A new data reduction scheme for the fragmentation testing of polymer composites

1995 ◽  
Vol 30 (3) ◽  
pp. 683-692 ◽  
Author(s):  
T. Lacroix ◽  
R. Keunings ◽  
M. Desaeger ◽  
I. Verpoest
Keyword(s):  
Polymer Composites ◽  
Data Reduction ◽  
Reduction Scheme

Adaptive Data Reduction Scheme for SSD-based Host-side Caches in VDI Storage

2013 ◽  
Author(s):  
Wanhui He ◽  
Nong Xiao ◽  
Fang Liu ◽  
Yuxuan Xing ◽  
Bo Chen
Keyword(s):  
Data Reduction ◽  
Reduction Scheme

Enhancing Density-Based Data Reduction Using Entropy

2006 ◽  
Vol 18 (2) ◽  
pp. 470-495 ◽  
Author(s):  
D. Huang ◽  
Tommy W. S. Chow
Keyword(s):  
Data Analysis ◽  
Density Estimation ◽  
Data Reduction ◽  
Large Data ◽  
Small Data ◽  
Reduction Scheme ◽  
Data Set ◽  
Data Subset ◽  
Large Data Set ◽  
Comparative Results

Data reduction algorithms determine a small data subset from a given large data set. In this article, new types of data reduction criteria, based on the concept of entropy, are first presented. These criteria can evaluate the data reduction performance in a sophisticated and comprehensive way. As a result, new data reduction procedures are developed. Using the newly introduced criteria, the proposed data reduction scheme is shown to be efficient and effective. In addition, an outlier-filtering strategy, which is computationally insignificant, is developed. In some instances, this strategy can substantially improve the performance of supervised data analysis. The proposed procedures are compared with related techniques in two types of application: density estimation and classification. Extensive comparative results are included to corroborate the contributions of the proposed algorithms.

Close-Range Photogrammetric Data Reduction Scheme

1968 ◽  
Vol 94 (2) ◽  
pp. 211-226
Author(s):  
Dennis E. Moellman ◽  
Houssam M. Karara
Keyword(s):  
Data Reduction ◽  
Reduction Scheme ◽  
Close Range

An Efficient Cluster-Based Routing Protocol for WSNs Using Time Series Prediction-Based Data Reduction Scheme

2013 ◽  
Vol 3 (3) ◽  
pp. 18-34 ◽  
Author(s):  
Dhirendra Pratap Singh ◽  
Vikrant Bhateja ◽  
Surender Kumar Soni
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Routing Protocol ◽  
Data Reduction ◽  
Energy Efficient ◽  
Physical Phenomenon ◽  
Cluster Formation ◽  
Time Series Prediction ◽  
Reduction Scheme ◽  
Cluster Based Routing

Wireless Sensor Networks (WSNs) consists of number of tiny, low cost and low power sensor nodes to monitor some physical phenomenon like temperature, pressure, vibration, seismic events, landslide detection etc. The communication subsystem in WSNs is primarily responsible for energy consumption. This becomes a constraint in these N/Ws owing to the usage of non-rechargeable battery having a limited power supply. Hence, the present work incorporates a time series prediction based data reduction scheme to develop an energy efficient routing protocol for minimizing energy consumption of WSNs. In this paper, three approaches have been devised involving energy efficient cluster based routing protocol with residual energy and distance based cluster head selection method. The cluster formation in the proposed approaches is different from Low Energy Adaptive Clustering Hierarchy (LEACH) routing protocol as its clusters will be fixed throughout the life of WSN. This leads to a reduction in overheads during cluster formation. Further, inter-cluster and intra-cluster transmissions are also minimized which results in an overall reduction in energy consumption of WSNs.

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