magnitudes. The conclusions can be used to optimise the track design in the early stage for better long-term structural performance of railway tracks.

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