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Learning Geo-Temporal Non-Stationary Failure and Recovery of Power Distribution [article]

Yun Wei and Chuanyi Ji and Floyd Galvan and Stephen Couvillon and George Orellana and James Momoh
2013 arXiv   pre-print
Second, spatial-temporal models of failure and recovery of power distribution are developed as geo-location based multivariate non-stationary GI(t)/G(t)/Infinity queues.  ...  Quantifiable approaches are lacking and needed to learn non-stationary behaviors of large-scale failure and recovery of power distribution.  ...  The authors would like to thank Chris Kung, Jae Won Choi, Daniel Burnham, Xinyu Dai and Michael Perez for data processing, Amanda Cox for providing parts of the data and helpful discussions, Anthony Kuh  ... 
arXiv:1304.7710v1 fatcat:btypkiwi2ndw3hn233ucb65osa

Resilience of Energy Infrastructure and Services: Modeling, Data Analytics, and Metrics

Chuanyi Ji, Yun Wei, H. Vincent Poor
2017 Proceedings of the IEEE  
In particular, a first aspect of this problem is how to model large-scale failures, recoveries and impacts, involving the infrastructure, service providers, customers, and weather.  ...  Large scale power failures induced by severe weather have become frequent and damaging in recent years, causing millions of people to be without electricity service for days.  ...  These models are based on stationary probability distributions while weather-induced failure-recovery processes are non-stationary [9] , [14] .  ... 
doi:10.1109/jproc.2017.2698262 fatcat:c4sgti7v5rfznnszbxo5vadwku

Non-Stationary Random Process for Large-Scale Failure and Recovery of Power Distribution

Yun Wei, Chuanyi Ji, Floyd Galvan, Stephen Couvillon, George Orellana, James Momoh
2016 Applied Mathematics  
First, a non-stationary random process is derived to characterize an entire life cycle of large-scale failure and recovery. Second, resilience is defined based on the non-stationary random process.  ...  Third, the non-stationary model and the resilience metric are applied to a real life example of large-scale disruptions due to Hurricane Ike.  ...  and Nikil Jayant for helpful discussions.  ... 
doi:10.4236/am.2016.73022 fatcat:7pdyzhvcf5e4xpffatpkg7wrui

Dynamic modeling and resilience for power distribution

Yun Wei, Chuanyi Ji, Floyd Galvan, Stephen Couvillon, George Orellana
2013 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm)  
The neighborhoods quantify correlated failures and recoveries due to topology and types of components in power distribution.  ...  This work develops an analytical formulation for large-scale failure and recovery of power distribution induced by severe weather.  ...  RESILIENCE The non-stationary spatial temporal model enables a novel resilience metric for power distribution.  ... 
doi:10.1109/smartgridcomm.2013.6687938 dblp:conf/smartgridcomm/WeiJGCO13 fatcat:3wabagtz2bbpbcaupimy4nwvca

Guest Editorial Learning in Nonstationary and Evolving Environments

Robi Polikar, Cesare Alippi
2014 IEEE Transactions on Neural Networks and Learning Systems  
methods and theories, as well as real-world applications that show the effectiveness of learning solutions directly from data.  ...  or financial data, network intrusion, spam and fraud detection, power demand and pricing, industrial quality inspection and complex dynamical systems, among others.  ...  Finally, in Learning Geo-Temporal Non-Stationary Failure and Recovery of Power Distribution, Wei et al. address the problem of system failure in the power distribution.  ... 
doi:10.1109/tnnls.2013.2283547 pmid:24806640 fatcat:bbf535c7tvcgbmfvqayxrxfwgi

From GPS and virtual globes to spatial computing - 2020

Shashi Shekhar, Steven Feiner, Walid G. Aref
2015 Geoinformatica  
May Yuan, University of Oklahoma * Spatial computing is used in a broad sense to include spatio-temporal computing and non-geographic spaces.  ...  Learning about the environmental impacts of mining via mountaintop removal is much more powerful when one can see the visual context via a simulated 3-d map.  ...  Data -Data Analytics How may machine learning techniques be generalized to address spatio-temporal challenges of auto-correlation, non-stationarity, heterogeneity, multi-scale, etc.?  ... 
doi:10.1007/s10707-015-0235-9 fatcat:42jd7y5xvbh4vbq6aq3st7d2xi

Front Matter: Volume 11763

Junhao Chu, Qifeng Yu, Huilin Jiang, Junhong Su
2021 Seventh Symposium on Novel Photoelectronic Detection Technology and Applications  
all solid state pulse source based on the power synthesis circuit 11763 5M Research on the mission oriented mobile model of aviation network 11763 5N Failure analysis method for laser initiated device  ...  2N 3D imaging model of airborne LiDAR system 11763 2O Guidance law based on zero effort miss and Q-learning algorithm 11763 2P Non-uniformity correction algorithm based on improved neural network vii  ...  Four 84 Detection method of tubular target leakage based on deep learning 11763 85 Design and calculation to reduce the polarization sensitivity of COCTS on GEO satellite 11763 86 Weak reflectivity  ... 
doi:10.1117/12.2592610 fatcat:zkgo4tuwiffmrle2gymou4iulq

Vehicular networks and the future of the mobile internet

Mario Gerla, Leonard Kleinrock
2011 Computer Networks  
In this paper we examine this interplay between wired and wireless and extract a message for the design of a more efficient Future Wireless Internet.  ...  After the review of vehicular applications and properties, we will offer an Internet history perspective to help understand how the mobile wireless network field has evolved from the early ARPANET and  ...  A critical component of the geo-routing address structure is the Geo Location Service (GLS) -a distributed service that maps a vehicle name to the set of most recent geo locations.  ... 
doi:10.1016/j.comnet.2010.10.015 fatcat:5smrrhafbzayflzojfonbxefve

A review of statistically-based landslide susceptibility models

Paola Reichenbach, Mauro Rossi, Bruce D. Malamud, Monika Mihir, Fausto Guzzetti
2018 Earth-Science Reviews  
, with an increasing preference towards machine learning methods in the recent years.  ...  We present graphical visualisations and discussions of commonalities and differences found as a function of region and time, revealing a significant heterogeneity of thematic data types and scales, modelling  ...  Verify the landslide spatial, temporal and size distributions.  ... 
doi:10.1016/j.earscirev.2018.03.001 fatcat:sor3l5riy5d6niylgfskwe4okq

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Haitao Yuan, Guoliang Li
2021 Data Science and Engineering  
With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here  ...  Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting).  ...  (b) Similar to [245] and [246] , which build learned indexes to accelerate the query on large scale of multi-dimensional data, we can use learned indexes to improve the distributed storage of spatio-temporal  ... 
doi:10.1007/s41019-020-00151-z fatcat:nnnnxnpo3bgk3l4hpr7kk2n4xa

Can collective memories shape fish distributions? A test, linking space-time occurrence models and population demographics

Jed I. Macdonald, Kai Logemann, Elias T. Krainski, Þorsteinn Sigurðsson, Colin M. Beale, Geir Huse, Solfrid S. Hjøllo, Guðrún Marteinsdóttir
2017 Ecography  
shifting distributions: a spatial similarity index (SSI) 19 To more formally quantify the spatial and temporal patterns of wintering we 20 constructed the SSI, a metric that unlike those designed for standardized  ...  This also meant that we could not determine which age 7 classes contributed to s, estimates of which were likely influenced by a substantial, and 8 unknown proportion of juveniles (i.e. age 0 to 2) in  ...  Lessons learned from stock collapse and recovery of North 6 Sea herring: a review. -ICES J. Mar. Sci. 67: 1875-1886. 7 Dormann, C. F. et al. 2013.  ... 
doi:10.1111/ecog.03098 fatcat:zguvdbcqk5ex7hd4d3dt3awhcu

2021 Index IEEE Transactions on Power Systems Vol. 36

2021 IEEE Transactions on Power Systems  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TPWRS Nov. 2021 5944-5947 Multiclass Learning-Aided Temporal Decomposition and Distributed Opti-Models Using Measurements.  ... 
doi:10.1109/tpwrs.2021.3125235 fatcat:n3ecyy2flnapzjz7clyrp7sx4a

Dragon-kings: Mechanisms, statistical methods and empirical evidence

D. Sornette, G. Ouillon
2012 The European Physical Journal Special Topics  
variety of natural and social systems.  ...  This introductory article presents the special Discussion and Debate volume "From black swans to dragon-kings, is there life beyond power laws?" published in Eur. Phys. J. Special Topics in May 2012.  ...  release rate is non-stationary, growing as t (1/α) −1 Clearly, such non-stationarity cannot be sustained over geological time scales, as this would imply the existence of earthquakes of magnitude corresponding  ... 
doi:10.1140/epjst/e2012-01559-5 fatcat:boehc3mte5huti2ozrisygdm34

Dragon-Kings: Mechanisms, Statistical Methods and Empirical Evidence

Didier Sornette, Guy Ouillon
2012 Social Science Research Network  
release rate is non-stationary, growing as t (1/α) −1 Clearly, such non-stationarity cannot be sustained over geological time scales, as this would imply the existence of earthquakes of magnitude corresponding  ...  Power-law (i.e. self-similar) distributions of the sizes of events suggest that mechanisms of nucleation and growth dynamics remain the same over the whole spectrum of relevant spatial and temporal scales  ... 
doi:10.2139/ssrn.2191590 fatcat:rb6wjvstlbgyje7pwlwg2y6qdy

2021 Index IEEE Transactions on Wireless Communications Vol. 20

2021 IEEE Transactions on Wireless Communications  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  Jiang, H., +, A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond. A General Wideband Non-Stationary Stochastic Channel Model for Intelli-trial Settings.  ... 
doi:10.1109/twc.2021.3135649 fatcat:bgd3vzb7pbee7jp75dnbucihmq
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