Abstract: An accurate and proficient artificial neural network
(ANN) based genetic algorithm (GA) is developed for predicting of
nanofluids viscosity. A genetic algorithm (GA) is used to optimize
the neural network parameters for minimizing the error between the
predictive viscosity and the experimental one. The experimental
viscosity in two nanofluids Al2O3-H2O and CuO-H2O from 278.15
to 343.15 K and volume fraction up to 15% were used from
literature. The result of this study reveals that GA-NN model is
outperform to the conventional neural nets in predicting the viscosity
of nanofluids with mean absolute relative error of 1.22% and 1.77%
for Al2O3-H2O and CuO-H2O, respectively. Furthermore, the results
of this work have also been compared with others models. The
findings of this work demonstrate that the GA-NN model is an
effective method for prediction viscosity of nanofluids and have
better accuracy and simplicity compared with the others models.
Abstract: Selection of the best possible set of suppliers has a
significant impact on the overall profitability and success of any
business. For this reason, it is usually necessary to optimize all
business processes and to make use of cost-effective alternatives for
additional savings. This paper proposes a new efficient context-aware
supplier selection model that takes into account possible changes of
the environment while significantly reducing selection costs. The
proposed model is based on data clustering techniques while
inspiring certain principles of online algorithms for an optimally
selection of suppliers. Unlike common selection models which re-run
the selection algorithm from the scratch-line for any decision-making
sub-period on the whole environment, our model considers the
changes only and superimposes it to the previously defined best set
of suppliers to obtain a new best set of suppliers. Therefore, any recomputation
of unchanged elements of the environment is avoided
and selection costs are consequently reduced significantly. A
numerical evaluation confirms applicability of this model and proves
that it is a more optimal solution compared with common static
selection models in this field.
Abstract: Differentiated impact of team sports (basketball, indoor soccer, handball) on general haemodynamics and aerobic potential of students who specialize in technical subjects is detected only on the fourth year of studies in the institute of higher education. Those who play basketball and indoor soccer have shown increase of stroke and minute volume of blood indices, pumping and contractile function of the heart, oxygenation of blood and oxygen delivery to tissues, aerobic energy supply and balance of sympathetic and parasympathetic activity of the nervous regulation mechanism of the circulatory system. Those who play handball have shown these indices statistically decreased. On the whole playing basketball and indoor soccer optimizes the strategy for adaptation of students to the studying process, but playing handball does the opposite thing. The leading factor for adaptation of students is: those who play basketball have increase of minute blood volume which stipulates velocity of the system blood circulation and well-timed oxygen delivery to tissues; those who play indoor soccer have increase of power and velocity of contractile function of the heart; those who play handball have increase of resistance of thorax to the system blood flow which minimizes contractile function of the heart, blood oxygen saturation and delivery of oxygen to tissues.
Abstract: There is a acute water problem especially in the dry
season in and around Perundurai (Erode district, Tamil Nadu, India)
where there are more number of tannery units. Hence an attempt was
made to use the waste water from tannery industry for construction
purpose. The mechanical properties such as compressive strength,
tensile strength, flexural strength etc were studied by casting various
concrete specimens in form of cube, cylinders and beams etc and
were found to be satisfactory. Hence some special properties such as
chloride attack, sulphate attack and chemical attack are considered
and comparatively studied with the conventional potable water. In
this experimental study the results of specimens prepared by using
treated and untreated tannery effluent were compared with the
concrete specimens prepared by using potable water. It was observed
that the concrete had some reduction in strength while subjected to
chloride attack, sulphate attack and chemical attack. So admixtures
were selected and optimized in suitable proportion to counter act the
adverse effects and the results were found to be satisfactory.
Abstract: Waste corn pulp was investigated as a potential feedstock during vermicomposting using Eisenia fetida. Corn pulp is the major staple food in Southern Africa and constitutes about 25% of the total organic waste. Wastecooked corn pulp was blended with cow dung in the ratio 6:1 respectively to optimize the vermicomposting process. The feedstock was allowed to vermicompost for 30 days. The vermicomposting took place in a 3- tray plastic worm bin. Moisture content, temperature, pH, and electrical conductivity were monitoreddaily. The NPK content was determined at day 30. During vermicomposting, moisture content increased from 27.68% to 52.41%, temperature ranged between 19- 25◦C, pH increased from 5.5 to 7.7, and electrical conductivity decreased from 80000μS/cm to 60000μS/cm. The ash content increased from 11.40% to 28.15%; additionally the volatile matter increased from 1.45% to 10.02%. An odorless, dark brown vermicompost was obtained. The vermicompost NPK content was 4.19%, 1.15%, and 6.18% respectively.
Abstract: Chemically defined Schlegel-s medium was modified
to improve production of cell growth and other metabolites that are
produced by fluorescent pseudomonad R62 strain. The modified
medium does not require pH control as pH changes are kept within ±
0.2 units of the initial pH 7.1 during fermentation. The siderophore
production was optimized for the fluorescent pseudomonad strain in
the modified medium containing 1% glycerol as a major carbon
source supplemented with 0.05% succinic acid and 0.5% Ltryptophan.
Indole-3 acetic acid (IAA) production was higher when
L-tryptophan was used at 0.5%. The 2,4- diacetylphloroglucinol
(DAPG) was higher with amended three trace elements in medium.
The optimized medium produced 2.28 g/l of dry cell mass and 900
mg/l of siderophore at the end of 36 h cultivation, while the
production levels of IAA and DAPG were 65 mg/l and 81 mg/l
respectively at the end of 48 h cultivation.
Abstract: Evolutionary Algorithms are population-based,
stochastic search techniques, widely used as efficient global
optimizers. However, many real life optimization problems often
require finding optimal solution to complex high dimensional,
multimodal problems involving computationally very expensive
fitness function evaluations. Use of evolutionary algorithms in such
problem domains is thus practically prohibitive. An attractive
alternative is to build meta models or use an approximation of the
actual fitness functions to be evaluated. These meta models are order
of magnitude cheaper to evaluate compared to the actual function
evaluation. Many regression and interpolation tools are available to
build such meta models. This paper briefly discusses the
architectures and use of such meta-modeling tools in an evolutionary
optimization context. We further present two evolutionary algorithm
frameworks which involve use of meta models for fitness function
evaluation. The first framework, namely the Dynamic Approximate
Fitness based Hybrid EA (DAFHEA) model [14] reduces
computation time by controlled use of meta-models (in this case
approximate model generated by Support Vector Machine
regression) to partially replace the actual function evaluation by
approximate function evaluation. However, the underlying
assumption in DAFHEA is that the training samples for the metamodel
are generated from a single uniform model. This does not take
into account uncertain scenarios involving noisy fitness functions.
The second model, DAFHEA-II, an enhanced version of the original
DAFHEA framework, incorporates a multiple-model based learning
approach for the support vector machine approximator to handle
noisy functions [15]. Empirical results obtained by evaluating the
frameworks using several benchmark functions demonstrate their
efficiency
Abstract: Fuzzy logic control (FLC) systems have been tested in
many technical and industrial applications as a useful modeling tool
that can handle the uncertainties and nonlinearities of modern control
systems. The main drawback of the FLC methodologies in the
industrial environment is challenging for selecting the number of
optimum tuning parameters.
In this paper, a method has been proposed for finding the optimum
membership functions of a fuzzy system using particle swarm
optimization (PSO) algorithm. A synthetic algorithm combined from
fuzzy logic control and PSO algorithm is used to design a controller
for a continuous stirred tank reactor (CSTR) with the aim of
achieving the accurate and acceptable desired results. To exhibit the
effectiveness of proposed algorithm, it is used to optimize the
Gaussian membership functions of the fuzzy model of a nonlinear
CSTR system as a case study. It is clearly proved that the optimized
membership functions (MFs) provided better performance than a
fuzzy model for the same system, when the MFs were heuristically
defined.
Abstract: KREISIG is a computer simulation program, firstly developed by Munawar (1994) in Germany to optimize signalized roundabout. The traffic movement is based on the car following theory. Turbine method has been implemented for signal setting. The program has then been further developed in Indonesia to meet the traffic characteristics in Indonesia by adjusting the sensitivity of the drivers. Trial and error method has been implemented to adjust the saturation flow. The saturation flow output has also been compared to the calculation method according to 1997 Indonesian Highway Capacity Manual. It has then been implemented to optimize signalized roundabout at Kleringan roundabout in Malioboro area, Yogyakarta, Indonesia. It is found that this method can optimize the signal setting of this roundabout. Therefore, it is recommended to use this program to optimize signalized roundabout.
Abstract: Understanding the consumption and production of
various metabolites of fibroblast conditioned media is needed for its
proper and optimized use in expansion of pluripotent stem cells. For
this purpose, we have used the HPLC method to analyse the
consumption of glucose and the production of lactate over time by
mouse embryonic fibroblasts. The experimental data have also been
compared with mathematical model fits. 0.025 moles of lactate was
produced after 72 hrs while the glucose concentration decreased from
0.017 moles to 0.011 moles. The mathematical model was able to
predict the trends of glucose consumption and lactate production.
Abstract: This paper presents a new approach for image
segmentation by applying Pillar-Kmeans algorithm. This
segmentation process includes a new mechanism for clustering the
elements of high-resolution images in order to improve precision and
reduce computation time. The system applies K-means clustering to
the image segmentation after optimized by Pillar Algorithm. The
Pillar algorithm considers the pillars- placement which should be
located as far as possible from each other to withstand against the
pressure distribution of a roof, as identical to the number of centroids
amongst the data distribution. This algorithm is able to optimize the
K-means clustering for image segmentation in aspects of precision
and computation time. It designates the initial centroids- positions
by calculating the accumulated distance metric between each data
point and all previous centroids, and then selects data points which
have the maximum distance as new initial centroids. This algorithm
distributes all initial centroids according to the maximum
accumulated distance metric. This paper evaluates the proposed
approach for image segmentation by comparing with K-means and
Gaussian Mixture Model algorithm and involving RGB, HSV, HSL
and CIELAB color spaces. The experimental results clarify the
effectiveness of our approach to improve the segmentation quality in
aspects of precision and computational time.
Abstract: Flour from Mucuna beans (Mucuna pruriens) were
used in producing texturized meat analogue using a single screw
extruder to monitor modifications on the proximate composition and
the functional properties at high moisture level. Response surface
methodology based on Box Behnken design at three levels of barrel
temperature (110, 120, 130°C), screw speed (100,120,140rpm) and
feed moisture (44, 47, 50%) were used in 17 runs. Regression models
describing the effect of variables on the product responses were
obtained. Descriptive profile analyses and consumer acceptability
test were carried out on optimized flavoured extruded meat analogue.
Responses were mostly affected by barrel temperature and moisture
level and to a lesser extent by screw speed. Optimization results
based on desirability concept indicated that a barrel temperature of
120.15°C, feed moisture of 47% and screw speed of 119.19 rpm
would produce meat analogue of preferable proximate composition,
functional and sensory properties which reveals consumers` likeness
for the product.
Abstract: We report on the development of a model to
understand why the range of experience with respect to HIV
infection is so diverse, especially with respect to the latency period.
To investigate this, an agent-based approach is used to extract highlevel
behaviour which cannot be described analytically from the set
of interaction rules at the cellular level. A network of independent
matrices mimics the chain of lymph nodes. Dealing with massively
multi-agent systems requires major computational effort. However,
parallelisation methods are a natural consequence and advantage of
the multi-agent approach and, using the MPI library, are here
implemented, tested and optimized. Our current focus is on the
various implementations of the data transfer across the network.
Three communications strategies are proposed and tested, showing
that the most efficient approach is communication based on the
natural lymph-network connectivity.
Abstract: This paper proposes the numerical simulation of the
investment casting of gold jewelry. It aims to study the behavior of
fluid flow during mould filling and solidification and to optimize the
process parameters, which lead to predict and control casting defects
such as gas porosity and shrinkage porosity. A finite difference
method, computer simulation software FLOW-3D was used to
simulate the jewelry casting process. The simplified model was
designed for both numerical simulation and real casting production.
A set of sensor acquisitions were allocated on the different positions
of the wax tree of the model to detect filling times, while a set of
thermocouples were allocated to detect the temperature during
casting and cooling. Those detected data were applied to validate the
results of the numerical simulation to the results of the real casting.
The resulting comparisons signify that the numerical simulation can
be used as an effective tool in investment-casting-process
optimization and casting-defect prediction.
Abstract: Over 90% of the world trade is carried by the
international shipping industry. As most of the countries are
developing, seaborne trade continues to expand to bring benefits for
consumers across the world. Studies show that world trade will
increase 70-80% through shipping in the next 15-20 years. Present
global fleet of 70000 commercial ships consumes approximately 200
million tonnes of diesel fuel a year and it is expected that it will be
around 350 million tonnes a year by 2020. It will increase the
demand for fuel and also increase the concentration of CO2 in the
atmosphere. So, it-s essential to control this massive fuel
consumption and CO2 emission. The idea is to utilize a diesel-wind
hybrid system for ship propulsion. Use of wind energy by installing
modern wing-sails in ships can drastically reduce the consumption of
diesel fuel. A huge amount of wind energy is available in oceans.
Whenever wind is available the wing-sails would be deployed and
the diesel engine would be throttled down and still the same forward
speed would be maintained. Wind direction in a particular shipping
route is not same throughout; it changes depending upon the global
wind pattern which depends on the latitude. So, the wing-sail
orientation should be such that it optimizes the use of wind energy.
We have made a computer programme in which by feeding the data
regarding wind velocity, wind direction, ship-motion direction; we
can find out the best wing-sail position and fuel saving for
commercial ships. We have calculated net fuel saving in certain
international shipping routes, for instance, from Mumbai in India to
Durban in South Africa. Our estimates show that about 8.3% diesel
fuel can be saved by utilizing the wind. We are also developing an
experimental model of the ship employing airfoils (small scale wingsail)
and going to test it in National Wind Tunnel Facility in IIT
Kanpur in order to develop a control mechanism for a system of
airfoils.
Abstract: In this paper, a nonlinear model predictive swing-up
and stabilizing sliding controller is proposed for an inverted
pendulum-cart system. In the swing up phase, the nonlinear model
predictive control is formulated as a nonlinear programming problem
with energy based objective function. By solving this problem at
each sampling instant, a sequence of control inputs that optimize the
nonlinear objective function subject to various constraints over a
finite horizon are obtained. Then, this control drives the pendulum to
a predefined neighborhood of the upper equilibrium point, at where
sliding mode based model predictive control is used to stabilize the
systems with the specified constraints. It is shown by the simulations
that, due to the way of formulating the problem, short horizon
lengths are sufficient for attaining the swing up goal.
Abstract: By employing BS (Base Station) cooperation we can
increase substantially the spectral efficiency and capacity of cellular
systems. The signals received at each BS are sent to a central unit that
performs the separation of the different MT (Mobile Terminal) using
the same physical channel. However, we need accurate sampling and
quantization of those signals so as to reduce the backhaul
communication requirements.
In this paper we consider the optimization of the quantizers for BS
cooperation systems. Four different quantizer types are analyzed and
optimized to allow better SQNR (Signal-to-Quantization Noise
Ratio) and BER (Bit Error Rate) performance.
Abstract: In this paper, we propose a single sample path based
algorithm with state aggregation to optimize the average rewards of
singularly perturbed Markov reward processes (SPMRPs) with a
large scale state spaces. It is assumed that such a reward process
depend on a set of parameters. Differing from the other kinds of
Markov chain, SPMRPs have their own hierarchical structure. Based
on this special structure, our algorithm can alleviate the load in the
optimization for performance. Moreover, our method can be applied
on line because of its evolution with the sample path simulated.
Compared with the original algorithm applied on these problems of
general MRPs, a new gradient formula for average reward
performance metric in SPMRPs is brought in, which will be proved
in Appendix, and then based on these gradients, the schedule of the
iteration algorithm is presented, which is based on a single sample
path, and eventually a special case in which parameters only
dominate the disturbance matrices will be analyzed, and a precise
comparison with be displayed between our algorithm with the old
ones which is aim to solve these problems in general Markov reward
processes. When applied in SPMRPs, our method will approach a fast
pace in these cases. Furthermore, to illustrate the practical value of
SPMRPs, a simple example in multiple programming in computer
systems will be listed and simulated. Corresponding to some practical
model, physical meanings of SPMRPs in networks of queues will be
clarified.
Abstract: Artificial Neural Network (ANN) has been
extensively used for classification of heart sounds for its
discriminative training ability and easy implementation. However, it
suffers from overparameterization if the number of nodes is not
chosen properly. In such cases, when the dataset has redundancy
within it, ANN is trained along with this redundant information that
results in poor validation. Also a larger network means more
computational expense resulting more hardware and time related
cost. Therefore, an optimum design of neural network is needed
towards real-time detection of pathological patterns, if any from heart
sound signal. The aims of this work are to (i) select a set of input
features that are effective for identification of heart sound signals and
(ii) make certain optimum selection of nodes in the hidden layer for a
more effective ANN structure. Here, we present an optimization
technique that involves Singular Value Decomposition (SVD) and
QR factorization with column pivoting (QRcp) methodology to
optimize empirically chosen over-parameterized ANN structure.
Input nodes present in ANN structure is optimized by SVD followed
by QRcp while only SVD is required to prune undesirable hidden
nodes. The result is presented for classifying 12 common
pathological cases and normal heart sound.
Abstract: In gas lifted oil fields, the lift gas should be distributed optimally among the wells which share gas from a common source to maximize total oil production. One of the objectives of the paper is to show that a linear MPC consisting of a control objective and an economic objective can be used both as an optimizer and a controller for gas lifted systems. The MPC is based on linearized model of the oil field developed from first principles modeling. Simulation results show that the total oil production is increased by 3.4%. Difficulties in accurately measuring the bottom hole pressure using sensors in harsh operating conditions can be resolved by using an Unscented Kalman Filter (UKF) for estimation. In oil fields where input disturbance (total supply of gas) is not measured, UKF can also be used for disturbance estimation. Increased total oil production due to optimization leads to increased profit.