Chaotic Features of Decomposed Time Series from Tidal River Water Level

This study assessed the characteristics of water-level time series of a tidal river by decomposing it into tide, wave, rainfall-runoff, and noise components. Especially, the analysis for chaotic behavior of each component was done by estimating the correlation dimension with phase-space reconstruction of time series and by using a close returns plot (CRP). Among the time series, the tide component showed chaotic characteristics to have a correlation dimension of 1.3. It was found out that the water level has stochastic characteristics showing the increasing trend of the correlation exponent in the embedding dimension. Other components also showed the stochastic characteristics. Then, the CRP was used to examine the characteristics of each component. The tide component showed the chaotic characteristics in its CRP. The CRP of water level showed an aperiodic characteristic which slightly strayed away from its periodicity, and this might be related to the tide component. This study showed that a low water level is mainly affected by a chaotic tide component through entropy information. Even though the water level did not show chaotic characteristics in the correlation dimension, it showed stochastic chaos characteristics in the CRP. Other components showed stochastic characteristics in the CRP. It was confirmed that the water level showed chaotic characteristics when it was not affected by rainfall and stochastic characteristics deviating from the bounded trajectory when water level rises due to rainfall. Therefore, we have shown that the water level related to the chaotic tide component can also have chaotic properties because water level is influenced by chaotic tide and rainfall shock, thus it showed stochastic chaos characteristics.

Modeling and Performance of Mine MIMO Channel Based on Geometric Stochastic Characteristics

MIMO technology has become one of the most promising content in mobile wireless communication, and it has shown positive application value in both application and innovation. On the basis of understanding the current development status of MIMO channel technology, this paper analyzes the standardization of MIMO to the future development trend according to the modeling of broadband dryness ratio of GBSM.

An Improved Model For Precise Point Positioning With Modernized Glogbal Positioning System

Recent developments in GPS positioning show that a user with a standalone GPS receiver can obtain positioning accuracy comparable to that of carrier-phase-based differential positioning. Such a technique is commonly known as precise point positioning (PPP). A significant challenge of PPP, however, is that it typically requires a minimum of 30 minutes to achieve centimeter- to decimeter-level accuracy. This relatively long convergence time is the result of un-modeled GPS residual errors. This thesis addresses error mitigation techniques to achieve near real-time PPP. To explore the full advantage of the modernized GPS L2C signal, it is essential to determine its stochastic characteristics and code bias. GPS measurements were collected in order to study the stochastic characteristics of the modernized GPS L2C signal. As a byproduct, the stochastic characteristics of the legacy GPS signals, namely C/A and P2 codes, were also determined and then used to verify the developed stochastic model of the modernized signal. The differential code biases between P2 and C2, DCB P2-C2, were also estimated using the Bernese GPS software. A major residual error component, which affects the convergence of PPP solution, is the higherorder ionospheric delay. We rigorously modeled the second-order ionospheric delay, which represents the bulk of higher-order ionospheric delay, for our PPP model. First, we investigated the effect of second-order ionospheric delay on GPS satellite orbit and clock corrections. Second, we used the estimated satellite orbit and clock corrections to process the GPS data from several IGS stations after correcting the data for the effect of second-order ionospheric delay. The results demonstrated an improvement of up to 25% in the precision of the estimated coordinates with the second-order ionospheric delay, as well as reduction of the convergence time of the estimated parameters by about 15%, depending on the geographic location and ionospheric and geomagnetic conditions. Between-satellite single-difference PPP algorithms were developed to cancel out the receiver clock error, receiver initial phase bias, and receiver hardware delay. The decoupled clock corrections, provided by NRCan, were also applied to account for the satellite hardware delay and satellite initial phase bias. GPS data collected from several IGS stations were processed using the un-differenced model, un-differenced decoupled clock model, between-satellite singledifference (BSSD) model, and between-satellite single-difference using the decoupled clock (BSSD-DC) model. The results showed that the proposed BSSD model significantly improved the PPP convergence time by 50% and improved the solution precision by more than 60% over the traditional un-differenced PPP model.

An Improved Model For Precise Point Positioning With Modernized Glogbal Positioning System

Recent developments in GPS positioning show that a user with a standalone GPS receiver can obtain positioning accuracy comparable to that of carrier-phase-based differential positioning. Such a technique is commonly known as precise point positioning (PPP). A significant challenge of PPP, however, is that it typically requires a minimum of 30 minutes to achieve centimeter- to decimeter-level accuracy. This relatively long convergence time is the result of un-modeled GPS residual errors. This thesis addresses error mitigation techniques to achieve near real-time PPP. To explore the full advantage of the modernized GPS L2C signal, it is essential to determine its stochastic characteristics and code bias. GPS measurements were collected in order to study the stochastic characteristics of the modernized GPS L2C signal. As a byproduct, the stochastic characteristics of the legacy GPS signals, namely C/A and P2 codes, were also determined and then used to verify the developed stochastic model of the modernized signal. The differential code biases between P2 and C2, DCB P2-C2, were also estimated using the Bernese GPS software. A major residual error component, which affects the convergence of PPP solution, is the higherorder ionospheric delay. We rigorously modeled the second-order ionospheric delay, which represents the bulk of higher-order ionospheric delay, for our PPP model. First, we investigated the effect of second-order ionospheric delay on GPS satellite orbit and clock corrections. Second, we used the estimated satellite orbit and clock corrections to process the GPS data from several IGS stations after correcting the data for the effect of second-order ionospheric delay. The results demonstrated an improvement of up to 25% in the precision of the estimated coordinates with the second-order ionospheric delay, as well as reduction of the convergence time of the estimated parameters by about 15%, depending on the geographic location and ionospheric and geomagnetic conditions. Between-satellite single-difference PPP algorithms were developed to cancel out the receiver clock error, receiver initial phase bias, and receiver hardware delay. The decoupled clock corrections, provided by NRCan, were also applied to account for the satellite hardware delay and satellite initial phase bias. GPS data collected from several IGS stations were processed using the un-differenced model, un-differenced decoupled clock model, between-satellite singledifference (BSSD) model, and between-satellite single-difference using the decoupled clock (BSSD-DC) model. The results showed that the proposed BSSD model significantly improved the PPP convergence time by 50% and improved the solution precision by more than 60% over the traditional un-differenced PPP model.

Stochastic perturbation of the Lighthill–Whitham–Richards model via the method of stochastic characteristics

In this paper we apply the method of stochastic characteristics to a Lighthill–Whitham–Richards model. The stochastic perturbation can be seen as errors in measurement of the traffic density. For concrete examples we solve the equation perturbed by a standard Brownian motion and the geometric Brownian motion without drift.

Perceptual rivalry with vibrotactile stimuli

In perceptual rivalry, ambiguous sensory information leads to dynamic changes in the perceptual interpretation of fixed stimuli. This phenomenon occurs when participants receive sensory stimuli that support two or more distinct interpretations; this results in spontaneous alternations between possible perceptual interpretations. Perceptual rivalry has been widely studied across different sensory modalities including vision, audition, and to a limited extent, in the tactile domain. Common features of perceptual rivalry across various ambiguous visual and auditory paradigms characterize the randomness of switching times and their dependence on input strength manipulations (Levelt’s propositions). It is still unclear whether the general characteristics of perceptual rivalry are preserved with tactile stimuli. This study aims to introduce a simple tactile stimulus capable of generating perceptual rivalry and explores whether general features of perceptual rivalry from other modalities extend to the tactile domain. Our results confirm that Levelt’s proposition II extends to tactile bistability, and that the stochastic characteristics of irregular perceptual alternations agree with non-tactile modalities. An analysis of correlations between subsequent perceptual phases reveals a significant positive correlation at lag 1 (as found in visual bistability), and a negative correlation for lag 2 (in contrast with visual bistability).

A Geometric Prediction Model of Surface Morphology in Micro-EDM Considering Stochastic Characteristics of Discharge Crater Size

The surface topography of micro-EDM is closely related to its application range, so it is important to accurately predict the surface morphology. Most of the existing models do not consider the stochastic characteristics and uncertainty of the discharge process, which could produce great differences with the experimental results. Aiming at these problems, the stochastic characteristic of the discharge crater size is studied, and a simulation model is established to predict the surface topography of EDM considering the stochastic characteristics of crater size. Firstly, the stochastic characteristics of the crater size were investigated through the finite-successive pulses discharge method and characterized from the perspective of probability theory. The stochastic characteristics was explained by analyzing discharge waveforms. Secondly, the response surface method (RSM) was used to map the corresponding relationship between the input machining parameters and the distribution characteristics of crater size. It was observed that the RSM model can accurately predict the distribution characteristics of crater size in the range of parameters selected in this study. Finally, models based on stochastic distribution of crater size (MSDS) and fixed crater size (MFS) were established respectively. By analyzing the characteristics of the simulated surfaces and the machined surface, it was found that the surface features of MSDS are closer to the real surface features. Compared with MFS, the roughness prediction error and the average of overall error of MSDS are reduced by 1.01% and 18.97% respectively. The results of this work are helpful to understand the randomness and realize the controllability of EDM.