Abstract: In this work a visual and reactive contour following
behaviour is learned by reinforcement. With artificial vision the
environment is perceived in 3D, and it is possible to avoid obstacles
that are invisible to other sensors that are more common in mobile
robotics. Reinforcement learning reduces the need for intervention in
behaviour design, and simplifies its adjustment to the environment,
the robot and the task. In order to facilitate its generalisation to other
behaviours and to reduce the role of the designer, we propose a
regular image-based codification of states. Even though this is much
more difficult, our implementation converges and is robust. Results
are presented with a Pioneer 2 AT on a Gazebo 3D simulator.
Abstract: To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
Abstract: Water samples were collected from river Pandu at six
stations where human and animal activities were high. Composite
samples were analyzed for dissolved oxygen (DO), biochemical
oxygen demand (BOD), chemical oxygen demand (COD) , pH values
during dry and wet seasons as well as the harmattan period. The total
data points were used to establish relationships between the
parameters and data were also subjected to statistical analysis and
expressed as mean ± standard error of mean (SEM) at a level of
significance of p
Abstract: Accurate demand forecasting is one of the most key
issues in inventory management of spare parts. The problem of
modeling future consumption becomes especially difficult for lumpy
patterns, which characterized by intervals in which there is no
demand and, periods with actual demand occurrences with large
variation in demand levels. However, many of the forecasting
methods may perform poorly when demand for an item is lumpy.
In this study based on the characteristic of lumpy demand patterns
of spare parts a hybrid forecasting approach has been developed,
which use a multi-layered perceptron neural network and a
traditional recursive method for forecasting future demands. In the
described approach the multi-layered perceptron are adapted to
forecast occurrences of non-zero demands, and then a conventional
recursive method is used to estimate the quantity of non-zero
demands. In order to evaluate the performance of the proposed
approach, their forecasts were compared to those obtained by using
Syntetos & Boylan approximation, recently employed multi-layered
perceptron neural network, generalized regression neural network
and elman recurrent neural network in this area. The models were
applied to forecast future demand of spare parts of Arak
Petrochemical Company in Iran, using 30 types of real data sets. The
results indicate that the forecasts obtained by using our proposed
mode are superior to those obtained by using other methods.
Abstract: This paper presents an approach for the determination of the optimal cutting parameters (spindle speed, feed rate, depth of cut and engagement) leading to minimum surface roughness in face milling of high silicon stainless steel by coupling neural network (NN) and Electromagnetism-like Algorithm (EM). In this regard, the advantages of statistical experimental design technique, experimental measurements, artificial neural network, and Electromagnetism-like optimization method are exploited in an integrated manner. To this end, numerous experiments on this stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness is created by using a back propogation neural network, then the optimization problem was solved by using EM optimization. Additional experiments were performed to validate optimum surface roughness value predicted by EM algorithm. It is clearly seen that a good agreement is observed between the predicted values by EM coupled with feed forward neural network and experimental measurements. The obtained results show that the EM algorithm coupled with back propogation neural network is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.
Abstract: We present an Electronic Nose (ENose), which is
aimed at identifying the presence of one out of two gases, possibly
detecting the presence of a mixture of the two. Estimation of the
concentrations of the components is also performed for a volatile
organic compound (VOC) constituted by methanol and acetone, for
the ranges 40-400 and 22-220 ppm (parts-per-million), respectively.
Our system contains 8 sensors, 5 of them being gas sensors (of the
class TGS from FIGARO USA, INC., whose sensing element is a tin
dioxide (SnO2) semiconductor), the remaining being a temperature
sensor (LM35 from National Semiconductor Corporation), a
humidity sensor (HIH–3610 from Honeywell), and a pressure sensor
(XFAM from Fujikura Ltd.).
Our integrated hardware–software system uses some machine
learning principles and least square regression principle to identify at
first a new gas sample, or a mixture, and then to estimate the
concentrations. In particular we adopt a training model using the
Support Vector Machine (SVM) approach with linear kernel to teach
the system how discriminate among different gases. Then we apply
another training model using the least square regression, to predict
the concentrations.
The experimental results demonstrate that the proposed
multiclassification and regression scheme is effective in the
identification of the tested VOCs of methanol and acetone with
96.61% correctness. The concentration prediction is obtained with
0.979 and 0.964 correlation coefficient for the predicted versus real
concentrations of methanol and acetone, respectively.
Abstract: The demands of taller structures are becoming imperative almost everywhere in the world in addition to the challenges of material and labor cost, project time line etc. This paper conducted a study keeping in view the challenging nature of high-rise construction with no generic rules for deflection minimizations and frequency control. The effects of cyclonic wind and provision of outriggers on 28-storey, 42-storey and 57-storey are examined in this paper and certain conclusions are made which would pave way for researchers to conduct further study in this particular area of civil engineering. The results show that plan dimensions have vital impacts on structural heights. Increase of height while keeping the plan dimensions same, leads to the reduction in the lateral rigidity. To achieve required stiffness increase of bracings sizes as well as introduction of additional lateral resisting system such as belt truss and outriggers is required.
Abstract: This paper deals with an adaptive multiuser detector for direct sequence code division multiple-access (DS-CDMA) systems. A modified receiver, precombinig LMMSE is considered under time varying channel environment. Detector updating is performed with two criterions, mean square estimation (MSE) and MOE optimization technique. The adaptive implementation issues of these two schemes are quite different. MSE criterion updates the filter weights by minimizing error between data vector and adaptive vector. MOE criterion together with canonical representation of the detector results in a constrained optimization problem. Even though the canonical representation is very complicated under time varying channels, it is analyzed with assumption of average power profile of multipath replicas of user of interest. The performance of both schemes is studied for practical SNR conditions. Results show that for poor SNR, MSE precombining LMMSE is better than the blind precombining LMMSE but for greater SNR, MOE scheme outperforms with better result.
Abstract: In this article, models based on quantitative analysis,
physical geometry and regression analysis are established, by using
analytic hierarchy process analysis, fuzzy cluster analysis, fuzzy
photographic and data fitting. The reasons of various leaf shapes
among different species and the differences between the leaf shapes on
same tree have been solved by using software, such as Eviews, VB and
Matlab. We also successfully estimate the leaf mass of a tree and the
correlation with the tree profile.
Abstract: The objective of this paper is the introduction to a
unified optimization framework for research and education. The
OPTILIB framework implements different general purpose algorithms
for combinatorial optimization and minimum search on standard continuous
test functions. The preferences of this library are the straightforward
integration of new optimization algorithms and problems
as well as the visualization of the optimization process of different
methods exploring the search space exclusively or for the real time
visualization of different methods in parallel. Further the usage of
several implemented methods is presented on the basis of two use
cases, where the focus is especially on the algorithm visualization.
First it is demonstrated how different methods can be compared
conveniently using OPTILIB on the example of different iterative
improvement schemes for the TRAVELING SALESMAN PROBLEM.
A second study emphasizes how the framework can be used to find
global minima in the continuous domain.
Abstract: Creative drama which interconnects with the concepts of play, theatre, animation and role playing is a field which can only be learnt and expressed through experiencing. This study about assessment of the drama teaching in preschools by children was conducted in 3 preschools in Ankara with participation of 12 children of 6 ages who had taken drama learning courses. Qualitative research approach and semi-structured interviewing technique were employed. The results of the study indicated that all of 12 children defined drama as a game and entertainment.
Abstract: Com Poisson distribution is capable of modeling the count responses irrespective of their mean variance relation and the parameters of this distribution when fitted to a simple cross sectional data can be efficiently estimated using maximum likelihood (ML) method. In the regression setup, however, ML estimation of the parameters of the Com Poisson based generalized linear model is computationally intensive. In this paper, we propose to use quasilikelihood (QL) approach to estimate the effect of the covariates on the Com Poisson counts and investigate the performance of this method with respect to the ML method. QL estimates are consistent and almost as efficient as ML estimates. The simulation studies show that the efficiency loss in the estimation of all the parameters using QL approach as compared to ML approach is quite negligible, whereas QL approach is lesser involving than ML approach.
Abstract: In medical therapy, laser has been widely used to conduct cosmetic, tumor and other treatments. During the process of laser irradiation, there may be thermal damage caused by excessive laser exposure. Thus, the establishment of a complete thermal analysis model is clinically helpful to physicians in reference data. In this study, porcine liver in place of tissue was subjected to laser irradiation to set up the experimental data considering the explored impact on surface thermal field and thermal damage region under different conditions of power, laser irradiation time, and distance between laser and porcine liver. In the experimental process, the surface temperature distribution of the porcine lever was measured by the infrared thermal imager. In the part of simulation, the bio heat transfer Pennes-s equation was solved by software SYSWELD applying in welding process. The double ellipsoid function as a laser source term is firstly considered in the prediction for surface thermal field and internal tissue damage. The simulation results are compared with the experimental data to validate the mathematical model established here in.
Abstract: This article deals with the numerical simulation of the
floor heating convector in 3D. Presented convector can operate in
two modes – cooling mode and heating mode. This initial numerical
simulation is focused on cooling mode of the convector. Models with
different temperature of the fins are compared and three various
shapes of the fins are examined as well. The objective of the work is
to predict air flow and heat transfer inside convector for further
optimalization of these devices. For the numerical simulation was
used commercial software Ansys Fluent.
Abstract: The kinetic properties of enzymes are often reported
using the apparent KM and Vmax appropriate to the standard
Michaelis-Menten enzyme. However, this model is inappropriate to
enzymes that have more than one substrate or where the rate
expression does not apply for other reasons. Consequently, it is
desirable to have a means of estimating the appropriate kinetic
parameters from the apparent values of KM and Vmax reported for each
substrate. We provide a means of estimating the range within which
the parameters should lie and apply the method to data for glutamate
dehydrogenase from the nematode parasite of sheep Teladorsagia
circumcincta.
Abstract: The paper presents a novel method for the 3D shaping
of different materials using a high-pressure abrasive water jet and a
flat target image. For steering movement process of the jet a principle
similar to raster image way of record and readout was used.
However, respective colors of pixel of such a bitmap are connected
with adequate jet feed rate that causes erosion of material with
adequate depth. Thanks to that innovation, one can observe spatial
imaging of the object. Theoretical basis as well as spatial model of
material shaping and experimental stand including steering program
are presented in. There are also presented methodic and some
experimental erosion results as well as practical example of object-s
bas-relief made of metal.
Abstract: Counting people from a video stream in a noisy environment is a challenging task. This project aims at developing a counting system for transport vehicles, integrated in a video surveillance product. This article presents a method for the detection and tracking of multiple faces in a video by using a model of first and second order local moments. An iterative process is used to estimate the position and shape of multiple faces in images, and to track them. the trajectories are then processed to count people entering and leaving the vehicle.
Abstract: There are various kinds of medical equipment which
requires relatively accurate positional adjustments for successful
treatment. However, patients tend to move without notice during a
certain span of operations. Therefore, it is common practice that
accompanying operators adjust the focus of the equipment. In this
paper, tracking controllers for medical equipment are suggested to
replace the operators. The tracking controllers use AHRS sensor
information to recognize the movements of patients. Sensor fusion is
applied to reducing the error magnitudes through linear Kalman filters.
The image processing of optical markers is included to adjust the
accumulation errors of gyroscope sensor data especially for yaw
angles.
The tracking controller reduces the positional errors between the
current focus of a device and the target position on the body of a
patient. Since the sensing frequencies of AHRS sensors are very high
compared to the physical movements, the control performance is
satisfactory. The typical applications are, for example, ESWT or
rTMS, which have the error ranges of a few centimeters.
Abstract: Smoke discharging is a main reason of air pollution
problem from industrial plants. The obstacle of a building has an
affect with the air pollutant discharge. In this research, a mathematical
model of the smoke dispersion from two sources and one source with
a structural obstacle is considered. The governing equation of the
model is an isothermal mass transfer model in a viscous fluid. The
finite element method is used to approximate the solutions of the
model. The triangular linear elements have been used for discretising
the domain, and time integration has been carried out by semi-implicit
finite difference method. The simulations of smoke dispersion in
cases of one chimney and two chimneys are presented. The maximum
calculated smoke concentration of both cases are compared. It is then
used to make the decision for smoke discharging and air pollutant
control problems on industrial area.
Abstract: We present here the results for a comparative study of
some techniques, available in the literature, related to the relevance
feedback mechanism in the case of a short-term learning. Only one
method among those considered here is belonging to the data mining
field which is the K-nearest neighbors algorithm (KNN) while the
rest of the methods is related purely to the information retrieval field
and they fall under the purview of the following three major axes:
Shifting query, Feature Weighting and the optimization of the
parameters of similarity metric. As a contribution, and in addition to
the comparative purpose, we propose a new version of the KNN
algorithm referred to as an incremental KNN which is distinct from
the original version in the sense that besides the influence of the
seeds, the rate of the actual target image is influenced also by the
images already rated. The results presented here have been obtained
after experiments conducted on the Wang database for one iteration
and utilizing color moments on the RGB space. This compact
descriptor, Color Moments, is adequate for the efficiency purposes
needed in the case of interactive systems. The results obtained allow
us to claim that the proposed algorithm proves good results; it even
outperforms a wide range of techniques available in the literature.