Abstract: Classifier fusion may generate more accurate
classification than each of the basic classifiers. Fusion is often based
on fixed combination rules like the product, average etc. This paper
presents decision templates as classifier fusion method for the
recognition of the handwritten English and Farsi numerals (1-9).
The process involves extracting a feature vector on well-known
image databases. The extracted feature vector is fed to multiple
classifier fusion. A set of experiments were conducted to compare
decision templates (DTs) with some combination rules. Results from
decision templates conclude 97.99% and 97.28% for Farsi and
English handwritten digits.
Abstract: Dextran is a D-glucose polymer which is produced by
Leuconostoc mesenteroides grown in a sucrose-rich media. The
organism was obtained from the Persian Type Culture Collection
(PTCC) and was transferred in MRS broth medium at 30°C and pH
6.8 for 24 h. After preparation of inoculums, organisms were
inoculated into five liquid fermentation media containing either
molasses or cheese whey or different combinations of cheese whey
and molasses. After certain fermentation period, the produced
dextran was separated and dried. Dextran yield was calculated and
significant differences in different media were observed.
Furthermore, FT-IR analysis was performed and the results showed
that there were no significant differences in the produced dextran
structures.
Abstract: Although there are many theories and discussion of leadership, the necessity of having a new leadership paradigm was emphasized. The existing leadership characteristic of instruction and control revealed its limitations. Market competition becomes fierce and economic recession never ends worldwide. Of the leadership theories, servant leadership was introduced recently and is in line with the environmental changes of the organization. Servant leadership is a combination of two words, 'servant' and 'leader' and can be defined as the role of the leader who focuses on doing voluntary work for others with altruistic ethics, makes members, customers, and local communities a priority, and makes a commitment to satisfying their needs. This leadership received attention as one field of leadership in the late 1990s and secured its legitimacy. This study discusses the existing research trends of leadership, the concept, behavior characteristics, and lower dimensions of servant leadership, compares servant leadership with the existing leadership researches and diagnoses if servant leadership is a useful concept for further leadership researches. Finally, this study criticizes the limitations in the existing researches on servant leadership.
Abstract: This paper discusses the performance modeling and availability analysis of Yarn Dyeing System of a Textile Industry. The Textile Industry is a complex and repairable engineering system. Yarn Dyeing System of Textile Industry consists of five subsystems arranged in series configuration. For performance modeling and analysis of availability, a performance evaluating model has been developed with the help of mathematical formulation based on Markov-Birth-Death Process. The differential equations have been developed on the basis of Probabilistic Approach using a Transition Diagram. These equations have further been solved using normalizing condition in order to develop the steady state availability, a performance measure of the system concerned. The system performance has been further analyzed with the help of decision matrices. These matrices provide various availability levels for different combinations of failure and repair rates for various subsystems. The findings of this paper are therefore, considered to be useful for the analysis of availability and determination of the best possible maintenance strategies which can be implemented in future to enhance the system performance.
Abstract: Wireless sensor network is formed with the combination of sensor nodes and sink nodes. Recently Wireless sensor network has attracted attention of the research community. The main application of wireless sensor network is security from different attacks both for mass public and military. However securing these networks, by itself is a critical issue due to many constraints like limited energy, computational power and lower memory. Researchers working in this area have proposed a number of security techniques for this purpose. Still, more work needs to be done.In this paper we provide a detailed discussion on security in wireless sensor networks. This paper will help to identify different obstacles and requirements for security of wireless sensor networks as well as highlight weaknesses of existing techniques.
Abstract: In this research we show that the dynamics of an action potential in a cell can be modeled with a linear combination of the dynamics of the gating state variables. It is shown that the modeling error is negligible. Our findings can be used for simplifying cell models and reduction of computational burden i.e. it is useful for simulating action potential propagation in large scale computations like tissue modeling. We have verified our finding with the use of several cell models.
Abstract: The sensitivity of orifice plate metering to disturbed
flow (either asymmetric or swirling) is a subject of great concern to
flow meter users and manufacturers. The distortions caused by pipe
fittings and pipe installations upstream of the orifice plate are major
sources of this type of non-standard flows. These distortions can alter
the accuracy of metering to an unacceptable degree. In this work, a
multi-scale object known as metal foam has been used to generate a
predetermined turbulent flow upstream of the orifice plate. The
experimental results showed that the combination of an orifice plate
and metal foam flow conditioner is broadly insensitive to upstream
disturbances. This metal foam demonstrated a good performance in
terms of removing swirl and producing a repeatable flow profile
within a short distance downstream of the device. The results of using
a combination of a metal foam flow conditioner and orifice plate for
non-standard flow conditions including swirling flow and asymmetric
flow show this package can preserve the accuracy of metering up to
the level required in the standards.
Abstract: In this paper, an improved technique for contingency
ranking using artificial neural network (ANN) is presented. The
proposed approach is based on multi-layer perceptrons trained by
backpropagation to contingency analysis. Severity indices in dynamic
stability assessment are presented. These indices are based on the
concept of coherency and three dot products of the system variables.
It is well known that some indices work better than others for a
particular power system. This paper along with test results using
several different systems, demonstrates that combination of indices
with ANN provides better ranking than a single index. The presented
results are obtained through the use of power system simulation
(PSS/E) and MATLAB 6.5 software.
Abstract: There have been widespread applications of fluidized beds in industries which are related to the combination of gas-solid particles during the last decade. For instance, in order to crack the catalyses in petrochemical industries or as a drier in food industries. High capacity of fluidized bed in heat and mass transfer has made this device very popular. In order to achieve a higher efficiency of fluidized beds, a particular attention has been paid to beds with pulsating air flow. In this paper, a fluidized bed device with pulsating flow has been designed and constructed. Size of particles have been used during the test are in the range of 40 to 100μm. The purpose of this experimental test is to investigate the air flow regime, observe the particles- movement and measure the pressure loss along the bed. The effects of pulsation can be evaluated by comparing the results for both continuous and pulsating flow. Results of both situations are compared for various gas speeds. Moreover the above experiment is numerically simulated by using Fluent software and its numerical results are compared with the experimental results.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: The present work encounters the solution of the defect identification problem with the use of an evolutionary algorithm combined with a simplex method. In more details, a Matlab implementation of Genetic Algorithms is combined with a Simplex method in order to lead to the successful identification of the defect. The influence of the location and the orientation of the depressed ellipsoidal flaw was investigated as well as the use of different amount of static data in the cost function. The results were evaluated according to the ability of the simplex method to locate the global optimum in each test case. In this way, a clear impression regarding the performance of the novel combination of the optimization algorithms, and the influence of the geometrical parameters of the flaw in defect identification problems was obtained.
Abstract: Carbon nanotubes (CNTs) with their high mechanical,
electrical, thermal and chemical properties are regarded as promising
materials for many different potential applications. Having unique
properties they can be used in a wide range of fields such as
electronic devices, electrodes, drug delivery systems, hydrogen
storage, textile etc. Catalytic chemical vapor deposition (CCVD) is a
common method for CNT production especially for mass production.
Catalysts impregnated on a suitable substrate are important for
production with chemical vapor deposition (CVD) method. Iron
catalyst and MgO substrate is one of most common catalyst-substrate
combination used for CNT. In this study, CNTs were produced by
CCVD of acetylene (C2H2) on magnesium oxide (MgO) powder
substrate impregnated by iron nitrate (Fe(NO3)3•9H2O) solution. The
CNT synthesis conditions were as follows: at synthesis temperatures
of 500 and 800°C multiwall and single wall CNTs were produced
respectively. Iron (Fe) catalysts were prepared by with Fe:MgO ratio
of 1:100, 5:100 and 10:100. The duration of syntheses were 30 and
60 minutes for all temperatures and catalyst percentages. The
synthesized materials were characterized by thermal gravimetric
analysis (TGA), transmission electron microscopy (TEM) and Raman
spectroscopy.
Abstract: The algorithm represents the DCT coefficients to concentrate signal energy and proposes combination and dictator to eliminate the correlation in the same level subband for encoding the DCT-based images. This work adopts DCT and modifies the SPIHT algorithm to encode DCT coefficients. The proposed algorithm also provides the enhancement function in low bit rate in order to improve the perceptual quality. Experimental results indicate that the proposed technique improves the quality of the reconstructed image in terms of both PSNR and the perceptual results close to JPEG2000 at the same bit rate.
Abstract: In this work we adopt a combination of Laplace
transform and the decomposition method to find numerical solutions
of a system of multi-pantograph equations. The procedure leads to a
rapid convergence of the series to the exact solution after computing a
few terms. The effectiveness of the method is demonstrated in some
examples by obtaining the exact solution and in others by computing
the absolute error which decreases as the number of terms of the series
increases.
Abstract: Decision fusion is one of hot research topics in
classification area, which aims to achieve the best possible
performance for the task at hand. In this paper, we
investigate the usefulness of this concept to improve change
detection accuracy in remote sensing. Thereby, outputs of
two fuzzy change detectors based respectively on
simultaneous and comparative analysis of multitemporal
data are fused by using fuzzy integral operators. This
method fuses the objective evidences produced by the
change detectors with respect to fuzzy measures that express
the difference of performance between them. The proposed
fusion framework is evaluated in comparison with some
ordinary fuzzy aggregation operators. Experiments carried
out on two SPOT images showed that the fuzzy integral was
the best performing. It improves the change detection
accuracy while attempting to equalize the accuracy rate in
both change and no change classes.
Abstract: In this paper I have developed a system for evaluating
the degree of fear emotion that the intelligent agent-based system
may feel when it encounters to a persecuting event. In this paper I
want to describe behaviors of emotional agents using human
behavior in terms of the way their emotional states evolve over time.
I have implemented a fuzzy inference system using Java
environment. As the inputs of this system, I have considered three
parameters related on human fear emotion. The system outputs can
be used in agent decision making process or choosing a person for
team working systems by combination the intensity of fear to other
emotion intensities.
Abstract: In the paper, the relative performances on spectral
classification of short exon and intron sequences of the human and
eleven model organisms is studied. In the simulations, all
combinations of sixteen one-sequence numerical representations, four
threshold values, and four window lengths are considered. Sequences
of 150-base length are chosen and for each organism, a total of
16,000 sequences are used for training and testing. Results indicate
that an appropriate combination of one-sequence numerical
representation, threshold value, and window length is essential for
arriving at top spectral classification results. For fixed-length
sequences, the precisions on exon and intron classification obtained
for different organisms are not the same because of their genomic
differences. In general, precision increases as sequence length
increases.
Abstract: Modern managements of water distribution system
(WDS) need water quality models that are able to accurately predict
the dynamics of water quality variations within the distribution system
environment. Before water quality models can be applied to solve
system problems, they should be calibrated. Although former
researchers use GA solver to calibrate relative parameters, it is
difficult to apply on the large-scale or medium-scale real system for
long computational time. In this paper a new method is designed
which combines both macro and detailed model to optimize the water
quality parameters. This new combinational algorithm uses radial
basis function (RBF) metamodeling as a surrogate to be optimized for
the purpose of decreasing the times of time-consuming water quality
simulation and can realize rapidly the calibration of pipe wall reaction
coefficients of chlorine model of large-scaled WDS. After two cases
study this method is testified to be more efficient and promising, and
deserve to generalize in the future.
Abstract: The separation of dissolved gas including dissolved oxygen can be used in breathing for a human under water. When one is suddenly wrecked or meets a tsunami, one is instantly drowned and cannot breathe under water. To avoid this crisis, when we meet waves, the dissolved gas separated from water by wave is used, while air can be used to breathe when we are about to escape from water. In this thesis, we investigated the separation characteristics of dissolved gas using the pipe type of hollow fiber membrane with polypropylene and the nude type of one with polysulfone. The hollow fiber membranes with good characteristics under water are used to separate the dissolved gas. The hollow fiber membranes with good characteristics in an air are used to transfer air. The combination of membranes with good separation characteristics under water and good transferring one in an air is used to breathe instantly under water to be alive at crisis. These results showed that polypropylene represented better performance than polysulfone under both of air and water conditions.
Abstract: Recent years have seen a growing trend towards the
integration of multiple information sources to support large-scale
prediction of protein-protein interaction (PPI) networks in model
organisms. Despite advances in computational approaches, the
combination of multiple “omic" datasets representing the same type
of data, e.g. different gene expression datasets, has not been
rigorously studied. Furthermore, there is a need to further investigate
the inference capability of powerful approaches, such as fullyconnected
Bayesian networks, in the context of the prediction of PPI
networks. This paper addresses these limitations by proposing a
Bayesian approach to integrate multiple datasets, some of which
encode the same type of “omic" data to support the identification of
PPI networks. The case study reported involved the combination of
three gene expression datasets relevant to human heart failure (HF).
In comparison with two traditional methods, Naive Bayesian and
maximum likelihood ratio approaches, the proposed technique can
accurately identify known PPI and can be applied to infer potentially
novel interactions.