Abstract: This paper presents a model predictive control (MPC)
of a utility interactive (UI) single phase inverter (SPI) for a
photovoltaic (PV) system at residential/distribution level. The
proposed model uses single-phase phase locked loop (PLL) to
synchronize SPI with the grid and performs MPC control in a dq
reference frame. SPI model consists of boost converter (BC),
maximum power point tracking (MPPT) control, and a full bridge
(FB) voltage source inverter (VSI). No PI regulators to tune and
carrier and modulating waves are required to produce switching
sequence. Instead, the operational model of VSI is used to synthesize
sinusoidal current and track the reference. Model is validated using a
three kW PV system at the input of UI-SPI in Matlab/Simulink.
Implementation and results demonstrate simplicity and accuracy, as
well as reliability of the model.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: Nonalcoholic fatty liver disease (NAFLD) has
increased in conjunction with obesity. The accuracy of risk factors
for detecting NAFLD in obese adolescents has not undergone a
formal evaluation. The aim of this study was to evaluate predictors of
NAFLD among Egyptian female obese adolescents. The study
included 162 obese female adolescents. All were subjected to
anthropometry, biochemical analysis and abdominal ultrasongraphic
assessment. Metabolic syndrome (MS) was diagnosed according to
the IDF criteria. Significant association between presence of MS and
NAFLD was observed. Obese adolescents with NAFLD had
significantly higher levels of ALT, triglycerides, fasting glucose,
insulin, blood pressure and HOMA-IR, whereas decreased HDL-C
levels as compared with obese cases without NAFLD. Receiver–
operating characteristic (ROC) curve analysis shows that ALT is a
sensitive predictor for NAFLD, confirming that ALT can be used as a
marker of NAFLD.
Abstract: The mathematical analysis on radiation obtained and
the development of the solar photovoltaic (PV) array groundwater
pumping is needed in the rural areas of Thohoyandou for sizing and
power performance subject to the climate conditions within the area.
A simple methodology approach is developed for the directed
coupled solar, controller and submersible ground water pump system.
The system consists of a PV array, pump controller and submerged
pump, battery backup and charger controller. For this reason, the
theoretical solar radiation is obtained for optimal predictions and
system performance in order to achieve different design and
operating parameters. Here the examination of the PV schematic
module in a Direct Current (DC) application is used for obtainable
maximum solar power energy for water pumping. In this paper, a
simple efficient photovoltaic water pumping system is presented with
its theoretical studies and mathematical modeling of photovoltaics
(PV) system.
Abstract: Mass flow measurement is the basis of most technoeconomic
formulations in the chemical industry. This calls for
reliable and accurate detection of mass flow. Flow measurement
laboratory experiments were conducted using various instruments.
These consisted of orifice plates, various sized rotameters, wet gas
meter and soap bubble meter. This work was aimed at evaluating
appropriate operating conditions and accuracy of the aforementioned
devices. The experimental data collected were compared to
theoretical predictions from Bernoulli’s equation and calibration
curves supplied by the instrument’s manufacturers. The results
obtained showed that rotameters were more reliable for measuring
high and low flow rates; while soap-bubble meters and wet-gas
meters were found to be suitable for measuring low flow rates. The
laboratory procedures and findings of the actual work can assist
engineering students and professionals in conducting their flow
measurement laboratory test work.
Abstract: Nanotechnology is the new cyber, according to several major leaders in this field. Just as cyber is entrenched across global society now, nano is poised to be major capabilities enabler of the next decades. Expert members from the National Nanotechnology Initiative (in U.S.) representing government and science disciplines say nano has great significance for the military and the general public. It is predicted that after next 15 years nanotechnology will replace information technology as the most economic technology platform. Nanotechnology has even wider applications than information technology.
Abstract: One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%
accuracy.
Abstract: Recent perceived climate variability raises concerns
with unprecedented hydrological phenomena and extremes.
Distribution and circulation of the waters of the Earth become
increasingly difficult to determine because of additional uncertainty
related to anthropogenic emissions. The world wide observed
changes in the large-scale hydrological cycle have been related to an
increase in the observed temperature over several decades. Although
the effect of change in climate on hydrology provides a general
picture of possible hydrological global change, new tools and
frameworks for modelling hydrological series with nonstationary
characteristics at finer scales, are required for assessing climate
change impacts. Of the downscaling techniques, dynamic
downscaling is usually based on the use of Regional Climate Models
(RCMs), which generate finer resolution output based on atmospheric
physics over a region using General Circulation Model (GCM) fields
as boundary conditions. However, RCMs are not expected to capture
the observed spatial precipitation extremes at a fine cell scale or at a
basin scale. Statistical downscaling derives a statistical or empirical
relationship between the variables simulated by the GCMs, called
predictors, and station-scale hydrologic variables, called predictands.
The main focus of the paper is on the need for using statistical
downscaling techniques for projection of local hydrometeorological
variables under climate change scenarios. The projections can be then
served as a means of input source to various hydrologic models to
obtain streamflow, evapotranspiration, soil moisture and other
hydrological variables of interest.
Abstract: A generalized vortex lattice method for complex
lifting surfaces with flap and aileron deflection is formulated. The
method is not restricted by the linearized theory assumption and
accounts for all standard geometric lifting surface parameters:
camber, taper, sweep, washout, dihedral, in addition to flap and
aileron deflection. Thickness is not accounted for since the physical
lifting body is replaced by a lattice of panels located on the mean
camber surface. This panel lattice setup and the treatment of different
wake geometries is what distinguish the present work form the
overwhelming majority of previous solutions based on the vortex
lattice method. A MATLAB code implementing the proposed
formulation is developed and validated by comparing our results to
existing experimental and numerical ones and good agreement is
demonstrated. It is then used to study the accuracy of the widely used
classical vortex-lattice method. It is shown that the classical approach
gives good agreement in the clean configuration but is off by as much
as 30% when a flap or aileron deflection of 30° is imposed. This
discrepancy is mainly due the linearized theory assumption
associated with the conventional method. A comparison of the effect
of four different wake geometries on the values of aerodynamic
coefficients was also carried out and it is found that the choice of the
wake shape had very little effect on the results.
Abstract: An analysis of the Australian Diabetes Screening
Study estimated undiagnosed diabetes mellitus [DM] prevalence in a
high risk general practice based cohort. DM prevalence varied from
9.4% to 18.1% depending upon the diagnostic criteria utilised with
age being a highly significant risk factor. Utilising the gold standard
oral glucose tolerance test, the prevalence of DM was 22-23% in
those aged >= 70 years and
Abstract: The construction of a new airport or the extension of
an existing one requires massive investments and many times public
private partnerships were considered in order to make feasible such
projects. One characteristic of these projects is uncertainty with
respect to financial and environmental impacts on the medium to long
term. Another one is the multistage nature of these types of projects.
While many airport development projects have been a success, some
others have turned into a nightmare for their promoters.
This communication puts forward a new approach for airport
investment risk assessment. The approach takes explicitly into
account the degree of uncertainty in activity levels prediction and
proposes milestones for the different stages of the project for
minimizing risk. Uncertainty is represented through fuzzy dual theory
and risk management is performed using dynamic programming. An
illustration of the proposed approach is provided.
Abstract: This study aims to examine the role of career
advancement and job security as predictors of employee commitment
to their organization. Data was collected from 580 frontline
employees attached to two departments of 29 luxury hotels in
Peninsular Malaysia. Statistical results using Partial Least Squares
technique provided support for the proposed hypotheses. In view of
the findings, theoretical and practical implications are discussed.
Abstract: In this paper, GSM signal strength was measured in
order to detect the type of the signal fading phenomenon using onedimensional
multilevel wavelet residual method and neural network
clustering to determine the average GSM signal strength received in
the study area. The wavelet residual method predicted that the GSM
signal experienced slow fading and attenuated with MSE of 3.875dB.
The neural network clustering revealed that mostly -75dB, -85dB and
-95dB were received. This means that the signal strength received in
the study is a weak signal.
Abstract: ‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. In this paper, a novel feature based image steganalysis technique is proposed. Various statistical moments have been used along with some similarity metric. The proposed steganalysis technique has been designed based on transformation in four wavelet domains, which include Haar, Daubechies, Symlets and Biorthogonal. Each domain is being subjected to various classifiers, namely K-nearest-neighbor, K* Classifier, Locally weighted learning, Naive Bayes classifier, Neural networks, Decision trees and Support vector machines. The experiments are performed on a large set of pictures which are available freely in image database. The system also predicts the different message length definitions.
Abstract: Second line antiretroviral therapy (ART) regimen is
used when patients fail their first line regimen. There are many
factors such as non-adherence, drug resistance as well as virological
and immunological failure that lead to second line highly active
antiretroviral therapy (HAART) regimen treatment failure. This study
was aimed at determining predictor factors to treatment failure with
second line HAART and analyzing median survival time.
An observational, retrospective study was conducted in Sungai
Buloh Hospital (HSB) to assess current status of HIV patients treated
with second line HAART regimen. Convenience sampling was used
and 104 patients were included based on the study’s inclusion and
exclusion criteria. Data was collected for six months i.e. from July
until December 2013. Data was then analysed using SPSS version 18.
Kaplan-Meier and Cox regression analyses were used to measure
median survival times and predictor factors for treatment failure.
The study population consisted mainly of male subjects, aged 30-
45 years, who were heterosexual, and had HIV infection for less than
6 years. The most common second line HAART regimen given was
lopinavir/ritonavir (LPV/r)-based combination. Kaplan-Meier
analysis showed that patients on LPV/r demonstrated longer median
survival times than patients on indinavir/ritonavir (IDV/r) based
combination (p
Abstract: Recently GPS data is used in a lot of studies to
automatically reconstruct travel patterns for trip survey. The aim is to
minimize the use of questionnaire surveys and travel diaries so as to
reduce their negative effects. In this paper data acquired from GPS and
accelerometer embedded in smart phones is utilized to predict the
mode of transportation used by the phone carrier. For prediction,
Support Vector Machine (SVM) and Adaptive boosting (AdaBoost)
are employed. Moreover a unique method to improve the prediction
results from these algorithms is also proposed. Results suggest that the
prediction accuracy of AdaBoost after improvement is relatively better
than the rest.
Abstract: Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
Abstract: This paper presents a regression model with
autocorrelated errors in which the inputs are social moods obtained by
analyzing the adjectives in Twitter posts using a document topic
model, where document topics are extracted using LDA. The
regression model predicts Dow Jones Industrial Average (DJIA) more
precisely than autoregressive moving-average models.
Abstract: In the present work, detailed analysis on flow characteristics of a pair of immiscible liquids through horizontal pipeline is simulated by using ANSYS FLUENT 6.2. Moderately viscous oil and water (viscosity ratio = 107, density ratio = 0.89 and interfacial tension = 0.024 N/m) have been taken as system fluids for the study. Volume of Fluid (VOF) method has been employed by assuming unsteady flow, immiscible liquid pair, constant liquid properties, and co-axial flow. Meshing has been done using GAMBIT. Quadrilateral mesh type has been chosen to account for the surface tension effect more accurately. From the grid independent study, we have selected 47037 number of mesh elements for the entire geometry. Simulation successfully predicts slug, stratified wavy, stratified mixed and annular flow, except dispersion of oil in water, and dispersion of water in oil. Simulation results are validated with horizontal literature data and good conformity is observed. Subsequently, we have simulated the hydrodynamics (viz., velocity profile, area average pressure across a cross section and volume fraction profile along the radius) of stratified wavy and annular flow at different phase velocities. The simulation results show that in the annular flow, total pressure of the mixture decreases with increase in oil velocity due to the fact that pipe cross section is completely wetted with water. Simulated oil volume fraction shows maximum at the centre in core annular flow, whereas, in stratified flow, maximum value appears at upper side of the pipeline. These results are in accord with the actual flow configuration. Our findings could be useful in designing pipeline for transportation of crude oil.
Abstract: The present environmental issues have made aircraft jet noise reduction a crucial problem in aero-acoustics research. Acoustic studies reveal that addition of chevrons to the nozzle reduces the sound pressure level reasonably with acceptable reduction in performance. In this paper comprehensive numerical studies on acoustic characteristics of different types of chevron nozzles have been carried out with non-reacting flows for the shape optimization of chevrons in supersonic nozzles for aerospace applications. The numerical studies have been carried out using a validated steady 3D density based, k-ε turbulence model. In this paper chevron with sharp edge, flat edge, round edge and U-type edge are selected for the jet acoustic characterization of supersonic nozzles. We observed that compared to the base model a case with round-shaped chevron nozzle could reduce 4.13% acoustic level with 0.6% thrust loss. We concluded that the prudent selection of the chevron shape will enable an appreciable reduction of the aircraft jet noise without compromising its overall performance. It is evident from the present numerical simulations that k-ε model can predict reasonably well the acoustic level of chevron supersonic nozzles for its shape optimization.