Abstract: As the gradual increase of the enterprise scale, the
firms may possess many manufacturing plants located in different
places geographically. This change will result in the multi-site
production planning problems under the environment of multiple
plants or production resources. Our research proposes the structural
framework to analyze the multi-site planning problems. The analytical
framework is composed of six elements: multi-site conceptual model,
product structure (bill of manufacturing), production strategy,
manufacturing capability and characteristics, production planning
constraints, and key performance indicators. As well as the discussion
of these six ingredients, we also review related literatures in this paper
to match our analytical framework. Finally we take a real-world
practical example of a TFT-LCD manufacturer in Taiwan to explain
our proposed analytical framework for the multi-site production
planning problems.
Abstract: Clustering in high dimensional space is a difficult
problem which is recurrent in many fields of science and
engineering, e.g., bioinformatics, image processing, pattern
reorganization and data mining. In high dimensional space some of
the dimensions are likely to be irrelevant, thus hiding the possible
clustering. In very high dimensions it is common for all the objects in
a dataset to be nearly equidistant from each other, completely
masking the clusters. Hence, performance of the clustering algorithm
decreases.
In this paper, we propose an algorithmic framework which
combines the (reduct) concept of rough set theory with the k-means
algorithm to remove the irrelevant dimensions in a high dimensional
space and obtain appropriate clusters. Our experiment on test data
shows that this framework increases efficiency of the clustering
process and accuracy of the results.
Abstract: Short Message Service (SMS) has grown in
popularity over the years and it has become a common way of
communication, it is a service provided through General System
for Mobile Communications (GSM) that allows users to send text
messages to others.
SMS is usually used to transport unclassified information, but
with the rise of mobile commerce it has become a popular tool for
transmitting sensitive information between the business and its
clients. By default SMS does not guarantee confidentiality and
integrity to the message content.
In the mobile communication systems, security (encryption)
offered by the network operator only applies on the wireless link.
Data delivered through the mobile core network may not be
protected. Existing end-to-end security mechanisms are provided
at application level and typically based on public key
cryptosystem.
The main concern in a public-key setting is the authenticity of
the public key; this issue can be resolved by identity-based (IDbased)
cryptography where the public key of a user can be derived
from public information that uniquely identifies the user.
This paper presents an encryption mechanism based on the IDbased
scheme using Elliptic curves to provide end-to-end security
for SMS. This mechanism has been implemented over the standard
SMS network architecture and the encryption overhead has been
estimated and compared with RSA scheme. This study indicates
that the ID-based mechanism has advantages over the RSA
mechanism in key distribution and scalability of increasing
security level for mobile service.
Abstract: Petroleum refineries discharged large amount of
wastewater -during the refining process- that contains hazardous
constituents that is hard to degrade. Anaerobic treatment process is
well known as an efficient method to degrade high strength
wastewaters. Up-flow Anaerobic Sludge Blanker (UASB) is a
common process used for various wastewater treatments. Two UASB
reactors were set up and operated in parallel to evaluate the treatment
efficiency of petroleum refinery wastewater. In this study four
organic volumetric loading rates were applied (i.e. 0.58, 0.89, 1.21
and 2.34 kg/m3·d), two loads to each reactor. Each load was applied
for a period of 60 days for the reactor to acclimatize and reach steady
state, and then the second load applied. The chemical oxygen demand
(COD) removals were satisfactory with the removal efficiencies at the
loadings applied were 78, 82, 83 and 81 % respectively.
Abstract: In the Enhanced Oil Recovery (EOR) method, use of Carbon dioxide flooding whereby CO2 is injected into an oil reservoir to increase output when extracting oil resulted significant recovery worldwide. The carbon dioxide function as a pressurizing agent when mixed into the underground crude oil will reduce its viscosity and will enable a rapid oil flow. Despite the CO2’s advantage in the oil recovery, it may result to asphaltene precipitation a problem that will cause the reduction of oil produced from oil wells. In severe cases, asphaltene precipitation can cause costly blockages in oil pipes and machinery. This paper presents reviews of several studies done on mathematical modeling of asphaltene precipitation. The synthesized result from several researches done on this topic can be used as guide in order to better understand asphaltene precipitation. Likewise, this can be used as initial reference for students, and new researchers doing study on asphaltene precipitation.
Abstract: The damage tolerance behavior of integrally and
conventional stiffened panel is investigated based on the fracture
mechanics and finite element analysis. The load bearing capability
and crack growth characteristic of both types of the stiffened panels
having same configuration subjected to distributed tensile load is
examined in this paper. A fourteen-stringer stiffened panel is
analyzed for a central skin crack propagating towards the adjacent
stringers. Stress intensity factors and fatigue crack propagation rates
of both types of the stiffened panels are then compared. The analysis
results show that integral stiffening causes higher stress intensity
factor than conventional stiffened panel as the crack tip passes
through the stringer and the integrally stiffened panel has less load
bearing capability than the riveted stiffened panel.
Abstract: Based on Rayleigh beam theory, the sub-impacts of a
free-free beam struck horizontally by a round-nosed rigid mass is
simulated by the finite difference method and the impact-separation
conditions. In order to obtain the sub-impact force, a uniaxial
compression elastic-plastic contact model is employed to analyze the
local deformation field on contact zone. It is found that the horizontal
impact is a complicated process including the elastic plastic
sub-impacts in sequence. There are two sub-zones of sub-impact. In
addition, it found that the elastic energy of the free-free beam is more
suitable for the Poisson collision hypothesis to explain compression
and recovery processes.
Abstract: In this paper, a nonlinear delay population model is investigated. Choosing the delay as a bifurcation parameter, we demonstrate that Hopf bifurcation will occur when the delay exceeds a critical value. Global existence of bifurcating periodic solutions is established. Numerical simulations supporting the theoretical findings are included.
Abstract: This frame work describes a computationally more
efficient and adaptive threshold estimation method for image
denoising in the wavelet domain based on Generalized Gaussian
Distribution (GGD) modeling of subband coefficients. In this
proposed method, the choice of the threshold estimation is carried out
by analysing the statistical parameters of the wavelet subband
coefficients like standard deviation, arithmetic mean and geometrical
mean. The noisy image is first decomposed into many levels to
obtain different frequency bands. Then soft thresholding method is
used to remove the noisy coefficients, by fixing the optimum
thresholding value by the proposed method. Experimental results on
several test images by using this method show that this method yields
significantly superior image quality and better Peak Signal to Noise
Ratio (PSNR). Here, to prove the efficiency of this method in image
denoising, we have compared this with various denoising methods
like wiener filter, Average filter, VisuShrink and BayesShrink.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.
Abstract: In this paper, we consider the problem of logic simplification for a special class of logic functions, namely complementary Boolean functions (CBF), targeting low power implementation using static CMOS logic style. The functions are uniquely characterized by the presence of terms, where for a canonical binary 2-tuple, D(mj) ∪ D(mk) = { } and therefore, we have | D(mj) ∪ D(mk) | = 0 [19]. Similarly, D(Mj) ∪ D(Mk) = { } and hence | D(Mj) ∪ D(Mk) | = 0. Here, 'mk' and 'Mk' represent a minterm and maxterm respectively. We compare the circuits minimized with our proposed method with those corresponding to factored Reed-Muller (f-RM) form, factored Pseudo Kronecker Reed-Muller (f-PKRM) form, and factored Generalized Reed-Muller (f-GRM) form. We have opted for algebraic factorization of the Reed-Muller (RM) form and its different variants, using the factorization rules of [1], as it is simple and requires much less CPU execution time compared to Boolean factorization operations. This technique has enabled us to greatly reduce the literal count as well as the gate count needed for such RM realizations, which are generally prone to consuming more cells and subsequently more power consumption. However, this leads to a drawback in terms of the design-for-test attribute associated with the various RM forms. Though we still preserve the definition of those forms viz. realizing such functionality with only select types of logic gates (AND gate and XOR gate), the structural integrity of the logic levels is not preserved. This would consequently alter the testability properties of such circuits i.e. it may increase/decrease/maintain the same number of test input vectors needed for their exhaustive testability, subsequently affecting their generalized test vector computation. We do not consider the issue of design-for-testability here, but, instead focus on the power consumption of the final logic implementation, after realization with a conventional CMOS process technology (0.35 micron TSMC process). The quality of the resulting circuits evaluated on the basis of an established cost metric viz., power consumption, demonstrate average savings by 26.79% for the samples considered in this work, besides reduction in number of gates and input literals by 39.66% and 12.98% respectively, in comparison with other factored RM forms.
Abstract: Predicting short term wind speed is essential in order
to prevent systems in-action from the effects of strong winds. It also
helps in using wind energy as an alternative source of energy, mainly
for Electrical power generation. Wind speed prediction has
applications in Military and civilian fields for air traffic control,
rocket launch, ship navigation etc. The wind speed in near future
depends on the values of other meteorological variables, such as
atmospheric pressure, moisture content, humidity, rainfall etc. The
values of these parameters are obtained from a nearest weather
station and are used to train various forms of neural networks. The
trained model of neural networks is validated using a similar set of
data. The model is then used to predict the wind speed, using the
same meteorological information. This paper reports an Artificial
Neural Network model for short term wind speed prediction, which
uses back propagation algorithm.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.
Abstract: The Niger Delta Region of Nigeria is home to about
20 million people and 40 different ethnic groups. The region has an
area of seventy thousand square kilometers (70,000 KM2) of
wetlands, formed primarily by sediments deposition and makes up
7.5 percent of Nigeria's total landmass. The notable ecological zones
in this region includes: coastal barrier islands; mangrove swamp
forests; fresh water swamps; and lowland rainforests. This incredibly
naturally-endowed ecosystem region, which contains one of the
highest concentrations of biodiversity on the planet, in addition to
supporting abundant flora and fauna, is threatened by the inhuman act
known as gas flaring. Gas flaring is the combustion of natural gas
that is associated with crude oil when it is pumped up from the
ground. In petroleum-producing areas such as the Niger Delta region
of Nigeria where insufficient investment was made in infrastructure
to utilize natural gas, flaring is employed to dispose of this associated
gas. This practice has impoverished the communities where it is
practiced, with attendant environmental, economic and health
challenges. This paper discusses the adverse environmental and
health implication associated with the practice, the role of
Government, Policy makers, Oil companies and the Local
communities aimed at bring this inhuman practice to a prompt end.
Abstract: Wireless Sensor Networks (WSNs) are wireless
networks consisting of number of tiny, low cost and low power
sensor nodes to monitor various physical phenomena like
temperature, pressure, vibration, landslide detection, presence of any
object, etc. The major limitation in these networks is the use of nonrechargeable
battery having limited power supply. The main cause of
energy consumption WSN is communication subsystem. This paper
presents an efficient grid formation/clustering strategy known as Grid
based level Clustering and Aggregation of Data (GCAD). The
proposed clustering strategy is simple and scalable that uses low duty
cycle approach to keep non-CH nodes into sleep mode thus reducing
energy consumption. Simulation results demonstrate that our
proposed GCAD protocol performs better in various performance
metrics.
Abstract: Particle Swarm Optimization (PSO) with elite PSO
parameters has been developed for power flow analysis under
practical constrained situations. Multiple solutions of the power flow
problem are useful in voltage stability assessment of power system.
A method of determination of multiple power flow solutions is
presented using a hybrid of Particle Swarm Optimization (PSO) and
local search technique. The unique and innovative learning factors of
the PSO algorithm are formulated depending upon the node power
mismatch values to be highly adaptive with the power flow problems.
The local search is applied on the pbest solution obtained by the PSO
algorithm in each iteration. The proposed algorithm performs reliably
and provides multiple solutions when applied on standard and illconditioned
systems. The test results show that the performances of
the proposed algorithm under critical conditions are better than the
conventional methods.
Abstract: Structural behavior of ring stiffened thick walled
cylinders made of functionally graded materials (FGMs) is
investigated in this paper. Functionally graded materials are inhomogeneous composites which are usually made from a mixture
of metal and ceramic. The gradient compositional variation of the
constituents from one surface to the other provides an elegant solution to the problem of high transverse shear stresses that are
induced when two dissimilar materials with large differences in material properties are bonded. FGM formation of the cylinder is
modeled by power-law exponent and the variation of characteristics is supposed to be in radial direction.
A finite element formulation is derived for the analysis. According to the property variation of the constituent materials in the radial
direction of the wall, it is not convenient to use conventional elements to model and analyze the structure of the stiffened FGM
cylinders. In this paper a new cylindrical super-element is used to model the finite element formulation and analyze the static and
modal behavior of stiffened FGM thick walled cylinders. By using
this super-element the number of elements, which are needed for
modeling, will reduce significantly and the process time is less in comparison with conventional finite element formulations. Results for static and modal analysis are evaluated and verified by
comparison to finite element formulation with conventional
elements. Comparison indicates a good conformity between results.
Abstract: In India, the quarrel between the budding human
populace and the planet-s unchanging supply of freshwater and
falling water tables has strained attention the reuse of gray water as
an alternative water resource in rural development. This paper
present the finest design of laboratory scale gray water treatment
plant, which is a combination of natural and physical operations such
as primary settling with cascaded water flow, aeration, agitation and
filtration, hence called as hybrid treatment process. The economical
performance of the plant for treatment of bathrooms, basins and
laundries gray water showed in terms of deduction competency of
water pollutants such as COD (83%), TDS (70%), TSS (83%), total
hardness (50%), oil and grease (97%), anions (46%) and cations
(49%). Hence, this technology could be a good alternative to treat
gray water in residential rural area.
Abstract: This paper applies Bayesian Networks to support
information extraction from unstructured, ungrammatical, and
incoherent data sources for semantic annotation. A tool has been
developed that combines ontologies, machine learning, and
information extraction and probabilistic reasoning techniques to
support the extraction process. Data acquisition is performed with the
aid of knowledge specified in the form of ontology. Due to the
variable size of information available on different data sources, it is
often the case that the extracted data contains missing values for
certain variables of interest. It is desirable in such situations to
predict the missing values. The methodology, presented in this paper,
first learns a Bayesian network from the training data and then uses it
to predict missing data and to resolve conflicts. Experiments have
been conducted to analyze the performance of the presented
methodology. The results look promising as the methodology
achieves high degree of precision and recall for information
extraction and reasonably good accuracy for predicting missing
values.
Abstract: There is a acute water problem especially in the dry
season in and around Perundurai (Erode district, Tamil Nadu, India)
where there are more number of tannery units. Hence an attempt was
made to use the waste water from tannery industry for construction
purpose. The mechanical properties such as compressive strength,
tensile strength, flexural strength etc were studied by casting various
concrete specimens in form of cube, cylinders and beams etc and
were found to be satisfactory. Hence some special properties such as
chloride attack, sulphate attack and chemical attack are considered
and comparatively studied with the conventional potable water. In
this experimental study the results of specimens prepared by using
treated and untreated tannery effluent were compared with the
concrete specimens prepared by using potable water. It was observed
that the concrete had some reduction in strength while subjected to
chloride attack, sulphate attack and chemical attack. So admixtures
were selected and optimized in suitable proportion to counter act the
adverse effects and the results were found to be satisfactory.