Abstract: Investment in a constructed facility represents a cost in
the short term that returns benefits only over the long term use of the
facility. Thus, the costs occur earlier than the benefits, and the owners
of facilities must obtain the capital resources to finance the costs of
construction. A project cannot proceed without an adequate
financing, and the cost of providing an adequate financing can be
quite large. For these reasons, the attention to the project finance is an
important aspect of project management. Finance is also a concern to
the other organizations involved in a project such as the general
contractor and material suppliers. Unless an owner immediately and
completely covers the costs incurred by each participant, these
organizations face financing problems of their own. At a more
general level, the project finance is the only one aspect of the general
problem of corporate finance. If numerous projects are considered
and financed together, then the net cash flow requirements constitute
the corporate financing problem for capital investment. Whether
project finance is performed at the project or at the corporate level
does not alter the basic financing problem .In this paper, we will first
consider facility financing from the owner's perspective, with due
consideration for its interaction with other organizations involved in a
project. Later, we discuss the problems of construction financing
which are crucial to the profitability and solvency of construction
contractors. The objective of this paper is to present the steps utilized
to determine the best combination of minimum project financing.
The proposed model considers financing; schedule and maximum net
area .The proposed model is called Project Financing and Schedule
Integration using Genetic Algorithms "PFSIGA". This model
intended to determine more steps (maximum net area) for any project
with a subproject. An illustrative example will demonstrate the
feature of this technique. The model verification and testing are put
into consideration.
Abstract: In this paper, a tooth shape optimization method for
cogging torque reduction in Permanent Magnet (PM) motors is
developed by using the Reduced Basis Technique (RBT) coupled by
Finite Element Analysis (FEA) and Design of Experiments (DOE)
methods. The primary objective of the method is to reduce the
enormous number of design variables required to define the tooth
shape. RBT is a weighted combination of several basis shapes. The
aim of the method is to find the best combination using the weights
for each tooth shape as the design variables. A multi-level design
process is developed to find suitable basis shapes or trial shapes at
each level that can be used in the reduced basis technique. Each level
is treated as a separated optimization problem until the required
objective – minimum cogging torque – is achieved. The process is
started with geometrically simple basis shapes that are defined by
their shape co-ordinates. The experimental design of Taguchi method
is used to build the approximation model and to perform
optimization. This method is demonstrated on the tooth shape
optimization of a 8-poles/12-slots PM motor.
Abstract: In a particular case of behavioural model reduction by ANNs, a validity domain shortening has been found. In mechanics, as in other domains, the notion of validity domain allows the engineer to choose a valid model for a particular analysis or simulation. In the study of mechanical behaviour for a cantilever beam (using linear and non-linear models), Multi-Layer Perceptron (MLP) Backpropagation (BP) networks have been applied as model reduction technique. This reduced model is constructed to be more efficient than the non-reduced model. Within a less extended domain, the ANN reduced model estimates correctly the non-linear response, with a lower computational cost. It has been found that the neural network model is not able to approximate the linear behaviour while it does approximate the non-linear behaviour very well. The details of the case are provided with an example of the cantilever beam behaviour modelling.
Abstract: Results are presented from a combined experimental
and modeling study undertaken to understand the effect of fuel spray
angle on soot production in turbulent liquid spray flames. The
experimental work was conducted in a cylindrical laboratory furnace
at fuel spray cone angle of 30º, 45º and 60º. Soot concentrations
inside the combustor are measured by filter paper technique. The soot
concentration is modeled by using the soot particle number density
and the mass density based acetylene concentrations. Soot oxidation
occurred by both hydroxide radicals and oxygen molecules. The
comparison of calculated results against experimental measurements
shows good agreement. Both the numerical and experimental results
show that the peak value of soot and its location in the furnace
depend on fuel spray cone angle. An increase in spray angle enhances
the evaporating rate and peak temperature near the nozzle. Although
peak soot concentration increase with enhance of fuel spray angle but
soot emission from the furnace decreases.
Abstract: Least Significant Bit (LSB) technique is the earliest
developed technique in watermarking and it is also the most simple,
direct and common technique. It essentially involves embedding the
watermark by replacing the least significant bit of the image data with
a bit of the watermark data. The disadvantage of LSB is that it is not
robust against attacks. In this study intermediate significant bit (ISB)
has been used in order to improve the robustness of the watermarking
system. The aim of this model is to replace the watermarked image
pixels by new pixels that can protect the watermark data against
attacks and at the same time keeping the new pixels very close to the
original pixels in order to protect the quality of watermarked image.
The technique is based on testing the value of the watermark pixel
according to the range of each bit-plane.
Abstract: This paper presents a new circuit arrangement for a
current-mode Wheatstone bridge that is suitable for low-voltage
integrated circuits implementation. Compared to the other proposed
circuits, this circuit features severe reduction of the elements number,
low supply voltage (1V) and low power consumption (
Abstract: The multiple traveling salesman problem (mTSP) can be used to model many practical problems. The mTSP is more complicated than the traveling salesman problem (TSP) because it requires determining which cities to assign to each salesman, as well as the optimal ordering of the cities within each salesman's tour. Previous studies proposed that Genetic Algorithm (GA), Integer Programming (IP) and several neural network (NN) approaches could be used to solve mTSP. This paper compared the results for mTSP, solved with Genetic Algorithm (GA) and Nearest Neighbor Algorithm (NNA). The number of cities is clustered into a few groups using k-means clustering technique. The number of groups depends on the number of salesman. Then, each group is solved with NNA and GA as an independent TSP. It is found that k-means clustering and NNA are superior to GA in terms of performance (evaluated by fitness function) and computing time.
Abstract: Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.
Abstract: The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.
Abstract: Environmental awareness and depletion of the
petroleum resources are among vital factors that motivate a number
of researchers to explore the potential of reusing natural fiber as an
alternative composite material in industries such as packaging,
automotive and building constructions. Natural fibers are available in
abundance, low cost, lightweight polymer composite and most
importance its biodegradability features, which often called “ecofriendly"
materials. However, their applications are still limited due
to several factors like moisture absorption, poor wettability and large
scattering in mechanical properties. Among the main challenges on
natural fibers reinforced matrices composite is their inclination to
entangle and form fibers agglomerates during processing due to
fiber-fiber interaction. This tends to prevent better dispersion of the
fibers into the matrix, resulting in poor interfacial adhesion between
the hydrophobic matrix and the hydrophilic reinforced natural fiber.
Therefore, to overcome this challenge, fiber treatment process is one
common alternative that can be use to modify the fiber surface
topology by chemically, physically or mechanically technique.
Nevertheless, this paper attempt to focus on the effect of
mercerization treatment on mechanical properties enhancement of
natural fiber reinforced composite or so-called bio composite. It
specifically discussed on mercerization parameters, and natural fiber
reinforced composite mechanical properties enhancement.
Abstract: The control of sprayer boom undesired vibrations pose a great challenge to investigators due to various disturbances and conditions. Sprayer boom movements lead to reduce of spread efficiency and crop yield. This paper describes the design of a novel control method for an active suspension system applying proportional-integral-derivative (PID) controller with an active force control (AFC) scheme integration of an iterative learning algorithm employed to a sprayer boom. The iterative learning as an intelligent method is principally used as a method to calculate the best value of the estimated inertia of the sprayer boom needed for the AFC loop. Results show that the proposed AFC-based scheme performs much better than the standard PID control technique. Also, this shows that the system is more robust and accurate.
Abstract: Sparse representation has long been studied and several
dictionary learning methods have been proposed. The dictionary
learning methods are widely used because they are adaptive. In this
paper, a new dictionary learning method for audio is proposed. Signals
are at first decomposed into different degrees of Intrinsic Mode
Functions (IMF) using Empirical Mode Decomposition (EMD)
technique. Then these IMFs form a learned dictionary. To reduce the
size of the dictionary, the K-means method is applied to the dictionary
to generate a K-EMD dictionary. Compared to K-SVD algorithm, the
K-EMD dictionary decomposes audio signals into structured
components, thus the sparsity of the representation is increased by
34.4% and the SNR of the recovered audio signals is increased by
20.9%.
Abstract: Content-based Image Retrieval (CBIR) aims at searching image databases for specific images that are similar to a given query image based on matching of features derived from the image content. This paper focuses on a low-dimensional color based indexing technique for achieving efficient and effective retrieval performance. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique. Then the cluster (region) mode is used as representative of the image in 3-D color space. The feature descriptor consists of the representative color of a region and is indexed using a spatial indexing method that uses *R -tree thus avoiding the high-dimensional indexing problems associated with the traditional color histogram. Alternatively, the images in the database are clustered based on region feature similarity using Euclidian distance. Only representative (centroids) features of these clusters are indexed using *R -tree thus improving the efficiency. For similarity retrieval, each representative color in the query image or region is used independently to find regions containing that color. The results of these methods are compared. A JAVA based query engine supporting query-by- example is built to retrieve images by color.
Abstract: The three steps of the standard one-way nested grid
for a regional scale of the third generation WAve Model Cycle 4
(WAMC4) is scrutinized. The model application is enabled to solve
the energy balance equation on a coarse resolution grid in order to
produce boundary conditions for a smaller area by the nested grid
technique. In the present study, the model takes a full advantage of the
fine resolution of wind fields in space and time produced by the available
U.S. Navy Global Atmospheric Prediction System (NOGAPS)
model with 1 degree resolution. The nested grid application of the
model is developed in order to gradually increase the resolution from
the open ocean towards the South China Sea (SCS) and the Gulf of
Thailand (GoT) respectively. The model results were compared with
buoy observations at Ko Chang, Rayong and Huahin locations which
were obtained from the Seawatch project. In addition, the results were
also compared with Satun based weather station which was provided
from Department of Meteorology, Thailand. The data collected from
this station presented the significant wave height (Hs) reached 12.85
m. The results indicated that the tendency of the Hs from the model
in the spherical coordinate propagation with deep water condition in
the fine grid domain agreed well with the Hs from the observations.
Abstract: Wireless Mesh Networks (WMNs) are an emerging
technology for last-mile broadband access. In WMNs, similar to ad
hoc networks, each user node operates not only as a host but also as a
router. User packets are forwarded to and from an Internet-connected
gateway in multi-hop fashion. The WMNs can be integrated with
other networking technologies i.e. ad hoc networks, to implement a
smooth network extension. The meshed topology provides good
reliability and scalability, as well as low upfront investments. Despite
the recent start-up surge in WMNs, much research remains to be
done in standardizing the functional parameters of WMNs to fully
exploit their full potential. An edifice of the security concerns of
these networks is authentication of a new client joining an integrated
ad hoc network and such a scenario will require execution of a multihop
authentication technique. Our endeavor in this paper is to
introduce a secure authentication technique, with light over-heads
that can be conveniently implemented for the ad-hoc nodes forming
clients of an integrated WMN, thus facilitating their inter-operability.
Abstract: In this paper, the computation of the electrical field distribution around AC high-voltage lines is demonstrated. The advantages and disadvantages of two different methods are described to evaluate the electrical field quantity. The first method is a seminumerical method using the laws of electrostatic techniques to simulate the two-dimensional electric field under the high-voltage overhead line. The second method which will be discussed is the finite element method (FEM) using specific boundary conditions to compute the two- dimensional electric field distributions in an efficient way.
Abstract: Embedded systems need to respect stringent real
time constraints. Various hardware components included in such
systems such as cache memories exhibit variability and therefore
affect execution time. Indeed, a cache memory access from an
embedded microprocessor might result in a cache hit where the
data is available or a cache miss and the data need to be fetched
with an additional delay from an external memory. It is therefore
highly desirable to predict future memory accesses during
execution in order to appropriately prefetch data without incurring
delays. In this paper, we evaluate the potential of several artificial
neural networks for the prediction of instruction memory
addresses. Neural network have the potential to tackle the nonlinear
behavior observed in memory accesses during program
execution and their demonstrated numerous hardware
implementation emphasize this choice over traditional forecasting
techniques for their inclusion in embedded systems. However,
embedded applications execute millions of instructions and
therefore millions of addresses to be predicted. This very
challenging problem of neural network based prediction of large
time series is approached in this paper by evaluating various neural
network architectures based on the recurrent neural network
paradigm with pre-processing based on the Self Organizing Map
(SOM) classification technique.
Abstract: Today's business environment requires that companies have access to highly relevant information in a matter of seconds.
Modern Business Intelligence tools rely on data structured mostly in traditional dimensional database schemas, typically represented by
star schemas. Dimensional modeling is already recognized as a
leading industry standard in the field of data warehousing although
several drawbacks and pitfalls were reported. This paper focuses on
the analysis of another data warehouse modeling technique - the
anchor modeling, and its characteristics in context with the standardized dimensional modeling technique from a query performance perspective. The results of the analysis show
information about performance of queries executed on database
schemas structured according to principles of each database modeling
technique.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: Natural frequencies and dynamic response of a spur
gear sector are investigated using a two dimensional finite element
model that offers significant advantages for dynamic gear analyses.
The gear teeth are analyzed for different operating speeds. A primary
feature of this modeling is determination of mesh forces using a
detailed contact analysis for each time step as the gears roll through
the mesh. ANSYS software has been used on the proposed model to
find the natural frequencies by Block Lanczos technique and
displacements and dynamic stresses by transient mode super position
method. The effect of rotational speed of the gear on the dynamic
response of gear tooth has been studied and design limits have been
discussed.