Abstract: Kepsut-Dursunbey volcanic field (KDVF) is located
in NW Turkey and contains various products of the post-collisional
Neogene magmatic activity. Two distinct volcanic suites have been
recognized; the Kepsut volcanic suite (KVS) and the Dursunbey
volcanic suite (DVS). The KVS includes basaltic trachyandesitebasaltic
andesite-andesite lavas and associated pyroclastic rocks. The
DVS consists of dacite-rhyodacite lavas and extensive pumice-ash
fall and flow deposits. Petrographical features (i.e. existence of
xenocrysts, glomerocrysts, and mixing-compatible textures) and
mineral chemistry of phenocryst assemblages of both suites provide
evidence for magma mixing/AFC. Calculated crystallization
pressures and temperatures give values of 5.7–7.0 kbar and 927–982
°C for the KVS and 3.7–5.3 kbar and 783-787°C for the DVS,
indicating separate magma reservoirs and crystallization in magma
chambers at deep and mid crustal levels, respectively. These
observations support the establishment and evolution of KDVF
magma system promoted by episodic basaltic inputs which may
generate and mix with crustal melts.
Abstract: In this paper, the modified optimal sliding mode control with a proposed method to design a sliding surface is presented. Because of the inability of the previous approach of the sliding mode method to design a bounded and suitable input, the new variation is proposed in the sliding manifold to obviate problems in a structural system. Although the sliding mode control is a powerful method to reject disturbances and noises, the chattering problem is not good for actuators. To decrease the chattering phenomena, the optimal control is added to the sliding mode control. Not only the proposed method can decline the intense variations in the inputs of the system but also it can produce the efficient responses respect to the sliding mode control and optimal control that are shown by performing some numerical simulations.
Abstract: This paper presents a methodology towards the emulation of the electrical power consumption of the RF device during the cellular phone/handset transmission mode using the LTE technology. The emulation methodology takes the physical environmental variables and the logical interface between the baseband and the RF system as inputs to compute the emulated power dissipation of the RF device. The emulated power, in between the measured points corresponding to the discrete values of the logical interface parameters is computed as a polynomial interpolation using polynomial basis functions. The evaluation of polynomial and spline curve fitting models showed a respective divergence (test error) of 8% and 0.02% from the physically measured power consumption. The precisions of the instruments used for the physical measurements have been modeled as intervals. We have been able to model the power consumption of the RF device operating at 5MHz using homotopy between 2 continuous power consumptions of the RF device operating at the bandwidths 3MHz and 10MHz.
Abstract: Series compensators have been used for many years,
to increase the stability and load ability of transmission line. They
compensate retarded or advanced volt drop of transmission lines
by placing advanced or retarded voltage in series with them to
compensate the effective reactance, which cause to increase load
ability of transmission lines. In this paper, two method of fuzzy
controller, based on power reference tracking and impedance
reference tracking have been developed on TCSC controller in
order to increase load ability and improving power oscillation
damping of system. In these methods, fire angle of thyristors are
determined directly through the special Rule-bases with the error
and change of error as the inputs. The simulation results of two
area four- machines power system show the good performance of
power oscillation damping in system. Comparison of this method
with classical PI controller shows the increasing speed of system
response in power oscillation damping.
Abstract: Earth reinforcing techniques have become useful and economical to solve problems related to difficult grounds and provide satisfactory foundation performance. In this context, this paper uses radial basis function neural network (RBFNN) for predicting the bearing pressure of strip footing on reinforced granular bed overlying weak soil. The inputs for the neural network models included plate width, thickness of granular bed and number of layers of reinforcements, settlement ratio, water content, dry density, cohesion and angle of friction. The results indicated that RBFNN model exhibited more than 84 % prediction accuracy, thereby demonstrating its application in a geotechnical problem.
Abstract: Mammals are known to use Interaural Intensity Difference (IID) to determine azimuthal position of high frequency sounds. In the Lateral Superior Olive (LSO) neurons have firing behaviours which vary systematicaly with IID. Those neurons receive excitatory inputs from the ipsilateral ear and inhibitory inputs from the contralateral one. The IID sensitivity of a LSO neuron is thought to be due to delay differences between both ears, delays due to different synaptic delays and to intensity-dependent delays. In this paper we model the auditory pathway until the LSO. Inputs to LSO neurons are at first numerous and differ in their relative delays. Spike Timing-Dependent Plasticity is then used to prune those connections. We compare the pruned neuron responses with physiological data and analyse the relationship between IID-s of teacher stimuli and IID sensitivities of trained LSO neurons.
Abstract: Soil organic carbon (SOC) plays a key role in soil
fertility, hydrology, contaminants control and acts as a sink or source
of terrestrial carbon content that can affect the concentration of
atmospheric CO2. SOC supports the sustainability and quality of
ecosystems, especially in semi-arid region. This study was
conducted to determine relative importance of 13 different
exploratory climatic, soil and geometric factors on the SOC contents
in one of the semiarid watershed zones in Iran. Two methods
canonical discriminate analysis (CDA) and feed-forward back
propagation neural networks were used to predict SOC. Stepwise
regression and sensitivity analysis were performed to identify
relative importance of exploratory variables. Results from sensitivity
analysis showed that 7-2-1 neural networks and 5 inputs in CDA
models output have highest predictive ability that explains %70 and
%65 of SOC variability. Since neural network models outperformed
CDA model, it should be preferred for estimating SOC.
Abstract: A two dimensional three segments coupled pendulum system that mathematically models human arm configuration was developed along with constructing and solving the equations of motions for this model using the energy (work) based approach of Lagrange. The equations of motion of the model were solved iteratively both as an initial value problem and as a two point boundary value problem. In the initial value problem solutions, both the initial system configuration (segment angles) and initial system velocity (segment angular velocities) were used as inputs, whereas, in the two point boundary value problem solutions initial and final configurations and time were used as inputs to solve for the trajectory of motion. The results suggest that the model solutions are sensitive to small changes in the dynamic forces applied to the system as well as to the initial and boundary conditions used. To overcome the system sensitivity a new approach is suggested.
Abstract: Many experimental results suggest that more precise spike timing is significant in neural information processing. We construct a self-organization model using the spatiotemporal pat-terns, where Spike-Timing Dependent Plasticity (STDP) tunes the conduction delays between neurons. We show that, for highly syn-chronized inputs, the fluctuation of conduction delays causes globally continuous and locally distributed firing patterns through the self-organization.
Abstract: Vehicle which are turning or maneuvering at high speeds
are susceptible to sliding and subsequently deviate from desired path. In
this paper the dynamics governing the Yaw/Roll behavior of a vehicle
has been simulated. Two different simulations have been used one for
the real vehicle, for which a fuzzy controller is designed to increase its
directional stability property. The other simulation is for a hypothetical
vehicle with much higher tire cornering stiffness which is capable of
developing the required lateral forces at the tire-ground patch contact to
attain the desired lateral acceleration for the vehicle to follow the
desired path without slippage. This simulation model is our reference
model.
The logic for keeping the vehicle on the desired track in the cornering
or maneuvering state is to have some braking forces on the inner or
outer tires based on the direction of vehicle deviation from the desired
path. The inputs to our vehicle simulation model is steer angle δ and
vehicle velocity V , and the outputs can be any kinematical parameters
like yaw rate, yaw acceleration, side slip angle, rate of side slip angle
and so on. The proposed fuzzy controller is a feed forward controller.
This controller has two inputs which are steer angle δ and vehicle
velocity V, and the output of the controller is the correcting moment M,
which guides the vehicle back to the desired track. To develop the
membership functions for the controller inputs and output and the fuzzy
rules, the vehicle simulation has been run for 1000 times and the
correcting moment have been determined by trial and error. Results of
the vehicle simulation with fuzzy controller are very promising
and show the vehicle performance is enhanced greatly over the
vehicle without the controller. In fact the vehicle performance
with the controller is very near the performance of the reference
ideal model.
Abstract: This paper provides the design steps of a robust Linear
Matrix Inequality (LMI) based iterative multivariable PID controller
whose duty is to drive a sample power system that comprises a
synchronous generator connected to a large network via a step-up
transformer and a transmission line. The generator is equipped with
two control-loops, namely, the speed/power (governor) and voltage
(exciter). Both loops are lumped in one where the error in the
terminal voltage and output active power represent the controller
inputs and the generator-exciter voltage and governor-valve position
represent its outputs. Multivariable PID is considered here because of
its wide use in the industry, simple structure and easy
implementation. It is also preferred in plants of higher order that
cannot be reduced to lower ones. To improve its robustness to
variation in the controlled variables, H∞-norm of the system transfer
function is used. To show the effectiveness of the controller, divers
tests, namely, step/tracking in the controlled variables, and variation
in plant parameters, are applied. A comparative study between the
proposed controller and a robust H∞ LMI-based output feedback is
given by its robustness to disturbance rejection. From the simulation
results, the iterative multivariable PID shows superiority.
Abstract: Adjacent Hall microsensors, comprising a silicon
substrate and four contacts, providing simultaneously two supply inputs and two differential outputs, are characterized. The voltage
related sensitivity is in the order of 0.11T-1, and a cancellation method for offset compensation is used, achieving residual offset in
the micro scale which is also compared to a single Hall plate.
Abstract: Artificial Neural Network (ANN)s can be modeled for
High Energy Particle analysis with special emphasis on shower core
location. The work describes the use of an ANN based system which
has been configured to predict locations of cores of showers in the
range 1010.5 to 1020.5 eV. The system receives density values as
inputs and generates coordinates of shower events recorded for values
captured by 20 core positions and 80 detectors in an area of 100
meters. Twenty ANNs are trained for the purpose and the positions
of shower events optimized by using cooperative ANN learning. The
results derived with variations of input upto 50% show success rates
in the range of 90s.
Abstract: This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.
Abstract: The innovative intelligent fuzzy weighted input
estimation method (FWIEM) can be applied to the inverse heat
transfer conduction problem (IHCP) to estimate the unknown
time-varying heat flux of the multilayer materials as presented in this
paper. The feasibility of this method can be verified by adopting the
temperature measurement experiment. The experiment modular may
be designed by using the copper sample which is stacked up 4
aluminum samples with different thicknesses. Furthermore, the
bottoms of copper samples are heated by applying the standard heat
source, and the temperatures on the tops of aluminum are measured by
using the thermocouples. The temperature measurements are then
regarded as the inputs into the presented method to estimate the heat
flux in the bottoms of copper samples. The influence on the estimation
caused by the temperature measurement of the sample with different
thickness, the processing noise covariance Q, the weighting factor γ ,
the sampling time interval Δt , and the space discrete interval Δx ,
will be investigated by utilizing the experiment verification. The
results show that this method is efficient and robust to estimate the
unknown time-varying heat input of the multilayer materials.
Abstract: In a competitive energy market, system reliability
should be maintained at all times. Power system operation being of
online in nature, the energy balance requirements must be satisfied to
ensure reliable operation the system. To achieve this, information
regarding the expected status of the system, the scheduled
transactions and the relevant inputs necessary to make either a
transaction contract or a transmission contract operational, have to be
made available in real time. The real time procedure proposed,
facilitates this. This paper proposes a quadratic curve learning
procedure, which enables a generator-s contribution to the retailer
demand, power loss of transaction in a line at the retail end and its
associated losses for an oncoming operating scenario to be predicted.
Matlab program was used to test in on a 24-bus IEE Reliability Test
System, and the results are found to be acceptable.
Abstract: In this paper methodology to exploit creeping wave
for body area network BAN communication reliability are described.
Creeping wave propagation effects are visualized & analyzed.
During this work Dipole, IA antennas various antennas were
redesigned using existing designs and their propagation
characteristics were verified for optimum performance when used on
BANs. These antennas were then applied on body shapes-including
rectangular, spherical and cylindrical so that all the effects of actual
human body can be taken nearly into account. Parametric simulation
scheme was devised so that on Body channel characterization can be
visualized at front, curved and back region. In the next phase
multiple inputs multiple output MIMO scheme was introduced where
virtual antennas were used in order to diminish the effects of
antennas on the propagation of waves. Results were, extracted and
analyzed at different heights. Finally based on comparative
measurement and analysis it was concluded that on body propagation
can be exploited to gain spatial diversity.
Abstract: Globalisation is a phenomenon that cannot be avoided.
As globalisation allowed free flow of inputs including labour, it may
affect job opportunities for the locals. Therefore, investigate the
determinants of labour supply is essential in understanding the
structure of labour market in the new era of globalization. The
objective of this article is to examine labour supply by taking into
account the globalisation effect. The study covers 3885 households in
Peninsular Malaysia who are chosen using stratified random
sampling. The labour supply model will be the basis for the analysis.
The basic labour supply determinants are own wage and non-labour
income. However, the extended labour supply model incorporates
other variables like spouse wage，number of children and
individuals characteristics like education level and age. Besides, the
globalization indicator will also be incorporated as another
independent variable.
Abstract: Sensors possess several properties of physical
measures. Whether devices that convert a sensed signal into an
electrical signal, chemical sensors and biosensors, thus all these
sensors can be considered as an interface between the physical and
electrical equipment. The problem is the analysis of the multitudes of
saved settings as input variables. However, they do not all have the
same level of influence on the outputs. In order to identify the most
sensitive parameters, those that can guide users in gathering
information on the ground and in the process of model calibration
and sensitivity analysis for the effect of each change made.
Mathematical models used for processing become very complex.
In this paper a fuzzy rule-based system is proposed as a solution
for this problem. The system collects the available signals
information from sensors. Moreover, the system allows the study of
the influence of the various factors that take part in the decision
system. Since its inception fuzzy set theory has been regarded as a
formalism suitable to deal with the imprecision intrinsic to many
problems. At the same time, fuzzy sets allow to use symbolic models.
In this study an example was applied for resolving variety of
physiological parameters that define human health state. The
application system was done for medical diagnosis help. The inputs
are the signals expressed the cardiovascular system parameters, blood
pressure, Respiratory system paramsystem was done, it will be able
to predict the state of patient according any input values.
Abstract: Construction cost in India is increasing at around 50
per cent over the average inflation levels. It have registered increase
of up to 15 per cent every year, primarily due to cost of basic
building materials such as steel, cement, bricks, timber and other
inputs as well as cost of labour. As a result, the cost of construction
using conventional building materials and construction is becoming
beyond the affordable limits particularly for low-income groups of
population as well as a large cross section of the middle - income
groups. Therefore, there is a need to adopt cost-effective construction
methods either by up-gradation of traditional technologies using local
resources or applying modern construction materials and techniques
with efficient inputs leading to economic solutions. This has become
the most relevant aspect in the context of the large volume of housing
to be constructed in both rural and urban areas and the consideration
of limitations in the availability of resources such as building
materials and finance. This paper makes an overview of the housing
status in India and adoption of appropriate and cost effective
technologies in the country.