Abstract: In this paper, a heuristic method for simultaneous
rescue robot path-planning and mission scheduling is introduced
based on project management techniques, multi criteria decision
making and artificial potential fields path-planning. Groups of
injured people are trapped in a disastrous situation. These people are
categorized into several groups based on the severity of their
situation. A rescue robot, whose ultimate objective is reaching
injured groups and providing preliminary aid for them through a path
with minimum risk, has to perform certain tasks on its way towards
targets before the arrival of rescue team. A decision value is assigned
to each target based on the whole degree of satisfaction of the criteria
and duties of the robot toward the target and the importance of
rescuing each target based on their category and the number of
injured people. The resulted decision value defines the strength of the
attractive potential field of each target. Dangerous environmental
parameters are defined as obstacles whose risk determines the
strength of the repulsive potential field of each obstacle. Moreover,
negative and positive energies are assigned to the targets and
obstacles, which are variable with respects to the factors involved.
The simulation results show that the generated path for two cases
studies with certain differences in environmental conditions and
other risk factors differ considerably.
Abstract: Environment today is featured with aging population,
increasing prevalence of chronic disease and complex of medical
treatment. Safe use of pharmaceutics relied very much on the efforts
made by both the health- related organizations and as well as the
government agencies. As far as the specialization concern in providing
health services to the patients, the government actively issued and
implemented the divisions of medical treatment and pharmaceutical to
improve the quality of care and to reduce medication errors and ensure
public health. Pharmaceutical sub-sector policy has been implemented
for 13 years. This study attempts to explore the factors that affect the
patients- behavior intention of refilling a prescription from a NHIB
pharmacy. Samples were those patients refilling their prescriptions
with the case NHIB pharmacies. A self-administered questionnaire
was used to collect respondents- information while the patients or
family members visit the pharmacy for the refilling. 1,200
questionnaires were dispatched in 37 pharmacies that randomly
selected from Pingtung City, Dongkang, Chaozhou, Hengchun areas.
732 responses were gained with 604 valid samples for further analyses.
Results of data analyses indicated that respondents- attitude,
subjective norm, perceived behavior control and behavior intentions
toward refilling behavior varied from some demographic variables to
another. This research also suggested adding actual behavior, either by
a self-report or observed, into the research.
Abstract: Model Predictive Control (MPC) is an established control
technique in a wide range of process industries. The reason for
this success is its ability to handle multivariable systems and systems
having input, output or state constraints. Neverthless comparing to
PID controller, the implementation of the MPC in miniaturized
devices like Field Programmable Gate Arrays (FPGA) and microcontrollers
has historically been very small scale due to its complexity in
implementation and its computation time requirement. At the same
time, such embedded technologies have become an enabler for future
manufacturing enterprisers as well as a transformer of organizations
and markets. In this work, we take advantage of these recent advances
in this area in the deployment of one of the most studied and applied
control technique in the industrial engineering. In this paper, we
propose an efficient firmware for the implementation of constrained
MPC in the performed STM32 microcontroller using interior point
method. Indeed, performances study shows good execution speed
and low computational burden. These results encourage to develop
predictive control algorithms to be programmed in industrial standard
processes. The PID anti windup controller was also implemented in
the STM32 in order to make a performance comparison with the
MPC. The main features of the proposed constrained MPC framework
are illustrated through two examples.
Abstract: A series of microarray experiments produces observations
of differential expression for thousands of genes across multiple
conditions.
Principal component analysis(PCA) has been widely used in
multivariate data analysis to reduce the dimensionality of the data in
order to simplify subsequent analysis and allow for summarization of
the data in a parsimonious manner. PCA, which can be implemented
via a singular value decomposition(SVD), is useful for analysis of
microarray data.
For application of PCA using SVD we use the DNA microarray
data for the small round blue cell tumors(SRBCT) of childhood
by Khan et al.(2001). To decide the number of components which
account for sufficient amount of information we draw scree plot.
Biplot, a graphic display associated with PCA, reveals important
features that exhibit relationship between variables and also the
relationship of variables with observations.
Abstract: A review of the literature found that Domestic
violence and child maltreatment co-occur in many families, the
purpose of this study attempts to emphasize the factors relating to
intra-family relationships (order point of view) on violence against
the children, For this purpose a survey technique on the sample size
amounted 200 students of governmental guidance schools of city of
Gilanegharb in country of Iran were considered. For measurement of
violence against the children (VAC) the CTS scaled has been used
.The results showed that children have experienced the violence more
than once during the last year. degree of order in family is high.
Explanation result indicated that the order variables in family
including collective thinking, empathy, communal co-circumstance
have significant effects on VAC.
Abstract: This research aims to create a model for analysis of student motivation behavior on e-Learning based on association rule mining techniques in case of the Information Technology for Communication and Learning Course at Suan Sunandha Rajabhat University. The model was created under association rules, one of the data mining techniques with minimum confidence. The results showed that the student motivation behavior model by using association rule technique can indicate the important variables that influence the student motivation behavior on e-Learning.
Abstract: One part of the total employee’s reward is apart from basic wages or salary, employee’s benefits and intangible remuneration also so called contingent (variable) pay. Contingent pay is connected to performance, contribution, cap competency or skills of individual employees, and to team’s or company-wide performance or to combination of few of the mentioned possibilities. Sometimes among the contingent pay is also incorporated the remuneration based on length of employment, when the financial reward is not connected to performance or skills, but to length of continuous employment either on one working position or in one level of remuneration scale. Main aim of this article is to define, based on available information, contingent pay, describe individual forms, its advantages and disadvantages and possibilities to utilization in practice; but also bring information not only about its extent and level of utilization of contingent pay by companies in one of the Czech Republic’s regions, but also mention their practical experience with this type of remuneration.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: The optimization problem using time scales is studied.
Time scale is a model of time. The language of time scales seems to
be an ideal tool to unify the continuous-time and the discrete-time
theories. In this work we present necessary conditions for a solution
of an optimization problem on time scales. To obtain that result we
use properties and results of the partial diamond-alpha derivatives for
continuous-multivariable functions. These results are also presented
here.
Abstract: The deviation between the target state variable and the
practical state variable should be used to form the state tending factor
of complex systems, which can reflect the process for the complex
system to tend rationalization. Relating to the system of basic
equations of complete factor synergetics consisting of twenty
nonlinear stochastic differential equations, the two new models are
considered to set, which should be called respectively the
rationalizing tendency model and the non- rationalizing tendency
model. Therefore we can extend the theory of programming with the
objective function & constraint condition suitable only for the realm
of man-s activities into the new analysis with the tendency function &
constraint condition suitable for all the field of complex system.
Abstract: Green house effect has becomes a serious concern in
many countries due to the increase consumption of the fossil fuel.
There have been many studies to find an alternative power source.
Wind energy found to be one of the most useful solutions to help in
overcoming the air pollution and global. There is no agreed solution
to conversion of wind energy to electrical energy. In this paper, the
advantages of using a Switched Reluctance Generator (SRG) for
wind energy applications. The theoretical study of the self excitation
of a SRG and the determination of the variable parameters in a SRG
design are discussed. The design parameters for the maximum power
output of the SRG are computed using Matlab simulation. The
designs of the circuit to control the variable parameters in a SRG to
provide the maximum power output are also discussed.
Abstract: Socioeconomic stability and development of a
country, can be describe by housing affordability. It is aimed to
ensure the housing provided as one of the key factors that is
affordable by every income earner group whether low-income,
middle income and high income group. This research carried out is to
find out affordability of home ownership level for first medium cost
landed-house by the middle-income group in Selangor, Malaysia. It is
also hope that it could be seen as able to contribute to the knowledge
and understanding on housing affordability level for the middleincome
group and variables that influenced the medium income
group-s ability to own first medium-cost houses.
Abstract: The paper is dealing by testing of ceramic cutting
tools with an interrupted machining. Tests will be provided on fixture
– interrupted cut simulator. This simulator has 4 mouldings on
circumference and cutting edge is put a shocks during 1 revolution.
Criteria of tool wear are destruction of cutting tool or 6000 shocks.
Like testing cutting tool material will be products of Sandvik
Coromant 6190, 620, 650 and 670. Machined materials was be steels
15 128 (13MoCrV6). Cutting speed (408 m.min-1 and 580 m.min-1)
and cutting feed (0,15 mm; 0,2 mm; 0,25 mm and 0,3 mm) were
variable parameters and cutting depth was constant parameter.
Abstract: This paper aims at developing a multilevel fuzzy
decision support model for urban rail transit planning schemes in
China under the background that China is presently experiencing an
unprecedented construction of urban rail transit. In this study, an
appropriate model using multilevel fuzzy comprehensive evaluation
method is developed. In the decision process, the followings are
considered as the influential objectives: traveler attraction,
environment protection, project feasibility and operation. In addition,
consistent matrix analysis method is used to determine the weights
between objectives and the weights between the objectives-
sub-indictors, which reduces the work caused by repeated
establishment of the decision matrix on the basis of ensuring the
consistency of decision matrix. The application results show that
multilevel fuzzy decision model can perfectly deal with the
multivariable and multilevel decision process, which is particularly
useful in the resolution of multilevel decision-making problem of
urban rail transit planning schemes.
Abstract: Recently, a lot of attention has been devoted to
advanced techniques of system modeling. PNN(polynomial neural
network) is a GMDH-type algorithm (Group Method of Data
Handling) which is one of the useful method for modeling nonlinear
systems but PNN performance depends strongly on the number of
input variables and the order of polynomial which are determined by
trial and error. In this paper, we introduce GPNN (genetic
polynomial neural network) to improve the performance of PNN.
GPNN determines the number of input variables and the order of all
neurons with GA (genetic algorithm). We use GA to search between
all possible values for the number of input variables and the order of
polynomial. GPNN performance is obtained by two nonlinear
systems. the quadratic equation and the time series Dow Jones stock
index are two case studies for obtaining the GPNN performance.
Abstract: This paper is mainly concerned with the application of
a novel technique of data interpretation for classifying measurements
of plasma columns in Tokamak reactors for nuclear fusion
applications. The proposed method exploits several concepts derived
from soft computing theory. In particular, Artificial Neural Networks
and Multi-Class Support Vector Machines have been exploited to
classify magnetic variables useful to determine shape and position of
the plasma with a reduced computational complexity. The proposed
technique is used to analyze simulated databases of plasma equilibria
based on ITER geometry configuration. As well as demonstrating the
successful recovery of scalar equilibrium parameters, we show that
the technique can yield practical advantages compared with earlier
methods.
Abstract: We study the spatial design of experiment and we want to select a most informative subset, having prespecified size, from a set of correlated random variables. The problem arises in many applied domains, such as meteorology, environmental statistics, and statistical geology. In these applications, observations can be collected at different locations and possibly at different times. In spatial design, when the design region and the set of interest are discrete then the covariance matrix completely describe any objective function and our goal is to choose a feasible design that minimizes the resulting uncertainty. The problem is recast as that of maximizing the determinant of the covariance matrix of the chosen subset. This problem is NP-hard. For using these designs in computer experiments, in many cases, the design space is very large and it's not possible to calculate the exact optimal solution. Heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective and exact solution not possible. We developed a GA algorithm to take advantage of the exploratory power of this algorithm. The successful application of this method is demonstrated in large design space. We consider a real case of design of experiment. In our problem, design space is very large and for solving the problem, we used proposed GA algorithm.
Abstract: In this study the adsorption of Cu (II) ions from aqueous solutions on synthetic zeolite NaA was evaluated. The effect of solution temperature and the determination of the kinetic parameters of adsorption of Cu(II) from aqueous solution on zeolite NaA is important in understanding the adsorption mechanism. Variables of the system include adsorption time, temperature (293- 328K), initial solution concentration and pH for the system. The sorption kinetics of the copper ions were found to be strongly dependent on pH (the optimum pH 3-5), solute ion concentration and temperature (293 – 328 K). It was found, the pseudo-second-order model was the best choice among all the kinetic models to describe the adsorption behavior of Cu(II) onto ziolite NaA, suggesting that the adsorption mechanism might be a chemisorptions process The activation energy of adsorption (Ea) was determined as Cu(II) 13.5 kJ mol-1. The low value of Ea shows that Cu(II) adsorption process by zeolite NaA may be an activated chemical adsorption. The thermodynamic parameters (ΔG0, ΔH0, and ΔS0) were also determined from the temperature dependence. The results show that the process of adsorption Cu(II) is spontaneous and endothermic process and rise in temperature favors the adsorption.
Abstract: In this paper, the differential quadrature method is applied to simulate natural convection in an inclined cubic cavity using velocity-vorticity formulation. The numerical capability of the present algorithm is demonstrated by application to natural convection in an inclined cubic cavity. The velocity Poisson equations, the vorticity transport equations and the energy equation are all solved as a coupled system of equations for the seven field variables consisting of three velocities, three vorticities and temperature. The coupled equations are simultaneously solved by imposing the vorticity definition at boundary without requiring the explicit specification of the vorticity boundary conditions. Test results obtained for an inclined cubic cavity with different angle of inclinations for Rayleigh number equal to 103, 104, 105 and 106 indicate that the present coupled solution algorithm could predict the benchmark results for temperature and flow fields. Thus, it is convinced that the present formulation is capable of solving coupled Navier-Stokes equations effectively and accurately.
Abstract: A kinetic model for propane dehydrogenation in an
industrial moving bed reactor is developed based on the reported
reaction scheme. The kinetic parameters and activity constant are
fine tuned with several sets of balanced plant data. Plant data at
different operating conditions is applied to validate the model and
the results show a good agreement between the model
predictions and plant observations in terms of the amount of main
product, propylene produced. The simulation analysis of key
variables such as inlet temperature of each reactor (Tinrx) and
hydrogen to total hydrocarbon ratio (H2/THC) affecting process
performance is performed to identify the operating condition to
maximize the production of propylene. Within the range of operating
conditions applied in the present studies, the operating condition to
maximize the propylene production at the same weighted average
inlet temperature (WAIT) is ΔTinrx1= -2, ΔTinrx2= +1, ΔTinrx3= +1 ,
ΔTinrx4= +2 and ΔH2/THC= -0.02. Under this condition, the surplus
propylene produced is 7.07 tons/day as compared with base case.