Abstract: Numerical studies on race car aerodynamics at wing
in ground effect have been carried out using a steady 3d, double
precision, pressure-based, and standard k-epsilon turbulence model.
Through various parametric analytical studies we have observed that
at a particular speed and ground clearance of the wings a favorable
negative lift was found high at a particular angle of attack for all the
physical models considered in this paper. The fact is that if the
ground clearance height to chord length (h/c) is too small, the
developing boundary layers from either side (the ground and the
lower surface of the wing) can interact, leading to an altered variation
of the aerodynamic characteristics at wing in ground effect. Therefore
a suitable ground clearance must be predicted throughout the racing
for a better performance of the race car, which obviously depends
upon the coupled effects of the topography, wing orientation with
respect to the ground, the incoming flow features and/or the race car
speed. We have concluded that for the design of high performance
and high speed race cars the adjustable wings capable to alter the
ground clearance and the angles of attack is the best design option for
any race car for racing safely with variable speeds.
Abstract: The effect of small non-parallelism of the base flow
on the stability of slightly curved mixing layers is analyzed in the
present paper. Assuming that the instability wavelength is much
smaller than the length scale of the variation of the base flow we
derive an amplitude evolution equation using the method of multiple
scales. The proposed asymptotic model provides connection between
parallel flow approximations and takes into account slow
longitudinal variation of the base flow.
Abstract: In this work, we study the problem of determining
the minimum scheduling length that can satisfy end-to-end (ETE)
traffic demand in scheduling-based multihop WSNs with cooperative
multiple-input multiple-output (MIMO) transmission scheme. Specifically,
we present a cross-layer formulation for the joint routing,
scheduling and stream control problem by incorporating various
power and rate adaptation schemes, and taking into account an
antenna beam pattern model and the signal-to-interference-and-noise
(SINR) constraint at the receiver. In the context, we also propose
column generation (CG) solutions to get rid of the complexity
requiring the enumeration of all possible sets of scheduling links.
Abstract: .Hardware realization of a Neural Network (NN), to a large extent depends on the efficient implementation of a single neuron. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. This paper discusses the issues involved in implementation of a multi-input neuron with linear/nonlinear excitation functions using FPGA. Implementation method with resource/speed tradeoff is proposed to handle signed decimal numbers. The VHDL coding developed is tested using Xilinx XC V50hq240 Chip. To improve the speed of operation a lookup table method is used. The problems involved in using a lookup table (LUT) for a nonlinear function is discussed. The percentage saving in resource and the improvement in speed with an LUT for a neuron is reported. An attempt is also made to derive a generalized formula for a multi-input neuron that facilitates to estimate approximately the total resource requirement and speed achievable for a given multilayer neural network. This facilitates the designer to choose the FPGA capacity for a given application. Using the proposed method of implementation a neural network based application, namely, a Space vector modulator for a vector-controlled drive is presented
Abstract: Business process automation is an important task in an
enterprise business environment software development. The
requirements of processing acceleration and automation level of
enterprises are inherently different from one organization to another.
We present a methodology and system for automation of business
process management system architecture by multi-agent collaboration
based on SOA. Design layer processes are modeled in semantic
markup language for web services application. At the core of our
system is considering certain types of human tasks to their further
automation across over multiple platform environments. An
improved abnormality processing with model for automation of
BPMS architecture by multi-agent collaboration based on SOA is
introduced. Validating system for efficiency of process automation,
an application for educational knowledge base instance would also be
described.
Abstract: InGaAsN and GaAsN epitaxial layers with similar
nitrogen compositions in a sample were successfully grown on a
GaAs (001) substrate by solid source molecular beam epitaxy. An
electron cyclotron resonance nitrogen plasma source has been used to
generate atomic nitrogen during the growth of the nitride layers. The
indium composition changed from sample to sample to give
compressive and tensile strained InGaAsN layers. Layer
characteristics have been assessed by high-resolution x-ray
diffraction to determine the relationship between the lattice constant
of the GaAs1-yNy layer and the fraction x of In. The objective was to
determine the In fraction x in an InxGa1-xAs1-yNy epitaxial layer which
exactly cancels the strain present in a GaAs1-yNy epitaxial layer with
the same nitrogen content when grown on a GaAs substrate.
Abstract: A dynamic of Bertrand duopoly game is analyzed, where players use different production methods and choose their prices with bounded rationality. The equilibriums of the corresponding discrete dynamical systems are investigated. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability of Nash equilibrium, as some parameters of the model are varied, gives rise to complex dynamics such as cycles of higher order and chaos. On this basis, we discover that an increase of adjustment speed of bounded rational player can make Bertrand market sink into the chaotic state. Finally, the complex dynamics, bifurcations and chaos are displayed by numerical simulation.
Abstract: The present paper deals with the most adopted technical
solutions for the enhancement of the lift force of a wing. In fact,
during several flight conditions (such as take off and landing), the
lift force needs to be dramatically enhanced. Both trailing edge
devices (such as flaps) and leading edge ones (such as slats) are
described. Finally, the most advanced aerodynamic solutions to avoid
the separation of the boundary layer from aircraft wings at high angles
of attack are reviewed.
Abstract: In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.
Abstract: This paper presents a novel method for inferring the
odor based on neural activities observed from rats- main olfactory
bulbs. Multi-channel extra-cellular single unit recordings were done
by micro-wire electrodes (tungsten, 50μm, 32 channels) implanted in
the mitral/tufted cell layers of the main olfactory bulb of anesthetized
rats to obtain neural responses to various odors. Neural response
as a key feature was measured by substraction of neural firing rate
before stimulus from after. For odor inference, we have developed a
decoding method based on the maximum likelihood (ML) estimation.
The results have shown that the average decoding accuracy is about
100.0%, 96.0%, 84.0%, and 100.0% with four rats, respectively. This
work has profound implications for a novel brain-machine interface
system for odor inference.
Abstract: Freeze concentration freezes or crystallises the water
molecules out as ice crystals and leaves behind a highly concentrated
solution. In conventional suspension freeze concentration where ice
crystals formed as a suspension in the mother liquor, separation of
ice is difficult. The size of the ice crystals is still very limited which
will require usage of scraped surface heat exchangers, which is very
expensive and accounted for approximately 30% of the capital cost.
This research is conducted using a newer method of freeze
concentration, which is progressive freeze concentration. Ice crystals
were formed as a layer on the designed heat exchanger surface. In
this particular research, a helical structured copper crystallisation
chamber was designed and fabricated. The effect of two operating
conditions on the performance of the newly designed crystallisation
chamber was investigated, which are circulation flowrate and coolant
temperature. The performance of the design was evaluated by the
effective partition constant, K, calculated from the volume and
concentration of the solid and liquid phase. The system was also
monitored by a data acquisition tool in order to see the temperature
profile throughout the process. On completing the experimental
work, it was found that higher flowrate resulted in a lower K, which
translated into high efficiency. The efficiency is the highest at 1000
ml/min. It was also found that the process gives the highest
efficiency at a coolant temperature of -6 °C.
Abstract: Composite pins of rubber dust collected from tyre
retreading centres of trucks, cars and buses etc.and epoxy with
weight percentages of 10. 15, and 20 % of rubber (weight fractions of
9, 13 and 17 % respectively) have been prepared in house with the
help of a split wooden mould. The pins were tested in a pin-on-disc
wear monitor to determine the co-efficient of friction and weight
losses with varying speeds, loads and time. The wear volume and
wear rates have also been found out for all these three specimens.. It
is observed that all the specimens have exhibited very low coefficient
of friction and low wear rates under dry sliding condition. Out of the
above three samples tested, the specimen with 10 % rubber dust by
weight has shown lowest wear rates. However a peculiar result i.e
decreasing trend has been obtained with 20% reinforcement of rubber
in epoxy while rubbed against steel at varying speeds. This might
have occurred due to high surface finish of the disc and formation of
a thin transfer layer from the composite
Abstract: The purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learning vector quantization are used. Two methods are utilized for multilayer perceptron. First method has single hidden layer and between 3-15 nodes, second method has two hidden layer and each layer has between 3-15 nodes. Overall classification rate of 86.88% is achieved.
Abstract: Artificial Intelligence based gaming is an interesting topic in the state-of-art technology. This paper presents an automation of a tradition Omani game, called Al-Hawalees. Its related issues are resolved and implemented using artificial intelligence approach. An AI approach called mini-max procedure is incorporated to make a diverse budges of the on-line gaming. If number of moves increase, time complexity will be increased in terms of propositionally. In order to tackle the time and space complexities, we have employed a back propagation neural network (BPNN) to train in off-line to make a decision for resources required to fulfill the automation of the game. We have utilized Leverberg- Marquardt training in order to get the rapid response during the gaming. A set of optimal moves is determined by the on-line back propagation training fashioned with alpha-beta pruning. The results and analyses reveal that the proposed scheme will be easily incorporated in the on-line scenario with one player against the system.
Abstract: Multi-Radio Multi-Channel Wireless Mesh Networks (MRMC-WMNs) operate at the backbone to access and route high volumes of traffic simultaneously. Such roles demand high network capacity, and long “online" time at the expense of accelerated transmission energy depletion and poor connectivity. This is the problem of transmission power control. Numerous power control methods for wireless networks are in literature. However, contributions towards MRMC configurations still face many challenges worth considering. In this paper, an energy-efficient power selection protocol called PMMUP is suggested at the Link-Layer. This protocol first divides the MRMC-WMN into a set of unified channel graphs (UCGs). A UCG consists of multiple radios interconnected to each other via a common wireless channel. In each UCG, a stochastic linear quadratic cost function is formulated. Each user minimizes this cost function consisting of trade-off between the size of unification states and the control action. Unification state variables come from independent UCGs and higher layers of the protocol stack. The PMMUP coordinates power optimizations at the network interface cards (NICs) of wireless mesh routers. The proposed PMMUP based algorithm converges fast analytically with a linear rate. Performance evaluations through simulations confirm the efficacy of the proposed dynamic power control.
Abstract: A continuum model is presented to study vdW
interaction on buckling analysis of multi-walled walled carbon
nanotube. In previous studies, only the vdW interaction between
adjacent two layers was considered and the vdW interaction between
the other two layers was neglected. The results show that the vdW
interaction cofficients are dependent on the change of interlayer
spacing and the radii of tubes. With increase of radii the vdW
coefficients approach a constant value. The numerical results show
that the effect of vdW interaction on the critical strain for a doublewalled
CNT is negligible when the radius is large enough for the
both the cases of before and after buckling.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: This paper deals with the design of a periodic output
feedback controller for a flexible beam structure modeled with
Timoshenko beam theory, Finite Element Method, State space
methods and embedded piezoelectrics concept. The first 3 modes are
considered in modeling the beam. The main objective of this work is
to control the vibrations of the beam when subjected to an external
force. Shear piezoelectric sensors and actuators are embedded into
the top and bottom layers of a flexible aluminum beam structure, thus
making it intelligent and self-adaptive. The composite beam is
divided into 5 finite elements and the control actuator is placed at
finite element position 1, whereas the sensor is varied from position 2
to 5, i.e., from the nearby fixed end to the free end. 4 state space
SISO models are thus developed. Periodic Output Feedback (POF)
Controllers are designed for the 4 SISO models of the same plant to
control the flexural vibrations. The effect of placing the sensor at
different locations on the beam is observed and the performance of
the controller is evaluated for vibration control. Conclusions are
finally drawn.
Abstract: BRI-STARS (BRIdge Stream Tube model for Alluvial
River Simulation) program was used to investigate the scour depth around bridge piers in some of the major river systems in Iran. Model
calibration was performed by collecting different field data. Field data are cataloged on three categories, first group of bridges that
their rivers bed are formed by fine material, second group of bridges
that their rivers bed are formed by sand material, and finally bridges that their rivers bed are formed by gravel or cobble materials.
Verification was performed with some field data in Fars Province. Results show that for wide piers, computed scour depth is more than
measured one. In gravel bed streams, computed scour depth is greater
than measured scour depth, the reason is due to formation of armor layer on bed of channel. Once this layer is eroded, the computed
scour depth is close to the measured one.
Abstract: This paper aims at numerically analysing the effect
of an active flow control (AFC) by a vortex generator jet (VGJ)
submerged in a boundary layer via Chimera Grids and Detached-
Eddy Simulation (DES). The performance of DES results are
judged against Reynolds-Averaged Navier-Stokes (RANS) and
compared with the experiments that showed an unsteady vortex
motion downstream of VGJ. Experimental results showed that
the mechanism of embedding logitudinal vortex structure in the
main stream flow is quite effective in increasing the near wall
momentum of separated aircraft wing. In order to simulate such
a flow configuration together with the VGJ, an efficient numerical
approach is required. This requirement is fulfilled by performing
the DES simulation over the flat plate using the DLR TAU Code.
The DES predictions identify the vortex region via smooth hybrid
length scale and predict the unsteady vortex motion observed in
the experiments. The DES results also showed that the sufficient
grid refinement in the vortex region resolves the turbulent scales
downstream of the VGJ, the spatial vortex core postion and nondimensional
momentum coefficient RVx .