Abstract: Enzymatic hydrolysis of starch from natural sources
finds potential application in commercial production of alcoholic
beverage and bioethanol. In this study the effect of starch
concentration, temperature, time and enzyme concentration were
studied and optimized for hydrolysis of Potato starch powder (of
mesh 80/120) into glucose syrup by immobilized (using Sodium
arginate) α-amylase using central composite design. The
experimental result on enzymatic hydrolysis of Potato starch was
subjected to multiple linear regression analysis using MINITAB 14
software. Positive linear effect of starch concentration, enzyme
concentration and time was observed on hydrolysis of Potato starch
by α-amylase. The statistical significance of the model was validated
by F-test for analysis of variance (p ≤ 0.01). The optimum value of
starch concentration, enzyme concentration, temperature, time and
were found to be 6% (w/v), 2% (w/v), 40°C and 80min respectively.
The maximum glucose yield at optimum condition was 2.34 mg/mL.
Abstract: In this paper, a set of experimental data has been used to assess the influence of abrasive water jet (AWJ) process parameters in cutting 6063-T6 aluminum alloy. The process variables considered here include nozzle diameter, jet traverse rate, jet pressure and abrasive flow rate. The effects of these input parameters are studied on depth of cut (h); one of most important characteristics of AWJ. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the AWJ process parameters. The objective is to determine a suitable set of process parameters that can produce a desired depth of cut, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure.
Abstract: Among many different methods that are used for
optimizing different engineering problems mathematical (numerical)
optimization techniques are very important because they can easily
be used and are consistent with most of engineering problems. Many
studies and researches are done on stability analysis of three
dimensional (3D) slopes and the relating probable slip surfaces and
determination of factors of safety, but in most of them force
equilibrium equations, as in simplified 2D methods, are considered
only in two directions. In other words for decreasing mathematical
calculations and also for simplifying purposes the force equilibrium
equation in 3rd direction is omitted. This point is considered in just a
few numbers of previous studies and most of them have only given a
factor of safety and they haven-t made enough effort to find the most
probable slip surface. In this study shapes of the slip surfaces are
modeled, and safety factors are calculated considering the force
equilibrium equations in all three directions, and also the moment
equilibrium equation is satisfied in the slip direction, and using
nonlinear programming techniques the shape of the most probable
slip surface is determined. The model which is used in this study is a
3D model that is composed of three upper surfaces which can cover
all defined and probable slip surfaces. In this research the meshing
process is done in a way that all elements are prismatic with
quadrilateral cross sections, and the safety factor is defined on this
quadrilateral surface in the base of the element which is a part of the
whole slip surface. The method that is used in this study to find the
most probable slip surface is the non-linear programming method in
which the objective function that must get optimized is the factor of
safety that is a function of the soil properties and the coordinates of
the nodes on the probable slip surface. The main reason for using
non-linear programming method in this research is its quick
convergence to the desired responses. The final results show a good
compatibility with the previously used classical and 2D methods and
also show a reasonable convergence speed.
Abstract: In this paper after reviewing some previous studies, in
order to optimize the above knee prosthesis, beside the inertial
properties a new controlling parameter is informed. This controlling
parameter makes the prosthesis able to act as a multi behavior system
when the amputee is opposing to different environments. This active
prosthesis with the new controlling parameter can simplify the
control of prosthesis and reduce the rate of energy consumption in
comparison to recently presented similar prosthesis “Agonistantagonist
active knee prosthesis".
In this paper three models are generated, a passive, an active, and
an optimized active prosthesis. Second order Taylor series is the
numerical method in solution of the models equations and the
optimization procedure is genetic algorithm.
Modeling the prosthesis which comprises this new controlling
parameter (SEP) during the swing phase represents acceptable results
in comparison to natural behavior of shank. Reported results in this
paper represent 3.3 degrees as the maximum deviation of models
shank angle from the natural pattern. The natural gait pattern belongs
to walking at the speed of 81 m/min.
Abstract: Ultrasonic machining (USM) is a non-traditional
machining process being widely used for commercial machining of
brittle and fragile materials such as glass, ceramics and
semiconductor materials. However, USM could be a viable
alternative for machining a tough material such as titanium; and this
aspect needs to be explored through experimental research. This
investigation is focused on exploring the use of ultrasonic machining
for commercial machining of pure titanium (ASTM Grade-I) and
evaluation of tool wear rate (TWR) under controlled experimental
conditions. The optimal settings of parameters are determined
through experiments planned, conducted and analyzed using Taguchi
method. In all, the paper focuses on parametric optimization of
ultrasonic machining of pure titanium metal with TWR as response,
and validation of the optimized value of TWR by conducting
confirmatory experiments.
Abstract: In this paper, a comparative study of application of
supervised and unsupervised learning algorithms on illumination
invariant face recognition has been carried out. The supervised
learning has been carried out with the help of using a bi-layered
artificial neural network having one input, two hidden and one output
layer. The gradient descent with momentum and adaptive learning
rate back propagation learning algorithm has been used to implement
the supervised learning in a way that both the inputs and
corresponding outputs are provided at the time of training the
network, thus here is an inherent clustering and optimized learning of
weights which provide us with efficient results.. The unsupervised
learning has been implemented with the help of a modified
Counterpropagation network. The Counterpropagation network
involves the process of clustering followed by application of Outstar
rule to obtain the recognized face. The face recognition system has
been developed for recognizing faces which have varying
illumination intensities, where the database images vary in lighting
with respect to angle of illumination with horizontal and vertical
planes. The supervised and unsupervised learning algorithms have
been implemented and have been tested exhaustively, with and
without application of histogram equalization to get efficient results.
Abstract: In this paper, the energy performance of a selected
UHDE Ammonia plant is optimized by conducting heat integration through waste heat recovery and the synthesis of a heat exchange
network (HEN). Minimum hot and cold utility requirements were estimated through IChemE spreadsheet. Supporting simulation was
carried out using HYSYS software. The results showed that there is
no need for heating utility while the required cold utility was found to
be around 268,714 kW. Hence a threshold pinch case was faced. Then, the hot and cold streams were matched appropriately. Also,
waste heat recovered resulted with savings in HP and LP steams of
approximately 51.0% and 99.6%, respectively. An economic analysis
on proposed HEN showed very attractive overall payback period not
exceeding 3 years. In general, a net saving approaching 35% was
achieved in implementing heat optimization of current studied UHDE Ammonia process.
Abstract: Structural representation and technology mapping of
a Boolean function is an important problem in the design of nonregenerative
digital logic circuits (also called combinational logic
circuits). Library aware function manipulation offers a solution to
this problem. Compact multi-level representation of binary networks,
based on simple circuit structures, such as AND-Inverter Graphs
(AIG) [1] [5], NAND Graphs, OR-Inverter Graphs (OIG), AND-OR
Graphs (AOG), AND-OR-Inverter Graphs (AOIG), AND-XORInverter
Graphs, Reduced Boolean Circuits [8] does exist in
literature. In this work, we discuss a novel and efficient graph
realization for combinational logic circuits, represented using a
NAND-NOR-Inverter Graph (NNIG), which is composed of only
two-input NAND (NAND2), NOR (NOR2) and inverter (INV) cells.
The networks are constructed on the basis of irredundant disjunctive
and conjunctive normal forms, after factoring, comprising terms with
minimum support. Construction of a NNIG for a non-regenerative
function in normal form would be straightforward, whereas for the
complementary phase, it would be developed by considering a virtual
instance of the function. However, the choice of best NNIG for a
given function would be based upon literal count, cell count and
DAG node count of the implementation at the technology
independent stage. In case of a tie, the final decision would be made
after extracting the physical design parameters.
We have considered AIG representation for reduced disjunctive
normal form and the best of OIG/AOG/AOIG for the minimized
conjunctive normal forms. This is necessitated due to the nature of
certain functions, such as Achilles- heel functions. NNIGs are found
to exhibit 3.97% lesser node count compared to AIGs and
OIG/AOG/AOIGs; consume 23.74% and 10.79% lesser library cells
than AIGs and OIG/AOG/AOIGs for the various samples considered.
We compare the power efficiency and delay improvement achieved
by optimal NNIGs over minimal AIGs and OIG/AOG/AOIGs for
various case studies. In comparison with functionally equivalent,
irredundant and compact AIGs, NNIGs report mean savings in power
and delay of 43.71% and 25.85% respectively, after technology
mapping with a 0.35 micron TSMC CMOS process. For a
comparison with OIG/AOG/AOIGs, NNIGs demonstrate average
savings in power and delay by 47.51% and 24.83%. With respect to
device count needed for implementation with static CMOS logic
style, NNIGs utilize 37.85% and 33.95% lesser transistors than their
AIG and OIG/AOG/AOIG counterparts.
Abstract: Complaints today have the ability to retain
customer loyalty using state of the art systems and strategies
in customer relationship management to analyze and respond
to a plethora of customer perception. The Majority of
companies are not aware of the beneficiary utilization of
customer complaints for the sake of quality improvements.
Also, some companies have problems determining how
resolution of complaints can be profitable. In this study, we
will define the problems and ascertain the importance of
customer management system on the companies. Furthermore,
we will determine the impact of such a system on efficiency,
confidence, profitability and customer complaints. Eventually,
we will develop methods and address the issues. In this paper,
we used an open-ended questionnaire and distributed that to
30 randomly chosen respondents which were the passengers in
an airport. We also define three hypotheses for our study and
we will validate each of them. Then using frequency, Chi-
Square and quality control method we optimized the size of
customers- negative feedback and improved the process of
customer retention.
Abstract: In this paper, a simulated annealing algorithm has been developed to optimize machining parameters in turning operation on cylindrical workpieces. The turning operation usually includes several passes of rough machining and a final pass of finishing. Seven different constraints are considered in a non-linear model where the goal is to achieve minimum total cost. The weighted total cost consists of machining cost, tool cost and tool replacement cost. The computational results clearly show that the proposed optimization procedure has considerably improved total operation cost by optimally determining machining parameters.
Abstract: The aim of this contribution is to present a new
approach in modeling the electrical activity of the human heart. A
recurrent artificial neural network is being used in order to exhibit a
subset of the dynamics of the electrical behavior of the human heart.
The proposed model can also be used, when integrated, as a
diagnostic tool of the human heart system.
What makes this approach unique is the fact that every model is
being developed from physiological measurements of an individual.
This kind of approach is very difficult to apply successfully in many
modeling problems, because of the complexity and entropy of the
free variables describing the complex system. Differences between
the modeled variables and the variables of an individual, measured at
specific moments, can be used for diagnostic purposes. The sensor
fusion used in order to optimize the utilization of biomedical sensors
is another point that this paper focuses on. Sensor fusion has been
known for its advantages in applications such as control and
diagnostics of mechanical and chemical processes.
Abstract: An optimized design of E/O and O/E for access points
of WiMAX RoF was carried out by evaluating RCE. The use of the
DFB-LD, a low input-impedance driving, a low distortion PIN-PD,
and a high gain EPHEMT amplifier is promising the cost-effective
design. For the uplink RoF design, the use of EDFA and EP-HEMT
amplifiers is necessity.
Abstract: An Ad hoc wireless network comprises of mobile
terminals linked and communicating with each other sans the aid of
traditional infrastructure. Optimized Link State Protocol (OLSR) is a
proactive routing protocol, in which routes are discovered/updated
continuously so that they are available when needed. Hello messages
generated by a node seeks information about its neighbor and if the
latter fails to respond to a specified number of hello messages
regulated by neighborhood hold time, the node is forced to assume
that the neighbor is not in range. This paper proposes to evaluate
OLSR routing protocol in a random mobility network having various
neighborhood hold time intervals. The throughput and delivery ratio
are also evaluated to learn about its efficiency for multimedia loads.
Abstract: Next Generation Wireless Network (NGWN) is
expected to be a heterogeneous network which integrates all different
Radio Access Technologies (RATs) through a common platform. A
major challenge is how to allocate users to the most suitable RAT for
them. An optimized solution can lead to maximize the efficient use
of radio resources, achieve better performance for service providers
and provide Quality of Service (QoS) with low costs to users.
Currently, Radio Resource Management (RRM) is implemented
efficiently for the RAT that it was developed. However, it is not
suitable for a heterogeneous network. Common RRM (CRRM) was
proposed to manage radio resource utilization in the heterogeneous
network. This paper presents a user level Markov model for a three
co-located RAT networks. The load-balancing based and service
based CRRM algorithms have been studied using the presented
Markov model. A comparison for the performance of load-balancing
based and service based CRRM algorithms is studied in terms of
traffic distribution, new call blocking probability, vertical handover
(VHO) call dropping probability and throughput.
Abstract: A highly optimized implementation of binary mixture
diffusion with no initial bulk velocity on graphics processors is
presented. The lattice Boltzmann model is employed for simulating
the binary diffusion of oxygen and nitrogen into each other with
different initial concentration distributions. Simulations have been
performed using the latest proposed lattice Boltzmann model that
satisfies both the indifferentiability principle and the H-theorem for
multi-component gas mixtures. Contemporary numerical
optimization techniques such as memory alignment and increasing
the multiprocessor occupancy are exploited along with some novel
optimization strategies to enhance the computational performance on
graphics processors using the C for CUDA programming language.
Speedup of more than two orders of magnitude over single-core
processors is achieved on a variety of Graphical Processing Unit
(GPU) devices ranging from conventional graphics cards to
advanced, high-end GPUs, while the numerical results are in
excellent agreement with the available analytical and numerical data
in the literature.
Abstract: In this paper, we proposed a novel receiver algorithm
for coherent underwater acoustic communications. The proposed
receiver is composed of three parts: (1) Doppler tracking and
correction, (2) Time reversal channel estimation and combining, and
(3) Joint iterative equalization and decoding (JIED). To reduce
computational complexity and optimize the equalization algorithm,
Time reversal (TR) channel estimation and combining is adopted to
simplify multi-channel adaptive decision feedback equalizer (ADFE)
into single channel ADFE without reducing the system performance.
Simultaneously, the turbo theory is adopted to form joint iterative
ADFE and convolutional decoder (JIED). In JIED scheme, the ADFE
and decoder exchange soft information in an iterative manner, which
can enhance the equalizer performance using decoding gain. The
simulation results show that the proposed algorithm can reduce
computational complexity and improve the performance of equalizer.
Therefore, the performance of coherent underwater acoustic
communications can be improved greatly.
Abstract: In this paper an ant colony optimization algorithm is
developed to solve the permutation flow shop scheduling problem. In
the permutation flow shop scheduling problem which has been vastly
studied in the literature, there are a set of m machines and a set of n
jobs. All the jobs are processed on all the machines and the sequence
of jobs being processed is the same on all the machines. Here this
problem is optimized considering two criteria, makespan and total
flow time. Then the results are compared with the ones obtained by
previously developed algorithms. Finally it is visible that our
proposed approach performs best among all other algorithms in the
literature.
Abstract: This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.
Abstract: This paper presents a systematic approach for designing Unified Power Flow Controller (UPFC) based supplementary damping controllers for damping low frequency oscillations in a single-machine infinite-bus power system. Detailed investigations have been carried out considering the four alternatives UPFC based damping controller namely modulating index of series inverter (mB), modulating index of shunt inverter (mE), phase angle of series inverter (δB ) and phase angle of the shunt inverter (δE ). The design problem of the proposed controllers is formulated as an optimization problem and Real- Coded Genetic Algorithm (RCGA) is employed to optimize damping controller parameters. Simulation results are presented and compared with a conventional method of tuning the damping controller parameters to show the effectiveness and robustness of the proposed design approach.
Abstract: This paper presents a procedure for modeling and tuning the parameters of Thyristor Controlled Series Compensation (TCSC) controller in a multi-machine power system to improve transient stability. First a simple transfer function model of TCSC controller for stability improvement is developed and the parameters of the proposed controller are optimally tuned. Genetic algorithm (GA) is employed for the optimization of the parameter-constrained nonlinear optimization problem implemented in a simulation environment. By minimizing an objective function in which the oscillatory rotor angle deviations of the generators are involved, transient stability performance of the system is improved. The proposed TCSC controller is tested on a multi-machine system and the simulation results are presented. The nonlinear simulation results validate the effectiveness of proposed approach for transient stability improvement in a multimachine power system installed with a TCSC. The simulation results also show that the proposed TCSC controller is also effective in damping low frequency oscillations.