Reconstruction of Cross-Sectional Data Using Implicit Solid Modelling

1996 ◽  
Author(s):  
Chek T. Lim ◽  
Mark T. Ensz
Keyword(s):  
Cross Sections ◽  
Data Sets ◽  
Solid Modelling ◽  
Cross Sectional ◽  
Data Set ◽  
A New Technique ◽  
Implicit Solid ◽  
The Cost ◽  
3D Solid ◽  
Spatial Locations

Abstract In this paper, we present a new technique for constructing mathematical representations of solids from cross-sectional data sets. A collection of 2D cross-sections is generated from the sliced data by merging circular primitives using Implicit Solid Modelling (ISM) techniques which approximate Boolean unions. The spatial locations and radii of the circles for each slice are determined through a nonlinear optimization process. The cost function employed in these optimizations is a measure of discrepancies in the distance from points to the boundary of the reconstructed cross-section. The starting configuration of the optimization, (i.e. initial size and location of the primitives) is determined from a 2D Delaunay triangulation of each slice of the data set. A morphing technique utilizing blending functions is applied to merge the implicit functions describing each slice into a 3D solid. The effectiveness of the algorithm is demonstrated through the reconstruction of several sample data sets, including a femur and a vertebra.

scholarly journals Six years of total ozone column measurements from SCIAMACHY nadir observations

2009 ◽  
Vol 2 (1) ◽  
pp. 87-98 ◽  
Author(s):  
C. Lerot ◽  
M. Van Roozendael ◽  
J. van Geffen ◽  
J. van Gent ◽  
C. Fayt ◽  
...  
Keyword(s):  
Cross Sections ◽  
Total Ozone ◽  
Large Scale ◽  
European Space Agency ◽  
Data Sets ◽  
Data Set ◽  
Ozone Data ◽  
Space Agency ◽  
German Aerospace ◽  
The Impact

Abstract. Total O3 columns have been retrieved from six years of SCIAMACHY nadir UV radiance measurements using SDOAS, an adaptation of the GDOAS algorithm previously developed at BIRA-IASB for the GOME instrument. GDOAS and SDOAS have been implemented by the German Aerospace Center (DLR) in the version 4 of the GOME Data Processor (GDP) and in version 3 of the SCIAMACHY Ground Processor (SGP), respectively. The processors are being run at the DLR processing centre on behalf of the European Space Agency (ESA). We first focus on the description of the SDOAS algorithm with particular attention to the impact of uncertainties on the reference O3 absorption cross-sections. Second, the resulting SCIAMACHY total ozone data set is globally evaluated through large-scale comparisons with results from GOME and OMI as well as with ground-based correlative measurements. The various total ozone data sets are found to agree within 2% on average. However, a negative trend of 0.2–0.4%/year has been identified in the SCIAMACHY O3 columns; this probably originates from instrumental degradation effects that have not yet been fully characterized.

The Legal Context of Turnout

2013 ◽  
Author(s):  
Jan E. Leighley ◽  
Jonathan Nagler
Keyword(s):  
Voter Turnout ◽  
State Level ◽  
Electoral Rules ◽  
Cross Sectional ◽  
Data Set ◽  
Election Administration ◽  
Legal Context ◽  
Electoral Impact ◽  
The Cost ◽  
Cost Of Voting

This chapter considers the electoral impact the new, wider array of voter registration and election administration laws using a new data set collected on state electoral rules between 1972 and 2008. States vary tremendously as to how easy it is to register and to vote, and previous research suggests that these laws affect who votes because they change the cost of voting. However, most of these studies rely on cross-sectional data, and usually consider the influence of one reform at a time. The chapter provides aggregate (state-level) analyses of the effects of changes in these rules on voter turnout. These analyses help us address the question of whether overall voter turnout has increased as a result of these legal changes. It finds modest effects of election day registration, of absentee voting, and of moving the closing date for registration closer to the election on overall turnout. The effect of early voting is less clear.

Classification with Local Clustering in Imbalanced Data Sets

2011 ◽  
Vol 219-220 ◽  
pp. 151-155 ◽  
Author(s):  
Hua Ji ◽  
Hua Xiang Zhang
Keyword(s):  
Data Distribution ◽  
Imbalanced Data ◽  
Support Vector ◽  
Data Sets ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Local Clustering ◽  
Rare Class ◽  
Novel Method ◽  
The Cost

In many real-world domains, learning from imbalanced data sets is always confronted. Since the skewed class distribution brings the challenge for traditional classifiers because of much lower classification accuracy on rare classes, we propose the novel method on classification with local clustering based on the data distribution of the imbalanced data sets to solve this problem. At first, we divide the whole data set into several data groups based on the data distribution. Then we perform local clustering within each group both on the normal class and the disjointed rare class. For rare class, the subsequent over-sampling is employed according to the different rates. At last, we apply support vector machines (SVMS) for classification, by means of the traditional tactic of the cost matrix to enhance the classification accuracies. The experimental results on several UCI data sets show that this method can produces much higher prediction accuracies on the rare class than state-of-art methods.

Rough Set Based Clustering Using Active Learning Approach

2011 ◽  
Vol 2 (4) ◽  
pp. 12-23 ◽  
Author(s):  
Rekha Kandwal ◽  
Prerna Mahajan ◽  
Ritu Vijay
Keyword(s):  
Active Learning ◽  
Rough Set ◽  
Rough Set Theory ◽  
Hamming Distance ◽  
Theoretical Background ◽  
Learning Approach ◽  
Data Sets ◽  
Data Set ◽  
The Cost ◽  
The Given

This paper revisits the problem of active learning and decision making when the cost of labeling incurs cost and unlabeled data is available in abundance. In many real world applications large amounts of data are available but the cost of correctly labeling it prohibits its use. In such cases, active learning can be employed. In this paper the authors propose rough set based clustering using active learning approach. The authors extend the basic notion of Hamming distance to propose a dissimilarity measure which helps in finding the approximations of clusters in the given data set. The underlying theoretical background for this decision is rough set theory. The authors have investigated our algorithm on the benchmark data sets from UCI machine learning repository which have shown promising results.

scholarly journals Normative Influences on Adolescents’ Self-Reported Pro-Environmental Behaviors: The Role of Parents and Friends

2017 ◽  
Vol 51 (3) ◽  
pp. 288-314 ◽  
Author(s):  
Silvia Collado ◽  
Henk Staats ◽  
Patricia Sancho
Keyword(s):  
Environmental Behavior ◽  
Cross Sectional Study ◽  
Data Sets ◽  
Bias Effect ◽  
Cross Sectional ◽  
Environmental Behaviors ◽  
Data Set ◽  
Personal Norms ◽  
Environmental Actions ◽  
Role Of Parents

Pro-environmental behavioral patterns are influenced by relevant others’ actions and expectations. Studies about the intergenerational transmission of environmentalism have demonstrated that parents play a major role in their children’s pro-environmental actions. However, little is known about how other social agents may shape youth’s environmentalism. This cross-sectional study concentrates on the role that parents and peers have in the regulation of 12- to 19-year-olds’ pro-environmental behaviors. We also consider the common response bias effect by examining the associations between parents, peers, and adolescents’ pro-environmentalism in two independent data sets. Data Set 1 ( N = 330) includes adolescents’ perceptions of relevant others’ behaviors. Data Set 2 ( N = 152) includes relevant others’ self-reported pro-environmental behavior. Our results show that parents’ and peers’ descriptive and injunctive norms have a direct effect on adolescents’ pro-environmental behavior and an indirect one, through personal norms. Adolescents seem to be accurate in the perception of their close ones’ environmental actions.

Stream Flow Measurement: Development of a Relationship between the Float Method and the Current Meter Method

2020 ◽  
Author(s):  
Ishmael Kanu
Keyword(s):  
Flow Measurement ◽  
River Flow ◽  
Measurement Development ◽  
Data Sets ◽  
Cross Sectional ◽  
Data Set ◽  
Flow Measurements ◽  
Float Method ◽  
Hydropower Potential ◽  
A Current

<p>In diverse developments such as hydropower potential assessment, flood mitigation studies, water supply, irrigation, bridge and culvert hydraulics, the magnitude of stream or river flows is a potential design input. Several methods of flow measurement exist; some basic and some more sophisticated. The sophisticated methods use equipment which, although they provide more accurate and reliable results, are invariably expensive and unaffordable by many institutions that depend greatly on flow records to plan and execute their projects. The need for skilled expertise in the use of these equipment and the associated maintenance problems preclude them from consideration in most projects developed and executed in developing regions such as Africa. For countries or institutions in these regions, there is a need for less expensive, but relatively reliable methods for stream or river flow measurement to be investigated; methods that require no equipment maintenance schemes. One such method is the float method in which the velocity of an object thrown in a river is measured by recording the time taken for the object to traverse a known distance and multiplying the velocity by the cross-sectional area of the river or stream. This method looks simplistic, but when flows obtained from it are correlated with those obtained from the more accurate and conventional methods, reliable results can be obtained. In this study, flow measurements were done at 42 different stream sections using the float method and a more reliable and generally accepted but expensive flow measurement method using a current meter. A statistical relationship was then developed between the flows obtained by the two methods by fitting a linear regression model to the set of data points obtained at the 42 locations on several reaches of selected streams in the western area of Freetown.  The study was conducted on streams with tranquil or laminar flow with flow magnitudes in the range of 0.39 m3/s to 4 m3/s in practically straight reaches with stable banks. The material of the stream beds was laterite soil. Thirty-two data sets were used to develop and calibrate the model and the remaining ten data sets were used to verify the model. The current meter method flows were regressed on the float method flows. For a significance level of 5%, the predicted flows of a current meter, given a float method flow, showed a high level of agreement with the observed current meter flows for the tested data set. </p>

scholarly journals Impacts of Sample Size and Quality-Adjusted Imputed Prices on Own-Price Elasticities Estimated Using Cross-Sectional Data

2003 ◽  
Vol 35 (2) ◽  
pp. 415-421
Author(s):  
Matthew C. Stockton
Keyword(s):  
Sample Size ◽  
Data Sets ◽  
Sample Sizes ◽  
Food Category ◽  
Cross Sectional ◽  
Data Set ◽  
Food Away From Home ◽  
Price Elasticities ◽  
Quality Adjustment

Cross-sectional data sets containing expenditure and quantity information are typically used to calculate quality-adjusted imputed prices. Do sample size and quality adjustment of price statistically alter estimates for own-price elasticities? This paper employs a data set pertaining to three food categories—pork, cheese, and food away from home—with four sample sizes for each food category. Twelve sample sizes were used for both adjusted and unadjusted prices to derive elasticities. No statistical differences were found between own-price elasticities among sample sizes. However, elasticities that were based on adjusted price imputations were significantly different from those that were based on unadjusted prices.

SHOCK PHYSICS DATA RECONSTRUCTION USING SUPPORT VECTOR REGRESSION

2006 ◽  
Vol 17 (09) ◽  
pp. 1313-1325 ◽  
Author(s):  
NIKITA A. SAKHANENKO ◽  
GEORGE F. LUGER ◽  
HANNA E. MAKARUK ◽  
JOYSREE B. AUBREY ◽  
DAVID B. HOLTKAMP
Keyword(s):  
Experimental Data ◽  
Support Vector ◽  
Data Sets ◽  
Shock Physics ◽  
Data Set ◽  
Velocity Surface ◽  
The Cost ◽  
Physical Phenomena ◽  
Physics Experiments ◽  
Data Estimation

This paper considers a set of shock physics experiments that investigate how materials respond to the extremes of deformation, pressure, and temperature when exposed to shock waves. Due to the complexity and the cost of these tests, the available experimental data set is often very sparse. A support vector machine (SVM) technique for regression is used for data estimation of velocity measurements from the underlying experiments. Because of good generalization performance, the SVM method successfully interpolates the experimental data. The analysis of the resulting velocity surface provides more information on the physical phenomena of the experiment. Additionally, the estimated data can be used to identify outlier data sets, as well as to increase the understanding of the other data from the experiment.

scholarly journals A Completeness and Complementarity Analysis of the Data Sources in the NOAA In Situ Sea Surface Temperature Quality Monitor (iQuam) System

2021 ◽  
Vol 13 (18) ◽  
pp. 3741
Author(s):  
Haifeng Zhang ◽  
Alexander Ignatov
Keyword(s):  
Sea Surface ◽  
Data Sets ◽  
Data Set ◽  
In Situ Data ◽  
Satellite Sst ◽  
Complementarity Analysis ◽  
The Cost ◽  
Marine Environment Monitoring ◽  
Drifting Buoys

In situ sea surface temperatures (SST) are the key component of the calibration and validation (Cal/Val) of satellite SST retrievals and data assimilation (DA). The NOAA in situ SST Quality Monitor (iQuam) aims to collect, from various sources, all available in situ SST data, and integrate them into a maximally complete, uniform, and accurate dataset to support these applications. For each in situ data type, iQuam strives to ingest data from several independent sources, to ensure most complete coverage, at the cost of some redundancy in data feeds. The relative completeness of various inputs and their consistency and mutual complementarity are often unknown and are the focus of this study. For four platform types customarily employed in satellite Cal/Val and DA (drifting buoys, tropical moorings, ships, and Argo floats), five widely known data sets are analyzed: (1) International Comprehensive Ocean-Atmosphere Data Set (ICOADS), (2) Fleet Numerical Meteorology and Oceanography Center (FNMOC), (3) Atlantic Oceanographic and Meteorological Laboratory (AOML), (4) Copernicus Marine Environment Monitoring Service (CMEMS), and (5) Argo Global Data Assembly Centers (GDACs). Each data set reports SSTs from one or more platform types. It is found that drifting buoys are more fully represented in FNMOC and CMEMS. Ships are reported in FNMOC and ICOADS, which are best used in conjunction with each other, but not in CMEMS. Tropical moorings are well represented in ICOADS, FNMOC, and CMEMS. Some CMEMS mooring reports are sampled every 10 min (compared to the standard 1 h sampling in all other datasets). The CMEMS Argo profiling data set is, as expected, nearly identical with those from the two Argo GDACs.

scholarly journals A framework for spatial normalization and voxelwise analysis of diffusion propagators in multiple MAP-MRI data sets

2019 ◽  
Author(s):  
Alexandru V. Avram ◽  
Adam S. Bernstein ◽  
M. Okan Irfanoglu ◽  
Craig C. Weinkauf ◽  
Martin Cota ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Basis Functions ◽  
Single Subject ◽  
Data Sets ◽  
Cross Sectional ◽  
Data Set ◽  
Diffusion Weighted Images ◽  
Multiple Data Sets ◽  
Robust Processing ◽  
Jensen Shannon Divergence

AbstractWe describe a pipeline for constructing a study-specific template of diffusion propagators measured with mean apparent propagator (MAP) MRI that supports direct voxelwise analysis of differences between propagators across multiple data sets. The pipeline leverages the fact that MAP-MRI is a generalization of diffusion tensor imaging (DTI) and combines simple and robust processing steps from existing tensor-based image registration methods. First, we compute a DTI study template which provides the reference frame and scaling parameters needed to construct a standardized set of MAP-MRI basis functions at each voxel in template space. Next, we transform each subjects diffusion data, including diffusion weighted images (DWIs) and gradient directions, from native to template space using the corresponding tensor-based deformation fields. Finally, we fit MAP coefficients in template space to the transformed DWIs of each subject using the standardized template of MAP basis functions. The consistency of MAP basis functions across all data sets in template space allows us to: 1. compute a template of propagators by directly averaging MAP coefficients and 2. quantify voxelwise differences between co-registered propagators using the angular dissimilarity, or a probability distance metric, such as the Jensen-Shannon Divergence. We illustrate the application of this method by generating a template of MAP propagators for a cohort of healthy volunteers and show a proof-of-principle example of how this pipeline may be used to detect subtle differences between propagators in a single-subject longitudinal clinical data set. The ability to standardize and analyze multiple clinical MAP-MRI data sets could improve assessments in cross-sectional and single-subject longitudinal clinical studies seeking to detect subtle microstructural changes, such as those occurring in mild traumatic brain injury (mTBI), or during the early stages of neurodegenerative diseases, or cancer.

Sign in / Sign up

Export Citation Format

Share Document