Abstract: Classification of electroencephalogram (EEG) signals
extracted during mental tasks is a technique that is actively pursued
for Brain Computer Interfaces (BCI) designs. In this paper, we
compared the classification performances of univariateautoregressive
(AR) and multivariate autoregressive (MAR) models
for representing EEG signals that were extracted during different
mental tasks. Multilayer Perceptron (MLP) neural network (NN)
trained by the backpropagation (BP) algorithm was used to classify
these features into the different categories representing the mental
tasks. Classification performances were also compared across
different mental task combinations and 2 sets of hidden units (HU): 2
to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different
mental tasks from 4 subjects were used in the experimental study and
combinations of 2 different mental tasks were studied for each
subject. Three different feature extraction methods with 6th order
were used to extract features from these EEG signals: AR
coefficients computed with Burg-s algorithm (ARBG), AR
coefficients computed with stepwise least square algorithm (ARLS)
and MAR coefficients computed with stepwise least square
algorithm. The best results were obtained with 20 to 100 HU using
ARBG. It is concluded that i) it is important to choose the suitable
mental tasks for different individuals for a successful BCI design, ii)
higher HU are more suitable and iii) ARBG is the most suitable
feature extraction method.
Abstract: In this paper, we propose a new method to distinguish
between arousal and relaxation states by using multiple features
acquired from a photoplethysmogram (PPG) and support vector
machine (SVM). To induce arousal and relaxation states in subjects, 2
kinds of sound stimuli are used, and their corresponding biosignals are
obtained using the PPG sensor. Two features–pulse to pulse interval
(PPI) and pulse amplitude (PA)–are extracted from acquired PPG
data, and a nonlinear classification between arousal and relaxation is
performed using SVM.
This methodology has several advantages when compared with
previous similar studies. Firstly, we extracted 2 separate features from
PPG, i.e., PPI and PA. Secondly, in order to improve the classification
accuracy, SVM-based nonlinear classification was performed.
Thirdly, to solve classification problems caused by generalized
features of whole subjects, we defined each threshold according to
individual features.
Experimental results showed that the average classification
accuracy was 74.67%. Also, the proposed method showed the better
identification performance than the single feature based methods.
From this result, we confirmed that arousal and relaxation can be
classified using SVM and PPG features.
Abstract: In this article, some methods are mentioned for developing the theatrical language by giving information of “theatrical language" since the arising of the language in obsolete terms, and today, and also by examining the problems. Being able to talk meaningfully in the theater stage is a skillful art. Maybe, to be able to convey the idea of the poet, his/her world outlook and his/her feelings from the bottom of the heart as such, also conveying the speech norms without breaking them to the ear of audience in a fascinating way in adverse of a repellent way is the most difficult one. Because of this, “the word is the mirror of the idea". The importance of the theatrical language should not be perceived as only a post, it is “as the yarn that the culture carpet is weaved from". Thereby, it is a tool which transposes our culture and our life style from generation to generation. At the time of creativeness, the “word" comes out from the poet, “the word and feeling" art comes out from the actor. If it was not so, the audience could read the texts of the work himself/herself instead of going to the theater in order to see the performance. The fundamental works by the Turkish, Kazakh and English scientists have been taken as a basis for the research done.
Abstract: This paper presents a subjective job scheduler based
on a 3-layer Backpropagation Neural Network (BPNN) and a greedy
alignment procedure in order formulates a real-life situation. The
BPNN estimates critical values of jobs based on the given subjective
criteria. The scheduler is formulated in such a way that, at each time
period, the most critical job is selected from the job queue and is
transferred into a single machine before the next periodic job arrives.
If the selected job is one of the oldest jobs in the queue and its
deadline is less than that of the arrival time of the current job, then
there is an update of the deadline of the job is assigned in order to
prevent the critical job from its elimination. The proposed
satisfiability criteria indicates that the satisfaction of the scheduler
with respect to performance of the BPNN, validity of the jobs and the
feasibility of the scheduler.
Abstract: Organic farmers across Saskatchewan face soil
phosphorus (P) shortages. Due to the restriction on inputs in organic
systems, farmers rely on crop rotation and naturally-occurring
arbuscular mycorrhizal fungi (AMF) for plant P supply. Crop rotation
is important for disease, pest, and weed management. Crops that are
not colonized by AMF (non-mycorrhizal) can decrease colonization
of a following crop. An experiment was performed to quantify soil P
cycling in four cropping sequences under organic management and
determine if mustard (non-mycorrhizal) was delaying the
colonization of subsequent wheat. Soils from the four cropping
sequences were measured for inorganic soil P (Pi), AMF spore
density (SD), phospholipid fatty acid analysis (PLFA, for AMF
biomarker counts), and alkaline phosphatase activity (ALPase,
related to AMF metabolic activity). Plants were measured for AMF
colonization and P content and uptake of above-ground biomass. A
lack of difference in AMF activity indicated that mustard was not
depressing colonization. Instead, AMF colonization was largely
determined by crop type and crop rotation.
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: The aim of the paper work is to investigate and predict
the static performance of journal bearing in turbulent flow condition
considering micropolar lubrication. The Reynolds equation has been
modified considering turbulent micropolar lubrication and is solved
for steady state operations. The Constantinescu-s turbulence model is
adopted using the coefficients. The analysis has been done for a
parallel and inertia less flow. Load capacity and friction factor have
been evaluated for various operating parameters.
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: In this paper, numerical simulations are performed to investigate the effect of disturbance block on flow field of the classical square lid-driven cavity. Attentions are focused on vortex formation and studying the effect of block position on its structure. Corner vortices are different upon block position and new vortices are produced because of the block. Finite volume method is used to solve Navier-Stokes equations and PISO algorithm is employed for the linkage of velocity and pressure. Verification and grid independency of results are reported. Stream lines are sketched to visualize vortex structure in different block positions.
Abstract: A new hybrid method to realise high-precision
distortion determination for optical ultra-precision 3D measurement
systems based on stereo cameras using active light projection is
introduced. It consists of two phases: the basic distortion
determination and the refinement. The refinement phase of the
procedure uses a plane surface and projected fringe patterns as
calibration tools to determine simultaneously the distortion of both
cameras within an iterative procedure. The new technique may be
performed in the state of the device “ready for measurement" which
avoids errors by a later adjustment. A considerable reduction of
distortion errors is achieved and leads to considerable improvements
of the accuracy of 3D measurements, especially in the precise
measurement of smooth surfaces.
Abstract: We investigate nonfactorizable contributions to
D → ¤Ç¤Ç decay modes. We perform isospin analysis of the
nonfactorizable contributions to these decays. Obtaining the
factorizable contributions from spectator-quark diagrams using
= 3 C N , we determine nonfactorizable amplitudes for these decays
and predict their branching ratios.
Abstract: An attempt has been made to develop a
seminumerical model to study temperature variations in dermal
layers of human limbs. The model has been developed for two
dimensional steady state case. The human limb has been assumed to
have elliptical cross section. The dermal region has been divided
into three natural layers namely epidermis, dermis and subdermal
tissues. The model incorporates the effect of important physiological
parameters like blood mass flow rate, metabolic heat generation, and
thermal conductivity of the tissues. The outer surface of the limb is
exposed to the environment and it is assumed that heat loss takes
place at the outer surface by conduction, convection, radiation, and
evaporation. The temperature of inner core of the limb also varies at
the lower atmospheric temperature. Appropriate boundary conditions
have been framed based on the physical conditions of the problem.
Cubic splines approach has been employed along radial direction and
Fourier series along angular direction to obtain the solution. The
numerical results have been computed for different values of
eccentricity resembling with the elliptic cross section of the human
limbs. The numerical results have been used to obtain the
temperature profile and to study the relationships among the various
physiological parameters.
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: Precast concrete has been widely adopted in public
housing construction of Hong Kong since the mid-1980s. While
pre-casting is considered an environmental friendly solution, there is
lack of study to investigate the life cycle performance of precast
concrete units. This study aims to bridge the knowledge gap by
providing a comprehensive life cycle assessment (LCA) study for two
precast elements namely façade and bathroom. The results show that
raw material is the most significant contributor of environmental
impact accounting for about 90% to the total impact. Furthermore,
human health is more affected by the production of precast concrete
than the ecosystems.
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: 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: The problem of incompressible steady flow simulation around an airfoil is discussed. For some simplest airfoils (circular, elliptical, Zhukovsky airfoils) the exact solution is known from complex analysis. It allows to compute the intensity of vortex layer which simulates the airfoil. Some modifications of the vortex element method are proposed and test computations are carried out. It-s shown that the these approaches are much more effective in comparison with the classical numerical scheme.
Abstract: Gas turbine systems with wet compression have a
potential for future power generation, since they can offer a high
efficiency and a high specific power with a relatively low cost. In this
study influence of ambient condition on the performance of the wet
compression process is investigated with a non-equilibrium analytical
modeling based on droplet evaporation. Transient behaviors of droplet
diameter and temperature of mixed air are investigated for various
ambient temperatures. Special attention is paid for the effects of
ambient temperature, pressure ratio, and water injection ratios on the
important wet compression variables including compressor outlet
temperature and compression work. Parametric studies show that
downing of the ambient temperature leads to lower compressor outlet
temperature and consequently lower consumption of compression
work even in wet compression processes.
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.