Abstract: To evaluate the ability to predict xerostomia after
radiotherapy, we constructed and compared neural network and
logistic regression models. In this study, 61 patients who completed a
questionnaire about their quality of life (QoL) before and after a full
course of radiation therapy were included. Based on this questionnaire,
some statistical data about the condition of the patients’ salivary
glands were obtained, and these subjects were included as the inputs of
the neural network and logistic regression models in order to predict
the probability of xerostomia. Seven variables were then selected from
the statistical data according to Cramer’s V and point-biserial
correlation values and were trained by each model to obtain the
respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65
for SSE, and 13.7% and 19.0% for MAPE, respectively. These
parameters demonstrate that both neural network and logistic
regression methods are effective for predicting conditions of parotid
glands.
Abstract: Freeways are originally designed to provide high
mobility to road users. However, the increase in population and
vehicle numbers has led to increasing congestions around the world.
Daily recurrent congestion substantially reduces the freeway capacity
when it is most needed. Building new highways and expanding the
existing ones is an expensive solution and impractical in many
situations. Intelligent and vision-based techniques can, however, be
efficient tools in monitoring highways and increasing the capacity of
the existing infrastructures. The crucial step for highway monitoring
is vehicle detection. In this paper, we propose one of such
techniques. The approach is based on artificial neural networks
(ANN) for vehicles detection and counting. The detection process
uses the freeway video images and starts by automatically extracting
the image background from the successive video frames. Once the
background is identified, subsequent frames are used to detect
moving objects through image subtraction. The result is segmented
using Sobel operator for edge detection. The ANN is, then, used in
the detection and counting phase. Applying this technique to the
busiest freeway in Riyadh (King Fahd Road) achieved higher than
98% detection accuracy despite the light intensity changes, the
occlusion situations, and shadows.
Abstract: The paper presents the optimization problem for the
multi-element synthetic transmit aperture method (MSTA) in
ultrasound imaging applications. The optimal choice of the transmit
aperture size is performed as a trade-off between the lateral
resolution, penetration depth and the frame rate. Results of the
analysis obtained by a developed optimization algorithm are
presented. Maximum penetration depth and the best lateral resolution
at given depths are chosen as the optimization criteria. The
optimization algorithm was tested using synthetic aperture data of
point reflectors simulated by Filed II program for Matlab® for the
case of 5MHz 128-element linear transducer array with 0.48 mm
pitch are presented. The visualization of experimentally obtained
synthetic aperture data of a tissue mimicking phantom and in vitro
measurements of the beef liver are also shown. The data were
obtained using the SonixTOUCH Research systemequipped with a
linear 4MHz 128 element transducerwith 0.3 mm element pitch, 0.28
mm element width and 70% fractional bandwidth was excited by one
sine cycle pulse burst of transducer's center frequency.
Abstract: The number of framework conceived for e-learning
constantly increase, unfortunately the creators of learning materials
and educational institutions engaged in e-formation adopt a
“proprietor" approach, where the developed products (courses,
activities, exercises, etc.) can be exploited only in the framework
where they were conceived, their uses in the other learning
environments requires a greedy adaptation in terms of time and
effort. Each one proposes courses whose organization, contents,
modes of interaction and presentations are unique for all learners,
unfortunately the latter are heterogeneous and are not interested by
the same information, but only by services or documents adapted to
their needs. Currently the new tendency for the framework
conceived for e-learning, is the interoperability of learning materials,
several standards exist (DCMI (Dublin Core Metadata Initiative)[2],
LOM (Learning Objects Meta data)[1], SCORM (Shareable Content
Object Reference Model)[6][7][8], ARIADNE (Alliance of Remote
Instructional Authoring and Distribution Networks for Europe)[9],
CANCORE (Canadian Core Learning Resource Metadata
Application Profiles)[3]), they converge all to the idea of learning
objects. They are also interested in the adaptation of the learning
materials according to the learners- profile. This article proposes an
approach for the composition of courses adapted to the various
profiles (knowledge, preferences, objectives) of learners, based on
two ontologies (domain to teach and educational) and the learning
objects.
Abstract: A complete spectral representation for the
electromagnetic field of planar multilayered waveguides
inhomogeneously filled with omega media is presented. The problem
of guided electromagnetic propagation is reduced to an eigenvalue
equation related to a 2 ´ 2 matrix differential operator. Using the
concept of adjoint waveguide, general bi-orthogonality relations for
the hybrid modes (either from the discrete or from the continuous
spectrum) are derived. For the special case of homogeneous layers
the linear operator formalism is reduced to a simple 2 ´ 2 coupling
matrix eigenvalue problem. Finally, as an example of application, the
surface and the radiation modes of a grounded omega slab waveguide
are analyzed.
Abstract: In this study thermodynamic performance analysis of a
combined organic Rankine cycle and ejector refrigeration cycle is
carried out for use of low-grade heat source in the form of sensible
energy. Special attention is paid to the effects of system parameters
including the turbine inlet temperature and turbine inlet pressure on the
characteristics of the system such as ratios of mass flow rate, net work
production, and refrigeration capacity as well as the coefficient of
performance and exergy efficiency of the system. Results show that
for a given source the coefficient of performance increases with
increasing of the turbine inlet pressure. However, the exergy
efficiency has an optimal condition with respect to the turbine inlet
pressure.
Abstract: The present models and simulation algorithms of intracellular stochastic kinetics are usually based on the premise that diffusion is so fast that the concentrations of all the involved species are homogeneous in space. However, recents experimental measurements of intracellular diffusion constants indicate that the assumption of a homogeneous well-stirred cytosol is not necessarily valid even for small prokaryotic cells. In this work a mathematical treatment of diffusion that can be incorporated in a stochastic algorithm simulating the dynamics of a reaction-diffusion system is presented. The movement of a molecule A from a region i to a region j of the space is represented as a first order reaction Ai k- ! Aj , where the rate constant k depends on the diffusion coefficient. The diffusion coefficients are modeled as function of the local concentration of the solutes, their intrinsic viscosities, their frictional coefficients and the temperature of the system. The stochastic time evolution of the system is given by the occurrence of diffusion events and chemical reaction events. At each time step an event (reaction or diffusion) is selected from a probability distribution of waiting times determined by the intrinsic reaction kinetics and diffusion dynamics. To demonstrate the method the simulation results of the reaction-diffusion system of chaperoneassisted protein folding in cytoplasm are shown.
Abstract: Deep and radical social reforms of the last century-s
nineties in many Eastern European countries caused changes in
Information Technology-s (IT) field. Inefficient information
technologies were rapidly replaced with forefront IT solutions, e.g.,
in Eastern European countries there is a high level penetration of
qualitative high-speed Internet. The authors have taken part in the
introduction of those changes in Latvia-s leading IT research
institute. Grounding on their experience authors in this paper offer an
IT services based model for analysis the mentioned changes- and
development processes in the higher education and research fields,
i.e., for research e-infrastructure-s development. Compare to the
international practice such services were developed in Eastern Europe
in an untraditional way, which provided swift and positive
technological changes.
Abstract: The conjugate gradient optimization algorithm
usually used for nonlinear least squares is presented and is
combined with the modified back propagation algorithm yielding
a new fast training multilayer perceptron (MLP) algorithm
(CGFR/AG). The approaches presented in the paper consist of
three steps: (1) Modification on standard back propagation
algorithm by introducing gain variation term of the activation
function, (2) Calculating the gradient descent on error with
respect to the weights and gains values and (3) the determination
of the new search direction by exploiting the information
calculated by gradient descent in step (2) as well as the previous
search direction. The proposed method improved the training
efficiency of back propagation algorithm by adaptively modifying
the initial search direction. Performance of the proposed method
is demonstrated by comparing to the conjugate gradient algorithm
from neural network toolbox for the chosen benchmark. The
results show that the number of iterations required by the
proposed method to converge is less than 20% of what is required
by the standard conjugate gradient and neural network toolbox
algorithm.
Abstract: Using mini modules of Tmotes, it is possible to automate a small personal area network. This idea can be extended to large networks too by implementing multi-hop routing. Linking the various Tmotes using Programming languages like Nesc, Java and having transmitter and receiver sections, a network can be monitored. It is foreseen that, depending on the application, a long range at a low data transfer rate or average throughput may be an acceptable trade-off. To reduce the overall costs involved, an optimum number of Tmotes to be used under various conditions (Indoor/Outdoor) is to be deduced. By analyzing the data rates or throughputs at various locations of Tmotes, it is possible to deduce an optimal number of Tmotes for a specific network. This paper deals with the determination of optimum distances to reduce the cost and increase the reliability of the entire sensor network with Wireless Local Loop (WLL) capability.
Abstract: In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.
Abstract: A mobile agent is a software which performs an
action autonomously and independently as a person or an
organizations assistance. Mobile agents are used for searching
information, retrieval information, filtering, intruder recognition in
networks, and so on. One of the important issues of mobile agent is
their security. It must consider different security issues in effective
and secured usage of mobile agent. One of those issues is the
integrity-s protection of mobile agents.
In this paper, the advantages and disadvantages of each method,
after reviewing the existing methods, is examined. Regarding to this
matter that each method has its own advantage or disadvantage, it
seems that by combining these methods, one can reach to a better
method for protecting the integrity of mobile agents. Therefore, this
method is provided in this paper and then is evaluated in terms of
existing method. Finally, this method is simulated and its results are
the sign of improving the possibility of integrity-s protection of
mobile agents.
Abstract: This research sought to discover the forms of
promotion and dissemination of traditional local wisdom that are
used to create occupations among the elderly at Noanmueng
Community, Muang Sub-District, Baan Doong District, Udornthani
Province. The criteria used to select the research sample group were:
having a role involved in the promotion and dissemination of
traditional local wisdom to create occupations among the elderly;
being an experienced person who the residents of Noanmueng
Community find trustworthy; and having lived in Noanmueng
Community for a long time so as to be able to see the development
and change that occurs. A total of 16 persons were thus selected. Data
was gathered through a qualitative study, using semi-structured indepth
interviews. The collected data was then summarized and
discussed according to the research objectives. Finally, the data was
presented in narrative format. Results found that the identifying
traditional local wisdom of the community (which grew from the
residents’ experience and beneficial usage in daily life, passed down
from generation to generation) was the weaving of cloth and
basketry. As for the manner of promotion and dissemination of
traditional local wisdom, these skills were passed down through
teaching by example to family members, relatives and others in the
community. This was largely the initiative of the elders or elderly
members of the community. In order for the promotion and
dissemination of traditional local wisdom to create occupations
among the elderly, the traditional local wisdom should be supported
in every way through participation of the community members. For
example, establish a museum of traditional local wisdom for the
collection of traditional local wisdom in various fields, both from the
past and present innovations. This would be a source of pride for the
community, simultaneously helping traditional local wisdom to
become widely known and to create income for the community’s
elderly. Additional ways include organizing exhibitions of products
made by traditional local wisdom, finding both domestic and
international markets, as well as building both domestic and
international networks aiming to find opportunities to market
products made by traditional local wisdom.
Abstract: This paper presents a novel two-phase hybrid optimization algorithm with hybrid genetic operators to solve the optimal control problem of a single stage hybrid manufacturing system. The proposed hybrid real coded genetic algorithm (HRCGA) is developed in such a way that a simple real coded GA acts as a base level search, which makes a quick decision to direct the search towards the optimal region, and a local search method is next employed to do fine tuning. The hybrid genetic operators involved in the proposed algorithm improve both the quality of the solution and convergence speed. The phase–1 uses conventional real coded genetic algorithm (RCGA), while optimisation by direct search and systematic reduction of the size of search region is employed in the phase – 2. A typical numerical example of an optimal control problem with the number of jobs varying from 10 to 50 is included to illustrate the efficacy of the proposed algorithm. Several statistical analyses are done to compare the validity of the proposed algorithm with the conventional RCGA and PSO techniques. Hypothesis t – test and analysis of variance (ANOVA) test are also carried out to validate the effectiveness of the proposed algorithm. The results clearly demonstrate that the proposed algorithm not only improves the quality but also is more efficient in converging to the optimal value faster. They can outperform the conventional real coded GA (RCGA) and the efficient particle swarm optimisation (PSO) algorithm in quality of the optimal solution and also in terms of convergence to the actual optimum value.
Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Abstract: Recently, a quality of motors is inspected by human
ears. In this paper, I propose two systems using a method of speech
recognition for automation of the inspection. The first system is based
on a method of linear processing which uses K-means and Nearest
Neighbor method, and the second is based on a method of non-linear
processing which uses neural networks. I used motor sounds in these
systems, and I successfully recognize 86.67% of motor sounds in the
linear processing system and 97.78% in the non-linear processing
system.
Abstract: In this article, a method has been offered to classify
normal and defective tiles using wavelet transform and artificial
neural networks. The proposed algorithm calculates max and min
medians as well as the standard deviation and average of detail
images obtained from wavelet filters, then comes by feature vectors
and attempts to classify the given tile using a Perceptron neural
network with a single hidden layer. In this study along with the
proposal of using median of optimum points as the basic feature and
its comparison with the rest of the statistical features in the wavelet
field, the relational advantages of Haar wavelet is investigated. This
method has been experimented on a number of various tile designs
and in average, it has been valid for over 90% of the cases. Amongst
the other advantages, high speed and low calculating load are
prominent.
Abstract: Arc welding creates a weld pool to realize continuity between pieces of assembly. The thermal history of the weld is dependent on heat transfer and fluid flow in the weld pool. The metallurgical transformation during welding and cooling are modeled in the literature only at solid state neglecting the fluid flow. In the present paper we associate a heat transfer – fluid flow and metallurgical model for the 16MnD5 steel. The metallurgical transformation model is based on Leblond model for the diffusion kinetics and on the Koistinen-Marburger equation for Marteniste transformation. The predicted thermal history and metallurgical transformations are compared to a simulation without fluid phase. This comparison shows the great importance of the fluid flow modeling.
Abstract: Energy efficient protocol design is the aim of current
researches in the area of sensor networks where limited power
resources impose energy conservation considerations. In this paper
we care for Medium Access Control (MAC) protocols and after an
extensive literature review, two adaptive schemes are discussed. Of
them, adaptive-rate MACs which were introduced for throughput
enhancement show the potency to save energy, even more than
adaptive-power schemes. Then we propose an allocation algorithm
for getting accurate and reliable results. Through a simulation study
we validated our claim and showed the power saving of adaptive-rate
protocols.
Abstract: In this study, workplace environmental monitoring
systems were established using USN(Ubiquitous Sensor Networks)
and LabVIEW. Although existing direct sampling methods enable
finding accurate values as of the time points of measurement, those
methods are disadvantageous in that continuous management and
supervision are difficult and costs for are high when those methods are
used. Therefore, the efficiency and reliability of workplace
management by supervisors are relatively low when those methods are
used. In this study, systems were established so that information on
workplace environmental factors such as temperatures, humidity and
noises is measured and transmitted to the PC in real time to enable
supervisors to monitor workplaces through LabVIEW on the PC.
When any accidents have occurred in workplaces, supervisors can
immediately respond through the monitoring system and this system
enables integrated workplace management and the prevention of
safety accidents. By introducing these monitoring systems, safety
accidents due to harmful environmental factors in workplaces can be
prevented and these monitoring systems will be also helpful in finding
out the correlation between safety accidents and occupational diseases
by comparing and linking databases established by this monitoring
system with existing statistical data.