Abstract: This paper considers the modelling of a non-stationary
bivariate integer-valued autoregressive moving average of order
one (BINARMA(1,1)) with correlated Poisson innovations. The
BINARMA(1,1) model is specified using the binomial thinning
operator and by assuming that the cross-correlation between the
two series is induced by the innovation terms only. Based on
these assumptions, the non-stationary marginal and joint moments
of the BINARMA(1,1) are derived iteratively by using some initial
stationary moments. As regards to the estimation of parameters of
the proposed model, the conditional maximum likelihood (CML)
estimation method is derived based on thinning and convolution
properties. The forecasting equations of the BINARMA(1,1) model
are also derived. A simulation study is also proposed where
BINARMA(1,1) count data are generated using a multivariate
Poisson R code for the innovation terms. The performance of
the BINARMA(1,1) model is then assessed through a simulation
experiment and the mean estimates of the model parameters obtained
are all efficient, based on their standard errors. The proposed model
is then used to analyse a real-life accident data on the motorway in
Mauritius, based on some covariates: policemen, daily patrol, speed
cameras, traffic lights and roundabouts. The BINARMA(1,1) model
is applied on the accident data and the CML estimates clearly indicate
a significant impact of the covariates on the number of accidents on
the motorway in Mauritius. The forecasting equations also provide
reliable one-step ahead forecasts.
Abstract: Lignin is a major constituent of woody biomass, and exists abundantly in nature. It is the major byproducts from the paper industry and bioethanol production processes. The byproducts are mainly used for low-valued applications. Instead, lignin can be converted into higher-valued gaseous fuel, thereby helping to curtail the ever-growing price of oil and to slow down the trend of global warming. Although biochemical treatment is capable of converting cellulose into liquid ethanol fuel, it cannot be applied to the conversion of lignin. Alternatively, it is possible to convert lignin into gaseous fuel thermochemically. In the present work, trans-4-hydroxycinnamic acid, a model compound for lignin, which closely resembles the basic building blocks of lignin, is gasified in an autoclave with ethanol at elevated temperatures and pressures, that are above the critical point of ethanol. Ethanol, instead of water, is chosen, because ethanol dissolves trans-4-hydroxycinnamic acid easily and helps to convert it into lighter gaseous species relatively well. The major operating parameters for the gasification reaction include temperature (673-873 K), reaction pressure (5-25 MPa) and feed concentration (0.05-0.3 M). Generally, more than 80% of the reactant, including trans-4-hydroxycinnamic acid and ethanol, were converted into gaseous products at an operating condition of 873 K and 5 MPa.
Abstract: The Com-Poisson (CMP) model is one of the most
popular discrete generalized linear models (GLMS) that handles
both equi-, over- and under-dispersed data. In longitudinal context,
an integer-valued autoregressive (INAR(1)) process that incorporates
covariate specification has been developed to model longitudinal
CMP counts. However, the joint likelihood CMP function is
difficult to specify and thus restricts the likelihood-based estimating
methodology. The joint generalized quasi-likelihood approach
(GQL-I) was instead considered but is rather computationally
intensive and may not even estimate the regression effects due
to a complex and frequently ill-conditioned covariance structure.
This paper proposes a new GQL approach for estimating the
regression parameters (GQL-III) that is based on a single score vector
representation. The performance of GQL-III is compared with GQL-I
and separate marginal GQLs (GQL-II) through some simulation
experiments and is proved to yield equally efficient estimates as
GQL-I and is far more computationally stable.
Abstract: In this paper, a system of linear matrix equations
is considered. A new necessary and sufficient condition for the
consistency of the equations is derived by means of the generalized
singular-value decomposition, and the explicit representation of the
general solution is provided.
Abstract: Supermarkets are the most electricity-intensive type of
commercial buildings. The unsuitable indoor environment of a
supermarket provided by abnormal HVAC operations incurs waste
energy consumption in refrigeration systems. This current study
briefly describes significantly solid backgrounds and proposes easyto-
use analysis terminology for investigating the impact of HVAC
operations on refrigeration power consumption using the field-test
data obtained from building automation system (BAS). With solid
backgrounds and prior knowledge, expected energy interactions
between HVAC and refrigeration systems are proposed through
Pearson’s correlation analysis (R value) by considering correlations
between equipment power consumption and dominantly independent
variables (driving force conditions).The R value can be conveniently
utilized to evaluate how strong relations between equipment
operations and driving force parameters are. The calculated R values
obtained from field data are compared to expected ranges of R values
computed by energy interaction methodology. The comparisons can
separate the operational conditions of equipment into faulty and
normal conditions. This analysis can simply investigate the condition
of equipment operations or building sensors because equipment could
be abnormal conditions due to routine operations or faulty
commissioning processes in field tests. With systematically solid and
easy-to-use backgrounds of interactions provided in the present
article, the procedures can be utilized as a tool to evaluate the proper
commissioning and routine operations of HVAC and refrigeration
systems to detect simple faults (e.g. sensors and driving force
environment of refrigeration systems and equipment set-point) and
optimize power consumption in supermarket buildings. Moreover,
the analysis will be used to further study the FDD research for
supermarkets in future.
Abstract: Let {Xi}i≥1 be a martingale difference sequence with
Xi = Si - Si-1. Under some regularity conditions, we show that
(X2
1+· · ·+X2N
n)-1/2SNn is asymptotically normal, where {Ni}i≥1
is a sequence of positive integer-valued random variables tending
to infinity. In a similar manner, a backward (or reverse) martingale
central limit theorem with random indices is provided.
Abstract: In this paper, Optimum adaptive loading algorithms
are applied to multicarrier system with Space-Time Block Coding
(STBC) scheme associated with space-time processing based on
singular-value decomposition (SVD) of the channel matrix over
Rayleigh fading channels. SVD method has been employed in
MIMO-OFDM system in order to overcome subchannel interference.
Chaw-s and Compello-s algorithms have been implemented to obtain
a bit and power allocation for each subcarrier assuming instantaneous
channel knowledge. The adaptive loaded SVD-STBC scheme is
capable of providing both full-rate and full-diversity for any number
of transmit antennas. The effectiveness of these techniques has
demonstrated through the simulation of an Adaptive loaded SVDSTBC
system, and the comparison shown that the proposed
algorithms ensure better performance in the case of MIMO.
Abstract: The Connection Admission Control (CAC) problem is formulated in this paper as a discrete time optimal control problem. The control variables account for the acceptance/ rejection of new connections and forced dropping of in-progress connections. These variables are constrained to meet suitable conditions which account for the QoS requirements (Link Availability, Blocking Probability, Dropping Probability). The performance index evaluates the total throughput. At each discrete time, the problem is solved as an integer-valued linear programming one. The proposed procedure was successfully tested against suitably simulated data.
Abstract: The problem of estimating time-varying regression is
inevitably concerned with the necessity to choose the appropriate
level of model volatility - ranging from the full stationarity of instant
regression models to their absolute independence of each other. In the
stationary case the number of regression coefficients to be estimated
equals that of regressors, whereas the absence of any smoothness
assumptions augments the dimension of the unknown vector by the
factor of the time-series length. The Akaike Information Criterion
is a commonly adopted means of adjusting a model to the given
data set within a succession of nested parametric model classes,
but its crucial restriction is that the classes are rigidly defined by
the growing integer-valued dimension of the unknown vector. To
make the Kullback information maximization principle underlying the
classical AIC applicable to the problem of time-varying regression
estimation, we extend it onto a wider class of data models in which
the dimension of the parameter is fixed, but the freedom of its values
is softly constrained by a family of continuously nested a priori
probability distributions.
Abstract: Thermal load calculations have been performed for
multi-layered walls that are composed of three different parts; a
common (sand and cement) plaster, and two types of locally
produced soft and hard bricks. The masonry construction of these
layered walls was based on concrete-backed stone masonry made of
limestone bricks joined by mortar. These multilayered walls are
forming the outer walls of the building envelope of a typical Libyan
house. Based on the periodic seasonal weather conditions, within the
Libyan cost region during summer and winter, measured thermal
conductivity values were used to implement such seasonal variation
of heat flow and the temperature variations through the walls. The
experimental measured thermal conductivity values were obtained
using the Hot Disk technique. The estimation of the thermal
resistance of the wall layers ( R-values) is based on measurements
and calculations. The numerical calculations were done using a
simplified analytical model that considers two different wall
constructions which are characteristics of such houses. According to
the obtained results, the R-values were quite low and therefore,
several suggestions have been proposed to improve the thermal
loading performance that will lead to a reasonable human comfort
and reduce energy consumption.