Abstract: For a given specific problem an efficient algorithm has
been the matter of study. However, an alternative approach orthogonal
to this approach comes out, which is called a reduction. In general
for a given specific problem this reduction approach studies how to
convert an original problem into subproblems. This paper proposes
a formal modeling language to support this reduction approach. We
show three examples from the wide area of learning problems. The
benefit is a fast prototyping of algorithms for a given new problem.
Abstract: In order to improve control performance and eliminate steady, a coupling compensation for 6-DOF parallel robot is presented. Taking dynamic load Tank Simulator as the research object, this paper analyzes the coupling of 6-DOC parallel robot considering the degree of freedom of the 6-DOF parallel manipulator. The coupling angle and coupling velocity are derived based on inverse kinematics model. It uses the mechanism-model combined method which takes practical moving track that considering the performance of motion controller and motor as its input to make the study. Experimental results show that the coupling compensation improves motion stability as well as accuracy. Besides, it decreases the dither amplitude of dynamic load Tank Simulator.
Abstract: In research on natural ventilation, and passive cooling
with forced convection, is essential to know how heat flows in a solid
object and the pattern of temperature distribution on their surfaces,
and eventually how air flows through and convects heat from the
surfaces of steel under roof. This paper presents some results from
running the computational fluid dynamic program (CFD) by
comparison between natural ventilation and forced convection within
roof attic that is received directly from solar radiation. The CFD
program for modeling air flow inside roof attic has been modified to
allow as two cases. First case, the analysis under natural ventilation,
is closed area in roof attic and second case, the analysis under forced
convection, is opened area in roof attic. These extend of all cases to
available predictions of variations such as temperature, pressure, and
mass flow rate distributions in each case within roof attic. The
comparison shows that this CFD program is an effective model for
predicting air flow of temperature and heat transfer coefficient
distribution within roof attic. The result shows that forced convection
can help to reduce heat transfer through roof attic and an around area
of steel core has temperature inner zone lower than natural
ventilation type. The different temperature on the steel core of roof
attic of two cases was 10-15 oK.
Abstract: Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.
Abstract: The fluid mechanics principle is used extensively in
designing axial flow fans and their associated equipment. This paper presents a computational fluid dynamics (CFD) modeling of air flow
distribution from a radiator axial flow fan used in an acid pump truck Tier4 (APT T4) Repower. This axial flow fan augments the transfer
of heat from the engine mounted on the APT T4.
CFD analysis was performed for an area weighted average static pressure difference at the inlet and outlet of the fan. Pressure contours, velocity vectors, and path lines were plotted for detailing
the flow characteristics for different orientations of the fan blade. The results were then compared and verified against known theoretical observations and actual experimental data. This study
shows that a CFD simulation can be very useful for predicting and understanding the flow distribution from a radiator fan for further
research work.
Abstract: Rural tourism has many economical, environmental, and socio-cultural benefits. However, the development of rural tourism compared to urban tourism is also faced with several challenges added to the disadvantages of rural tourism. The aim of this study is to design a model of the factors affecting the motivations of rural tourists, in an attempt to improve the understanding of rural tourism motivation for the development of that form of tourism. The proposed model is based on a sound theoretical framework. It was designed following a literature review of tourism motivation theoretical frameworks and of rural tourism motivation factors. The tourism motivation theoretical framework that fitted to the best all rural tourism motivation factors was then chosen as the basis for the proposed model. This study hence found that the push and pull tourism motivation framework and the inner and outer directed values theory are the most adequate theoretical frameworks for the modeling of rural tourism motivation.
Abstract: The social force model which belongs to the
microscopic pedestrian studies has been considered as the supremacy
by many researchers and due to the main feature of reproducing the
self-organized phenomena resulted from pedestrian dynamic. The
Preferred Force which is a measurement of pedestrian-s motivation to
adapt his actual velocity to his desired velocity is an essential term on
which the model was set up. This Force has gone through stages of
development: first of all, Helbing and Molnar (1995) have modeled
the original force for the normal situation. Second, Helbing and his
co-workers (2000) have incorporated the panic situation into this
force by incorporating the panic parameter to account for the panic
situations. Third, Lakoba and Kaup (2005) have provided the
pedestrians some kind of intelligence by incorporating aspects of the
decision-making capability. In this paper, the authors analyze the
most important incorporations into the model regarding the preferred
force. They make comparisons between the different factors of these
incorporations. Furthermore, to enhance the decision-making ability
of the pedestrians, they introduce additional features such as the
familiarity factor to the preferred force to let it appear more
representative of what actually happens in reality.
Abstract: Natural resources management including water resources requires reliable estimations of time variant environmental parameters. Small improvements in the estimation of environmental parameters would result in grate effects on managing decisions. Noise reduction using wavelet techniques is an effective approach for preprocessing of practical data sets. Predictability enhancement of the river flow time series are assessed using fractal approaches before and after applying wavelet based preprocessing. Time series correlation and persistency, the minimum sufficient length for training the predicting model and the maximum valid length of predictions were also investigated through a fractal assessment.
Abstract: The paper focuses on the enhanced stiffness modeling
of robotic manipulators by taking into account influence of the external force/torque acting upon the end point. It implements the
virtual joint technique that describes the compliance of manipulator elements by a set of localized six-dimensional springs separated by
rigid links and perfect joints. In contrast to the conventional
formulation, which is valid for the unloaded mode and small
displacements, the proposed approach implicitly assumes that the loading leads to the non-negligible changes of the manipulator posture and corresponding amendment of the Jacobian. The
developed numerical technique allows computing the static
equilibrium and relevant force/torque reaction of the manipulator for
any given displacement of the end-effector. This enables designer
detecting essentially nonlinear effects in elastic behavior of
manipulator, similar to the buckling of beam elements. It is also proposed the linearization procedure that is based on the inversion of
the dedicated matrix composed of the stiffness parameters of the
virtual springs and the Jacobians/Hessians of the active and passive
joints. The developed technique is illustrated by an application example that deals with the stiffness analysis of a parallel
manipulator of the Orthoglide family
Abstract: Web usage mining algorithms have been widely
utilized for modeling user web navigation behavior. In this study we
advance a model for mining of user-s navigation pattern. The model
makes user model based on expectation-maximization (EM)
algorithm.An EM algorithm is used in statistics for finding maximum
likelihood estimates of parameters in probabilistic models, where the
model depends on unobserved latent variables. The experimental
results represent that by decreasing the number of clusters, the log
likelihood converges toward lower values and probability of the
largest cluster will be decreased while the number of the clusters
increases in each treatment.
Abstract: This paper proposes a new decision making approch
based on quantitative possibilistic influence diagrams which are
extension of standard influence diagrams in the possibilistic framework.
We will in particular treat the case where several expert
opinions relative to value nodes are available. An initial expert assigns
confidence degrees to other experts and fixes a similarity threshold
that provided possibility distributions should respect. To illustrate our
approach an evaluation algorithm for these multi-source possibilistic
influence diagrams will also be proposed.
Abstract: Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.
Abstract: In this paper, we are interested in attitude control of a satellite, which using wheels of reaction, by state feedback. First, we develop a method allowing us to put the control and its integral in the state-feedback form. Then, by using the theorem of Gronwall- Bellman, we put the sufficient conditions so that the nonlinear system modeling the satellite is stabilisable and observed by state feedback.
Abstract: This article proposes an Ant Colony Optimization
(ACO) metaheuristic to minimize total makespan for scheduling a set
of jobs and assign workers for uniformly related parallel machines.
An algorithm based on ACO has been developed and coded on a
computer program Matlab®, to solve this problem. The paper
explains various steps to apply Ant Colony approach to the problem
of minimizing makespan for the worker assignment & jobs
scheduling problem in a parallel machine model and is aimed at
evaluating the strength of ACO as compared to other conventional
approaches. One data set containing 100 problems (12 Jobs, 03
machines and 10 workers) which is available on internet, has been
taken and solved through this ACO algorithm. The results of our
ACO based algorithm has shown drastically improved results,
especially, in terms of negligible computational effort of CPU, to
reach the optimal solution. In our case, the time taken to solve all 100
problems is even lesser than the average time taken to solve one
problem in the data set by other conventional approaches like GA
algorithm and SPT-A/LMC heuristics.
Abstract: The objective of this paper is to analyze the
performance of a double-sided axial flux permanent magnet
brushless DC (AFPM BLDC) motor with two-phase winding.
To study the motor operation, a mathematical dynamic model
has been proposed for motor, which became the basis for
simulations that were performed using MATLAB/SIMULINK
software package. The results of simulations were presented
in form of the waveforms of selected quantities and the
electromechanical characteristics performed by the motor. The
calculation results show that the two-phase motor version
develops smooth torque and reaches high efficiency. The twophase
motor can be applied where more smooth torque is
required. Finally a study on the influence of switching angle
on motor performance shows that when advance switching
technique is used, the motor operates with the highest
efficiency.
Abstract: In this paper, novel techniques in increasing the accuracy
and speed of convergence of a Feed forward Back propagation
Artificial Neural Network (FFBPNN) with polynomial activation
function reported in literature is presented. These technique was
subsequently used to determine the coefficients of Autoregressive
Moving Average (ARMA) and Autoregressive (AR) system. The
results obtained by introducing sequential and batch method of weight
initialization, batch method of weight and coefficient update, adaptive
momentum and learning rate technique gives more accurate result
and significant reduction in convergence time when compared t the
traditional method of back propagation algorithm, thereby making
FFBPNN an appropriate technique for online ARMA coefficient
determination.
Abstract: Modeling of Panel Zone (PZ) seismic behavior,
because of its role in overall ductility and lateral stiffness of steel
moment frames, has been considered a challenge for years. There are
some studies regarding the effects of different doubler plates
thicknesses and geometric properties of PZ on its seismic behavior.
However, there is not much investigation on the effects of number of
provided continuity plates in case of presence of one triangular
haunch, two triangular haunches and rectangular haunch (T shape
haunches) for exterior columns. In this research first detailed finite
element models of 12tested connection of SAC joint venture were
created and analyzed then obtained cyclic behavior backbone curves
of these models besides other FE models for similar tests were used
for neural network training. Then seismic behavior of these data is
categorized according to continuity plate-s arrangements and
differences in type of haunches. PZ with one-sided haunches have
little plastic rotation. As the number of continuity plates increases
due to presence of two triangular haunches (four continuity plate),
there will be no plastic rotation, in other words PZ behaves in its
elastic range. In the case of rectangular haunch, PZ show more plastic
rotation in comparison with one-sided triangular haunch and
especially double-sided triangular haunches. Moreover, the models
that will be presented in case of triangular one-sided and double-
sided haunches and rectangular haunches as a result of this study
seem to have a proper estimation of PZ seismic behavior.
Abstract: This research’s objective is to select the model with
most accurate value by using Neural Network Technique as a way to
filter potential students who enroll in IT course by Electronic learning
at Suan Suanadha Rajabhat University. It is designed to help students
selecting the appropriate courses by themselves. The result showed
that the most accurate model was 100 Folds Cross-validation which
had 73.58% points of accuracy.
Abstract: This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.
Abstract: Workload and resource management are two essential functions provided at the service level of the grid software infrastructure. To improve the global throughput of these software environments, workloads have to be evenly scheduled among the available resources. To realize this goal several load balancing strategies and algorithms have been proposed. Most strategies were developed in mind, assuming homogeneous set of sites linked with homogeneous and fast networks. However for computational grids we must address main new issues, namely: heterogeneity, scalability and adaptability. In this paper, we propose a layered algorithm which achieve dynamic load balancing in grid computing. Based on a tree model, our algorithm presents the following main features: (i) it is layered; (ii) it supports heterogeneity and scalability; and, (iii) it is totally independent from any physical architecture of a grid.