Abstract: Preparation and negotiation of innovative and future
projects can be characterized as a strategic-type decision situation,
involving many uncertainties and an unpredictable environment.
We will focus in this paper on the bidding process. It includes cooperative
and strategic decisions.
Our approach for bidding process knowledge capitalization is
aimed at information management in project-oriented organizations,
based on the MUSIC (Management and Use of Co-operative
Information Systems) model.
We will show how to capitalize the company strategic knowledge
and also how to organize the corporate memory. The result of the
adopted approach is improvement of corporate memory quality.
Abstract: The aim of this work is to study the elastic transfer
phenomenon which takes place in the elastic scattering of 16O on 12C
at energies near the Coulomb barrier. Where, the angular distribution
decrease steadily with increasing the scattering angle, then the cross
section will increase at backward angles due to the α-transfer process.
This reaction was also studied at different energies for tracking the
nuclear rainbow phenomenon. The experimental data of the angular
distribution at these energies were compared to the calculation
predictions. The optical potential codes such as SPIVAL and
Distorted Wave Born Approximation (DWUCK5) were used in
analysis.
Abstract: This research was conducted to develop a correlation
between microstructure of HSLA steel and the mechanical properties
that occur as a result of both laser and mechanical forming processes
of the metal. The technique of forming flat metals by applying laser
beams is a relatively new concept in the manufacturing industry.
However, the effects of laser energy on the stability of metal alloy
phases have not yet been elucidated in terms of phase
transformations and microhardness. In this work, CO2 laser source
was used to irradiate the surface of a flat metal then the
microstructure and microhardness of the metal were studied on the
formed specimen. The extent to which the microstructure changed
depended on the heat inputs of up to 1000 J/cm2 with cooling rates of
about 4.8E+02 K/s. Experimental results revealed that the irradiated
surface of a HSLA steel had transformed to austenitic structure
during the heating process.
Abstract: Moulded parts contribute to more than 70% of
components in products. However, common defects particularly in
plastic injection moulding exist such as: warpage, shrinkage, sink
marks, and weld lines. In this paper Taguchi experimental design
methods are applied to reduce the warpage defect of thin plate
Acrylonitrile Butadiene Styrene (ABS) and are demonstrated in two
levels; namely, orthogonal arrays of Taguchi and the Analysis of
Variance (ANOVA). Eight trials have been run in which the optimal
parameters that can minimize the warpage defect in factorial
experiment are obtained. The results obtained from ANOVA
approach analysis with respect to those derived from MINITAB
illustrate the most significant factors which may cause warpage in
injection moulding process. Moreover, ANOVA approach in
comparison with other approaches like S/N ratio is more accurate and
with the interaction of factors it is possible to achieve higher and the
better outcomes.
Abstract: Implementing quality assurance in higher education establishments is the main focus of the reform process currently undertaken by the Ministry of Higher Education and Scientific Research in the Kurdistan Region of Iraq. The reform agenda has involved attempts to improve academic quality and management processes in universities, technical institutions and colleges. The central challenge for the reform process is to produce change in higher education in a region where administration is described as centralized and bureaucratic. To make these changes, there should be a well-designed plans and follow up processes in order to monitor progress and develop responses to obstacles. Lack of skills, resources, political dilemmas, poor motivation, and readiness to face the consequences of change are factors which will determine the success of the reform process.
Abstract: Titanium nitride (TiN) has been synthesized using the
sheet plasma negative ion source (SPNIS). The parameters used for
its effective synthesis has been determined from previous
experiments and studies. In this study, further enhancement of the
deposition rate of TiN synthesis and advancement of the SPNIS
operation is presented. This is primarily achieved by the addition of
Sm-Co permanent magnets and a modification of the configuration in
the TiN deposition process. The magnetic enhancement is aimed at
optimizing the sputtering rate and the sputtering yield of the process.
The Sm-Co permanent magnets are placed below the Ti target for
better sputtering by argon. The Ti target is biased from –250V to –
350V and is sputtered by Ar plasma produced at discharge current of
2.5–4A and discharge potential of 60–90V. Steel substrates of
dimensions 20x20x0.5mm3 were prepared with N2:Ar volumetric
ratios of 1:3, 1:5 and 1:10. Ocular inspection of samples exhibit
bright gold color associated with TiN. XRD characterization
confirmed the effective TiN synthesis as all samples exhibit the (200)
and (311) peaks of TiN and the non-stoichiometric Ti2N (220) facet.
Cross-sectional SEM results showed increase in the TiN deposition
rate of up to 0.35μm/min. This doubles what was previously obtained
[1]. Scanning electron micrograph results give a comparative
morphological picture of the samples. Vickers hardness results gave
the largest hardness value of 21.094GPa.
Abstract: This paper presents a critical study about the
application of Neural Networks to ion-exchange process. Ionexchange
is a complex non-linear process involving many factors
influencing the ions uptake mechanisms from the pregnant solution.
The following step includes the elution. Published data presents
empirical isotherm equations with definite shortcomings resulting in
unreliable predictions. Although Neural Network simulation
technique encounters a number of disadvantages including its “black
box", and a limited ability to explicitly identify possible causal
relationships, it has the advantage to implicitly handle complex
nonlinear relationships between dependent and independent
variables. In the present paper, the Neural Network model based on
the back-propagation algorithm Levenberg-Marquardt was developed
using a three layer approach with a tangent sigmoid transfer function
(tansig) at hidden layer with 11 neurons and linear transfer function
(purelin) at out layer. The above mentioned approach has been used
to test the effectiveness in simulating ion exchange processes. The
modeling results showed that there is an excellent agreement between
the experimental data and the predicted values of copper ions
removed from aqueous solutions.
Abstract: With deep development of software reuse, componentrelated
technologies have been widely applied in the development of
large-scale complex applications. Component identification (CI) is
one of the primary research problems in software reuse, by analyzing
domain business models to get a set of business components with high
reuse value and good reuse performance to support effective reuse.
Based on the concept and classification of CI, its technical stack is
briefly discussed from four views, i.e., form of input business models,
identification goals, identification strategies, and identification
process. Then various CI methods presented in literatures are
classified into four types, i.e., domain analysis based methods,
cohesion-coupling based clustering methods, CRUD matrix based
methods, and other methods, with the comparisons between these
methods for their advantages and disadvantages. Additionally, some
insufficiencies of study on CI are discussed, and the causes are
explained subsequently. Finally, it is concluded with some
significantly promising tendency about research on this problem.
Abstract: In this work a new platform for mobile-health systems is
presented. System target application is providing decision support to
rescue corps or military medical personnel in combat areas. Software
architecture relies on a distributed client-server system that manages a
wireless ad-hoc networks hierarchy in which several different types of
client operate. Each client is characterized for different hardware and
software requirements. Lower hierarchy levels rely in a network of
completely custom devices that store clinical information and patient
status and are designed to form an ad-hoc network operating in the
2.4 GHz ISM band and complying with the IEEE 802.15.4 standard
(ZigBee). Medical personnel may interact with such devices, that are
called MICs (Medical Information Carriers), by means of a PDA
(Personal Digital Assistant) or a MDA (Medical Digital Assistant),
and transmit the information stored in their local databases as well as
issue a service request to the upper hierarchy levels by using IEEE
802.11 a/b/g standard (WiFi). The server acts as a repository that
stores both medical evacuation forms and associated events (e.g., a
teleconsulting request). All the actors participating in the diagnostic
or evacuation process may access asynchronously to such repository
and update its content or generate new events. The designed system
pretends to optimise and improve information spreading and flow
among all the system components with the aim of improving both
diagnostic quality and evacuation process.
Abstract: In this contribution a newly developed e-learning environment is presented, which incorporates Intelligent Agents and Computational Intelligence Techniques. The new e-learning environment is constituted by three parts, the E-learning platform Front-End, the Student Questioner Reasoning and the Student Model Agent. These parts are distributed geographically in dispersed computer servers, with main focus on the design and development of these subsystems through the use of new and emerging technologies. These parts are interconnected in an interoperable way, using web services for the integration of the subsystems, in order to enhance the user modelling procedure and achieve the goals of the learning process.
Abstract: Serial hierarchical support vector machine (SHSVM)
is proposed to discriminate three brain tissues which are white matter
(WM), gray matter (GM), and cerebrospinal fluid (CSF). SHSVM
has novel classification approach by repeating the hierarchical
classification on data set iteratively. It used Radial Basis Function
(rbf) Kernel with different tuning to obtain accurate results. Also as
the second approach, segmentation performed with DAGSVM
method. In this article eight univariate features from the raw DTI data
are extracted and all the possible 2D feature sets are examined within
the segmentation process. SHSVM succeed to obtain DSI values
higher than 0.95 accuracy for all the three tissues, which are higher
than DAGSVM results.
Abstract: Chemical reaction and diffusion are important phenomena in quantitative neurobiology and biophysics. The knowledge of the dynamics of calcium Ca2+ is very important in cellular physiology because Ca2+ binds to many proteins and regulates their activity and interactions Calcium waves propagate inside cells due to a regenerative mechanism known as calcium-induced calcium release. Buffer-mediated calcium diffusion in the cytosol plays a crucial role in the process. A mathematical model has been developed for calcium waves by assuming the buffers are in equilibrium with calcium i.e., the rapid buffering approximation for a one dimensional unsteady state case. This model incorporates important physical and physiological parameters like dissociation rate, diffusion rate, total buffer concentration and influx. The finite difference method has been employed to predict [Ca2+] and buffer concentration time course regardless of the calcium influx. The comparative studies of the effect of the rapid buffered diffusion and kinetic parameters of the model on the concentration time course have been performed.
Abstract: Square pipes (pipes with square cross sections) are
being used for various industrial objectives, such as machine
structure components and housing/building elements. The utilization
of them is extending rapidly and widely. Hence, the out-put of those
pipes is increasing and new application fields are continually
developing.
Due to various demands in recent time, the products have to
satisfy difficult specifications with high accuracy in dimensions. The
reshaping process design of pipes with square cross sections;
however, is performed by trial and error and based on expert-s
experience.
In this paper, a computer-aided simulation is developed based on
the 2-D elastic-plastic method with consideration of the shear
deformation to analyze the reshaping process. Effect of various
parameters such as diameter of the circular pipe and mechanical
properties of metal on product dimension and quality can be
evaluated by using this simulation. Moreover, design of reshaping
process include determination of shrinkage of cross section,
necessary number of stands, radius of rolls and height of pipe at each
stand, are investigated. Further, it is shown that there are good
agreements between the results of the design method and the
experimental results.
Abstract: This is an application research presenting the
improvement of production quality using the six sigma solutions and
the analyses of benefit-cost ratio. The case of interest is the
production of tile-concrete. Such production has faced with the
problem of high nonconforming products from an inappropriate
surface coating and had low process capability based on the strength
property of tile. Surface coating and tile strength are the most critical
to quality of this product. The improvements followed five stages of
six sigma solutions. After the improvement, the production yield was
improved to 80% as target required and the defective products from
coating process was remarkably reduced from 29.40% to 4.09%. The
process capability based on the strength quality was increased from
0.87 to 1.08 as customer oriented. The improvement was able to save
the materials loss for 3.24 millions baht or 0.11 million dollars. The
benefits from the improvement were analyzed from (1) the reduction
of the numbers of non conforming tile using its factory price for
surface coating improvement and (2) the materials saved from the
increment of process capability. The benefit-cost ratio of overall
improvement was high as 7.03. It was non valuable investment in
define, measure, analyses and the initial of improve stages after that
it kept increasing. This was due to there were no benefits in define,
measure, and analyze stages of six sigma since these three stages
mainly determine the cause of problem and its effects rather than
improve the process. The benefit-cost ratio starts existing in the
improve stage and go on. Within each stage, the individual benefitcost
ratio was much higher than the accumulative one as there was an
accumulation of cost since the first stage of six sigma. The
consideration of the benefit-cost ratio during the improvement
project helps make decisions for cost saving of similar activities
during the improvement and for new project. In conclusion, the
determination of benefit-cost ratio behavior through out six sigma
implementation period provides the useful data for managing quality
improvement for the optimal effectiveness. This is the additional
outcome from the regular proceeding of six sigma.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process
Abstract: Limited competition has been a serious concern in infrastructure procurement. Importantly, however, there are normally a number of potential bidders initially showing interest in proposed projects. This paper focuses on tackling the question why these initially interested bidders fade out. An empirical problem is that no bids of fading-out firms are observable. They could decide not to enter the process at the beginning of the tendering or may be technically disqualified at any point in the selection process. The paper applies the double selection model to procurement data from road development projects in developing countries and shows that competition ends up restricted, because bidders are self-selective and auctioneers also tend to limit participation depending on the size of contracts.Limited competition would likely lead to high infrastructure procurement costs, threatening fiscal sustainability and economic growth.
Abstract: The objective of this study is to design an adaptive
neuro-fuzzy inference system (ANFIS) for estimation of surface
roughness in grinding process. The Used data have been generated
from experimental observations when the wheel has been dressed
using a rotary diamond disc dresser. The input parameters of model
are dressing speed ratio, dressing depth and dresser cross-feed rate
and output parameter is surface roughness. In the experimental
procedure the grinding conditions are constant and only the dressing
conditions are varied. The comparison of the predicted values and the
experimental data indicates that the ANFIS model has a better
performance with respect to back-propagation neural network
(BPNN) model which has been presented by the authors in previous
work for estimation of the surface roughness.
Abstract: Software reliability, defined as the probability of a
software system or application functioning without failure or errors
over a defined period of time, has been an important area of research
for over three decades. Several research efforts aimed at developing
models to improve reliability are currently underway. One of the
most popular approaches to software reliability adopted by some of
these research efforts involves the use of operational profiles to
predict how software applications will be used. Operational profiles
are a quantification of usage patterns for a software application. The
research presented in this paper investigates an innovative multiagent
framework for automatic creation and management of
operational profiles for generic distributed systems after their release
into the market. The architecture of the proposed Operational Profile
MAS (Multi-Agent System) is presented along with detailed
descriptions of the various models arrived at following the analysis
and design phases of the proposed system. The operational profile in
this paper is extended to comprise seven different profiles. Further,
the criticality of operations is defined using a new composed metrics
in order to organize the testing process as well as to decrease the time
and cost involved in this process. A prototype implementation of the
proposed MAS is included as proof-of-concept and the framework is
considered as a step towards making distributed systems intelligent
and self-managing.
Abstract: The use of electronic sensors in the electronics
industry has become increasingly popular over the past few years,
and it has become a high competition product. The frequency
adjustment process is regarded as one of the most important process
in the electronic sensor manufacturing process. Due to inaccuracies
in the frequency adjustment process, up to 80% waste can be caused
due to rework processes; therefore, this study aims to provide a
preliminary understanding of the role of parameters used in the
frequency adjustment process, and also make suggestions in order to
further improve performance. Four parameters are considered in this
study: air pressure, dispensing time, vacuum force, and the distance
between the needle tip and the product. A full factorial design for
experiment 2k was considered to determine those parameters that
significantly affect the accuracy of the frequency adjustment process,
where a deviation in the frequency after adjustment and the target
frequency is expected to be 0 kHz. The experiment was conducted on
two levels, using two replications and with five center-points added.
In total, 37 experiments were carried out. The results reveal that air
pressure and dispensing time significantly affect the frequency
adjustment process. The mathematical relationship between these
two parameters was formulated, and the optimal parameters for air
pressure and dispensing time were found to be 0.45 MPa and 458 ms,
respectively. The optimal parameters were examined by carrying out
a confirmation experiment in which an average deviation of 0.082
kHz was achieved.