Abstract: The driven processes of Wiener and Lévy are known
self-standing Gaussian-Markov processes for fitting non-linear
dynamical Vasciek model. In this paper, a coincidental Gaussian
density stationarity condition and autocorrelation function of the
two driven processes were established. This led to the conflation
of Wiener and Lévy processes so as to investigate the efficiency
of estimates incorporated into the one-dimensional Vasciek model
that was estimated via the Maximum Likelihood (ML) technique.
The conditional laws of drift, diffusion and stationarity process
was ascertained for the individual Wiener and Lévy processes as
well as the commingle of the two processes for a fixed effect
and Autoregressive like Vasciek model when subjected to financial
series; exchange rate of Naira-CFA Franc. In addition, the model
performance error of the sub-merged driven process was miniature
compared to the self-standing driven process of Wiener and Lévy.
Abstract: This study was designed to find the best stochastic model (using of time series analysis) for annual extreme streamflow (peak and maximum streamflow) of Karkheh River at Iran. The Auto-regressive Integrated Moving Average (ARIMA) model used to simulate these series and forecast those in future. For the analysis, annual extreme streamflow data of Jelogir Majin station (above of Karkheh dam reservoir) for the years 1958–2005 were used. A visual inspection of the time plot gives a little increasing trend; therefore, series is not stationary. The stationarity observed in Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots of annual extreme streamflow was removed using first order differencing (d=1) in order to the development of the ARIMA model. Interestingly, the ARIMA(4,1,1) model developed was found to be most suitable for simulating annual extreme streamflow for Karkheh River. The model was found to be appropriate to forecast ten years of annual extreme streamflow and assist decision makers to establish priorities for water demand. The Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS) codes were used to determinate of the best model for this series.
Abstract: Remotely sensed data are a significant source for monitoring and updating databases for land use/cover. Nowadays, changes detection of urban area has been a subject of intensive researches. Timely and accurate data on spatio-temporal changes of urban areas are therefore required. The data extracted from multi-temporal satellite images are usually non-stationary. In fact, the changes evolve in time and space. This paper is an attempt to propose a methodology for changes detection in urban area by combining a non-stationary decomposition method and stochastic modeling. We consider as input of our methodology a sequence of satellite images I1, I2, … In at different periods (t = 1, 2, ..., n). Firstly, a preprocessing of multi-temporal satellite images is applied. (e.g. radiometric, atmospheric and geometric). The systematic study of global urban expansion in our methodology can be approached in two ways: The first considers the urban area as one same object as opposed to non-urban areas (e.g. vegetation, bare soil and water). The objective is to extract the urban mask. The second one aims to obtain a more knowledge of urban area, distinguishing different types of tissue within the urban area. In order to validate our approach, we used a database of Tres Cantos-Madrid in Spain, which is derived from Landsat for a period (from January 2004 to July 2013) by collecting two frames per year at a spatial resolution of 25 meters. The obtained results show the effectiveness of our method.
Abstract: Both steady and unsteady turbulent mixed convection
heat transfer in a 3D lid-driven enclosure, which has constant heat
flux on the middle of bottom wall and with isothermal moving
sidewalls, is reported in this paper for working fluid with Prandtl
number Pr = 0.71. The other walls are adiabatic and stationary. The
dimensionless parameters used in this research are Reynolds number,
Re = 5000, 10000 and 15000, and Richardson number, Ri = 1 and 10.
The simulations have been done by using different turbulent methods
such as RANS, URANS, and LES. The effects of using different k-ε
models such as standard, RNG and Realizable k-ε model are
investigated. Interesting behaviours of the thermal and flow fields
with changing the Re or Ri numbers are observed. Isotherm and
turbulent kinetic energy distributions and variation of local Nusselt
number at the hot bottom wall are studied as well. The local Nusselt
number is found increasing with increasing either Re or Ri number.
In addition, the turbulent kinetic energy is discernibly affected by
increasing Re number. Moreover, the LES results have shown good
ability of this method in predicting more detailed flow structures in
the cavity.
Abstract: Future flood can be predicted using the probable
maximum flood (PMF). PMF is calculated using the historical
discharge or rainfall data considering the other climatic parameters
remaining stationary. However climate is changing globally and the
key climatic variables are temperature, evaporation, rainfall and sea
level rise are likely to change. To develop scenarios to a basin or
catchment scale these important climatic variables should be
considered. Nowadays scenario based on climatic variables is more
suitable than PMF. Six scenarios were developed for a large Fitzroy
basin and presented in this paper.
Abstract: The main objective of the research in this paper is to empirically assess the causal relationship of private savings and economic growth in the Republic of Croatia. Households’ savings are approximated by household deposits in banks, while domestic income is approximated by industrial production volume indices. Vector Autoregression model and Granger causality tests are used to in order to analyse the relationship among private savings and economic growth. Since ADF unit root tests have shown that both mentioned series are non stationary at levels, series are first differenced in order to become stationary. Therefore, VAR model is estimated with percentage change in private savings and percentage change in domestic income, which can be interpreted as economic growth in case of positive percentage change in domestic income. The Granger causality test has shown that there is no causal relationship among private savings and economic growth in Croatia. The impulse response functions have shown that the impact of shock in domestic income on private savings change is stronger than the impact of private saving on growth. Variance decompositions show that both economic growth and private saving change explain the largest part of its own forecast variance. The research has shown that the link between private savings economic and growth in Croatia is weak, what is in line with relevant empirical research in small open economies.
Abstract: This paper aims to examine whether a bubble is present in the housing market of China. Thus, we use the housing price-to-income ratios and housing price-to-rent ratios of 35 cities from 1998 to 2010. The methods of the panel KSS unit root test with a Fourier function and the SPSM process are likewise used. The panel KSS unit root test with a Fourier function considers the problem of non-linearity and structural changes, and the SPSM process can avoid the stationary time series from dominating the result-generated bias. Through a rigorous empirical study, we determine that the housing price-to-income ratios are stationary in 34 of the 35 cities in China. Only Xining is non-stationary. The housing price-to-rent ratios are stationary in 32 of the 35 cities in China. Chengdu, Fuzhou, and Zhengzhou are non-stationary. Overall, the housing bubbles are not a serious problem in China at the time.
Abstract: The Random Coefficient Dynamic Regression (RCDR)
model is to developed from Random Coefficient Autoregressive
(RCA) model and Autoregressive (AR) model. The RCDR model
is considered by adding exogenous variables to RCA model. In this
paper, the concept of the Maximum Likelihood (ML) method is used
to estimate the parameter of RCDR(1,1) model. Simulation results
have shown the AIC and BIC criterion to compare the performance of
the the RCDR(1,1) model. The variables as the stationary and weakly
stationary data are good estimates where the exogenous variables
are weakly stationary. However, the model selection indicated that
variables are nonstationarity data based on the stationary data of the
exogenous variables.
Abstract: We consider optimal channel equalization for MIMO
(multi-input/multi-output) time-varying channels in the sense of
MMSE (minimum mean-squared-error), where the observation noise
can be non-stationary. We show that all ZF (zero-forcing) receivers
can be parameterized in an affine form which eliminates completely
the ISI (inter-symbol-interference), and optimal channel equalizers
can be designed through minimization of the MSE (mean-squarederror)
between the detected signals and the transmitted signals,
among all ZF receivers. We demonstrate that the optimal channel
equalizer is a modified Kalman filter, and show that under the AWGN
(additive white Gaussian noise) assumption, the proposed optimal
channel equalizer minimizes the BER (bit error rate) among all
possible ZF receivers. Our results are applicable to optimal channel
equalization for DWMT (discrete wavelet multitone), multirate transmultiplexers,
OFDM (orthogonal frequency division multiplexing),
and DS (direct sequence) CDMA (code division multiple access)
wireless data communication systems. A design algorithm for optimal
channel equalization is developed, and several simulation examples
are worked out to illustrate the proposed design algorithm.
Abstract: This paper develops driver reaction-time models for
car-following analysis based on human factors. The reaction time
was classified as brake-reaction time (BRT) and
acceleration/deceleration reaction time (ADRT). The BRT occurs
when the lead vehicle is barking and its brake light is on, while the
ADRT occurs when the driver reacts to adjust his/her speed using the
gas pedal only. The study evaluates the effect of driver
characteristics and traffic kinematic conditions on the driver reaction
time in a car-following environment. The kinematic conditions
introduced urgency and expectancy based on the braking behaviour
of the lead vehicle at different speeds and spacing. The kinematic
conditions were used for evaluating the BRT and are classified as
normal, surprised, and stationary. Data were collected on a driving
simulator integrated into a real car and included the BRT and ADRT
(as dependent variables) and driver-s age, gender, driving experience,
driving intensity (driving hours per week), vehicle speed, and
spacing (as independent variables). The results showed that there was
a significant difference in the BRT at normal, surprised, and
stationary scenarios and supported the hypothesis that both urgency
and expectancy had significant effects on BRT. Driver-s age, gender,
speed, and spacing were found to be significant variables for the
BRT in all scenarios. The results also showed that driver-s age and
gender were significant variables for the ADRT. The research
presented in this paper is part of a larger project to develop a driversensitive
in-vehicle rear-end collision warning system.
Abstract: This study applies the sequential panel selection
method (SPSM) procedure proposed by Chortareas and Kapetanios
(2009) to investigate the time-series properties of energy
consumption in 50 US states from 1963 to 2009. SPSM involves the
classification of the entire panel into a group of stationary series and
a group of non-stationary series to identify how many and which
series in the panel are stationary processes. Empirical results obtained
through SPSM with the panel KSS unit root test developed by Ucar
and Omay (2009) combined with a Fourier function indicate that
energy consumption in all the 50 US states are stationary. The results
of this study have important policy implications for the 50 US states.