scholarly journals A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis

Sensors ◽  
2009 ◽  
Vol 10 (1) ◽  
pp. 241-253 ◽  
Author(s):  
Daqi Zhu ◽  
Jie Bai ◽  
Simon Yang
Keyword(s):  
Fault Diagnosis ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Sensor Systems ◽  
Principle Component ◽  
Diagnosis Method

scholarly journals Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoming Xu ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Principal Components ◽  
Information Entropy ◽  
Principle Component Analysis ◽  
Information Gain ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Original Dataset ◽  
Diagnosis Method

In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

Four challenges for cognitive neuroscience and the cortico-hippocampal division of memory

2003 ◽  
Vol 26 (6) ◽  
pp. 681-682
Author(s):  
Harry Howard
Keyword(s):  
Independent Component Analysis ◽  
Recent Work ◽  
Cognitive Neuroscience ◽  
Principle Component Analysis ◽  
Pattern Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Principle Component ◽  
Complementary Methods ◽  
Current State

Jackendoff's criticisms of the current state of theorization in cognitive neuroscience are defused by recent work on the computational complementarity of the hippocampus and neocortex. Such considerations lead to a grounding of Jackendoff's processing model in the complementary methods of pattern analysis effected by independent component analysis (ICA) and principle component analysis (PCA).

Sign in / Sign up

Export Citation Format

Share Document