Data Fusion Analysis For Test Validation System

2009 ◽  
Author(s):  
Mark H. Hammond ◽  
Christian Minor ◽  
Susan Rose-Pehrsson
Keyword(s):  
Data Fusion ◽  
Test Validation ◽  
Fusion Analysis

Data Fusion Analysis for Range Test Validation System

2010 ◽  
Author(s):  
Christian P. Minor ◽  
Mark H. Hammond ◽  
Susan L. Rose-Pehrsson
Keyword(s):  
Data Fusion ◽  
Test Validation ◽  
Fusion Analysis ◽  
Range Test

scholarly journals Metabolic phenotype-microRNA data fusion analysis of the systemic consequences of Roux-en-Y gastric bypass surgery

2015 ◽  
Vol 39 (7) ◽  
pp. 1126-1134 ◽  
Author(s):  
Q Wu ◽  
J V Li ◽  
F Seyfried ◽  
C W le Roux ◽  
H Ashrafian ◽  
...  
Keyword(s):  
Gastric Bypass ◽  
Data Fusion ◽  
Bypass Surgery ◽  
Gastric Bypass Surgery ◽  
Metabolic Phenotype ◽  
Fusion Analysis

Data fusion analysis applied to different climate change models: An application to the energy consumptions of a building office

2019 ◽  
Vol 196 ◽  
pp. 240-254 ◽  
Author(s):  
Francesco Guarino ◽  
Daniele Croce ◽  
Ilenia Tinnirello ◽  
Maurizio Cellura
Keyword(s):  
Climate Change ◽  
Data Fusion ◽  
Change Models ◽  
Energy Consumptions ◽  
Fusion Analysis

Chromatographic and spectroscopic data fusion analysis for interpretation of photodegradation processes

2011 ◽  
Vol 1218 (51) ◽  
pp. 9260-9268 ◽  
Author(s):  
Sílvia Mas ◽  
Romà Tauler ◽  
Anna de Juan
Keyword(s):  
Data Fusion ◽  
Spectroscopic Data ◽  
Fusion Analysis

scholarly journals Data Fusion Analysis based Active Thermography for SNR Enhancement

2019 ◽  
Author(s):  
Wontae Kim ◽  
Ranjit Shrestha, ◽  
Manyong Choi
Keyword(s):  
Data Fusion ◽  
Active Thermography ◽  
Fusion Analysis

scholarly journals Multisource Heterogeneous Data Fusion Analysis of Regional Digital Construction Based on Machine Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Mengmeng Jiang ◽  
Qiong Wu ◽  
Xuetao Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Data Fusion ◽  
Heterogeneous Data ◽  
False Alarms ◽  
Network Algorithms ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Fusion Analysis

In modern urban construction, digitalization has become a trend, but the single source of information of traditional algorithms can not meet people’s needs, so the data fusion technology needs to draw estimation and judgment from multisource data to increase the confidence of data, improve reliability, and reduce uncertainty. In order to understand the influencing factors of regional digitalization, this paper conducts multisource heterogeneous data fusion analysis based on regional digitalization of machine learning, using decision tree and artificial neural network algorithm, compares the management efficiency and satisfaction of school population under different algorithms, and understands the data fusion and construction under different algorithms. According to the results, decision-making tree and artificial neural network algorithms were more efficient than traditional methods in building regional digitization, and their magnitude was about 60% higher. More importantly, the machine learning-based methods in multisource heterogeneous data fusion have been better than traditional calculation methods both in computational efficiency and misleading rate with respect to false alarms and missed alarms. This shows that machine learning methods can play an important role in the analysis of multisource heterogeneous data fusion in regional digital construction.

scholarly journals Ultrasonic Health Monitoring of Lithium-Ion Batteries

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 751 ◽  
Author(s):  
Yi Wu ◽  
Youren Wang ◽  
Winco K. C. Yung ◽  
Michael Pecht
Keyword(s):  
Data Fusion ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Management ◽  
Early Failure ◽  
Battery Management Systems ◽  
Ultrasonic Sensing ◽  
Battery Degradation ◽  
Fusion Analysis ◽  
Fusion Approach

Because of the complex physiochemical nature of the lithium-ion battery, it is difficult to identify the internal changes that lead to battery degradation and failure. This study develops an ultrasonic sensing technique for monitoring the commercial lithium-ion pouch cells and demonstrates this technique through experimental studies. Data fusion analysis is implemented using the ultrasonic sensing data to construct a new battery health indicator, thus extending the capabilities of traditional battery management systems. The combination of the ultrasonic sensing and data fusion approach is validated and shown to be effective for degradation assessment as well as early failure indication.

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