Abstract: This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear underlying phenomena contained in the data set and to partly eliminate the burden of having to specify completely the structure of the model. Two different types of neural networks were used for the application of pulping problem. A three layer feed forward neural networks, using the Preconditioned Conjugate Gradient (PCG) methods were used in this investigation. Preconditioning is a method to improve convergence by lowering the condition number and increasing the eigenvalues clustering. The idea is to solve the modified odified problem M-1 Ax= M-1b where M is a positive-definite preconditioner that is closely related to A. We mainly focused on Preconditioned Conjugate Gradient- based training methods which originated from optimization theory, namely Preconditioned Conjugate Gradient with Fletcher-Reeves Update (PCGF), Preconditioned Conjugate Gradient with Polak-Ribiere Update (PCGP) and Preconditioned Conjugate Gradient with Powell-Beale Restarts (PCGB). The behavior of the PCG methods in the simulations proved to be robust against phenomenon such as oscillations due to large step size.
Abstract: Using vision based solution in intelligent vehicle application often needs large memory to handle video stream and image process which increase complexity of hardware and software. In this paper, we present a FPGA implement of a vision based lane departure warning system. By taking frame of videos, the line gradient of line is estimated and the lane marks are found. By analysis the position of lane mark, departure of vehicle will be detected in time. This idea has been implemented in Xilinx Spartan6 FPGA. The lane departure warning system used 39% logic resources and no memory of the device. The average availability is 92.5%. The frame rate is more than 30 frames per second (fps).
Abstract: In this paper, many techniques for blind identification of moving average (MA) process are presented. These methods utilize third- and fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed (i.i.d) non-Gaussian sequence that is not observed. Two nonlinear optimization algorithms, namely the Gradient Descent and the Gauss-Newton algorithms are exposed. An algorithm based on the joint-diagonalization of the fourth-order cumulant matrices (FOSI) is also considered, as well as an improved version of the classical C(q, 0, k) algorithm based on the choice of the Best 1-D Slice of fourth-order cumulants. To illustrate the effectiveness of our methods, various simulation examples are presented.
Abstract: The mechanism behind the electromigration and
thermomigration failure in flip-chip solder joints with Cu-pillar bumps
was investigated in this paper through using finite element method.
Hot spot and the current crowding occurrs in the upper corner of
copper column instead of solders of the common solder ball. The
simulation results show that the change in thermal gradient is
noticeable, which might greatly affect the reliability of solder joints
with Cu-pillar bumps under current stressing. When the average
applied current density is increased from 1×104 A/cm2 to 3×104 A/cm2
in solders, the thermal gradient would increase from 74 K/cm to 901
K/cm at an ambient temperature of 25°C. The force from thermal
gradient of 901 K/cm can nearly induce thermomigration by itself.
With the increase in applied current, the thermal gradient is growing. It
is proposed that thermomigration likely causes a serious reliability
issue for Cu column based interconnects.
Abstract: The parametrical study of Shrouded Contra-rotating
Rotor was done in this paper based on 2D axisymmetric simulations.
The calculations were made with an actuator disk as double rotor
model. It objects to explore and quantify the effects of different shroud
geometry parameters mainly using the performance of power loading
(PL), which could evaluate the whole propulsion system capability as
5 Newtontotal thrust generationfor hover demand. The numerical
results show that:The increase of nozzle radius is desired but limited
by the flow separation, its optimal design is around 1.15 times rotor
radius, the viscosity effects greatly constraint the influence of nozzle
shape, the divergent angle around 10.5° performs best for chosen
nozzle length;The parameters of inlet such as leading edge curvature,
radius and internal shape do not affect thrust great but play an
important role in pressure distribution which could produce most part
of shroud thrust, they should be chosen according to the reduction of
adverse pressure gradients to reduce the risk of boundary separation.
Abstract: In this study, stress distributions on dental implants
made of functionally graded biomaterials (FGBM) are investigated
numerically. The implant body is considered to be subjected to axial
compression loads. Numerical problem is assumed to be 2D, and
ANSYS commercial software is used for the analysis. The cross
section of the implant thread varies as varying the height (H) and the
width (t) of the thread. According to thread dimensions of implant
and material properties of FGBM, equivalent stress distribution on
the implant is determined and presented with contour plots along
with the maximum equivalent stress values. As a result, with
increasing material gradient parameter (n), the equivalent stress
decreases, but the minimum stress distribution increases. Maximum
stress values decrease with decreasing implant radius (r). Maximum
von Mises stresses increases with decreasing H when t is constant.
On the other hand, the stress values are not affected by variation of t
in the case of H = constant.
Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.
Abstract: This work contains information about the influence low-level optical irradiation on sperm motility of sturgeon fish. On the basis of given and earlier received data the following conclusion has been made. Among the photophysical processes of a resonant and not resonant nature (oriented action of light; action of gradient forces; dipole-dipole interaction; termooptical processes), which are capable to cause the photobiological effects depended on such laserspecific characteristics as polarization and coherency, determining influence belongs to oriented action of light and dipole-dipole interactions among the processes studied in the present work.
Abstract: In a travelling wave thermoacoustic device, the
regenerator sandwiched between a pair of (hot and cold) heat
exchangers constitutes the so-called thermoacoustic core, where the
thermoacoustic energy conversion from heat to acoustic power takes
place. The temperature gradient along the regenerator caused by the
two heat exchangers excites and maintains the acoustic wave in the
resonator. The devices are called travelling wave thermoacoustic
systems because the phase angle difference between the pressure and
velocity oscillation is close to zero in the regenerator. This paper
presents the construction and testing of a thermoacoustic engine
equipped with a ceramic regenerator, made from a ceramic material
that is usually used as catalyst substrate in vehicles- exhaust systems,
with fine square channels (900 cells per square inch). The testing
includes the onset temperature difference (minimum temperature
difference required to start the acoustic oscillation in an engine), the
acoustic power output, thermal efficiency and the temperature profile
along the regenerator.
Abstract: Incompressible Navier-Stokes equations are reviewed
in this work. Three-dimensional Navier-Stokes equations are solved
analytically. The Mathematical derivation shows that the solutions
for the zero and constant pressure gradients are similar. Descriptions
of the proposed formulation and validation against two laminar
experiments and three different turbulent flow cases are reported in
this paper. Even though, the analytical solution is derived for nonreacting
flows, it could reproduce trends for cases including
combustion.
Abstract: With the presence of a uniform vertical magnetic field and suspended particles, thermocapillary instability in a horizontal liquid layer is investigated. The resulting eigenvalue is solved by the Galerkin technique for various basic temperature gradients. It is found that the presence of magnetic field always has a stability effect of increasing the critical Marangoni number.
Abstract: Study is on the vibration of thin cylindrical shells made of a functionally gradient material (FGM) composed of stainless steel and nickel is presented. The effects of the FGM configuration are studied by studying the frequencies of FG cylindrical shells. In this case FG cylindrical shell has Nickel on its inner surface and stainless steel on its outer surface. The study is carried out based on third order shear deformation shell theory. The objective is to study the natural frequencies, the influence of constituent volume fractions and the effects of configurations of the constituent materials on the frequencies. The properties are graded in the thickness direction according to the volume fraction power-law distribution. Results are presented on the frequency characteristics, the influence of the constituent various volume fractions on the frequencies.
Abstract: The design of a gravity dam is performed through an
interactive process involving a preliminary layout of the structure
followed by a stability and stress analysis. This study presents a
method to define the optimal top width of gravity dam with genetic
algorithm. To solve the optimization task (minimize the cost of the
dam), an optimization routine based on genetic algorithms (GAs) was
implemented into an Excel spreadsheet. It was found to perform well
and GA parameters were optimized in a parametric study. Using the
parameters found in the parametric study, the top width of gravity
dam optimization was performed and compared to a gradient-based
optimization method (classic method). The accuracy of the results
was within close proximity. In optimum dam cross section, the ratio
of is dam base to dam height is almost equal to 0.85, and ratio of dam
top width to dam height is almost equal to 0.13. The computerized
methodology may provide the help for computation of the optimal
top width for a wide range of height of a gravity dam.
Abstract: As the global climate changes, the threat from
landslides and debris flows increases. Learning how a watershed
initiates landslides under abnormal rainfall conditions and predicting
landslide magnitude and frequency distribution is thus important.
Landslides show a power-law distribution in the frequency-area
distribution. The distribution curve shows an exponent gradient 1.0 in
the Sandpile model test. Will the landslide frequency-area statistics
show a distribution similar to the Sandpile model under extreme
rainfall conditions? The purpose of the study is to identify the extreme
rainfall-induced landslide frequency-area distribution in the Laonong
River Basin in southern Taiwan. Results of the analysis show that a
lower gradient of landslide frequency-area distribution could be
attributed to the transportation and deposition of debris flow areas that
are included in the landslide area.
Abstract: A semi-active control strategy for suspension
systems of passenger cars is presented employing
Magnetorheological (MR) dampers. The vehicle is modeled with
seven DOFs including the, roll pitch and bounce of car body, and
the vertical motion of the four tires. In order to design an optimal
controller based on the actuator constraints, a Linear-Quadratic
Regulator (LQR) is designed. The design procedure of the LQR
consists of selecting two weighting matrices to minimize the energy
of the control system. This paper presents a hybrid optimization
procedure which is a combination of gradient-based and
evolutionary algorithms to choose the weighting matrices with
regards to the actuator constraint. The optimization algorithm is
defined based on maximum comfort and actuator constraints. It is
noted that utilizing the present control algorithm may significantly
reduce the vibration response of the passenger car, thus, providing
a comfortable ride.
Abstract: This paper is concerned with studying the forgetting factor of the recursive least square (RLS). A new dynamic forgetting factor (DFF) for RLS algorithm is presented. The proposed DFF-RLS is compared to other methods. Better performance at convergence and tracking of noisy chirp sinusoid is achieved. The control of the forgetting factor at DFF-RLS is based on the gradient of inverse correlation matrix. Compared with the gradient of mean square error algorithm, the proposed approach provides faster tracking and smaller mean square error. In low signal-to-noise ratios, the performance of the proposed method is superior to other approaches.
Abstract: This paper presents an improved image segmentation
model with edge preserving regularization based on the
piecewise-smooth Mumford-Shah functional. A level set formulation
is considered for the Mumford-Shah functional minimization in
segmentation, and the corresponding partial difference equations are
solved by the backward Euler discretization. Aiming at encouraging
edge preserving regularization, a new edge indicator function is
introduced at level set frame. In which all the grid points which is used
to locate the level set curve are considered to avoid blurring the edges
and a nonlinear smooth constraint function as regularization term is
applied to smooth the image in the isophote direction instead of the
gradient direction. In implementation, some strategies such as a new
scheme for extension of u+ and u- computation of the grid points and
speedup of the convergence are studied to improve the efficacy of the
algorithm. The resulting algorithm has been implemented and
compared with the previous methods, and has been proved efficiently
by several cases.
Abstract: The Minimal Residual (MR) is modified for adaptive
filtering application. Three forms of MR based algorithm are
presented: i) the low complexity SPCG, ii) MREDSI, and iii)
MREDSII. The low complexity is a reduced complexity version of a
previously proposed SPCG algorithm. Approximations introduced
reduce the algorithm to an LMS type algorithm, but, maintain the
superior convergence of the SPCG algorithm. Both MREDSI and
MREDSII are MR based methods with Euclidean direction of search.
The choice of Euclidean directions is shown via simulation to give
better misadjustment compared to their gradient search counterparts.
Abstract: The main aim of this work is to establish the
capabilities of new green buildings to ascertain off-grid electricity
generation based on the integration of wind turbines in the
conceptual model of a rotating tower [2] in Dubai. An in depth
performance analysis of the WinWind 3.0MW [3] wind turbine is
performed. Data based on the Dubai Meteorological Services is
collected and analyzed in conjunction with the performance analysis
of this wind turbine. The mathematical model is compared with
Computational Fluid Dynamics (CFD) results based on a conceptual
rotating tower design model. The comparison results are further
validated and verified for accuracy by conducting experiments on a
scaled prototype of the tower design. The study concluded that
integrating wind turbines inside a rotating tower can generate enough
electricity to meet the required power consumption of the building,
which equates to a wind farm containing 9 horizontal axis wind
turbines located at an approximate area of 3,237,485 m2 [14].
Abstract: Several works regarding facial recognition have dealt with methods which identify isolated characteristics of the face or with templates which encompass several regions of it. In this paper a new technique which approaches the problem holistically dispensing with the need to identify geometrical characteristics or regions of the face is introduced. The characterization of a face is achieved by randomly sampling selected attributes of the pixels of its image. From this information we construct a set of data, which correspond to the values of low frequencies, gradient, entropy and another several characteristics of pixel of the image. Generating a set of “p" variables. The multivariate data set with different polynomials minimizing the data fitness error in the minimax sense (L∞ - Norm) is approximated. With the use of a Genetic Algorithm (GA) it is able to circumvent the problem of dimensionality inherent to higher degree polynomial approximations. The GA yields the degree and values of a set of coefficients of the polynomials approximating of the image of a face. By finding a family of characteristic polynomials from several variables (pixel characteristics) for each face (say Fi ) in the data base through a resampling process the system in use, is trained. A face (say F ) is recognized by finding its characteristic polynomials and using an AdaBoost Classifier from F -s polynomials to each of the Fi -s polynomials. The winner is the polynomial family closer to F -s corresponding to target face in data base.