Abstract: This paper presents an authoring tool which makes a
user easily and intuitively design vibrotactile sensation. A mobile
hardware platform powered by ANDROID, a multi-purpose haptic
driver and a linear resonance actuator are used to implement the
system of the presented authoring tool. The tool allows users to easily
and simply create a vibrotactile sensation by drawing vibrotactile
images and to feel the sensation by rubbing drawn images on the touch
screen of a mobile device. The tool supports a graphical interface for
designing, editing and playing vibrotactile images as well as a
pre-defined file format for save and open.
Abstract: Power System Security is a major concern in real time
operation. Conventional method of security evaluation consists of
performing continuous load flow and transient stability studies by
simulation program. This is highly time consuming and infeasible
for on-line application. Pattern Recognition (PR) is a promising
tool for on-line security evaluation. This paper proposes a Support
Vector Machine (SVM) based binary classification for static and
transient security evaluation. The proposed SVM based PR approach
is implemented on New England 39 Bus and IEEE 57 Bus systems.
The simulation results of SVM classifier is compared with the other
classifier algorithms like Method of Least Squares (MLS), Multi-
Layer Perceptron (MLP) and Linear Discriminant Analysis (LDA)
classifiers.
Abstract: In order to be able to automatically differentiate
between two modes of permanent flow of a liquid simulating blood,
it was imperative to put together a data bank. Thus, the acquisition of
the various amplitude spectra of the Doppler signal of this liquid in
laminar flow and other spectra in turbulent flow enabled us to
establish an automatic difference between the two modes. According
to the number of parameters and their nature, a comparative study
allowed us to choose the best classifier.
Abstract: Biplot can be used to evaluate cultivars for their oil
percent potential and stability and to evaluate trial sites for their
discriminating ability and representativeness. Multi-environmental
trial (MET) data for oil percent of 10 open pollinating sunflower
cultivars were analyzed to investigate the genotype-environment
interactions. The genotypes were evaluated in four locations with
different climatic conditions in Iran in 2010. In each location, a
Randomized Complete Block design with four replications was used.
According to both mean and stability, Zaria, Master and R453, had
highest performances among all cultivars. The graphical analysis
identified best cultivar for each environment. Cultivars Berezans and
Record performed best in Khoy and Islamabad. Zaria and R453 were
the best genotypes in Sari and Karaj followed by Master and Favorit.
The GGE bi-plot indicated two mega-environments, group one
contained Karaj, Khoy and Islamabad and the second group
contained Sari. The best discriminating location was Karaj followed
with Khoy, Islamabad and Sari. The best representative genotypes
were Zaria, R453, Master and Favorit. Ranking of ten cultivars based
their oil percent was as Zaria > R453 ≈ Master ≈ Favorit > Record ≈
Berezans > Sor > Lakumka > Bulg3 > Bulg5.
Abstract: This paper describes an application of a dual satellite
geolocation (DSG) system on identifying and locating the unknown
source of uplink sweeping interference. The geolocation system
integrates the method of joint time difference of arrival (TDOA) and
frequency difference of arrival (FDOA) with ephemeris correction
technique which successfully demonstrated high accuracy in
interference source location. The factors affecting the location error
were also discussed.
Abstract: Since the presentation of the backpropagation algorithm, a vast variety of improvements of the technique for training a feed forward neural networks have been proposed. This article focuses on two classes of acceleration techniques, one is known as Local Adaptive Techniques that are based on weightspecific only, such as the temporal behavior of the partial derivative of the current weight. The other, known as Dynamic Adaptation Methods, which dynamically adapts the momentum factors, α, and learning rate, η, with respect to the iteration number or gradient. Some of most popular learning algorithms are described. These techniques have been implemented and tested on several problems and measured in terms of gradient and error function evaluation, and percentage of success. Numerical evidence shows that these techniques improve the convergence of the Backpropagation algorithm.
Abstract: Internet Access Technologies (IAT) provide a means
through which Internet can be accessed. The choice of a suitable
Internet technology is increasingly becoming an important issue to
ISP clients. Currently, the choice of IAT is based on discretion and
intuition of the concerned managers and the reliance on ISPs. In this
paper we propose a model and designs algorithms that are used in the
Internet access technology specification. In the proposed model, three
ranking approaches are introduced; concurrent ranking, stepwise
ranking and weighted ranking. The model ranks the IAT based on
distance measures computed in ascending order while the global
ranking system assigns weights to each IAT according to the position
held in each ranking technique, determines the total weight of a
particular IAT and ranks them in descending order. The final output
is an objective ranking of IAT in descending order.
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: Binary Decision Diagrams (BDDs) are useful data
structures for symbolic Boolean manipulations. BDDs are used in
many tasks in VLSI/CAD, such as equivalence checking, property
checking, logic synthesis, and false paths. In this paper we describe a
new approach for the realization of a BDD package. To perform
manipulations of Boolean functions, the proposed approach does not
depend on the recursive synthesis operation of the IF-Then-Else
(ITE). Instead of using the ITE operation, the basic synthesis
algorithm is done using Boolean NOR operation.
Abstract: In this paper comparison of Reflector Antenna
analyzing techniques based on wave and ray nature of optics is
presented for an offset reflector antenna using GRASP (General
Reflector antenna Analysis Software Package) software. The results
obtained using PO (Physical Optics), PTD (Physical theory of
Diffraction), and GTD (Geometrical Theory of Diffraction) are
compared. The validity of PO and GTD techniques in regions around
the antenna, caustic behavior of GTD in main beam, and deviation of
GTD in case of near-in sidelobes of radiation pattern are discussed.
The comparison for far-out sidelobes predicted by PO, PO + PTD
and GTD is described. The effect of Direct Radiations from feed
which results in feed selection for the system is addressed.
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: Aggression is a behavior that cannot be approved by
the society. Vandalism which is aggression towards objects is an
action that tends to damage public or personal property. The
behaviors that are described as vandalism can often be observed in
the schools as well. According to Zwier and Vaughan (1)
previous research about the reasons of and precautionary measures
for vandalism in schools can be grouped in three tendency categories:
conservative, liberal and radical. In this context, the main aim of this
study is to discover which ideological tendency of the reasons of
school vandalism is adopted by the teachers and what are their
physical, environmental, school system and societal solutions for
vandalism. A total of 200 teachers participated in this study, and the
mean age was 34.20 years (SD = 6.54). The sample was made up of
109 females and 91 males. For the analysis of the data, SPSS 15.00,
frequency, percentage, and t-test were used. The research showed
that the teachers have tendencies in the order of conservative, liberal
and radical for the reasons of vandalism. The research also showed
that the teachers do not have any tendency for eliminating vandalism
physically and general solutions on the level of society; on the other
hand they mostly adopt a conservative tendency in terms of
precautions against vandalism in the school system. Second most,
they adopt the liberal tendency in terms of precautions against
vandalism in the school system. . It is observed that the findings of
this study are comparable to the existing literature on the subject.
Future studies should be conducted with multiple variants and
bigger sampling.
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.
Abstract: This paper presents how the real-time chatter
prevention can be realized by feedback of acoustic cutting signal, and
the efficacy of the proposed adaptive spindle speed tuning algorithm is
verified by intensive experimental simulations. A pair of
microphones, perpendicular to each other, is used to acquire the
acoustic cutting signal resulting from milling chatter. A real-time
feedback control loop is constructed for spindle speed compensation
so that the milling process can be ensured to be within the stability
zone of stability lobe diagram. Acoustic Chatter Signal Index (ACSI)
and Spindle Speed Compensation Strategy (SSCS) are proposed to
quantify the acoustic signal and actively tune the spindle speed
respectively. By converting the acoustic feedback signal into ACSI,
an appropriate Spindle Speed Compensation Rate (SSCR) can be
determined by SSCS based on real-time chatter level or ACSI.
Accordingly, the compensation command, referred to as Added-On
Voltage (AOV), is applied to increase/decrease the spindle motor
speed. By inspection on the precision and quality of the workpiece
surface after milling, the efficacy of the real-time chatter prevention
strategy via acoustic signal feedback is further assured.
Abstract: We investigate the ZnO role in the inherent protection
of old manuscripts to protect them against environmental damaging
effect of ultraviolet radiation, pollutant gasses, mold and bacteria. In
this study a cellulosic nanocomposite of ZnO were used as protective
coating on the surface of paper fibers. This layered nanocomposite
can act as a consolidate materials too. Furthermore, to determine how
well paper works screen objects from the damaging effects, two
accelerated aging mechanisms due to light and heat are discussed.
Results show good stability of papers with nanocomposite coating.
Also, a good light stability was shown in the colored paper that
treated with this nanocomposite. Furthermore, to demonstrate the
degree of antifungal and antibacterial properties of coated papers,
papers was treated with four common molds and bacteria and the
good preventive effects of coated paper against molds and bacteria
are described.
Abstract: With the advent of emerging personal computing paradigms such as ubiquitous and mobile computing, Web contents are becoming accessible from a wide range of mobile devices. Since these devices do not have the same rendering capabilities, Web contents need to be adapted for transparent access from a variety of client agents. Such content adaptation results in better rendering and faster delivery to the client device. Nevertheless, Web content adaptation sets new challenges for semantic markup. This paper presents an advanced components platform, called MorfeoSMC, enabling the development of mobility applications and services according to a channel model based on Services Oriented Architecture (SOA) principles. It then goes on to describe the potential for integration with the Semantic Web through a novel framework of external semantic annotation of mobile Web contents. The role of semantic annotation in this framework is to describe the contents of individual documents themselves, assuring the preservation of the semantics during the process of adapting content rendering, as well as to exploit these semantic annotations in a novel user profile-aware content adaptation process. Semantic Web content adaptation is a way of adding value to and facilitates repurposing of Web contents (enhanced browsing, Web Services location and access, etc).
Abstract: The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.
Abstract: Computer-mediated communication technologies which provide for virtual communities have typically evolved in a cross-dichotomous manner, such that technical constructs of the technology have evolved independently from the social environment of the community. The present paper analyses some limitations of current implementations of computer-mediated communication technology that are implied by such a dichotomy, and discusses their inhibiting effects on possible developments of virtual communities. A Socio-Technical Indicator Model is introduced that utilizes integrated feedback to describe, simulate and operationalise increasing representativeness within a variety of structurally and parametrically diverse systems. In illustration, applications of the model are briefly described for financial markets and for eco-systems. A detailed application is then provided to resolve the aforementioned technical limitations of moderation on the evolution of virtual communities. The application parameterises virtual communities to function as self-transforming social-technical systems which are sensitive to emergent and shifting community values as products of on-going communications within the collective.
Abstract: Software reuse can be considered as the most realistic
and promising way to improve software engineering productivity and
quality. Automated assistance for software reuse involves the
representation, classification, retrieval and adaptation of components.
The representation and retrieval of components are important to
software reuse in Component-Based on Software Development
(CBSD). However, current industrial component models mainly focus
on the implement techniques and ignore the semantic information
about component, so it is difficult to retrieve the components that
satisfy user-s requirements. This paper presents a method of business
component retrieval based on specification matching to solve the
software reuse of enterprise information system. First, a business
component model oriented reuse is proposed. In our model, the
business data type is represented as sign data type based on XML,
which can express the variable business data type that can describe the
variety of business operations. Based on this model, we propose
specification match relationships in two levels: business operation
level and business component level. In business operation level, we
use input business data types, output business data types and the
taxonomy of business operations evaluate the similarity between
business operations. In the business component level, we propose five
specification matches between business components. To retrieval
reusable business components, we propose the measure of similarity
degrees to calculate the similarities between business components.
Finally, a business component retrieval command like SQL is
proposed to help user to retrieve approximate business components
from component repository.