Abstract: Development of new generation bio-tribological,
multilayer coatings opens an avenue for fabrication of future hightech
functional surfaces. In the presented work, nano-composite,
Cr/CrN+[Cr/ a-C:H implanted by metallic nanocrystals] multilayer
coatings have been developed for surface protection of medical tools.
Thin films were fabricated by a hybrid Pulsed Laser Deposition
technique. Complex microstructure analysis of nanomultilayer
coatings, subjected to mechanical and biological tests, were
performed by means of transmission electron microscopy (TEM).
Microstructure characterization revealed the layered arrangement of
Cr23C6 nanoparticles in multilayer structure. Influence of deposition
conditions on bio-tribological properties of the coatings was studied.
The bio-tests were used as a screening tool for the analyzed
nanomultilayer coatings before they could be deposited on medical
tools. Bio-medical tests were done using fibroblasts. The mechanical
properties of the coatings were investigated by means of a ball-ondisc
mechanical test. The micro hardness was done using Berkovich
indenter. The scratch adhesion test was done using Rockwell
indenter. From the bio-tribological point of view, the optimal
properties had the C106_1 material.
Abstract: Polymer Electrolyte Membrane Fuel Cell (PEMFC) is
such a time-vary nonlinear dynamic system. The traditional linear
modeling approach is hard to estimate structure correctly of PEMFC
system. From this reason, this paper presents a nonlinear modeling of
the PEMFC using Neural Network Auto-regressive model with
eXogenous inputs (NNARX) approach. The multilayer perception
(MLP) network is applied to evaluate the structure of the NNARX
model of PEMFC. The validity and accuracy of NNARX model are
tested by one step ahead relating output voltage to input current from
measured experimental of PEMFC. The results show that the obtained
nonlinear NNARX model can efficiently approximate the dynamic
mode of the PEMFC and model output and system measured output
consistently.
Abstract: An Australian manufacturer has fabricated an
innovative GFRP sandwich panel made from E-glass fiber skin and a
modified phenolic core for structural applications. Debonding, which
refers to separation of skin from the core material in composite
sandwiches, is one of the most common types of damage in
composites. The presence of debonding is of great concern because it
not only severely affects the stiffness but also modifies the dynamic
behaviour of the structure. Generally it is seen that the majority of
research carried out has been concerned about the delamination of
laminated structures whereas skin-core debonding has received
relatively minor attention. Furthermore it is observed that research
done on composite slabs having multiple skin-core debonding is very
limited. To address this gap, a comprehensive research investigating
dynamic behaviour of composite panels with single and multiple
debonding is presented. The study uses finite-element modelling and
analyses for investigating the influence of debonding on free
vibration behaviour of single and multilayer composite sandwich
panels. A broad parametric investigation has been carried out by
varying debonding locations, debonding sizes and support conditions
of the panels in view of both single and multiple debonding.
Numerical models were developed with Strand7 finite element
package by innovatively selecting the suitable elements to diligently
represent their actual behavior. Three-dimensional finite element
models were employed to simulate the physically real situation as
close as possible, with the use of an experimentally and numerically
validated finite element model. Comparative results and conclusions
based on the analyses are presented. For similar extents and locations
of debonding, the effect of debonding on natural frequencies appears
greatly dependent on the end conditions of the panel, giving greater
decrease in natural frequency when the panels are more restrained.
Some modes are more sensitive to debonding and this sensitivity
seems to be related to their vibration mode shapes. The fundamental
mode seems generally the least sensitive mode to debonding with
respect to the variation in free vibration characteristics. The results
indicate the effectiveness of the developed three dimensional finite
element models in assessing debonding damage in composite
sandwich panels.
Abstract: Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods
Abstract: A model was constructed to predict the amount of
solar radiation that will make contact with the surface of the earth in
a given location an hour into the future. This project was supported
by the Southern Company to determine at what specific times during
a given day of the year solar panels could be relied upon to produce
energy in sufficient quantities. Due to their ability as universal
function approximators, an artificial neural network was used to
estimate the nonlinear pattern of solar radiation, which utilized
measurements of weather conditions collected at the Griffin, Georgia
weather station as inputs. A number of network configurations and
training strategies were utilized, though a multilayer perceptron with
a variety of hidden nodes trained with the resilient propagation
algorithm consistently yielded the most accurate predictions. In
addition, a modeled direct normal irradiance field and adjacent
weather station data were used to bolster prediction accuracy. In later
trials, the solar radiation field was preprocessed with a discrete
wavelet transform with the aim of removing noise from the
measurements. The current model provides predictions of solar
radiation with a mean square error of 0.0042, though ongoing efforts
are being made to further improve the model’s accuracy.
Abstract: In this paper, the improvement by deconvolution of
the depth resolution in Secondary Ion Mass Spectrometry (SIMS)
analysis is considered. Indeed, we have developed a new Tikhonov-
Miller deconvolution algorithm where a priori model of the solution
is included. This is a denoisy and pre-deconvoluted signal obtained
from: firstly, by the application of wavelet shrinkage algorithm,
secondly by the introduction of the obtained denoisy signal in an
iterative deconvolution algorithm. In particular, we have focused the
light on the effect of the iterations number on the evolution of the
deconvoluted signals. The SIMS profiles are multilayers of Boron in
Silicon matrix.
Abstract: To develop AZ91D magnesium alloys with improved
properties, we have applied TiN and VN/TiN multilayer coatings
using DC magnetron sputter technique. Coating structure, surface
morphology, chemical bonding and corrosion resistance of coatings
were analyzed by x-ray diffraction (XRD), scanning electron
microscope (SEM), x-ray photoelectron spectroscopy (XPS), and
tafel extrapolation method, respectively. XPS analysis reveal that VN
overlayer reacts with oxygen at the VN/TiN interface and forms more
stable TiN layer. Morphological investigations and the corrosion
results show that VN/TiN multilayer thin film coatings are quite
effective to optimize the corrosion resistance of Mg alloys.
Abstract: Fuzzy inference method based approach to the
forming of modular intellectual system of assessment the quality of
communication services is proposed. Developed under this approach
the basic fuzzy estimation model takes into account the
recommendations of the International Telecommunication Union in
respect of the operation of packet switching networks based on IPprotocol.
To implement the main features and functions of the fuzzy
control system of quality telecommunication services it is used
multilayer feedforward neural network.
Abstract: A relationship between face and signature biometrics
is established in this paper. A new approach is developed to predict
faces from signatures by using artificial intelligence. A multilayer
perceptron (MLP) neural network is used to generate face details
from features extracted from signatures, here face is the physical
biometric and signatures is the behavioural biometric. The new
method establishes a relationship between the two biometrics and
regenerates a visible face image from the signature features.
Furthermore, the performance efficiencies of our new technique are
demonstrated in terms of minimum error rates compared to published
work.
Abstract: This paper presents a comparative study between two
neural network models namely General Regression Neural Network
(GRNN) and Back Propagation Neural Network (BPNN) are used
to estimate radial overcut produced during Electrical Discharge
Machining (EDM). Four input parameters have been employed:
discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and
discharge voltage (V). Recently, artificial intelligence techniques, as
it is emerged as an effective tool that could be used to replace
time consuming procedures in various scientific or engineering
applications, explicitly in prediction and estimation of the complex
and nonlinear process. The both networks are trained, and the
prediction results are tested with the unseen validation set of the
experiment and analysed. It is found that the performance of both the
networks are found to be in good agreement with average percentage
error less than 11% and the correlation coefficient obtained for the
validation data set for GRNN and BPNN is more than 91%. However,
it is much faster to train GRNN network than a BPNN and GRNN is
often more accurate than BPNN. GRNN requires more memory space
to store the model, GRNN features fast learning that does not require
an iterative procedure, and highly parallel structure. GRNN networks
are slower than multilayer perceptron networks at classifying new
cases.
Abstract: Several meteorological parameters were used for the
prediction of monthly average daily global solar radiation on
horizontal using recurrent neural networks (RNNs). Climatological
data and measures, mainly air temperature, humidity, sunshine
duration, and wind speed between 1995 and 2007 were used to design
and validate a feed forward and recurrent neural network based
prediction systems. In this paper we present our reference system
based on a feed-forward multilayer perceptron (MLP) as well as the
proposed approach based on an RNN model. The obtained results
were promising and comparable to those obtained by other existing
empirical and neural models. The experimental results showed the
advantage of RNNs over simple MLPs when we deal with time series
solar radiation predictions based on daily climatological data.
Abstract: Cd1−xZnxS thins films have been fabricated from ZnS/CdS/ZnS multilayer thin film systems, by using the vacuum deposition method; the Rutherford backscattering (RBS) technique have been applied in order to determine the: structure, composition, depth profile, and stoichiometric of these films. The influence of the chemical and heat treatments on the produced films also have been investigated; the RBS spectra of the films showed that homogenous Cd1−xZnxS can be synthesized with x=0.45.
Abstract: Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multilayered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Further, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.
Abstract: In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion detection system (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw dataset for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle component analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. This optimal feature subset is used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.
Abstract: This paper involves a study of the heavy metal pollution of the soils around one of cement plants in Libya called Suk-Alkhameas and surrounding urban areas caused by cement kiln dust (CKD) emitted. Samples of soil was collected from sites at four directions around the cement factory at distances 250m, 1000m, and 3000m from the factory and at (0-10)cm deep in the soil. These samples are analyzed for Fe (iii), Zn(ii), and Pb (ii) as major pollutants. These values are compared with soils at 25 Km distances from the factory as a reference or control samples. The results show that the concentration of Fe ions in the surface soil was within the acceptable range of 1000ppm. However, for Zn and Pb ions the concentrations at the east and north sides of the factory were found six fold higher than the benchmark level. This high value was attributed to the wind which blows usually from south to north and from west to east. This work includes an investigation of the adsorption isotherms and adsorption efficiency of CKD as adsorbent of heavy metal ions (Fe (iii), Zn(ii), and Pb(ii)) from the polluted soils of Suk-Alkameas city. The investigation was conducted in batch and fixed bed column flow technique. The adsorption efficiency of the studied heavy metals ions removals onto CKD depends on the pH of the solution. The optimum pH values are found to be in the ranges of 8-10 and decreases at lower pH values. The removal efficiency of these heavy metals ions ranged from 93% for Pb, 94% for Zn, and 98% for Fe ions for 10 g.l-1 adsorbent concentration. The maximum removal efficiency of these ions was achieved at 50-60 minutes contact times at which equilibrium is reached. Fixed bed column experimental measurements are also made to evaluate CKD as an adsorbent for the heavy metals. Results obtained are with good agreement with Langmuir and Drachsal assumption of multilayer formation on the adsorbent surface.
Abstract: LTCC (Low Temperature Co-fired Ceramics) being the most advantageous technology towards the multilayer substrates for various applications, demands an extensive study of its raw materials. In the present work, a series of CuxMg1-xNb2O6 (x=0,0.4,0.6,1) has been prepared using sol-gel synthesis route and sintered at a temperature of 900°C to study its applicability for LTCC technology as the firing temperature is 900°C in this technology. The phase formation has been confirmed using X-ray Diffraction. Thermal properties like thermal conductivity and thermal expansion being very important aspect as the former defines the heat flow to avoid thermal instability in layers and the later provides the dimensional congruency of the dielectric material and the conductors, are studied here over high temperature up to the firing temperature. Although the values are quite satisfactory from substrate requirement point view, results have shown anomaly over temperature. The anomalous thermal behavior has been further analyzed using TG-DTA.
Abstract: One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This study evaluates the susceptibility of Nigerian banks to failure with a view to identifying ratios and financial data that are sensitive to solvency of the bank. Further, a predictive model is generated to guide all stakeholders in the industry. Thirty quoted banks that had published Annual Reports for the year preceding the consolidation i.e. year 2004 were selected. They were examined for distress using the Multilayer Perceptron Neural Network Analysis. The model was used to analyze further reforms by the Central Bank of Nigeria using published Annual Reports of twenty quoted banks for the year 2008 and 2011. The model can thus be used for future prediction of failure in the Nigerian banking system.
Abstract: The importance of logistics has changed enormously in the last few decades. While logistics was formerly one of the core functions of most companies, logistics or at least parts of their functions are nowadays outsourced to external logistic service providers in terms of contracts. As a result of this shift new business models like the fourth party logistics provider emerged, which designs, plans and monitors the resulting logistics networks. This new business model and topics such as Synchromodality or Big Data impose new requirements on the underlying IT, which cannot be met with conventional concepts and approaches.
In this paper, the challenges of logistics network monitoring are outlined by using a scenario. The most common layers in a logical multilayered architecture for an information system are used to point out the arising challenges for IT. In addition, first appropriate solution approaches are introduced.
Abstract: A filtering problem of almost incompressible liquid chemical compound in the porous inhomogeneous 3D domain is studied. In this work general approaches to the solution of twodimensional filtering problems in ananisotropic, inhomogeneous and multilayered medium are developed, and on the basis of the obtained results mathematical models are constructed (according to Ollendorff method) for studying the certain engineering and technical problem of filtering the almost incompressible liquid chemical compound in the porous inhomogeneous 3D domain. For some of the formulated mathematical problems with additional requirements for the structure of the porous inhomogeneous medium, namely, its isotropy, spatial periodicity of its permeability coefficient, solution algorithms are proposed. Continuation of the current work titled ”On one mathematical model for filtration of weakly compressible chemical compound in the porous heterogeneous 3D medium. Part II: Determination of the reference directions of anisotropy and permeabilities on these directions” will be prepared in the shortest terms by the authors.
Abstract: In this study tree types of multilayer gas barrier plastic packaging films were compared using life cycle assessment as a tool for resource efficient and environmentally low-impact materials selection. The first type of multilayer packaging film (PET-AlOx/LDPE) consists of polyethylene terephthalate with barrier layer AlOx (PET-AlOx) and low density polyethylene (LDPE). The second type of polymer film (PET/PE-EVOH-PE) is made of polyethylene terephthalate (PET) and co-extrusion film PE-EVOH-PE as barrier layer. And the third one type of multilayer packaging film (PET-PVOH/LDPE) is formed from polyethylene terephthalate with barrier layer PVOH (PET-PVOH) and low density polyethylene (LDPE).
All of analyzed packaging has significant impact to resource depletion, because of raw materials extraction and energy use and production of different kind of plastics. Nevertheless the impact generated during life cycle of functional unit of II type of packaging (PET/PE-EVOH-PE) was about 25% lower than impact generated by I type (PET-AlOx/LDPE) and III type (PET-PVOH/LDPE) of packaging.
Result revealed that the contribution of different gas barrier type to the overall environmental problem of packaging is not significant. The impact are mostly generated by using energy and materials during raw material extraction and production of different plastic materials as plastic polymers material as PE, LDPE and PET, but not gas barrier materials as AlOx, PVOH and EVOH.
The LCA results could be useful in different decision-making processes, for selecting resource efficient and environmentally low-impact materials.