Abstract: Since dealing with high dimensional data is
computationally complex and sometimes even intractable, recently
several feature reductions methods have been developed to reduce
the dimensionality of the data in order to simplify the calculation
analysis in various applications such as text categorization, signal
processing, image retrieval, gene expressions and etc. Among feature
reduction techniques, feature selection is one the most popular
methods due to the preservation of the original features.
In this paper, we propose a new unsupervised feature selection
method which will remove redundant features from the original
feature space by the use of probability density functions of various
features. To show the effectiveness of the proposed method, popular
feature selection methods have been implemented and compared.
Experimental results on the several datasets derived from UCI
repository database, illustrate the effectiveness of our proposed
methods in comparison with the other compared methods in terms of
both classification accuracy and the number of selected features.
Abstract: Stock portfolio selection is a classic problem in finance,
and it involves deciding how to allocate an institution-s or an individual-s
wealth to a number of stocks, with certain investment objectives
(return and risk). In this paper, we adopt the classical Markowitz
mean-variance model and consider an additional common realistic
constraint, namely, the cardinality constraint. Thus, stock portfolio
optimization becomes a mixed-integer quadratic programming problem
and it is difficult to be solved by exact optimization algorithms.
Chemical Reaction Optimization (CRO), which mimics the molecular
interactions in a chemical reaction process, is a population-based
metaheuristic method. Two different types of CRO, named canonical
CRO and Super Molecule-based CRO (S-CRO), are proposed to solve
the stock portfolio selection problem. We test both canonical CRO
and S-CRO on a benchmark and compare their performance under
two criteria: Markowitz efficient frontier (Pareto frontier) and Sharpe
ratio. Computational experiments suggest that S-CRO is promising
in handling the stock portfolio optimization problem.
Abstract: This paper is to clarify the relationship of individual investor types, risk tolerance and herding bias. The questionnaire survey investigation is conducted to collect 389 valid and voluntary individual investors and to examine how the risk tolerance plays as a mediator between four types of personality and herding bias. Based on featuring BB&K model and reviewing the prior literature of psychology, a linear structural model are constructed and further used to evaluate the path of herding formation through the analysis of Structural Equation Modeling (SEM). The results showed that more impetuous investors would be prone to herding bias directly, but rather exhibit higher risk tolerance. However, risk tolerance would fully mediate between the level of confidence (i.e., confident or anxious) and herding bias, but not mediate between the method of action (careful or impetuous) for individual investors.
Abstract: In this paper we propose new method for
simultaneous generating multiple quantiles corresponding to given
probability levels from data streams and massive data sets. This
method provides a basis for development of single-pass low-storage
quantile estimation algorithms, which differ in complexity, storage
requirement and accuracy. We demonstrate that such algorithms may
perform well even for heavy-tailed data.
Abstract: The electrochemical coagulation of a kaolin
suspension was investigated at the currents of 0.06, 0.12, 0.22, 0.44,
0.85 A (corresponding to 0.68, 1.36, 2.50, 5.00, 9.66 mA·cm-2,
respectively) for the contact time of 5, 10, 20, 30, and 50 min. The
TSS removal efficiency at currents of 0.06 A, 0.12 A and 0.22 A
increased with the amount of iron generated by the sacrificial anode,
while the removal efficiencies did not increase proportionally with
the amount of iron generated at the currents of 0.44 and 0.85 A,
where electroflotation was clearly observed. Zeta potential
measurement illustrated the presence of the highly positive charged
particles created by sorption of highly charged polymeric metal
hydroxyl species onto the negative surface charged kaolin particles at
both low and high applied currents. The disappearance of the
individual peaks after certain contact times indicated the attraction
between these positive and negative charged particles causing
agglomeration. It was concluded that charge neutralization of the
individual species was not the only mechanism operating in the
electrocoagulation process at any current level, but electrostatic
attraction was likely to co-operate or mainly operate.
Abstract: The development of Artificial Neural Networks
(ANNs) is usually a slow process in which the human expert has to
test several architectures until he finds the one that achieves best
results to solve a certain problem. This work presents a new
technique that uses Genetic Programming (GP) for automatically
generating ANNs. To do this, the GP algorithm had to be changed in
order to work with graph structures, so ANNs can be developed. This
technique also allows the obtaining of simplified networks that solve
the problem with a small group of neurons. In order to measure the
performance of the system and to compare the results with other
ANN development methods by means of Evolutionary Computation
(EC) techniques, several tests were performed with problems based
on some of the most used test databases. The results of those
comparisons show that the system achieves good results comparable
with the already existing techniques and, in most of the cases, they
worked better than those techniques.
Abstract: A product development for green logistics model using
the fuzzy analytic network process method is presented for evaluating
the relationships among the product design, the manufacturing
activities, and the green supply chain. In the product development
stage, there can be alternative ways to design the detailed components
to satisfy the design concept and product requirement. In different
design alternative cases, the manufacturing activities can be different.
In addition, the manufacturing activities can affect the green supply
chain of the components and product. In this research, a fuzzy analytic
network process evaluation model is presented for evaluating the
criteria in product design, manufacturing activities, and green supply
chain. The comparison matrices for evaluating the criteria among the
three groups are established. The total relational values between the
three groups represent the relationships and effects. In application, the
total relational values can be used to evaluate the design alternative
cases for decision-making to select a suitable design case and the green
supply chain. In this presentation, an example product is illustrated. It
shows that the model is useful for integrated evaluation of design and
manufacturing and green supply chain for the purpose of product
development for green logistics.
Abstract: Arc welding is an important joining process widely used in many industrial applications including production of automobile, ships structures and metal tanks. In welding process, the moving electrode causes highly non-uniform temperature distribution that leads to residual stresses and different deviations, especially buckling distortions in thin plates. In order to control the deviations and increase the quality of welded plates, a fixture can be used as a practical and low cost method with high efficiency. In this study, a coupled thermo-mechanical finite element model is coded in the software ANSYS to simulate the behavior of thin plates located by a 3-2-1 positioning system during the welding process. Computational results are compared with recent similar works to validate the finite element models. The agreement between the result of proposed model and other reported data proves that finite element modeling can accurately predict the behavior of welded thin plates.
Abstract: The purpose of this study is to design a portable virtual
piano. By utilizing optical fiber gloves and the virtual piano software
designed by this study, the user can play the piano anywhere at any
time. This virtual piano consists of three major parts: finger tapping
identification, hand movement and positioning identification, and
MIDI software sound effect simulation. To play the virtual piano, the
user wears optical fiber gloves and simulates piano key tapping
motions. The finger bending information detected by the optical fiber
gloves can tell when piano key tapping motions are made. Images
captured by a video camera are analyzed, hand locations and moving
directions are positioned, and the corresponding scales are found. The
system integrates finger tapping identification with information about
hand placement in relation to corresponding piano key positions, and
generates MIDI piano sound effects based on this data. This
experiment shows that the proposed method achieves an accuracy rate
of 95% for determining when a piano key is tapped.
Abstract: Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.
Abstract: In this paper an algorithm for fast wavelength calibration of Optical Spectrum Analyzers (OSAs) using low power reference gas spectra is proposed. In existing OSAs a reference spectrum with low noise for precise detection of the reference extreme values is needed. To generate this spectrum costly hardware with high optical power is necessary. With this new wavelength calibration algorithm it is possible to use a noisy reference spectrum and therefore hardware costs can be cut. With this algorithm the reference spectrum is filtered and the key information is extracted by segmenting and finding the local minima and maxima. Afterwards slope and offset of a linear correction function for best matching the measured and theoretical spectra are found by correlating the measured with the stored minima. With this algorithm a reliable wavelength referencing of an OSA can be implemented on a microcontroller with a calculation time of less than one second.
Abstract: This paper presents an approach for the design of
fuzzy logic power system stabilizers using genetic algorithms. In the
proposed fuzzy expert system, speed deviation and its derivative
have been selected as fuzzy inputs. In this approach the parameters of
the fuzzy logic controllers have been tuned using genetic algorithm.
Incorporation of GA in the design of fuzzy logic power system
stabilizer will add an intelligent dimension to the stabilizer and
significantly reduces computational time in the design process. It is
shown in this paper that the system dynamic performance can be
improved significantly by incorporating a genetic-based searching
mechanism. To demonstrate the robustness of the genetic based
fuzzy logic power system stabilizer (GFLPSS), simulation studies on
multimachine system subjected to small perturbation and three-phase
fault have been carried out. Simulation results show the superiority
and robustness of GA based power system stabilizer as compare to
conventionally tuned controller to enhance system dynamic
performance over a wide range of operating conditions.
Abstract: This article presents the implementation of several
different e/b-Learning collaborative activities, used to improve the
students learning process in an high school Polytechnic Institution. A
new learning model arises, based on a combination between face-toface
and distance leaning. Learning is now becoming centered with
the development of collaborative activities, and its actors (teachers
and students) have to be re-socialized to a new e/b-Learning
paradigm. Measuring approaches are proposed for this model and
results are presented, showing prospective correlation between
students learning success and the use of online collaborative
activities.
Abstract: This paper presents an alternate approach that uses
artificial neural network to simulate the flood level dynamics in a
river basin. The algorithm was developed in a decision support
system environment in order to enable users to process the data. The
decision support system is found to be useful due to its interactive
nature, flexibility in approach and evolving graphical feature and can
be adopted for any similar situation to predict the flood level. The
main data processing includes the gauging station selection, input
generation, lead-time selection/generation, and length of prediction.
This program enables users to process the flood level data, to
train/test the model using various inputs and to visualize results. The
program code consists of a set of files, which can as well be modified
to match other purposes. This program may also serve as a tool for
real-time flood monitoring and process control. The running results
indicate that the decision support system applied to the flood level
seems to have reached encouraging results for the river basin under
examination. The comparison of the model predictions with the
observed data was satisfactory, where the model is able to forecast
the flood level up to 5 hours in advance with reasonable prediction
accuracy. Finally, this program may also serve as a tool for real-time
flood monitoring and process control.
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.
Abstract: Cow milk, is a product of the mammary gland and
soymilk is a beverage made from soybeans; it is the liquid that
remains after soybeans are soaked. In this research effort, we
compared nutritional parameters of this two kind milk such as total
fat, fiber, protein, minerals (Ca, Fe and P), fatty acids, carbohydrate,
lactose, water, total solids, ash, pH, acidity and calories content in
one cup (245 g). Results showed soymilk contains 4.67 grams of fat,
0.52 of fatty acids, 3.18 of fiber, 6.73 of protein, 4.43 of
carbohydrate, 0.00 of lactose, 228.51 of water, 10.40 of total solids
and 0.66 of ash, also 9.80 milligrams of Ca, 1.42 of Fe, and 120.05 of
P, 79 Kcal of calories, pH=6.74 and acidity was 0.24%. Cow milk
contains 8.15 grams of fat, 5.07 of fatty acids, 0.00 of fiber, 8.02 of
protein, 11.37 of carbohydrate, ´Çá4.27 of lactose, 214.69 of water,
12.90 of total solids, 1.75 of ash, 290.36 milligrams of Ca, 0.12 of
Fe, and 226.92 of P, 150 Kcal of calories, pH=6.90 and acidity was
0.21% . Soy milk is one of plant-based complete proteins and cow
milk is a rich source of nutrients as well. Cow milk is containing near
twice as much fat as and ten times more fatty acids do soymilk. Cow
milk contains greater amounts of mineral (except Fe) it contain more
than three hundred times the amount of Ca and nearly twice the
amount of P as does soymilk but soymilk contains more Fe (ten time
more) than does cow milk. Cow milk and soy milk contain nearly
identical amounts of protein and water and fiber is a big plus, dairy
has none. Although what we choose to drink is really a mater of
personal preference and our health objectives but looking at the
comparison, soy looks like healthier choices.
Abstract: The oleaginous yeasts Lipomyces starkey were grown
in the presence of dairy industry wastewaters (DIW). The yeasts were
able to degrade the organic components of DIW and to produce a
significant fraction of their biomass as triglycerides.
When using DIW from the Ricotta cheese production or residual
whey as growth medium, the L. starkey could be cultured without
dilution nor external organic supplement. On the contrary, the yeasts
could only partially degrade the DIW from the Mozzarella cheese
production, due to the accumulation of a metabolic product beyond
the threshold of toxicity. In this case, a dilution of the DIW was
required to obtain a more efficient degradation of the carbon
compounds and an higher yield in oleaginous biomass.
The fatty acid distribution of the microbial oils obtained showed a
prevalence of oleic acid, and is compatible with the production of a II
generation biodiesel offering a good resistance to oxidation as well as
an excellent cold-performance.
Abstract: Graph partitioning is a NP-hard problem with multiple
conflicting objectives. The graph partitioning should minimize the
inter-partition relationship while maximizing the intra-partition
relationship. Furthermore, the partition load should be evenly
distributed over the respective partitions. Therefore this is a multiobjective
optimization problem (MOO). One of the approaches to
MOO is Pareto optimization which has been used in this paper. The
proposed methods of this paper used to improve the performance are
injecting best solutions of previous runs into the first generation of
next runs and also storing the non-dominated set of previous
generations to combine with later generation's non-dominated set.
These improvements prevent the GA from getting stuck in the local
optima and increase the probability of finding more optimal
solutions. Finally, a simulation research is carried out to investigate
the effectiveness of the proposed algorithm. The simulation results
confirm the effectiveness of the proposed method.
Abstract: Experiments were carried out at the Faculty of Food
Technology of Latvia University of Agriculture (LLU). Soft cheese
Kleo produced in Latvia was packed in a biodegradable PLA without
barrierproperties and VC999 BioPack lidding film PLA, coated with
a barrier of pure silicon oxide (SiOx) and in combination with
modified atmosphere (MAP) the influence on the shelf life was
investigated and compared with some conventional (OPP, PE/PA,
PE/OPA and Multibarrier 60) polymer film impact. Modified
atmosphere consisted of carbon dioxide CO2 (E 290) 30% and
nitrogen N2 (E 941) 70%. The analyzable samples were stored at the
temperature of +4.0±0.5 °C up to 32 days- and analyzed before
packaging and in the 0, 5th, 11th, 15th, 18th, 22nd, 25th, 29th and 32nd
day of storage. The shelf life was extended along to 32 days, good
outside appearance and lactic acid aroma was observed.
Abstract: Customarily, the LMTD correction factor, FT, is used
to screen alternative designs for a heat exchanger. Designs with
unacceptably low FT values are discarded. In this paper, authors have
proposed a more fundamental criterion, based on feasibility of a
multipass exchanger as the only criteria, followed by economic
optimization. This criterion, coupled with asymptotic energy targets,
provide the complete optimization space in a heat exchanger network
(HEN), where cost-optimization of HEN can be performed with only
Heat Recovery Approach temperature (HRAT) and number-of-shells
as variables.