Iteration Algorithms and Structural Models for Increase of the Accuracy of Electrical Signal Transducers

2005 ◽  
Vol 64 (2) ◽  
pp. 155-165
Author(s):  
L. I. Volgin
Author(s):  
Caroline Wehner ◽  
Ulrike Maaß ◽  
Marius Leckelt ◽  
Mitja D. Back ◽  
Matthias Ziegler

Abstract. The structure, correlates, and assessment of the Dark Triad are widely discussed in several fields of psychology. Based on the German version of the Short Dark Triad (SDT), we add to this by (a) providing a competitive test of existing structural models, (b) testing the nomological network, and (c) proposing an ultrashort 9-item version of the SDT (uSDT). A sample of N = 969 participants provided data on the SDT and a range of further measures. Our competitive test of five structural models revealed that fit indices and nomological network assumptions were best met in a three-factor model, with separate factors for psychopathy, Machiavellianism, and narcissism. The results provided an extensive overview of the raw, unique, and shared associations of Dark Triad dimensions with narcissism facets, sadism, impulsivity, self-esteem, sensation seeking, the Big Five, maladaptive personality traits, sociosexual orientation, and behavioral criteria. Finally, the uSDT exhibited satisfactory psychometric properties. The highest overlap in expected relations between SDT and uSDT, and convergent and discriminant measures was also found for the three-factor model. Our study underlines the utility of a three-factor model of the Dark Triad, extends findings on its nomological network, and provides an ultrashort instrument.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 439-446
Author(s):  
Gildas Diguet ◽  
Gael Sebald ◽  
Masami Nakano ◽  
Mickaël Lallart ◽  
Jean-Yves Cavaillé

Magneto Rheological Elastomers (MREs) are composite materials based on an elastomer filled by magnetic particles. Anisotropic MRE can be easily manufactured by curing the material under homogeneous magnetic field which creates column of particles. The magnetic and elastic properties are actually coupled making these MREs suitable for energy conversion. From these remarkable properties, an energy harvesting device is considered through the application of a DC bias magnetic induction on two MREs as a metal piece is applying an AC shear strain on them. Such strain therefore changes the permeabilities of the elastomers, hence generating an AC magnetic induction which can be converted into AC electrical signal with the help of a coil. The device is simulated with a Finite Element Method software to examine the effect of the MRE parameters, the DC bias magnetic induction and applied shear strain (amplitude and frequency) on the resulting electrical signal.


2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.


2006 ◽  
Vol 176 (8) ◽  
pp. 833 ◽  
Author(s):  
Gari N. Sarkisov
Keyword(s):  

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 857-864
Author(s):  
S. N. Gangadharan ◽  
E. Nikolaidis ◽  
K. Lee ◽  
R. T. Haftka ◽  
R. Burdisso

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