A new delay distribution model to take long-term degradation into account

2013 ◽  
Author(s):  
S. Tsukiyama ◽  
M. Fukui ◽  
T. Kambe
Keyword(s):  
Distribution Model ◽  
Delay Distribution

Limited Capacity of Railways for Coal Transportation in India: Evidence from ArcGIS-based Load Flow Study

2020 ◽  
pp. 001946622095310
Author(s):  
Rajesh Gupta ◽  
Ekaterina Kozyreva ◽  
Pavel Chistyakov ◽  
Petr Lavrinenko ◽  
Igor Smirnov
Keyword(s):  
Load Flow ◽  
Distribution Model ◽  
Limited Capacity ◽  
Rail Network ◽  
Coal Transportation ◽  
Traffic Volumes ◽  
Using Data ◽  
Flow Study

How much coal will India need to transport in future and is the rail network poised to handle that requirement are two important questions for the emerging economy. To find answers to these questions, this study creates a distribution model of coal freight traffic on Indian Railways, analyzing the sufficiency of infrastructure for future economic needs. Using data on spatial distribution of coal mines, coal traffic volumes and rail sectional capacities, this study creates sectional capacity maps as main visual tool for analysis. Sections with bottlenecks are identified for next ten years’ coal transport need of the country. The simulation done in this study finds 15% under-delivery for the 900mT coal demand in the country by 2030 due to transport bottlenecks. Based on this analysis, the article presents the conclusions on possible influence of existing conditions of coal transportation on India’s economy in the long-term period and also considers the role of dedicated freight.

A Monte Carlo Model for Pitting Corrosion in Phosphor Bronze Tape Used in Underground Power Transmission Cables

CORROSION ◽  
10.5006/3347 ◽  
2020 ◽  
Vol 76 (1) ◽  
pp. 82-92
Author(s):  
Lixin Zhang ◽  
Simon Gill ◽  
Sivashangari Gnanasambandam ◽  
Maurizio Foresta ◽  
Jingzhe Pan ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Pitting Corrosion ◽  
Depth Distribution ◽  
Power Transmission ◽  
Monte Carlo Model ◽  
Probability Of Failure ◽  
Distribution Model ◽  
Pit Depth ◽  
Random Nature

Life of underground oil-filled power transmission cables used with phosphor bronze tapes is greatly reduced by pitting corrosion and hence accurate prediction of the pit growth in these tapes becomes essential. In the present work, the probability distribution of corrosion pit depth on phosphor bronze tapes is calculated using probabilistic Monte Carlo simulations and compared with the measured pit depth distribution on samples of broken tapes which have been in service for about 50 y. This Monte Carlo simulation is performed on every stable pit that nucleates, propagates, and repassivates on the metal surface. Due to the random nature of pitting corrosion, the probability of failure of this class of cables can be simulated based on the Monte Carlo model. This paper shows that the simulated pit depth distribution is very similar to the experimental data. The results demonstrate that the Monte Carlo model by Engelhardt and Macdonald can be effectively applied to long-term field data of phosphor bronze tapes, even over 50 y. In addition, the probability of failure due to pitting corrosion can be evaluated analytically, without need of conducting expensive and time-consuming experimental campaigns. Therefore, this probabilistic pit depth distribution model will be a powerful tool in the decision-making strategy for the replacement of underground power transmission cables near their end of life.

Adaptive Image Feature Reduction for Object Tracking

2014 ◽  
Vol 989-994 ◽  
pp. 3605-3608
Author(s):  
Cong Lin ◽  
Chi Man Pun
Keyword(s):  
Object Tracking ◽  
Adaptive Learning ◽  
Computational Cost ◽  
Texture Feature ◽  
Feature Reduction ◽  
Image Feature ◽  
Distribution Model ◽  
Appearance Model ◽  
Art Object

A novel adaptive image feature reduction approach for object tracking using vectorized texture feature is proposed in this paper. Our contributions are three-fold: 1) a statistical discriminative appearance model using texture feature was proposed. 2) Majority of dimensions of the features are removed by judging their errors of the chosen distribution model. The remaining dimensions are most discriminative ones for classification task. The dimension reduction has advantages of reducing the computational cost in classification stage. 3) An adaptive learning rate was proposed to handle drifts caused by long term occlusion. Preliminary experimental results are satisfactory and compared to state-of-the-art object tracking methods.

scholarly journals Covid-19 crisis

2020 ◽  
Vol 1 (1) ◽  
pp. 50-61
Author(s):  
Petra Kelecic
Keyword(s):  
Coming Out ◽  
Retail Banking ◽  
Distribution Model ◽  
Market Penetration ◽  
Key Factors ◽  
Distribution Models ◽  
Customer Expectations ◽  
Long Time ◽  
New Phase

The coronavirus situation is causing widespread concern and economic hardship for society, consumers and businesses worldwide. As for entire world, COVD-19 is the most serious challenge to financial institutions in long time. Banks are called upon to manage this new phase with urgency and aptness, to help deflect a worldwide recession. Until recently, majority of banks were focused on empowering both the psychical and digital distribution models. The “new normal” calls for reassessing of priorities and pushes new distribution model where psychical and digital are combined and act as one, with interconnected capabilities. This paper highlights importance of long-term positioning in post covid world, as market forces and customer behavior potentially change coming out of this crisis. In order to manage revenue and customer expectations, new customer-centric and digital based ecosystem should be established, leveraging on the latest technologies, aiming at increasing remote sales and market penetration. Having that in mind, article aims to shed light on key factors important for retail banking success in this “new future”.

Characterizing Wind Speed and Air Density for Wind Energy Estimation

2009 ◽  
Author(s):  
Xiuli Qu ◽  
Jing Shi
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Density Distribution ◽  
North Dakota ◽  
Distribution Model ◽  
Energy Potential ◽  
Energy Estimation ◽  
Air Density ◽  
Wind Energy Potential

Wind energy is the fastest growing renewable energy source in the past decade. To estimate the wind energy potential for a specific site, the long-term wind data need to be analyzed and accurately modeled. Wind speed and air density are the two key parameters for wind energy potential calculation, and their characteristics determine the long-term wind energy estimation. In this paper, we analyze the wind speed and air density data obtained from two observation sites in North Dakota and Colorado, and the variations of wind speed and air density in long term are demonstrated. We obtain univariate statistical distributions for the two parameters respectively. Excellent fitting performance can be achieved for wind speed for both sites using conventional univariate probability distribution functions, but fitting air density distribution for the North Dakota site appears to be less accurate. Furthermore, we adopt Farlie-Gumbel-Morgenstern approach to construct joint bivariate distributions to describe wind speed and air density simultaneously. Overall, satisfactory goodness-of-fit values are achieved with the joint distribution models, but the fitting performance is slightly worse compared with the univariate distributions. Further research is needed to improve air density distribution model and the joint bivariate distribution model for wind speed and air density.

Stochastic stress-testing approach for assessing resilience of urban water systems from source to tap

2021 ◽  
Author(s):  
Dionysios Nikolopoulos ◽  
Panagiotis Kossieris ◽  
Christos Makropoulos
Keyword(s):  
Stress Testing ◽  
Water Cycle ◽  
Spatial Scales ◽  
Water System ◽  
Water Systems ◽  
Distribution Model ◽  
Generation Model ◽  
Urban Water ◽  
Urban Water Systems

<p>Urban water systems are designed with the goal of delivering their service for several decades.  The infrastructure will inevitably face long-term uncertainty in a multitude of parameters from the hydroclimatic and socioeconomic realms (e.g., climate change, limited supply of water in terms quantity and acceptable quality, population growth, shifting demand patterns, industrialization), as well as from the conceptual realm of the decision maker (e.g., changes in policy, system maintenance incentives, investment rate, expansion plans). Because urban water systems are overly complex, a holistic analysis involves the use of various models that individually pertain to a smaller sub-system and a variety of metrics to assess performance, whereas the analysis is accomplished at different temporal and spatial scales for each sub-system. In this work, we integrate a water resources management model with a water distribution model and a water demand generation model at smaller (household and district) scale, allowing us to simulate urban water systems “from source to tap”, covering the entire water cycle. We also couple a stochastic simulation module that supports the representation of uncertainty throughout the water cycle. The performance of the integrated system under long term uncertainty is assessed with the novel measure of system’s resilience i.e. the degree to which a water system continues to perform under progressively increasing disturbance. This evaluation is essentially a framework of systematic stress-testing, where the disturbance is described via stochastically changing parameters in an ensemble of scenarios that represent future world views. The framework is showcased through a synthesized case study of a medium-sized urban water system.</p><p><strong>Acknowledgement</strong></p><p>This research is carried out / funded in the context of the project “A resilience assessment framework for water supply infrastructure under long-term uncertainty: A Source-to-Tap methodology integrating state of the art computational tools” (MIS 5049174) under the call for proposals “Researchers' support with an emphasis on young researchers- 2nd Cycle”. The project is co-financed by Greece and the European Union (European Social Fund- ESF) by the Operational Programme Human Resources Development, Education and Lifelong Learning 2014-2020.”</p>

scholarly journals Dynamic Simulation of the Crown Net Photosynthetic Rate for Young Larix olgensis Henry Trees

Forests ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 321
Author(s):  
Qiang Liu ◽  
Longfei Xie ◽  
Fengri Li
Keyword(s):  
Net Photosynthetic Rate ◽  
Dynamic Simulation ◽  
Photosynthetic Rate ◽  
Elevation Angle ◽  
Distribution Model ◽  
Co2 Uptake ◽  
Relative Depth ◽  
Larix Olgensis ◽  
Larix Olgensis Henry

Numerical integration of the instantaneous net photosynthetic rate (An) is a common method for calculating the long-term CO2 uptake of trees, and accurate dynamic simulation of the crown An has been receiving substantial attention. Tree characteristics are challenging to assess given their aerodynamically coarse crown properties, spatiotemporal variation in leaf functional traits and microenvironments. Therefore, the variables associated with the dynamic variations in the crown An must be identified. The relationships of leaf temperature (Tleaf), the vapor pressure deficit (VPD), leaf mass per area (LMA) and the relative depth into the crown (RDINC) with the parameters of the photosynthetic light-response (PLR) model of Larix olgensis Henry were analyzed. The LMA, RDINC and VPD were highly correlated with the maximum net photosynthetic rate (Amax). The VPD was the key variable that mainly determined the variation in the apparent quantum yield (AQY). Tleaf exhibited a significant exponential correlation with the dark respiration rate (Rd). According to the above correlations, the crown PLR model of L. olgensis trees was constructed by linking VPD, LMA and RDINC to the original PLR equation. The model performed well, with a high coefficient of determination (R2) value (0.883) and low root mean square error (RMSE) value (1.440 μmol m−2 s−1). The extinction coefficient (k) of different pseudowhorls within a crown was calculated by the Beer–Lambert equation based on the observed photosynthetically active radiation (PAR) distribution. The results showed that k was not a constant value but varied with the RDINC, solar elevation angle (ψ) and cumulative leaf area of the whole crown (CLA). Thus, we constructed a k model by reparameterizing the power function of RDINC with the ψ and CLA, and the PAR distribution within a crown was therefore well estimated (R2 = 0.698 and RMSE = 174.4 μmol m−2 s−1). Dynamic simulation of the crown An for L. olgensis trees was achieved by combining the crown PLR model and dynamic PAR distribution model. Although the models showed some weakened physiological biochemical processes during photosynthesis, they enabled the estimation of long-term CO2 uptake for an L. olgensis plantation, and the results could be easily fitted to gas-exchange measurements.

American Prices and Urban Inequality Since 1820

1976 ◽  
Vol 36 (2) ◽  
pp. 303-333 ◽  
Author(s):  
Jeffrey G. Williamson
Keyword(s):  
Income Distribution ◽  
Consumer Goods ◽  
Dominant Role ◽  
Distribution Model ◽  
Crucial Element ◽  
Urban Inequality ◽  
Long Term Trends

This article examines the forces that appear to have driven long-term trends in American urban inequality. The changing structure of consumer goods' prices is shown to have played a significant—but not dominant—role in every phase of increasing and decreasing nominal inequality from 1820 to 1929. The revealed symmetry in movement between the urban price and income structure suggests that a successful macro-distribution model must explain both historical phenomena. Finally, the article concludes that technological imbalance was a crucial element in shaping peacetime patterns of income distribution.

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