Comparison of Newton’s Integral in the Space and Frequency Domains

2005 ◽  
pp. 386-391 ◽  
Author(s):  
M. Kuhn ◽  
K. Seitz
Keyword(s):  
Frequency Domains ◽  
Newton’S Integral

Frequency domains of resting state default mode network activity in schizophrenia

2014 ◽  
Vol 45 (01) ◽  
Author(s):  
G Mingoia ◽  
K Langbein ◽  
M Dietzek ◽  
G Wagner ◽  
S Smesny ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Resting State ◽  
Network Activity ◽  
Default Mode ◽  
Frequency Domains

A Design of Comfortable Dental Treatment Sound Based on Chord-forming in Time and Frequency Domains

2015 ◽  
Vol 135 (12) ◽  
pp. 1565-1573
Author(s):  
Yoshitaka Ohshio ◽  
Daisuke Ikefuji ◽  
Yuko Suhara ◽  
Masato Nakayama ◽  
Takanobu Nishiura
Keyword(s):  
Dental Treatment ◽  
Frequency Domains ◽  
Time And Frequency Domains

Fuzzy Extreme Analysis in Time- and Frequency Domains

2011 ◽  
Vol 57 (4) ◽  
Author(s):  
Włodzimierz Pogribny ◽  
Marcin Drzycimski ◽  
Zdzisław Drzycimski
Keyword(s):  
Frequency Domains ◽  
Time And Frequency Domains

Effect of economic indicators, biomass energy on human development in China

2021 ◽  
pp. 0958305X2110220
Author(s):  
Ngo Thai Hung
Keyword(s):  
Human Development ◽  
Global Financial Crisis ◽  
Trade Openness ◽  
Biomass Energy ◽  
Wavelet Coherence ◽  
Causality Test ◽  
Frequency Domains ◽  
Bidirectional Relationship ◽  
The Global Financial Crisis

Previous studies ignored the distinction between short, medium, and long term by decomposing macroeconomic variables and human development index at different time scales. We re-visit the causal association between biomass energy (BIO), economic growth (GDP), trade openness (TRO), industrialization (IND), foreign direct investment (FDI), and human development (HDI) in China on a quarterly scale by scale basis for the period 1990 to 2019 using the tools of wavelet, i.e., wavelet correlation, wavelet coherence and scale by scale Granger causality test. The main findings uncover that IND, TRO, GDP, and BIO positively drive the HDI at low and medium frequencies, while FDI negatively impacts HDI during the sample period. Additionally, there is a bidirectional relationship between GDP and HDI at different time and frequency domains. Specifically, we discover that the positive co-movement is more robust in the aftermath of the global financial crisis, particularly for HDI, BIO, GDP, and TRO at medium frequencies throughout the period under research. Our empirical insights have significant implications for achieving human development sustainability in China.

scholarly journals Application of Ultra Narrow Band Modulation in Enhanced Loran System

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4347
Author(s):  
Boyun Lyu ◽  
Yu Hua ◽  
Jiangbin Yuan ◽  
Shifeng Li
Keyword(s):  
Narrow Band ◽  
Data Rate ◽  
Modulation Method ◽  
Phase Shift Keying ◽  
Spectrum Utilization ◽  
Frequency Domains ◽  
Shift Keying ◽  
Modulation Methods ◽  
Binary Phase Shift Keying ◽  
Signal Time

The Enhanced Loran (eLoran) system is valued for its important role in the positioning, navigation, and timing fields; however, with its current modulation methods, low data rate restricts its development. Ultra narrow band (UNB) modulation is a modulation method with extremely high spectrum utilization. If UNB modulation can be applied to the eLoran system, it will be very helpful. The extended binary phase shift keying modulation in UNB modulation is selected for a detailed study, parameters and application model are designed according to its unique characteristics of signal time and frequency domains, and it is verified through simulation that the application of this modulation not only meets the design constraints of the eLoran system but also does not affect the reception of the respective signals of both parties. Several feasible schemes are compared, analyzed, and selected. Studies have revealed that application of UNB modulation in the eLoran system is feasible, and it will increase the data rate of the system by dozens of times.

Composite load modeling by spatial-temporal deep attention network based on wide-area monitoring systems

2021 ◽  
pp. 1-12
Author(s):  
Omid Izadi Ghafarokhi ◽  
Mazda Moattari ◽  
Ahmad Forouzantabar
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Wide Area ◽  
Load Modeling ◽  
Wide Area Monitoring ◽  
Training Performance ◽  
Spatial Features ◽  
Temporal Features ◽  
Frequency Domains ◽  
Area Monitoring

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.

Global Nonlinear Modelling of Gas Turbine Dynamics Using NARMAX Structures

2001 ◽  
Author(s):  
Neophytos Chiras ◽  
Ceri Evans ◽  
David Rees
Keyword(s):  
Gas Turbine ◽  
Nonlinear Model ◽  
Linear Models ◽  
Search Space ◽  
Frequency Domain Analysis ◽  
Superior Performance ◽  
Large Signal ◽  
Nonlinear Modelling ◽  
Frequency Domains ◽  
Identification Techniques

This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modelling and nonlinear analysis is used to restrict the search space for nonlinear modelling. The nonlinear model is then validated using large-signal data and its superior performance illustrated by comparison with a linear model. This paper illustrates how periodic test signals, frequency domain analysis and identification techniques, and time-domain NARMAX modelling can be effectively combined to enhance the modelling of an aircraft gas turbine.

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