Abstract: Missing data yields many analysis challenges. In case of complex survey design, in addition to dealing with missing data, researchers need to account for the sampling design to achieve useful inferences. Methods for incorporating sampling weights in neural network imputation were investigated to account for complex survey designs. An estimate of variance to account for the imputation uncertainty as well as the sampling design using neural networks will be provided. A simulation study was conducted to compare estimation results based on complete case analysis, multiple imputation using a Markov Chain Monte Carlo, and neural network imputation. Furthermore, a public-use dataset was used as an example to illustrate neural networks imputation under a complex survey design
Abstract: The angular distribution of Compton scattering of two
quanta originating in the annihilation of a positron with an electron
is investigated as a quantum key distribution (QKD) mechanism in
the gamma spectral range. The geometry of coincident Compton
scattering is observed on the two sides as a way to obtain partially
correlated readings on the quantum channel. We derive the noise
probability density function of a conceptually equivalent prepare
and measure quantum channel in order to evaluate the limits of the
concept in terms of the device secrecy capacity and estimate it at
roughly 1.9 bits per 1 000 annihilation events. The high error rate
is well above the tolerable error rates of the common reconciliation
protocols; therefore, the proposed key agreement protocol by public
discussion requires key reconciliation using classical error-correcting
codes. We constructed a prototype device based on the readily
available monolithic detectors in the least complex setup.
Abstract: In this paper, the effect of receive and/or transmit
antenna spacing on the performance (BER vs. SNR) of multipleantenna
systems is determined by using an RCS (Radar Cross
Section) channel model. In this physical model, the scatterers
existing in the propagation environment are modeled by their RCS so
that the correlation of the receive signal complex amplitudes, i.e.,
both magnitude and phase, can be estimated. The proposed RCS
channel model is then compared with classical models.
Abstract: Sedimentation process resulting from soil erosion in
the water basin especially in arid and semi-arid where poor
vegetation cover in the slope of the mountains upstream could
contribute to sediment formation. The consequence of sedimentation
not only makes considerable change in the morphology of the river
and the hydraulic characteristics but would also have a major
challenge for the operation and maintenance of the canal network
which depend on water flow to meet the stakeholder-s requirements.
For this reason mathematical modeling can be used to simulate the
effective factors on scouring, sediment transport and their settling
along the waterways. This is particularly important behind the
reservoirs which enable the operators to estimate the useful life of
these hydraulic structures. The aim of this paper is to simulate the
sedimentation and erosion in the eastern and western water intake
structures of the Dez Diversion weir using GSTARS-3 software. This
is done to estimate the sedimentation and investigate the ways in
which to optimize the process and minimize the operational
problems. Results indicated that the at the furthest point upstream of
the diversion weir, the coarser sediment grains tended to settle. The
reason for this is the construction of the phantom bridge and the
outstanding rocks just upstream of the structure. The construction of
these along the river course has reduced the momentum energy
require to push the sediment loads and make it possible for them to
settle wherever the river regime allows it. Results further indicated a
trend for the sediment size in such a way that as the focus of study
shifts downstream the size of grains get smaller and vice versa. It
was also found that the finding of the GSTARS-3 had a close
proximity with the sets of the observed data. This suggests that the
software is a powerful analytical tool which can be applied in the
river engineering project with a minimum of costs and relatively
accurate results.
Abstract: This paper deals with the effect of a power transformer’s vector group on the basic voltage sag characteristics during unbalanced faults at a meshed or radial power network. Specifically, the propagation of voltage sags through a power transformer is studied with advanced short-circuit analysis. A smart method to incorporate this effect on analytical mathematical expressions is proposed. Based on this methodology, the positive effect of transformers of certain vector groups on the mitigation of the expected number of voltage sags per year (sag frequency) at the terminals of critical industrial customers can be estimated.
Abstract: The distance between two objects is an important
problem in CAGD, CAD and CG etc. It will be presented in this paper
that a simple and quick method to estimate the distance between a
point and a Bezier curve on a Bezier surface.
Abstract: The paper aims at investigating influence of medium
capacity on linear adsorbed solute dispersion into chemically
heterogeneous fixed beds. A discrete chemical heterogeneity
distribution is considered in the one-dimensional advectivedispersive
equation. The partial differential equation is solved using
finite volumes method based on the Adam-Bashforth algorithm.
Increased dispersion is estimated by comparing breakthrough curves
second order moments and keeping identical hydrodynamic
properties. As a result, dispersion increase due to chemical
heterogeneity depends on the column size and surprisingly on the
solid capacity. The more intense capacity is, the more important
solute dispersion is. Medium length which is known to favour this
effect vanishing according to the linear adsorption in fixed bed seems
to create nonmonotonous variation of dispersion because of the
heterogeneity. This nonmonotonous behaviour is also favoured by
high capacities.
Abstract: This paper presents a wavelet transform and Support
Vector Machine (SVM) based algorithm for estimating fault location
on transmission lines. The Discrete wavelet transform (DWT) is used
for data pre-processing and this data are used for training and testing
SVM. Five types of mother wavelet are used for signal processing to
identify a suitable wavelet family that is more appropriate for use in
estimating fault location. The results demonstrated the ability of SVM
to generalize the situation from the provided patterns and to
accurately estimate the location of faults with varying fault resistance.
Abstract: The log periodogram regression is widely used in empirical
applications because of its simplicity, since only a least squares
regression is required to estimate the memory parameter, d, its good
asymptotic properties and its robustness to misspecification of the
short term behavior of the series. However, the asymptotic distribution
is a poor approximation of the (unknown) finite sample distribution
if the sample size is small. Here the finite sample performance of different
nonparametric residual bootstrap procedures is analyzed when
applied to construct confidence intervals. In particular, in addition to
the basic residual bootstrap, the local and block bootstrap that might
adequately replicate the structure that may arise in the errors of the
regression are considered when the series shows weak dependence in
addition to the long memory component. Bias correcting bootstrap
to adjust the bias caused by that structure is also considered. Finally,
the performance of the bootstrap in log periodogram regression based
confidence intervals is assessed in different type of models and how
its performance changes as sample size increases.
Abstract: For improving the efficiency of human 3D tracking, we
present an algorithm to track 3D Arm Motion. First, the Hierarchy
Limb Model (HLM) is proposed based on the human 3D skeleton
model. Second, via graph decomposition, the arm motion state space,
modeled by HLM, can be discomposed into two low dimension
subspaces: root nodes and leaf nodes. Finally, Rao-Blackwellised
Particle Filter is used to estimate the 3D arm motion. The result of
experiment shows that our algorithm can advance the computation
efficiency.
Abstract: Estimates of temperature values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to design of solar energy systems. The scope of this research is to investigate the efficiency of data mining techniques in estimating minimum, maximum and mean temperature values. For this reason, a number of experiments have been conducted with well-known regression algorithms using temperature data from the city of Patras in Greece. The performance of these algorithms has been evaluated using standard statistical indicators, such as Correlation Coefficient, Root Mean Squared Error, etc.
Abstract: Periodicities in the environmetric time series can be
idyllically assessed by utilizing periodic models. In this
communication fugitive emission of gases from open sewer channel
Lyari which follows periodic behaviour are approximated by
employing periodic autoregressive model of order p. The orders of
periodic model for each season are selected through the examination
of periodic partial autocorrelation or information criteria. The
parameters for the selected order of season are estimated individually
for each emitted air toxin. Subsequently, adequacies of fitted models
are established by examining the properties of the residual for each
season. These models are beneficial for schemer and administrative
bodies for the improvement of implemented policies to surmount
future environmental problems.
Abstract: The Constraints imposed by non-thermal
leptogenesis on the survival of the neutrino mass models describing
the presently available neutrino mass patterns, are studied
numerically. We consider the Majorana CP violating phases coming
from right-handed Majorana mass matrices to estimate the baryon
asymmetry of the universe, for different neutrino mass models
namely quasi-degenerate, inverted hierarchical and normal
hierarchical models, with tribimaximal mixings. Considering two
possible diagonal forms of Dirac neutrino mass matrix as either
charged lepton or up-quark mass matrix, the heavy right-handed
mass matrices are constructed from the light neutrino mass matrix.
Only the normal hierarchical model leads to the best predictions of
baryon asymmetry of the universe, consistent with observations in
non-thermal leptogenesis scenario.
Abstract: Constant amplitude fatigue crack growth (FCG) tests
were performed on dissimilar metal welded plates of Type 316L
Stainless Steel (SS) and IS 2062 Grade A Carbon steel (CS). The
plates were welded by TIG welding using SS E309 as electrode. FCG
tests were carried on the Side Edge Notch Tension (SENT)
specimens of 5 mm thickness, with crack initiator (notch) at base
metal region (BM), weld metal region (WM) and heat affected zones
(HAZ). The tests were performed at a test frequency of 10 Hz and at
load ratios (R) of 0.1 & 0.6. FCG rate was found to increase with
stress ratio for weld metals and base metals, where as in case of
HAZ, FCG rates were almost equal at high ΔK. FCG rate of HAZ of
stainless steel was found to be lowest at low and high ΔK. At
intermediate ΔK, WM showed the lowest FCG rate. CS showed
higher crack growth rate at all ΔK. However, the scatter band of data
was found to be narrow. Fracture toughness (Kc) was found to vary
in different locations of weldments. Kc was found lowest for the
weldment and highest for HAZ of stainless steel. A novel method of
characterizing the FCG behavior using an Infrared thermography
(IRT) camera was attempted. By monitoring the temperature rise at
the fast moving crack tip region, the amount of plastic deformation
was estimated.
Abstract: Innovations and innovative activity get the increasing
value for successful financial and economic activity of the countries
and regions. The level of innovative sphere development determines
place of a country or a region in world economy and forms a basis of
steady economic growth. This article is devoted to different aspects
of organization of the national economic safety in the conditions of
innovative development, its problems, risks and threats. Economy
can be considered as aspiring for transition to innovative way only
with finding of economic safety: financial independence, power
stability and technological progress. There are statistical indicators,
defining the level of economic security and factors, threatening
economic safety of the state. The research is based on the analysis of
factors and indicators in conditions of innovative development. The
paper is illustrated by the examples of possible estimated system of
the economic safety level.
Abstract: This study aims to assess the environmental hazards
from radon exhalation rate in the soil samples in selected locations in
Iraqi Kurdistan, using passive (CR-39NTDs) and active (RAD7)
detecting method. Radon concentration, effective radium content and
radon exhalation rate were estimated in soil samples that collected at
the depth level of 30 cm inside 124 houses. The results show that the
emanation rate for radon gas was variation from location to other,
depending on the geological formation. Most health risks come from
emanation of radon and its daughter due to its contribution for indoor
radon, so the results showed that there is a linear relationship
between the ratio of soil and indoor radon concentration (CSoil Rn222/
Cindoor Rn222) and the effective radium content in soil samples. The
results show that radon concentration has high and low values in
Hajyawa city and Er. Tyrawa Qr, respectively. A comparison
between our results with that mentioned in international reports was
done.
Abstract: Parametric models have been quite popular for
studying human growth, particularly in relation to biological
parameters such as peak size velocity and age at peak size velocity.
Longitudinal data are generally considered to be vital for fittinga
parametric model to individual-specific data, and for studying the
distribution of these biological parameters in a human population.
However, cross-sectional data are easier to obtain than longitudinal
data. In this paper, we present a method of combining longitudinal
and cross-sectional data for the purpose of estimating the distribution
of the biological parameters. We demonstrate, through simulations in
the special case ofthePreece Baines model, how estimates based on
longitudinal data can be improved upon by harnessing the
information contained in cross-sectional data.We study the extent of
improvement for different mixes of the two types of data, and finally
illustrate the use of the method through data collected by the Indian
Statistical Institute.
Abstract: For decades, the defense business has been plagued by
not having a reliable, deterministic method to know when the Kalman
filter solution for passive ranging application is reliable for use by the
fighter pilot. This has made it hard to accurately assess when the
ranging solution can be used for situation awareness and weapons
use. To date, we have used ad hoc rules-of-thumb to assess when we
think the estimate of the Kalman filter standard deviation on range is
reliable. A reliable algorithm has been developed at BAE Systems
Electronics & Integrated Solutions that monitors the Kalman gain
matrix elements – and a patent is pending. The “settling" of the gain
matrix elements relates directly to when we can assess the time when
the passive ranging solution is within the 10 percent-of-truth value.
The focus of the paper is on surface-based passive ranging – but the
method is applicable to airborne targets as well.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dimensional fuzzy decision region is approximated by union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision regions are determined by estimating the parameters of each hyperellipsoid.Genetic Algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the learned decision region can be arbitrarily complex.