Abstract: Text Mining is an important step of Knowledge
Discovery process. It is used to extract hidden information from notstructured
o semi-structured data. This aspect is fundamental because
much of the Web information is semi-structured due to the nested
structure of HTML code, much of the Web information is linked,
much of the Web information is redundant. Web Text Mining helps
whole knowledge mining process to mining, extraction and
integration of useful data, information and knowledge from Web
page contents.
In this paper, we present a Web Text Mining process able to
discover knowledge in a distributed and heterogeneous multiorganization
environment. The Web Text Mining process is based on
flexible architecture and is implemented by four steps able to
examine web content and to extract useful hidden information
through mining techniques. Our Web Text Mining prototype starts
from the recovery of Web job offers in which, through a Text Mining
process, useful information for fast classification of the same are
drawn out, these information are, essentially, job offer place and
skills.
Abstract: In this study the elastic-plastic stress distribution in
weld-bonded joint, fabricated from austenitic stainless steel (AISI
304) sheet of 1.00 mm thickness and Epoxy adhesive Araldite 2011,
subjected to axial loading is investigated. This is needed to improve
design procedures and welding codes, and saving efforts in the
cumbersome experiments and analysis. Therefore, a complete 3-D
finite element modelling and analysis of spot welded, bonded and
weld-bonded joints under axial loading conditions is carried out. A
comprehensive systematic experimental program is conducted to
determine many properties and quantities, of the base metals and the
adhesive, needed for FE modelling, such like the elastic – plastic
properties, modulus of elasticity, fracture limit, the nugget and heat
affected zones (HAZ) properties, etc. Consequently, the finite
element models developed, for each case, are used to evaluate
stresses distributions across the entire joint, in both the elastic and
plastic regions. The stress distribution curves are obtained,
particularly in the elastic regions and found to be consistent and in
excellent agreement with the published data. Furthermore, the
stresses distributions are obtained in the weld-bonded joint and
display the best results with almost uniform smooth distribution
compared to spot and bonded cases. The stress concentration peaks at
the edges of the weld-bonded region, are almost eliminated resulting
in achieving the strongest joint of all processes.
Abstract: Conventional industrial monitoring systems are
tedious, inefficient and the at times integrity of the data is
unreliable. The objective of this system is to monitor industrial
processes specifically the fluid level which will measure the
instantaneous fluid level parameter and respond by text
messaging the exact value of the parameter to the user when
being enquired by a privileged access user. The development of
the embedded program code and the circuit for fluid level
measuring are discussed as well. Suggestions for future
implementations and efficient remote monitoring works are
included.
Abstract: This paper proposes a VPN Accelerator Board
(VPN-AB), a virtual private network (VPN) protocol designed for
trust channel security system (TCSS). TCSS supports safety
communication channel between security nodes in internet. It
furnishes authentication, confidentiality, integrity, and access control
to security node to transmit data packets with IPsec protocol. TCSS
consists of internet key exchange block, security association block,
and IPsec engine block. The internet key exchange block negotiates
crypto algorithm and key used in IPsec engine block. Security
Association blocks setting-up and manages security association
information. IPsec engine block treats IPsec packets and consists of
networking functions for communication. The IPsec engine block
should be embodied by H/W and in-line mode transaction for high
speed IPsec processing. Our VPN-AB is implemented with high speed
security processor that supports many cryptographic algorithms and
in-line mode. We evaluate a small TCSS communication environment,
and measure a performance of VPN-AB in the environment. The
experiment results show that VPN-AB gets a performance throughput
of maximum 15.645Gbps when we set the IPsec protocol with
3DES-HMAC-MD5 tunnel mode.
Abstract: This paper describes the development of a WLAN
propagation model, using Spectral Analyzer measurements. The
signal is generated by two Access Points (APs) on the base floor at
the administrative Communication School of ESPOL building. In
general, users do not have a Q&S reference about a wireless network;
however, this depends on the level signal as a function of frequency,
distance and other path conditions between receiver and transmitter.
Then, power density of the signal decrease as it propagates through
space and data transfer rate is affected. This document evaluates and
implements empirical mathematical formulation for the
characterization of WLAN radio wave propagation on two aisles of
the building base floor.
Abstract: High pressure adsorption of carbon dioxide on zeolite
13X was investigated in the pressure range (0 to 4) Mpa and
temperatures 298, 308 and 323K. The data fitting is accomplished
with the Toth, UNILAN, Dubinin-Astakhov and virial adsorption
models which are generally used for micro porous adsorbents such as
zeolites. Comparison with experimental data from the literature
indicated that the virial model would best determine results. These
results may be partly attributed to the flexibility of the virial model
which can accommodate as many constants as the data warrants.
Abstract: The following paper shows an interactive tool which
main purpose is to teach how to play a flute. It consists of three
stages the first one is the instruction and teaching process through a
software application, the second is the practice part when the user
starts to play the flute (hardware specially designed for this
application) this flute is capable of capturing how is being played the
flute and the final stage is the one in which the data captured are sent
to the software and the user is evaluated in order to give him / she a
correction or an acceptance
Abstract: This work is focused on the numerical prediction of the fracture resistance of a flat stiffened panel made of the aluminium alloy 2024 T3 under a monotonic traction condition. The performed numerical simulations have been based on the micromechanical Gurson-Tvergaard (GT) model for ductile damage. The applicability of the GT model to this kind of structural problems has been studied and assessed by comparing numerical results, obtained by using the WARP 3D finite element code, with experimental data available in literature. In the sequel a home-made procedure is presented, which aims to increase the residual strength of a cracked stiffened aluminum panel and which is based on the stochastic design improvement (SDI) technique; a whole application example is then given to illustrate the said technique.
Abstract: One of the most important parts of a cement factory is
the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral
movement of air and materials, together with chemical reactions take
place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only
in exceptional case; however, a large number of the involved parameter were crossed out and an approximation model was
presented instead. This issue caused many problems for designing a
cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using
nonlinear identification technique on the Locally Linear Neuro-
Fuzzy (LLNF) model. For the first time, a simulator model as well as
a predictor one with a precise fifteen minute prediction horizon for a
cement rotary kiln is presented. These models are trained by
LOLIMOT algorithm which is an incremental tree-structure
algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these
models. The data collected from White Saveh Cement Company is used for modeling.
Abstract: In this paper, we consider the problem of tracking
multiple maneuvering targets using switching multiple target motion
models. With this paper, we aim to contribute in solving the problem
of model-based body motion estimation by using data coming from
visual sensors. The Interacting Multiple Model (IMM) algorithm is
specially designed to track accurately targets whose state and/or
measurement (assumed to be linear) models changes during motion
transition. However, when these models are nonlinear, the IMM
algorithm must be modified in order to guarantee an accurate track.
In this paper we propose to avoid the Extended Kalman filter because
of its limitations and substitute it with the Unscented Kalman filter
which seems to be more efficient especially according to the
simulation results obtained with the nonlinear IMM algorithm (IMMUKF).
To resolve the problem of data association, the JPDA
approach is combined with the IMM-UKF algorithm, the derived
algorithm is noted JPDA-IMM-UKF.
Abstract: Recently, the health of retired National Football
League players, particularly lineman has been investigated. A number of studies have reported increased cardiometabolic risk, premature ardiovascular disease and incidence of type 2 diabetes. Rugby union players have somatotypes very similar
to National Football league players which suggest that rugby players may have similar health risks. The International Golden Oldies World Rugby Festival (GORF) provided a
unique opportunity to investigate the demographics of veteran rugby players. METHODOLOGIES: A cross-sectional, observational study was completed using an online web-based
questionnaire that consisted of medical history and
physiological measures. Data analysis was completed using a one sample t-test (50yrs) and Chi-square test. RESULTS: A total of 216 veteran rugby competitors
(response rate = 6.8%) representing 10 countries, aged 35-72 yrs (mean 51.2, S.D. ±8.0), participated in the online survey. As a group, the incidence of current smokers was low at 8.8%
(avg 72.4 cigs/wk) whilst the percentage consuming alcohol
was high (93.1% (avg 11.2 drinks/wk). Competitors reported
the following top six chronic diseases/disorders; hypertension
(18.6%), arthritis (OA/RA, 11.5%), asthma (9.3%),
hyperlipidemia (8.2%), diabetes (all types, 7.5%) and gout (6%), there were significant differences between groups with
regard to cancer (all types) and migraines. When compared to
the Australian general population (Australian Bureau of Statistics data, n=18,000), GORF competitors had a
Climstein Mike, Walsh Joe (corresponding author) and Burke Stephen
School of Exercise Science, Australian Catholic University, 25A Barker Road,
Strathfield, Sydney, NSW, 2016, Australia (e-mail:
[email protected], [email protected],
[email protected]).
John Best is with Orthosports, 160 Belmore Rd., Randwick, Sydney,NSW
2031, Australia (e-mail: [email protected]).
Heazlewood, Ian Timothy is with School of Environmental and Life
Sciences, Faculty Education, Health and Science, Charles Darwin University,
Precinct Yellow Building 2, Charles Darwin University, NT 0909, Australia
(e-mail: [email protected]).
Kettunen Jyrki Arcada University of Applied Sciences, Jan-Magnus
Janssonin aukio 1, FI-00550, Helsinki, Finland (e-mail:
[email protected]).
Adams Kent is with California State University Monterey Bay, Kinesiology Department, 100 Campus Center, Seaside, CA., 93955, USA (email: [email protected]).
DeBeliso Mark is with Department of Physical Education and Human
Performance, Southern Utah University, 351 West University Blvd, Cedar
City, Utah, USA (e-mail: [email protected]).
significantly lower incidence of anxiety (p
Abstract: This paper describes a platform that faces the main
research areas for e-learning educational contents. Reusability tackles
the possibility to use contents in different courses reducing costs and
exploiting available data from repositories. In our approach the
production of educational material is based on templates to reuse
learning objects. In terms of interoperability the main challenge lays
on reaching the audience through different platforms. E-learning
solution must track social consumption evolution where nowadays
lots of multimedia contents are accessed through the social networks.
Our work faces it by implementing a platform for generation of
multimedia presentations focused on the new paradigm related to
social media. The system produces videos-courses on top of web
standard SMIL (Synchronized Multimedia Integration Language)
ready to be published and shared. Regarding interfaces it is
mandatory to satisfy user needs and ease communication. To
overcome it the platform deploys virtual teachers that provide natural
interfaces while multimodal features remove barriers to pupils with
disabilities.
Abstract: This paper presents the application of Intelligent
Techniques to the various duties of Intelligent Condition Monitoring
Systems (ICMS) for Unmanned Aerial Vehicle (UAV) Robots. These
Systems are intended to support these Intelligent Robots in the event
of a Fault occurrence. Neural Networks are used for Diagnosis, whilst
Fuzzy Logic is intended for Prognosis and Remedy. The ultimate
goals of ICMS are to save large losses in financial cost, time and
data.
Abstract: This paper is an extension of a previous work where a diagonally implicit harmonic balance method was developed and applied to simulate oscillatory motions of pitching airfoil and wing. A more detailed study on the accuracy, convergence, and the efficiency of the method is carried out in the current paperby varying the number of harmonics in the solution approximation. As the main advantage of the method is itsusage for the design optimization of the unsteady problems, its application to more practical case of rotor flow analysis during forward flight is carried out and compared with flight test data and time-accurate computation results.
Abstract: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, a density based clustering algorithm (DCBRD) is presented, relying on a knowledge acquired from the data by dividing the data space into overlapped regions. The proposed algorithm discovers arbitrary shaped clusters, requires no input parameters and uses the same definitions of DBSCAN algorithm. We performed an experimental evaluation of the effectiveness and efficiency of it, and compared this results with that of DBSCAN. The results of our experiments demonstrate that the proposed algorithm is significantly efficient in discovering clusters of arbitrary shape and size.
Abstract: A transient heat transfer mathematical model for the
prediction of temperature distribution in the car body during primer
baking has been developed by considering the thermal radiation and
convection in the furnace chamber and transient heat conduction
governing equations in the car framework. The car cockpit is
considered like a structure with six flat plates, four vertical plates
representing the car doors and the rear and front panels. The other
two flat plates are the car roof and floor. The transient heat
conduction in each flat plate is modeled by the lumped capacitance
method. Comparison with the experimental data shows that the heat
transfer model works well for the prediction of thermal behavior of
the car body in the curing furnace, with deviations below 5%.
Abstract: In this paper a data miner based on the learning
automata is proposed and is called LA-miner. The LA-miner extracts
classification rules from data sets automatically. The proposed
algorithm is established based on the function optimization using
learning automata. The experimental results on three benchmarks
indicate that the performance of the proposed LA-miner is
comparable with (sometimes better than) the Ant-miner (a data miner
algorithm based on the Ant Colony optimization algorithm) and CNZ
(a well-known data mining algorithm for classification).
Abstract: Effect of high temperature exposure on properties of cement mortar containing municipal solid waste incineration (MSWI) bottom ash as partial natural aggregate replacement is analyzed in the paper. The measurements of mechanical properties, bulk density, matrix density, total open porosity, sorption and desorption isotherms are done on samples exposed to the temperatures of 20°C to 1000°C. TGA analysis is performed as well. Finally, the studied samples are analyzed by IR spectroscopy in order to evaluate TGA data.
Abstract: This paper describes a novel and effective approach to content-based image retrieval (CBIR) that represents each image in the database by a vector of feature values called “Standard deviation of mean vectors of color distribution of rows and columns of images for CBIR". In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. This paper describes the approach that uses contents as feature vector for retrieval of similar images. There are several classes of features that are used to specify queries: colour, texture, shape, spatial layout. Colour features are often easily obtained directly from the pixel intensities. In this paper feature extraction is done for the texture descriptor that is 'variance' and 'Variance of Variances'. First standard deviation of each row and column mean is calculated for R, G, and B planes. These six values are obtained for one image which acts as a feature vector. Secondly we calculate variance of the row and column of R, G and B planes of an image. Then six standard deviations of these variance sequences are calculated to form a feature vector of dimension six. We applied our approach to a database of 300 BMP images. We have determined the capability of automatic indexing by analyzing image content: color and texture as features and by applying a similarity measure Euclidean distance.
Abstract: Data mining is an extraordinarily demanding field referring to extraction of implicit knowledge and relationships, which are not explicitly stored in databases. A wide variety of methods of data mining have been introduced (classification, characterization, generalization...). Each one of these methods includes more than algorithm. A system of data mining implies different user categories,, which mean that the user-s behavior must be a component of the system. The problem at this level is to know which algorithm of which method to employ for an exploratory end, which one for a decisional end, and how can they collaborate and communicate. Agent paradigm presents a new way of conception and realizing of data mining system. The purpose is to combine different algorithms of data mining to prepare elements for decision-makers, benefiting from the possibilities offered by the multi-agent systems. In this paper the agent framework for data mining is introduced, and its overall architecture and functionality are presented. The validation is made on spatial data. Principal results will be presented.