Influence of Different Signal Characteristics on PAR Model Stability

2015 ◽  
pp. 89-104
Author(s):  
Agnieszka Wylomanska ◽  
Jakub Obuchowski ◽  
Radosław Zimroz ◽  
Harry Hurd
Keyword(s):  
Model Stability ◽  
Signal Characteristics

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

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.

Fatigue crack detection method using analysis of vibration signal

2017 ◽  
Vol 1 (20) ◽  
pp. 63-74 ◽  
Author(s):  
Arkadiusz Rychlik ◽  
Krzysztof Ligier
Keyword(s):  
Crack Detection ◽  
Fatigue Cracks ◽  
Fatigue Cracking ◽  
Vibration Signal ◽  
Technical Condition ◽  
Visual Methods ◽  
Signal Characteristics ◽  
Fatigue Crack Detection ◽  
Sample Structure ◽  
Change Identification

This paper discusses the method used to identify the process involving fatigue cracking of samples on the basis of selected vibration signal characteristics. Acceleration of vibrations has been chosen as a diagnostic signal in the analysis of sample cross section. Signal characteristics in form of change in vibration amplitudes and corresponding changes in FFT spectrum have been indicated for the acceleration. The tests were performed on a designed setup, where destruction process was caused by the force of inertia of the sample. Based on the conducted tests, it was found that the demonstrated sample structure change identification method may be applied to identify the technical condition of the structure in the aspect of loss of its continuity and its properties (e.g.: mechanical and fatigue cracks). The vibration analysis results have been verified by penetration and visual methods, using a scanning electron microscope.

Seismic signal characteristics and interpretation of the 2020 “6.17” Danba landslide dam failure hazard chain process

Landslides ◽  
2021 ◽  
Author(s):  
Yan Yan ◽  
Yifei Cui ◽  
Dingzhu Liu ◽  
Hui Tang ◽  
Yongjian Li ◽  
...  
Keyword(s):  
Landslide Dam ◽  
Seismic Signal ◽  
Dam Failure ◽  
Chain Process ◽  
Signal Characteristics

scholarly journals An In-Depth Assessment of the New BDS-3 B1C and B2a Signals

2021 ◽  
Vol 13 (4) ◽  
pp. 788
Author(s):  
Qinghua Zhang ◽  
Yongxing Zhu ◽  
Zhengsheng Chen
Keyword(s):  
Carrier Phase ◽  
Density Ratio ◽  
Signal Strength ◽  
Satellite Orbit ◽  
Satellite System ◽  
Earth Orbit ◽  
Base Line ◽  
Signal Characteristics ◽  
Better Than

An in-depth and comprehensive assessment of new observations from BDS-3 satellites is presented, with the main focus on the Carrier-to-Noise density ratio (C/N0), the quality of code and carrier phase observations for B1C and B2a signal. The signal characteristics of geosynchronous earth orbit (GEO), inclined geosynchronous satellite orbit (IGSO) and medium earth orbit (MEO) satellites of BDS-3 were grouped and compared, respectively. The evaluation results of the new B1C and B2a signals of BDS-3 were compared with the previously B1I/B2I/B3I signals and the interoperable signals of GPS, Galileo and quasi-zenith satellite system (QZSS) were compared simultaneously. As expected, the results clearly show that B1C and B2a have better signal strength and higher accuracy, including code and carrier phase observations. The C/N0 of the B2a signal is about 3 dB higher than other signals. One exception is the code observation accuracy of B3I, which value is less than 0.15 m. The carrier precision of B1C and B2a is better than that of B1I/B2I/B3I. Despite difference-in-difference (DD) observation quantity or zero-base line evaluation is adopted, while B1C is about 0.3 mm higher carrier precision than B2a. The BDS-3 MEO satellite and GPS, Galileo, and QZSS satellites have the same level of signal strength, code and phase observation accuracy at the interoperable frequency, namely 1575.42 MHz and 1176.45 MHz which are very suitable for the co-position application.

scholarly journals Efficient symbolic computation of approximated small-signal characteristics of analog integrated circuits

1995 ◽  
Vol 30 (3) ◽  
pp. 327-330 ◽  
Author(s):  
P. Wambacq ◽  
F.V. Fernandez ◽  
G. Gielen ◽  
W. Sansen ◽  
A. Rodriguez-Vazquez
Keyword(s):  
Integrated Circuits ◽  
Symbolic Computation ◽  
Analog Integrated Circuits ◽  
Small Signal ◽  
Signal Characteristics
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