Abstract: To study the dynamic mechanics response of asphalt
pavement under the temperature load and vehicle loading, asphalt
pavement was regarded as multilayered elastic half-space system, and
theory analysis was conducted by regarding dynamic modulus of
asphalt mixture as the parameter. Firstly, based on the dynamic
modulus test of asphalt mixture, function relationship between the
dynamic modulus of representative asphalt mixture and temperature
was obtained. In addition, the analytical solution for thermal stress in
single layer was derived by using Laplace integral transformation and
Hankel integral transformation respectively by using thermal
equations of equilibrium. The analytical solution of calculation model
of thermal stress in asphalt pavement was derived by transfer matrix
of thermal stress in multilayer elastic system. Finally, the variation of
thermal stress in pavement structure was analyzed. The result shows
that there is obvious difference between the thermal stress based on
dynamic modulus and the solution based on static modulus. So the
dynamic change of parameter in asphalt mixture should be taken into
consideration when theoretical analysis is taken out.
Abstract: A compact Ultra Wide Band (UWB) antenna with coplanar
waveguide feed has been designed and results are verified in
this paper. The antenna has been designed on FR4 substrate with
dielectric constant (εr) of 4.4 and dimensions of 32mm x 26mm x
0.8mm. The presented antenna shows return loss characteristics in the
band of 3.1 to 10.6 GHz as prescribed by FCC, USA. Parametric
studies have been done and results thus obtained have been
presented. Simulated results have been verified on Rohde & Swartz
VNA. The measured results are in good agreement with simulated
results which make the presented antenna suitable to be used for
wearable applications. Performance analysis of antenna has also been
shown in the presence of three layered Human Arm model. Results
obtained in presence of Human Arm model has been compared with
that in free space.
Abstract: Most of the existing video streaming protocols
provide video services without considering security aspects in
decentralized mobile ad-hoc networks. The security policies adapted
to the currently existing non-streaming protocols, do not comply with
the live video streaming protocols resulting in considerable
vulnerability, high bandwidth consumption and unreliability which
cause severe security threats, low bandwidth and error prone
transmission respectively in video streaming applications. Therefore
a synergized methodology is required to reduce vulnerability and
bandwidth consumption, and enhance reliability in the video
streaming applications in MANET. To ensure the security measures
with reduced bandwidth consumption and improve reliability of the
video streaming applications, a Secure Low-bandwidth Video
Streaming through Reliable Multipath Propagation (SLVRMP)
protocol architecture has been proposed by incorporating the two
algorithms namely Secure Low-bandwidth Video Streaming
Algorithm and Reliable Secure Multipath Propagation Algorithm
using Layered Video Coding in non-overlapping zone routing
network topology. The performances of the proposed system are
compared to those of the other existing secure multipath protocols
Sec-MR, SPREAD using NS 2.34 and the simulation results show
that the performances of the proposed system get considerably
improved.
Abstract: Typical load-bearing biological materials like bone,
mineralized tendon and shell, are biocomposites made from both
organic (collagen) and inorganic (biomineral) materials. This
amazing class of materials with intrinsic internally designed
hierarchical structures show superior mechanical properties with
regard to their weak components from which they are formed.
Extensive investigations concentrating on static loading conditions
have been done to study the biological materials failure. However,
most of the damage and failure mechanisms in load-bearing
biological materials will occur whenever their structures are exposed
to dynamic loading conditions. The main question needed to be
answered here is: What is the relation between the layout and
architecture of the load-bearing biological materials and their
dynamic behavior? In this work, a staggered model has been
developed based on the structure of natural materials at nanoscale and
Finite Element Analysis (FEA) has been used to study the dynamic
behavior of the structure of load-bearing biological materials to
answer why the staggered arrangement has been selected by nature to
make the nanocomposite structure of most of the biological materials.
The results showed that the staggered structures will efficiently
attenuate the stress wave rather than the layered structure.
Furthermore, such staggered architecture is effectively in charge of
utilizing the capacity of the biostructure to resist both normal and
shear loads. In this work, the geometrical parameters of the model
like the thickness and aspect ratio of the mineral inclusions selected
from the typical range of the experimentally observed feature sizes
and layout dimensions of the biological materials such as bone and
mineralized tendon. Furthermore, the numerical results validated with
existing theoretical solutions. Findings of the present work emphasize
on the significant effects of dynamic behavior on the natural
evolution of load-bearing biological materials and can help scientists
to design bioinspired materials in the laboratories.
Abstract: A novel method is presented for obtaining the stress
field induced by an edge dislocation in a multilayered composite. To
demonstrate the applications of the obtained solution, we consider the
problem of an interfacial crack in a periodically layered bimaterial
medium. The crack is modelled as a continuous distribution of edge
dislocations and the Distributed Dislocation Technique (DDT) is
utilized to obtain numerical results for the energy release rate (ERR).
The numerical implementation of the dislocation solution in
MATLAB is also provided.
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: Non contact evaluation of the thickness of paint
coatings can be attempted by different destructive and nondestructive
methods such as cross-section microscopy, gravimetric mass
measurement, magnetic gauges, Eddy current, ultrasound or
terahertz. Infrared thermography is a nondestructive and non-invasive
method that can be envisaged as a useful tool to measure the surface
thickness variations by analyzing the temperature response. In this
paper, the thermal quadrupole method for two layered samples heated
up with a pulsed excitation is firstly used. By analyzing the thermal
responses as a function of thermal properties and thicknesses of both
layers, optimal parameters for the excitation source can be identified.
Simulations show that a pulsed excitation with duration of ten
milliseconds allows obtaining a substrate-independent thermal
response. Based on this result, an experimental setup consisting of a
near-infrared laser diode and an Infrared camera was next used to
evaluate the variation of paint coating thickness between 60 μm and
130 μm on two samples. Results show that the parameters extracted
for thermal images are correlated with the estimated thicknesses by
the Eddy current methods. The laser pulsed thermography is thus an
interesting alternative nondestructive method that can be moreover
used for nonconductive substrates.
Abstract: Applications of the Hausdorff space and its mappings
into tangent spaces are outlined, including their fractal dimensions
and self-similarities. The paper details this theory set up and further
describes virtualizations and atomization of manufacturing processes.
It demonstrates novel concurrency principles that will guide
manufacturing processes and resources configurations. Moreover,
varying levels of details may be produced by up folding and breaking
down of newly introduced generic models. This choice of layered
generic models for units and systems aspects along specific aspects
allows research work in parallel to other disciplines with the same
focus on all levels of detail. More credit and easier access are granted
to outside disciplines for enriching manufacturing grounds. Specific
mappings and the layers give hints for chances for interdisciplinary
outcomes and may highlight more details for interoperability
standards, as already worked on the international level. The new rules
are described, which require additional properties concerning all
involved entities for defining distributed decision cycles, again on the
base of self-similarity. All properties are further detailed and assigned
to a maturity scale, eventually displaying the smartness maturity of a
total shopfloor or a factory. The paper contributes to the intensive
ongoing discussion in the field of intelligent distributed
manufacturing and promotes solid concepts for implementations of
Cyber Physical Systems and the Internet of Things into
manufacturing industry, like industry 4.0, as discussed in German-speaking
countries.
Abstract: Polysulfone (PSU) is a specialty engineering polymer
having various industrial applications. PSU is especially used in
waste water treatment membranes due to its good mechanical
properties, structural and chemical stability. But it is a hydrophobic
material and therefore its surface aim to pollute easily. In order to
resolve this problem and extend the properties of membrane, PSU
surface is rendered hydrophilic by addition of the sepiolite
nanofibers. Sepiolite is one of the natural clays, which is a hydrate
magnesium silicate fiber, also one of the well known layered clays of
the montmorillonites where has several unique channels and pores
within. It has also moisture durability, strength and low price.
Sepiolite channels give great capacity of absorption and good surface
properties. In this study, nanocomposites of commercial PSU and
Sepiolite were prepared by solvent mixing method. Different organic
solvents and their mixtures were used. Rheological characteristics of
PSU-Sepiolite solvent mixtures were analyzed, the solubility of
nanocomposite content in those mixtures were studied.
Abstract: In this paper, extract of papaya leaves are used as a
natural dye and combined by variations of solvent concentration
applied on DSSC (Dye-Sensitized Solar Cell). Indonesian geographic
located on the equator line occasions the magnitude of the potential
to develop organic solar cells made from extracts of chlorophyll as a
substitute for inorganic materials or synthetic dye on DSSC material.
Dye serves as absorbing photons which are then converted into
electrical energy. A conductive coated glass layer called TCO
(Transparent Conductive Oxide) is used as a substrate of electrode.
TiO2 nanoparticles as binding dye molecules, redox couple iodide/
tri-iodide as the electrolyte and carbon as the counter electrode in the
DSSC are used. TiO2 nanoparticles, organic dyes, electrolytes, and
counter electrode are arranged and combined with the layered
structure of the photo-catalyst absorption layer. Dye absorption
measurements using a spectrophotometer at 400-800 nm light
spectrum produces a total amount of chlorophyll 80.076 mg/l. The
test cell at 7 watt LED light with 5000 lux luminescence was
obtained Voc and Isc of 235.5 mV and 14 μA, respectively.
Abstract: Artificial Neural Network (ANN) can be trained using
back propagation (BP). It is the most widely used algorithm for
supervised learning with multi-layered feed-forward networks.
Efficient learning by the BP algorithm is required for many practical
applications. The BP algorithm calculates the weight changes of
artificial neural networks, and a common approach is to use a twoterm
algorithm consisting of a learning rate (LR) and a momentum
factor (MF). The major drawbacks of the two-term BP learning
algorithm are the problems of local minima and slow convergence
speeds, which limit the scope for real-time applications. Recently the
addition of an extra term, called a proportional factor (PF), to the
two-term BP algorithm was proposed. The third increases the speed
of the BP algorithm. However, the PF term also reduces the
convergence of the BP algorithm, and criteria for evaluating
convergence are required to facilitate the application of the three
terms BP algorithm. Although these two seem to be closely related,
as described later, we summarize various improvements to overcome
the drawbacks. Here we compare the different methods of
convergence of the new three-term BP algorithm.
Abstract: Securing the confidential data transferred via wireless
network remains a challenging problem. It is paramount to ensure
that data are accessible only by the legitimate users rather than by the
attackers. One of the most serious threats to organization is jamming,
which disrupts the communication between any two pairs of nodes.
Therefore, designing an attack-defending scheme without any packet
loss in data transmission is an important challenge. In this paper,
Dependence based Malicious Route Defending DMRD Scheme has
been proposed in multi path routing environment to prevent jamming
attack. The key idea is to defend the malicious route to ensure
perspicuous transmission. This scheme develops a two layered
architecture and it operates in two different steps. In the first step,
possible routes are captured and their agent dependence values are
marked using triple agents. In the second step, the dependence values
are compared by performing comparator filtering to detect malicious
route as well as to identify a reliable route for secured data
transmission. By simulation studies, it is observed that the proposed
scheme significantly identifies malicious route by attaining lower
delay time and route discovery time; it also achieves higher
throughput.
Abstract: This paper presents a real-time visualization technique
and filtering of classified LiDAR point clouds. The visualization is
capable of displaying filtered information organized in layers by the
classification attribute saved within LiDAR datasets. We explain the
used data structure and data management, which enables real-time
presentation of layered LiDAR data. Real-time visualization is
achieved with LOD optimization based on the distance from the
observer without loss of quality. The filtering process is done in two
steps and is entirely executed on the GPU and implemented using
programmable shaders.
Abstract: This paper aims at finding a suitable neural network
for monitoring congestion level in electrical power systems. In this
paper, the input data has been framed properly to meet the target
objective through supervised learning mechanism by defining normal
and abnormal operating conditions for the system under study. The
congestion level, expressed as line congestion index (LCI), is
evaluated for each operating condition and is presented to the NN
along with the bus voltages to represent the input and target data.
Once, the training goes successful, the NN learns how to deal with a
set of newly presented data through validation and testing
mechanism. The crux of the results presented in this paper rests on
performance comparison of a multi-layered feed forward neural
network with eleven types of back propagation techniques so as to
evolve the best training criteria. The proposed methodology has been
tested on the standard IEEE-14 bus test system with the support of
MATLAB based NN toolbox. The results presented in this paper
signify that the Levenberg-Marquardt backpropagation algorithm
gives best training performance of all the eleven cases considered in
this paper, thus validating the proposed methodology.
Abstract: Montmorillonite (MMT) is a very abundant clay mineral and is versatile such that it can be chemically or physically altered by changing the ions between the sheets of its layered structure. This clay mineral can be prepared into functional nanoparticles that can be used as fillers in other nanomaterials such as nanofibers to achieve special properties. In this study, two types of iron-modified MMT, Iron-MMT (FeMMT) and Zero Valent Iron-MMT (ZVIMMT) were synthesized via ion exchange technique. The modified clay was incorporated in polymer nanofibers which were produced using a process called electrospinning. ICP analysis confirmed that clay modification was successful where there is an observed decrease in the concentration of Na and an increase in the concentration of Fe after ion exchange. XRD analysis also confirmed that modification took place because of the changes in the d-spacing of Na-MMT from 11.5 Å to 13.6 Å and 12.6 Å after synthesis of FeMMT and ZVIMMT, respectively. SEM images of the electrospun nanofibers revealed that the ZVIMMT-filled fibers have a smaller average diameter than the FeMMT-filled fibers because of the lower resistance of the suspensions of the former to the elongation force from the applied electric field. The resistance to the electric field was measured by getting the bulk voltage of the suspensions.
Abstract: This work had three stages. In the first stage was
examined pull-out process for steel fiber was embedded into a
concrete by one end and was pulled out of concrete under the angle to
pulling out force direction. Angle was varied. On the obtained forcedisplacement
diagrams were observed jumps. For such mechanical
behavior explanation, fiber channel in concrete surface microscopical
experimental investigation, using microscope KEYENCE VHX2000,
was performed.
At the second stage were obtained diagrams for load- crack
opening displacement for breaking homogeneously reinforced and
layered fiberconcrete prisms (with dimensions 10x10x40cm)
subjected to 4-point bending. After testing was analyzed main crack.
At the third stage elaborated prediction model for the fiberconcrete
beam, failure under bending, using the following data: a) diagrams
for fibers pulling out at different angles; b) experimental data about
steel-straight fibers locations in the main crack. Experimental and
theoretical (modeling) data were compared.
Abstract: Fiber reinforced concrete is important material for load bearing structural elements. Usually fibers are homogeneously distributed in a concrete body having arbitrary spatial orientations. At the same time, in many situations, fiber concrete with oriented fibers is more optimal. Is obvious, that is possible to create constructions with oriented short fibers in them, in different ways. Present research is devoted to one of such approaches- fiber reinforced concrete prisms having dimensions 100mm ×100mm ×400mmwith layers of non-homogeneously distributed fibers inside them were fabricated.
Simultaneously prisms with homogeneously dispersed fibers were produced for reference as well. Prisms were tested under four point bending conditions. During the tests vertical deflection at the center of every prism and crack opening were measured (using linear displacements transducers in real timescale). Prediction results were discussed.
Abstract: This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.
The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.
Abstract: This work was focused in to study the compatibility, dispersion and exfoliation of modified nanoclays in biodegradable polymers and evaluate its effect on the physical, mechanical and thermal properties on the biodegradable matrix used. The formulations have been developed with polylactic acid (PLA) and organically modified montmorillonite-type commercial nanoclays (Cloisite 15, Cloisite 20, and Cloisite 30B) in the presence of a plasticizer agent, specifically Polyethylene Glycol of low molecular weight. Different compositions were evaluated, in order to identify the influence of each nanoclayin the polymeric matrix. The mixtures were characterized by thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), X-ray diffraction (DRX), transmission electron microscopy (TEM) and Tensile Test. These tests have allowed understanding the behavior of each of the mixtures developed.
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.