Abstract: Software Development Risks Identification (SDRI),
using Fault Tree Analysis (FTA), is a proposed technique to identify
not only the risk factors but also the causes of the appearance of the
risk factors in software development life cycle. The method is based
on analyzing the probable causes of software development failures
before they become problems and adversely affect a project. It uses
Fault tree analysis (FTA) to determine the probability of a particular
system level failures that are defined by A Taxonomy for Sources of
Software Development Risk to deduce failure analysis in which an
undesired state of a system by using Boolean logic to combine a
series of lower-level events. The major purpose of this paper is to use
the probabilistic calculations of Fault Tree Analysis approach to
determine all possible causes that lead to software development risk
occurrence
Abstract: The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations.
As a results of this, Computational Fluid Dynamic (CFD) solvers are
widely used in the aeronautical field. These solvers require the correct
selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on
the proper choice of these parameters.
In this paper we create an expert system capable of making an
accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver.
Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time
required for the convergence of a CFD solver.
Abstract: Software estimation accuracy is among the greatest
challenges for software developers. This study aimed at building and
evaluating a neuro-fuzzy model to estimate software projects
development time. The forty-one modules developed from ten
programs were used as dataset. Our proposed approach is compared
with fuzzy logic and neural network model and Results show that the
value of MMRE (Mean of Magnitude of Relative Error) applying
neuro-fuzzy was substantially lower than MMRE applying fuzzy
logic and neural network.
Abstract: MinC plays an important role in bacterial cell division
system by inhibiting FtsZ assembly. However, the molecular
mechanism of the action is poorly understood. E. coli MinC Nterminus
domain was purified and crystallized using 1.4 M sodium
citrate pH 6.5 as a precipitant. X-ray diffraction data was collected
and processed to 2.3 Å from a native crystal. The crystal belonged to
space group P212121, with the unit cell parameters a = 52.7, b = 54.0,
c = 64.7 Å. Assuming the presence of two molecules in the
asymmetric unit, the Matthews coefficient value is 1.94 Å3 Da-1,
which corresponds to a solvent content of 36.5%. The overall
structure of MinCN is observed as a dimer form through anti-parallel
ß-strand interaction.
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 we compare the response of linear and
nonlinear neural network-based prediction schemes in prediction of
received Signal-to-Interference Power Ratio (SIR) in Direct
Sequence Code Division Multiple Access (DS/CDMA) systems. The
nonlinear predictor is Multilayer Perceptron MLP and the linear
predictor is an Adaptive Linear (Adaline) predictor. We solve the
problem of complexity by using the Minimum Mean Squared Error
(MMSE) principle to select the optimal predictors. The optimized
Adaline predictor is compared to optimized MLP by employing
noisy Rayleigh fading signals with 1.8 GHZ carrier frequency in an
urban environment. The results show that the Adaline predictor can
estimates SIR with the same error as MLP when the user has the
velocity of 5 km/h and 60 km/h but by increasing the velocity up-to
120 km/h the mean squared error of MLP is two times more than
Adaline predictor. This makes the Adaline predictor (with lower
complexity) more suitable than MLP for closed-loop power control
where efficient and accurate identification of the time-varying
inverse dynamics of the multi path fading channel is required.
Abstract: Supplier selection is a multi criteria decision-making process that comprises tangible and intangible factors. The majority of previous supplier selection techniques do not consider strategic perspective. Besides, uncertainty is one of the most important obstacles in supplier selection. For the first, time in this paper, the idea of the algorithm " Knapsack " is used to select suppliers Moreover, an attempt has to be made to take the advantage of a simple numerical method for solving model .This is an innovation to resolve any ambiguity in choosing suppliers. This model has been tried in the suppliers selected in a competitive environment and according to all desired standards of quality and quantity to show the efficiency of the model, an industry sample has been uses.
Abstract: Reconfigurable optical add/drop multiplexers
(ROADMs) can be classified into three categories based on their
underlying switching technologies. Category I consists of a single
large optical switch; category II is composed of a number of small
optical switches aligned in parallel; and category III has a single
optical switch and only one wavelength being added/dropped. In this
paper, to evaluate the wavelength-routing capability of ROADMs of
category-II in dynamic optical networks,the dynamic traffic models
are designed based on Bernoulli, Poisson distributions for smooth
and regular types of traffic. Through Analytical and Simulation
results, the routing power of cat-II of ROADM networks for two
traffic models are determined.
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: Foundation of tower crane serves to ensure stability
against vertical and horizontal forces. If foundation stress is not
sufficient, tower crane may be subject to overturning, shearing or
foundation settlement. Therefore, engineering review of stable support
is a highly critical part of foundation design. However, there are not
many professionals who can conduct engineering review of tower
crane foundation and, if any, they have information only on a small
number of cranes in which they have hands-on experience. It is also
customary to rely on empirical knowledge and tower crane renter-s
recommendations rather than designing foundation on the basis of
engineering knowledge. Therefore, a foundation design automation
system considering not only lifting conditions but also overturning
risk, shearing and vertical force may facilitate production of foolproof
foundation design for experts and enable even non-experts to utilize
professional knowledge that only experts can access now. This study
proposes Automatic Design Algorithm for the Tower Crane
Foundations considering load and horizontal force.
Abstract: The bypass exhaust system of a 160 MW combined cycle has been modeled and analyzed using numerical simulation in 2D prospective. Analysis was carried out using the commercial numerical simulation software, FLUENT 6.2. All inputs were based on the technical data gathered from working conditions of a Siemens V94.2 gas turbine, installed in the Yazd power plant. This paper deals with reduction of pressure drop in bypass exhaust system using turning vanes mounted in diverter box in order to alleviate turbulent energy dissipation rate above diverter box. The geometry of such turning vanes has been optimized based on the flow pattern at diverter box inlet. The results show that the use of optimized turning vanes in diverter box can improve the flow pattern and eliminate vortices around sharp edges just before the silencer. Furthermore, this optimization could decrease the pressure drop in bypass exhaust system and leads to higher plant efficiency.
Abstract: Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.
Abstract: In this paper we designed and implemented a new
ensemble of classifiers based on a sequence of classifiers which were
specialized in regions of the training dataset where errors of its
trained homologous are concentrated. In order to separate this
regions, and to determine the aptitude of each classifier to properly
respond to a new case, it was used another set of classifiers built
hierarchically. We explored a selection based variant to combine the
base classifiers. We validated this model with different base
classifiers using 37 training datasets. It was carried out a statistical
comparison of these models with the well known Bagging and
Boosting, obtaining significantly superior results with the
hierarchical ensemble using Multilayer Perceptron as base classifier.
Therefore, we demonstrated the efficacy of the proposed ensemble,
as well as its applicability to general problems.
Abstract: The inherent iterative nature of product design and development poses significant challenge to reduce the product design and development time (PD). In order to shorten the time to market, organizations have adopted concurrent development where multiple specialized tasks and design activities are carried out in parallel. Iterative nature of work coupled with the overlap of activities can result in unpredictable time to completion and significant rework. Many of the products have missed the time to market window due to unanticipated or rather unplanned iteration and rework. The iterative and often overlapped processes introduce greater amounts of ambiguity in design and development, where the traditional methods and tools of project management provide less value. In this context, identifying critical metrics to understand the iteration probability is an open research area where significant contribution can be made given that iteration has been the key driver of cost and schedule risk in PD projects. Two important questions that the proposed study attempts to address are: Can we predict and identify the number of iterations in a product development flow? Can we provide managerial insights for a better control over iteration? The proposal introduces the concept of decision points and using this concept intends to develop metrics that can provide managerial insights into iteration predictability. By characterizing the product development flow as a network of decision points, the proposed research intends to delve further into iteration probability and attempts to provide more clarity.
Abstract: The mechanical and tribological properties in WC-Co
coatings are strongly affected by hardness and elasticity
specifications. The results revealed the effect of spraying distance on
microhardness and elasticity modulus of coatings. The metallurgical
studies have been made on coated samples using optical microscopy,
scanning electron microscopy (SEM).
Abstract: The aim of the present study is to analyze the generation of osteoporotic vertebral bone induced by lack of calcium during growth period and analyze its effects for disc degeneration, based on biomechanical and histomorphometrical study. Mechanical and histomorphological characteristics of lumbar vertebral bones and discs of rats with calcium free diet (CFD) were detected and tracked by using high resolution in-vivo micro-computed tomography (in-vivo micro-CT), finite element (FE) and histological analysis. Twenty female Sprague-Dawley rats (6 weeks old, approximate weight 170g) were randomly divided into two groups (CFD group: 10, NOR group: 10). The CFD group was maintained on a refmed calcium-controlled semisynthetic diet without added calcium, to induce osteoporosis. All lumbar (L 1-L6) were scanned by using in vivo micro-CT with 35i.un resolution at 0, 4, 8 weeks to track the effects of CFD on the generation of osteoporosis. The fmdings of the present study indicated that calcium insufficiency was the main factor in the generation of osteoporosis and it induced lumbar vertebral disc degeneration. This study is a valuable experiment to firstly evaluate osteoporotic vertebral bone and disc degeneration induced by lack of calcium during growth period from a biomechanical and histomorphometrical point of view.
Abstract: Shortening of natural resources will impose greater
limitations of electric energy consumption in various fields including
water treatment technologies. Small water treatment installations
supplied with electric energy from solar sources are perfect example of
zero-emission technology. Possibility of solar energy application, as
one of the alternative energy resources for decontamination processes
is strongly dependent on geographical location. Various examples of
solar driven water purification systems are given and design of
solar-water treatment installation based on ozone for the geographical
conditions in Poland are presented.
Abstract: Over half of the total electricity consumption is used in buildings. Air-conditioning and electric lighting are the two main resources of electricity consumption in high rise buildings. One way to reduce electricity consumption would be to limit heat gain into buildings, therefore reduce the demand for air-conditioning during hot summer months especially in hot regions. On the other hand natural daylight can be used to reduce the use of electricity for artificial lighting. In this paper effective factors on minimizing heat gain and achieving required day light were reviewed .As daylight always accompanied by solar heat gain. Also interactions between heat gain and daylight were discussed through previous studies and equations which are related to heat gain and day lighting especially in high rise buildings. As a result importance of building-s form and its component on energy consumption in buildings were clarified.
Abstract: In cancer progress, the optical properties of tissues
like absorption and scattering coefficient change, so by these
changes, we can trace the progress of cancer, even it can be applied
for pre-detection of cancer. In this paper, we investigate the effects of
changes of optical properties on light penetrated into tissues. The
diffusion equation is widely used to simulate light propagation into
biological tissues. In this study, the boundary integral method (BIM)
is used to solve the diffusion equation. We illustrate that the changes
of optical properties can modified the reflectance or penetrating light.
Abstract: Off-grid Photovoltaic (PV) systems are empowering
technology in underdeveloped countries like Ethiopia where many
people live far away from the modern world. Where there is
relatively low energy consumption, providing energy from grid
systems is not commercially cost-effective. As a result, significant
people groups worldwide stay without access to electricity. One
remote village in northern Ethiopia was selected by the United
Nations for a pilot project to improve its living conditions. As part of
this comprehensive project, an intelligent charge controller circuit for
Off-grid PV systems was designed for the clinic in that village. In
this paper, design aspects of an intelligent charge controller unit and
its load driver circuits are discussed for an efficient utilization of PVbased
supply systems.