2012 Conference on Intelligent Data Understanding最新文献

Introduction to astroML: Machine learning for astrophysics astroML简介:天体物理学的机器学习

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382200

J. Vanderplas, A. Connolly, Ž. Ivezić, Alexander G. Gray

引用次数: 177

Improved Interferometric Synthetic Aperture Radar processing via advanced co-registration and phase correction techniques 通过先进的共配准和相位校正技术改进干涉合成孔径雷达处理

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382195

M. Shahbazi, M. Motagh

引用次数: 2

Detection and characterization of non-transiting extra-solar planets in Kepler Data using reflected light variations 利用反射光变化在开普勒数据中探测和表征非凌日系外行星

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382198

K. Knuth, B. Placek, Zachary Richards

引用次数: 3

Time series change detection using segmentation: A case study for land cover monitoring 使用分割的时间序列变化检测:土地覆盖监测的案例研究

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382202

Varun Mithal, Zachary O'Connor, K. Steinhaeuser, S. Boriah, Vipin Kumar, C. Potter, S. Klooster

{"title":"Time series change detection using segmentation: A case study for land cover monitoring","authors":"Varun Mithal, Zachary O'Connor, K. Steinhaeuser, S. Boriah, Vipin Kumar, C. Potter, S. Klooster","doi":"10.1109/CIDU.2012.6382202","DOIUrl":"https://doi.org/10.1109/CIDU.2012.6382202","url":null,"abstract":"Segmentation of a time series attempts to divide it into homogeneous subsequences, such that each of these segments are different from each other. A typical segmentation framework involves selecting a model that is used to represent the segment. In this paper, we investigate segmentation scores based on difference between models and propose two approaches for normalizing the difference based score. The first approach uses permutation testing to assign a p-value to model difference. The second approach builds on bootstrapping methodology used in statistics which estimates the null distribution of complex statistics whose standard errors are not analytically derivable by generating alternative versions of the data by a resampling strategy. More specifically, given a time series with either a single or two segments, we propose a method to estimate the distribution of model difference statistic for each segment. The proposed approach allows normalizing model difference statistic when complex models are being used in the segmentation algorithm. We study the strengths and weaknesses of the two normalizing approaches in the context of characteristics of land cover data such as seasonality and noise using synthetic and real data sets. We show that relative performance of normalization approaches can vary significantly depending on the characteristics of the data. We illustrate the utility of these approaches for detection of deforestation in Mato Grosso (Brazil).","PeriodicalId":270712,"journal":{"name":"2012 Conference on Intelligent Data Understanding","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116242221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Aircraft anomaly detection using performance models trained on fleet data 使用机队数据训练的性能模型进行飞机异常检测

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382196

D. Gorinevsky, B. Matthews, Rodney A. Martin

引用次数: 40

Characterization of traffic and structure in the U.S. airport network 美国机场网络的交通和结构特征

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382193

V. Mehta, Feanil Patel, Y. Glina, Matthew C. Schmidt, B. A. Miller, N. Bliss

引用次数: 4

A technique to improve the quality of accumulated fields 一种提高累积场质量的技术

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382188

V. Lakshmanan, Madison Miller, Travis M. Smith

引用次数: 1

Mining time-lagged relationships in spatio-temporal climate data 时空气候数据的时间滞后关系挖掘

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382194

Jaya Kawale, S. Liess, Vipin Kumar, Upmanu Lall, A. Ganguly

{"title":"Mining time-lagged relationships in spatio-temporal climate data","authors":"Jaya Kawale, S. Liess, Vipin Kumar, Upmanu Lall, A. Ganguly","doi":"10.1109/CIDU.2012.6382194","DOIUrl":"https://doi.org/10.1109/CIDU.2012.6382194","url":null,"abstract":"Time series data in climate are often characterized by a delayed relationship between two variables, for example precipitation and temperature anomalies occurring at a place might also occur at another place after some time. These lagged relations generally signify the time lag between the cause and the effect or the spread of a common cause and are important to study and understand as they can aid in prediction. Identifying lagged relationships in climate data is challenging due to the various complex dependencies present in the data like spatial and temporal auto-correlation, seasonality, trends and long distance teleconnections. In this paper, we present a general framework for finding all pairs of lagged positive and negative relations that can exist in a given spatio-temporal dataset. We use a graph based approach based upon the concept of shared reciprocal nearest neighbor to generate cluster pairs of locations sharing similar or opposing behavior for every time lag. Our framework can be generalized to extract multivariate lagged relationships across different variables thus can be used to understand the lagged response of one variable on another. We show the utility of our approach by extracting some of the known delayed relationships like the Madden Julian Oscillation (MJO) and the Pacific North American (PNA) pattern at different lags using the sea level pressure dataset provided by the NCEP/NCAR. Our approach can be broadly applied to other problems in spatio-temporal domain to extract lagged relationships.","PeriodicalId":270712,"journal":{"name":"2012 Conference on Intelligent Data Understanding","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123718224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Data visualization techniques for airspace flow modeling 空域流动建模的数据可视化技术

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382187

A. Marzuoli, C. Hurter, E. Feron

引用次数: 14

Hierarchical structure of the Madden-Julian oscillation in infrared brightness temperature revealed through nonlinear Laplacian spectral analysis 通过非线性拉普拉斯光谱分析揭示了红外亮温中Madden-Julian振荡的层次结构

2012 Conference on Intelligent Data Understanding Pub Date : 2012-12-24 DOI: 10.1109/CIDU.2012.6382201

D. Giannakis, W. Tung, A. Majda

{"title":"Hierarchical structure of the Madden-Julian oscillation in infrared brightness temperature revealed through nonlinear Laplacian spectral analysis","authors":"D. Giannakis, W. Tung, A. Majda","doi":"10.1109/CIDU.2012.6382201","DOIUrl":"https://doi.org/10.1109/CIDU.2012.6382201","url":null,"abstract":"The convection-coupled tropical atmospheric motions are highly nonlinear and multiscaled, and play a major role in weather and climate predictability in both the tropics and mid-latitudes. In this work, nonlinear Laplacian spectral analysis (NLSA) is applied to extract spatiotemporal modes of variability in tropical dynamics from satellite observations. Blending qualitative analysis of dynamical systems, singular spectrum analysis (SSA), and spectral graph theory, NLSA has been shown to capture intermittency, rare events, and other nonlinear dynamical features not accessible through classical SSA. Applied to 1983-2006 satellite infrared brightness temperature data averaged over the global tropical belt, the method reveals a wealth of spatiotemporal patterns, most notably the 30-90-day Madden-Julian oscillation (MJO). Using the Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment period as an example, representative modes associated with the MJO are reconstructed. The recovered modes augment Nakazawa's classical hierarchical structure of intraseasonal variability with intermediate modes between the fundamental MJO envelope and super cloud clusters.","PeriodicalId":270712,"journal":{"name":"2012 Conference on Intelligent Data Understanding","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123413561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16