Something from (almost) nothing: an overview of tire modeling in F1 using minimal data sets

2018 ◽  
pp. 901-909
Author(s):  
Vasilis Tsinias
Keyword(s):  
Data Sets ◽  
Minimal Data ◽  
Tire Modeling

Quantization based Sequence Generation and Subsequence Pruning for Data Mining Applications

2012 ◽  
pp. 94-110 ◽  
Author(s):  
T. Ravindra Babu ◽  
M. Narasimha Murty ◽  
S. V. Subrahmanya
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Large Datasets ◽  
Superior Performance ◽  
Data Sets ◽  
Sequence Generation ◽  
Data Compaction ◽  
Clustering And Classification ◽  
Minimal Data

Data Mining deals with efficient algorithms for dealing with large data. When such algorithms are combined with data compaction, they would lead to superior performance. Approaches to deal with large data include working with representatives of data instead of entire data. The representatives should preferably be generated with minimal data scans. In the current chapter we discuss working with methods of lossy and non-lossy data compression methods combined with clustering and classification of large datasets. We demonstrate the working of such schemes on two large data sets.

Quantization based Sequence Generation and Subsequence Pruning for Data Mining Applications

Data Mining ◽  
2013 ◽  
pp. 734-750
Author(s):  
T. Ravindra Babu ◽  
M. Narasimha Murty ◽  
S. V. Subrahmanya
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Large Datasets ◽  
Superior Performance ◽  
Data Sets ◽  
Sequence Generation ◽  
Data Compaction ◽  
Clustering And Classification ◽  
Minimal Data

Data Mining deals with efficient algorithms for dealing with large data. When such algorithms are combined with data compaction, they would lead to superior performance. Approaches to deal with large data include working with representatives of data instead of entire data. The representatives should preferably be generated with minimal data scans. In the current chapter we discuss working with methods of lossy and non-lossy data compression methods combined with clustering and classification of large datasets. We demonstrate the working of such schemes on two large data sets.

scholarly journals Predicting natural streamflows in regulated snowmelt-driven watersheds using regionalization methods

2013 ◽  
Vol 10 (7) ◽  
pp. 9435-9476
Author(s):  
D. Kim ◽  
J. Kaluarachchi
Keyword(s):  
Snow Water Equivalent ◽  
Precipitation Index ◽  
Data Availability ◽  
Data Sets ◽  
Tank Model ◽  
Natural Flow ◽  
Snow Water ◽  
Minimal Data ◽  
Canal Networks ◽  
Ungauged Watersheds

Abstract. Predicting streamflows in snow-fed watersheds in the Western United States is important for water allocation. Since many of these watersheds are heavily regulated through canal networks and reservoirs, predicting expected natural flows and therefore water availability under limited data is always a challenge. This study investigates the applicability of the flow duration curve (FDC) method for predicting natural flows in gauged and ungauged snow-fed watersheds. Point snow observations, air temperature, precipitation, and snow water equivalent, are used to simulate snowmelt process with SNOW-17 model and extended to streamflow generation by a FDC method with modified current precipitation index. For regulated (ungauged) watersheds, a parametric regional FDC method is applied to reconstruct natural flow. For comparison, a simplified Tank Model is used as well. The proximity regionalization method is used to generate streamflow using the Tank Model in ungauged watersheds. The results show that the FDC method can produce acceptable natural flow estimates in both gauged and ungauged watersheds under data limited conditions. The performance of the FDC method is better in watersheds with relatively low evapotranspiration (ET). Multiple donor data sets including current precipitation index are recommended to reduce uncertainty of the regional FDC method for ungauged watersheds. In spite of its simplicity, the FDC method can perform better than the Tank Model under minimal data availability.

Misuse of ‘Break-the-Glass' Policies in Hospitals

2018 ◽  
Vol 12 (3) ◽  
pp. 100-122
Author(s):  
Benjamin Stark ◽  
Heiko Gewald ◽  
Heinrich Lautenbacher ◽  
Ulrich Haase ◽  
Siegmar Ruff
Keyword(s):  
Health Information ◽  
Information Needs ◽  
Medical Center ◽  
Real Life ◽  
Hospital Setting ◽  
Data Access ◽  
Data Sets ◽  
Data Set ◽  
Real Life Data ◽  
Minimal Data

This article describes how the information about an individual's personal health is among ones most sensitive and important intangible belongings. When health information is misused, serious non-revertible damage can be caused, e.g. through making intimidating details public or leaking it to employers, insurances etc. Therefore, health information needs to be treated with the highest degree of confidentiality. In practice it proves difficult to achieve this goal. In a hospital setting medical staff across departments often needs to access patient data without directly obvious reasons, which makes it difficult to distinguish legitimate from illegitimate access. This article provides a mechanism to classify transactions at a large university medical center into plausible and questionable data access using a real-life data set of more than 60,000 transactions. The classification mechanism works with minimal data requirements and unsupervised data sets. The results were evaluated through manual cross-checks internally and by a group of external experts. Consequently, the hospital's data protection officer is now able to focus on analyzing questionable transactions instead of checking random samples.

From acquisition footprints to true amplitude

Geophysics ◽  
2002 ◽  
Vol 67 (3) ◽  
pp. 830-839 ◽  
Author(s):  
Stéphane Gesbert
Keyword(s):  
Time Lapse ◽  
Theoretical Framework ◽  
Data Sets ◽  
Variable Geometry ◽  
Quantitative Interpretation ◽  
Prestack Depth Migration ◽  
Seismic Surveys ◽  
Depth Migration ◽  
Minimal Data ◽  
Imaging Artifacts

This paper addresses the issue of the sensitivity of 3‐D prestack depth migration (PSDM) with respect to the acquisition geometry of 3‐D seismic surveys. Using the theoretical framework of PSDM, I show how acquisition‐related imaging artifacts—the acquisition footprints—can arise. I then show how the acquisition footprint can be suppressed in two steps by (1) partitioning the 3‐D survey into minimal data sets, each to be migrated separately, and (2) applying a robust variable‐geometry PSDM quadrature. The validity of the method is demonstrated on synthetic parallel and antiparallel multistreamer data and cross‐spread data. The proposed two‐step solution can play an important role in projects where amplitude integrity and fidelity are paramount, e.g., quantitative interpretation and time‐lapse surveying. The concept of minimal data also fills a gap in understanding the relation between acquisition and imaging.

scholarly journals Unique parameter identification for cardiac diagnosis in critical care using minimal data sets

2010 ◽  
Vol 99 (1) ◽  
pp. 75-87 ◽  
Author(s):  
C.E. Hann ◽  
J.G. Chase ◽  
T. Desaive ◽  
C.B. Froissart ◽  
J. Revie ◽  
...  
Keyword(s):  
Critical Care ◽  
Parameter Identification ◽  
Data Sets ◽  
Minimal Data ◽  
Unique Parameter ◽  
Cardiac Diagnosis

scholarly journals Mapping the landscape of global programmes to evaluate health interventions in pregnancy: the need for harmonised approaches, standards and tools

2018 ◽  
Vol 3 (5) ◽  
pp. e001053 ◽  
Author(s):  
Patrick L F Zuber ◽  
Allisyn C Moran ◽  
Doris Chou ◽  
Françoise Renaud ◽  
Christine Halleux ◽  
...  
Keyword(s):  
Low Income ◽  
Health Interventions ◽  
Data Sets ◽  
Middle Income ◽  
Childhood Health ◽  
The Sustainable Development ◽  
Early Childhood Health ◽  
Effective Interventions ◽  
Minimal Data ◽  
Development Goals

Pregnant women and their babies are among the populations most vulnerable to untoward health outcomes. Yet current standards for evaluating health interventions cannot be met during pregnancy because of lack of adequate evidence. The situation is even more concerning in low-income and middle-income countries, where the need for effective interventions is the greatest. Meeting the Sustainable Development Goals for health will require strengthened attention to maternal and child health. In this paper we examine ongoing initiatives aimed at improving the assessment of maternal interventions. We review current methodologies to monitor outcomes of maternal interventions and identify where harmonisation is needed. Based on this analysis we identify settings where different minimal data sets should be considered taking into consideration the clinical realities. Stronger coordination mechanisms and a roadmap to support harmonised monitoring of maternal interventions across programmes and partners, working on improving pregnancy and early childhood health events, will greatly enhance ability to generate evidence-based policies.

scholarly journals A Comparison of Nursing Minimal Data Sets

1998 ◽  
Vol 5 (2) ◽  
pp. 152-163 ◽  
Author(s):  
W. T. F. Goossen ◽  
P. J. M. M. Epping ◽  
T. Feuth ◽  
T. W. N. Dassen ◽  
A. Hasman ◽  
...  
Keyword(s):  
Data Sets ◽  
Minimal Data
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