Abstract: In this paper numerous robust fitting procedures are considered in estimating spatial variograms. In spatial statistics, the conventional variogram fitting procedure (non-linear weighted least squares) suffers from the same outlier problem that has plagued this method from its inception. Even a 3-parameter model, like the variogram, can be adversely affected by a single outlier. This paper uses the Hogg-Type adaptive procedures to select an optimal score function for a rank-based estimator for these non-linear models. Numeric examples and simulation studies will demonstrate the robustness, utility, efficiency, and validity of these estimates.
Abstract: In this paper, a brief review of the corrosion mechanism in buried pipe and modes of failure is provided together with the available corrosion models. Moreover, the sensitivity analysis is performed to understand the influence of corrosion model parameters on the remaining life estimation. Further, the probabilistic analysis is performed to propagate the uncertainty in the corrosion model on the estimation of the renaming life of the pipe. Finally, the comparison among the corrosion models on the basis of the remaining life estimation will be provided to improve the renewal plan.
Abstract: This paper presents an analytical study on the
behavior of reinforced concrete walls with rectangular cross section.
Several experiments on such walls have been selected to be studied.
Database from various experiments were collected and nominal shear
wall strengths have been calculated using formulas, such as those of
the ACI (American), NZS (New Zealand), Mexican (NTCC), and
Wood and Barda equations. Subsequently, nominal shear wall
strengths from the formulas were compared with the ultimate shear
wall strengths from the database. These formulas vary substantially in
functional form and do not account for all variables that affect the
response of walls. There is substantial scatter in the predicted values
of ultimate shear strength. Two new semi empirical equations are
developed using data from tests of 57 walls for transitions walls and
27 for slender walls with the objective of improving the prediction of
peak strength of walls with the most possible accurate.
Abstract: The present paper discusses the prediction of gas-liquid two-phase frictional pressure drop in a 2.12 mm horizontal circular minichannel using Artificial Neural Network (ANN). The experimental results are obtained with air as gas phase and water as liquid phase. The superficial gas velocity is kept in the range of 0.0236 m/s to 0.4722 m/s while the values of 0.0944 m/s, 0.1416 m/s and 0.1889 m/s are considered for superficial liquid velocity. The experimental results are predicted using different Artificial Neural Network (ANN) models. Networks used for prediction are radial basis, generalised regression, linear layer, cascade forward back propagation, feed forward back propagation, feed forward distributed time delay, layer recurrent, and Elman back propagation. Transfer functions used for networks are Linear (PURELIN), Logistic sigmoid (LOGSIG), tangent sigmoid (TANSIG) and Gaussian RBF. Combination of networks and transfer functions give different possible neural network models. These models are compared for Mean Absolute Relative Deviation (MARD) and Mean Relative Deviation (MRD) to identify the best predictive model of ANN.
Abstract: To increase the temperature contrast in thermal
images, the characteristics of the electrical conductivity and thermal
imaging modalities can be combined. In this experimental study, it is
objected to observe whether the temperature contrast created by the
tumor tissue can be improved just due to the current application
within medical safety limits. Various thermal breast phantoms are
developed to simulate the female breast tissue. In vitro experiments
are implemented using a thermal infrared camera in a controlled
manner. Since experiments are implemented in vitro, there is no
metabolic heat generation and blood perfusion. Only the effects and
results of the electrical stimulation are investigated. Experimental
study is implemented with two-dimensional models. Temperature
contrasts due to the tumor tissues are obtained. Cancerous tissue is
determined using the difference and ratio of healthy and tumor
images. 1 cm diameter single tumor tissue causes almost 40 °mC
temperature contrast on the thermal-breast phantom. Electrode
artifacts are reduced by taking the difference and ratio of background
(healthy) and tumor images. Ratio of healthy and tumor images show
that temperature contrast is increased by the current application.
Abstract: In this work we make a bifurcation analysis for a
single compartment representation of Traub model, one of the most
important conductance-based models. The analysis focus in two
principal parameters: current and leakage conductance. Study of
stable and unstable solutions are explored; also Hop-bifurcation and
frequency interpretation when current varies is examined. This study
allows having control of neuron dynamics and neuron response when
these parameters change. Analysis like this is particularly important
for several applications such as: tuning parameters in learning
process, neuron excitability tests, measure bursting properties of the
neuron, etc. Finally, a hardware implementation results were
developed to corroborate these results.
Abstract: The cities of Johannesburg and Pretoria both located in the Gauteng province are separated by a distance of 58 km. The traffic queues on the Ben Schoeman freeway which connects these two cities can stretch for almost 1.5 km. Vehicle traffic congestion impacts negatively on the business and the commuter’s quality of life. The goal of this paper is to identify variables that influence the flow of traffic and to design a vehicle traffic prediction model, which will predict the traffic flow pattern in advance. The model will unable motorist to be able to make appropriate travel decisions ahead of time. The data used was collected by Mikro’s Traffic Monitoring (MTM). Multi-Layer perceptron (MLP) was used individually to construct the model and the MLP was also combined with Bagging ensemble method to training the data. The cross—validation method was used for evaluating the models. The results obtained from the techniques were compared using predictive and prediction costs. The cost was computed using combination of the loss matrix and the confusion matrix. The predicted models designed shows that the status of the traffic flow on the freeway can be predicted using the following parameters travel time, average speed, traffic volume and day of month. The implications of this work is that commuters will be able to spend less time travelling on the route and spend time with their families. The logistics industry will save more than twice what they are currently spending.
Abstract: The acceptance of sustainable products by the final
consumer is still one of the challenges of the industry, which
constantly seeks alternative approaches to successfully be accepted in
the global market. A large set of methods and approaches have been
discussed and analysed throughout the literature. Considering the current need for sustainable development and the
current pace of consumption, the need for a combined solution
towards the development of new products became clear, forcing
researchers in product development to propose alternatives to the
previous standard product development models. This paper presents, through a systemic analysis of the literature
on product development, eco-design and consumer involvement, a set
of alternatives regarding consumer involvement towards the
development of sustainable products and how these approaches could
help improve the sustainable industry’s establishment in the general
market. Still being developed in the course of the author’s PhD, the initial
findings of the research show that the understanding of the benefits of
sustainable behaviour lead to a more conscious acquisition and
eventually to the implementation of sustainable change in the
consumer. Thus this paper is the initial approach towards the
development of new sustainable products using the fashion industry
as an example of practical implementation and acceptance by the
consumers. By comparing the existing literature and critically analysing it, this
paper concluded that the consumer involvement is strategic to
improve the general understanding of sustainability and its features.
The use of consumers and communities has been studied since the
early 90s in order to exemplify uses and to guarantee a fast
comprehension. The analysis done also includes the importance of
this approach for the increase of innovation and ground breaking
developments, thus requiring further research and practical
implementation in order to better understand the implications and
limitations of this methodology.
Abstract: In this paper, a linear mixed model which has two
random effects is broken up into two models. This thesis gets
the parameter estimation of the original model and an estimation’s
statistical qualities based on these two models. Then many important
properties are given by comparing this estimation with other general
estimations. At the same time, this paper proves the analysis of
variance estimate (ANOVAE) about σ2 of the original model is equal
to the least-squares estimation (LSE) about σ2 of these two models.
Finally, it also proves that this estimation is better than ANOVAE
under Stein function and special condition in some degree.
Abstract: Background modeling and subtraction in video
analysis has been widely used as an effective method for moving
objects detection in many computer vision applications. Recently, a
large number of approaches have been developed to tackle different
types of challenges in this field. However, the dynamic background
and illumination variations are the most frequently occurred problems
in the practical situation. This paper presents a favorable two-layer
model based on codebook algorithm incorporated with local binary
pattern (LBP) texture measure, targeted for handling dynamic
background and illumination variation problems. More specifically,
the first layer is designed by block-based codebook combining with
LBP histogram and mean value of each RGB color channel. Because
of the invariance of the LBP features with respect to monotonic
gray-scale changes, this layer can produce block wise detection results
with considerable tolerance of illumination variations. The pixel-based
codebook is employed to reinforce the precision from the output of the
first layer which is to eliminate false positives further. As a result, the
proposed approach can greatly promote the accuracy under the
circumstances of dynamic background and illumination changes.
Experimental results on several popular background subtraction
datasets demonstrate very competitive performance compared to
previous models.
Abstract: Lead being a toxic heavy metal that mankind is
exposed to the highest levels of this metal. There are different sources
of environmental pollution with lead as lead alkyl additives in petrol
and manufacturing processes. The contaminated atmosphere in urban
and industrial areas by lead in Egypt may lead to the contamination
of foods beside the other different sources. The present investigation
studied the risk assessment of lead in some Egyptian edible
vegetables and fruits collected from different environments in Greater
Cairo Governorate, i.e. industrial, heavy traffic and rural areas. A
total of 325 leafy and fruity vegetables and fruits samples belonging
to 11, 6 and 4 different species, respectively were randomly collected
from markets of the three main models. Data indicated the variation
of lead levels in different three areas. The highest levels of lead were
detected in the samples collected from industrial and traffic areas.
However, the lowest levels were found in the rural areas. It could be
concluded that determination of lead levels in foods from different
localities and environments at regularly is very important.
Abstract: The paper focuses on the benefits of business process
modeling. Although this discipline is developing for many years,
there is still necessity of creating new opportunities to meet the ever
increasing users’ needs. Because one of these needs is related to the
conversion of business process models from one standard to another,
the authors have developed a converter between BPMN and EPC
standards using workflow patterns as intermediate tool. Nowadays
there are too many systems for business process modeling. The
variety of output formats is almost the same as the systems
themselves. This diversity additionally hampers the conversion of the
models. The presented study is aimed at discussing problems due to
differences in the output formats of various modeling environments.
Abstract: Although it is fully impossible to ensure that a software system is quite secure, developing an acceptable secure software system in a convenient platform is not unreachable. In this paper, we attempt to analyze software development life cycle (SDLC) models from the hardware systems and circuits point of view. To date, the SDLC models pay merely attention to the software security from the software perspectives. In this paper, we present new features for SDLC stages to emphasize the role of systems and circuits in developing secure software system through the software development stages, the point that has not been considered previously in the SDLC models.
Abstract: The education sector is constantly faced with rapid
changes in technologies in terms of ensuring that the curriculum is up
to date and in terms of making sure that students are aware of these
technological changes. This challenge can be seen as the motivation
for this study, which is to examine the factors affecting computing
students’ awareness of the latest Information Technologies (ICTs).
The aim of this study is divided into two sub-objectives which are:
the selection of relevant theories and the design of a conceptual
model to support it as well as the empirical testing of the designed
model. The first objective is achieved by a review of existing
literature on technology adoption theories and models. The second
objective is achieved using a survey of computing students in the four
universities of the KwaZulu-Natal province of South Africa. Data
collected from this survey is analyzed using Statistical package for
the Social Science (SPSS) using descriptive statistics, ANOVA and
Pearson correlations. The main hypothesis of this study is that there is
a relationship between the demographics and the prior conditions of
the computing students and their awareness of general ICT trends and
of Digital Switch Over (DSO) a new technology which involves the
change from analog to digital television broadcasting in order to
achieve improved spectrum efficiency. The prior conditions of the
computing students that were considered in this study are students’
perceived exposure to career guidance and students’ perceived
curriculum currency. The results of this study confirm that gender,
ethnicity, and high school computing course affect students’
perceived curriculum currency while high school location affects
students’ awareness of DSO. The results of this study also confirm
that there is a relationship between students prior conditions and their
awareness of general ICT trends and DSO in particular.
Abstract: A growing demand is felt today for realistic 3D
models enabling the cognition and popularization of historical-artistic
heritage. Evaluation and preservation of Cultural Heritage is
inextricably connected with the innovative processes of gaining,
managing, and using knowledge. The development and perfecting of
techniques for acquiring and elaborating photorealistic 3D models,
made them pivotal elements for popularizing information of objects
on the scale of architectonic structures.
Abstract: The need to save time and cost of soil testing at the
planning stage of road work has necessitated developing predictive
models. This study proposes a model for predicting the dry density of
lateritic soils stabilized with corn cob ash (CCA) and blended cement
- CCA. Lateritic soil was first stabilized with CCA at 1.5, 3.0, 4.5 and
6% of the weight of soil and then stabilized with the same
proportions as replacement for cement. Dry density, specific gravity,
maximum degree of saturation and moisture content were determined
for each stabilized soil specimen, following standard procedure.
Polynomial equations containing alpha and beta parameters for CCA
and blended CCA-cement were developed. Experimental values were
correlated with the values predicted from the Matlab curve fitting
tool, and the Solver function of Microsoft Excel 2010. The correlation
coefficient (R2) of 0.86 was obtained indicating that the model could
be accepted in predicting the maximum dry density of CCA stabilized
soils to facilitate quick decision making in roadworks.
Abstract: Extreme formation is a theoretical concept of selfsustain
flight when a big airliner is followed by a small UAV glider
flying in the airliner wake vortex. The paper presents results of a
climb analysis with the goal to lift the gliding UAV to airliners cruise
altitude. Wake vortex models, the UAV drag polar and basic
parameters and airliner’s climb profile are introduced at first.
Afterwards, flight performance of the UAV in a wake vortex is
evaluated by analytical methods. Time history of optimal distance
between an airliner and the UAV during a climb is determined. The
results are encouraging. Therefore available UAV drag margin for
electricity generation is figured out for different vortex models.
Abstract: In this paper, we present an optimization technique or
a learning algorithm using the hybrid architecture by combining the
most popular sequence recognition models such as Recurrent Neural
Networks (RNNs) and Hidden Markov models (HMMs). In order to
improve the sequence/pattern recognition/classification performance
by applying a hybrid/neural symbolic approach, a gradient descent
learning algorithm is developed using the Real Time Recurrent
Learning of Recurrent Neural Network for processing the knowledge
represented in trained Hidden Markov Models. The developed hybrid
algorithm is implemented on automata theory as a sample test beds
and the performance of the designed algorithm is demonstrated and
evaluated on learning the deterministic finite state automata.
Abstract: The knitted fabric suffers a deformation in its
dimensions due to stretching and tension factors, transverse and
longitudinal respectively, during the process in rectilinear knitting
machines so it performs a dry relaxation shrinkage procedure and
thermal action of prefixed to obtain stable conditions in the knitting.
This paper presents a dry relaxation shrinkage prediction of Bordeaux
fiber using a feed forward neural network and linear regression
models. Six operational alternatives of shrinkage were predicted. A
comparison of the results was performed finding neural network
models with higher levels of explanation of the variability and
prediction. The presence of different reposes is included. The models
were obtained through a neural toolbox of Matlab and Minitab
software with real data in a knitting company of Southern
Guanajuato. The results allow predicting dry relaxation shrinkage of
each alternative operation.
Abstract: The work reported through this paper is an
experimental work conducted on High Performance Concrete (HPC)
with super plasticizer with the aim to develop some models suitable
for prediction of compressive strength of HPC mixes. In this study,
the effect of varying proportions of fly ash (0% to 50% @ 10%
increment) on compressive strength of high performance concrete has
been evaluated. The mix designs studied were M30, M40 and M50 to
compare the effect of fly ash addition on the properties of these
concrete mixes. In all eighteen concrete mixes that have been
designed, three were conventional concretes for three grades under
discussion and fifteen were HPC with fly ash with varying
percentages of fly ash. The concrete mix designing has been done in
accordance with Indian standard recommended guidelines. All the
concrete mixes have been studied in terms of compressive strength at
7 days, 28 days, 90 days, and 365 days. All the materials used have
been kept same throughout the study to get a perfect comparison of
values of results. The models for compressive strength prediction
have been developed using Linear Regression method (LR), Artificial
Neural Network (ANN) and Leave-One-Out Validation (LOOV)
methods.