Multi-scale Data Reduction Algorithm of Proteomic Mass Spectrum

10.5580/243c ◽  
2006 ◽  
Vol 5 (1) ◽  
Keyword(s):  
Mass Spectrum ◽  
Data Reduction ◽  
Reduction Algorithm ◽  
Multi Scale ◽  
Scale Data

scholarly journals MorphoNet: an interactive online morphological browser to explore complex multi-scale data

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Bruno Leggio ◽  
Julien Laussu ◽  
Axel Carlier ◽  
Christophe Godin ◽  
Patrick Lemaire ◽  
...  
Keyword(s):  
Multi Scale ◽  
Scale Data

scholarly journals Development of a Data Reduction algorithm for Optical Wide Field Patrol

2013 ◽  
Vol 30 (3) ◽  
pp. 193-206 ◽  
Author(s):  
Sun-Youp Park ◽  
Kang-Hoon Keum ◽  
Seong-Whan Lee ◽  
Ho Jin ◽  
Yung-Sik Park ◽  
...  
Keyword(s):  
Data Reduction ◽  
Wide Field ◽  
Reduction Algorithm

scholarly journals Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153971 ◽  
Author(s):  
Tianxiang Cui ◽  
Yujie Wang ◽  
Rui Sun ◽  
Chen Qiao ◽  
Wenjie Fan ◽  
...  
Keyword(s):  
Primary Production ◽  
River Basin ◽  
Heihe River Basin ◽  
Heihe River ◽  
Multi Scale ◽  
Scale Data ◽  
The Heihe River Basin

In-Process Data Fusion for Process Monitoring and Control of Metal Additive Manufacturing

2021 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Simin Li ◽  
Jennifer Li ◽  
Yande Ndiaye ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Control ◽  
Data Fusion ◽  
Real Time ◽  
Process Data ◽  
Multi Scale ◽  
And Control ◽  
Fusion Framework ◽  
Scale Data ◽  
Metal Additive

Abstract To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the use of multi-modal and in-process sensing techniques to model, monitor and control the process. The data generated from these sensors and process actuators are fused in various ways to advance our understanding of the process and to estimate both process status and part-in-progress states. This paper presents a hierarchical in-process data fusion framework for MAM, consisting of pointwise, trackwise, layerwise and partwise data analytics. Data fusion can be performed at raw data, feature, decision or mixed levels. The multi-scale data fusion framework is illustrated in detail using a laser powder bed fusion process for anomaly detection, material defect isolation, and part quality prediction. The multi-scale data fusion can be generally applied and integrated with real-time MAM process control, near-real-time layerwise repairing and buildwise decision making. The framework can be utilized by the AM research and standards community to rapidly develop and deploy interoperable tools and standards to analyze, process and exploit two or more different types of AM data. Common engineering standards for AM data fusion systems will dramatically improve the ability to detect, identify and locate part flaws, and then derive optimal policies for process control.

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