Abstract: This paper presents a developed method for
controlling multi-renewable energy generators. The control system
depends basically on three sensors (wind anemometer, solar sensor,
and voltage sensor). These sensors represent PLC-s analogue inputs.
Controlling the output voltage supply can be achieved by an
enhanced method of interlocking between the renewable energy
generators, depending on those sensors and output contactors.
Abstract: This article presents new current-mode oscillator circuits using CDTAs which is designed from block diagram. The proposed circuits consist of two CDTAs and two grounded capacitors. The condition of oscillation and the frequency of oscillation can be adjusted by electronic method. The circuits have high output impedance and use only grounded capacitors without any external resistor which is very appropriate to future development into an integrated circuit. The results of PSPICE simulation program are corresponding to the theoretical analysis.
Abstract: This research proposes a methodology for patent-citation-based technology input-output analysis by applying the patent information to input-output analysis developed for the dependencies among different industries. For this analysis, a technology relationship matrix and its components, as well as input and technology inducement coefficients, are constructed using patent information. Then, a technology inducement coefficient is calculated by normalizing the degree of citation from certain IPCs to the different IPCs (International patent classification) or to the same IPCs. Finally, we construct a Dependency Structure Matrix (DSM) based on the technology inducement coefficient to suggest a useful application for this methodology.
Abstract: It is known that an analog Hopfield neural network
with time delay can generate the outputs which are similar to the
human electroencephalogram. To gain deeper insights into the
mechanisms of rhythm generation by the Hopfield neural networks
and to study the effects of noise on their activities, we investigated
the behaviors of the networks with symmetric and asymmetric
interneuron connections. The neural network under the study consists
of 10 identical neurons. For symmetric (fully connected) networks all
interneuron connections aij = +1; the interneuron connections for
asymmetric networks form an upper triangular matrix with non-zero
entries aij = +1. The behavior of the network is described by 10
differential equations, which are solved numerically. The results of
simulations demonstrate some remarkable properties of a Hopfield
neural network, such as linear growth of outputs, dependence of
synchronization properties on the connection type, huge
amplification of oscillation by the external uniform noise, and the
capability of the neural network to transform one type of noise to
another.
Abstract: A self tuning PID control strategy using reinforcement
learning is proposed in this paper to deal with the control of wind
energy conversion systems (WECS). Actor-Critic learning is used to
tune PID parameters in an adaptive way by taking advantage of the
model-free and on-line learning properties of reinforcement learning
effectively. In order to reduce the demand of storage space and to
improve the learning efficiency, a single RBF neural network is used
to approximate the policy function of Actor and the value function of
Critic simultaneously. The inputs of RBF network are the system
error, as well as the first and the second-order differences of error.
The Actor can realize the mapping from the system state to PID
parameters, while the Critic evaluates the outputs of the Actor and
produces TD error. Based on TD error performance index and
gradient descent method, the updating rules of RBF kernel function
and network weights were given. Simulation results show that the
proposed controller is efficient for WECS and it is perfectly
adaptable and strongly robust, which is better than that of a
conventional PID controller.
Abstract: This paper presents recent work on the improvement
of the robotics vision based control strategy for underwater pipeline
tracking system. The study focuses on developing image processing
algorithms and a fuzzy inference system for the analysis of the
terrain. The main goal is to implement the supervisory fuzzy learning
control technique to reduce the errors on navigation decision due to
the pipeline occlusion problem. The system developed is capable of
interpreting underwater images containing occluded pipeline, seabed
and other unwanted noise. The algorithm proposed in previous work
does not explore the cooperation between fuzzy controllers,
knowledge and learnt data to improve the outputs for underwater
pipeline tracking. Computer simulations and prototype simulations
demonstrate the effectiveness of this approach. The system accuracy
level has also been discussed.
Abstract: Gas turbine air inlet cooling is a useful method for
increasing output for regions where significant power demand and
highest electricity prices occur during the warm months. Inlet air
cooling increases the power output by taking advantage of the gas
turbine-s feature of higher mass flow rate when the compressor inlet
temperature decreases. Different methods are available for reducing
gas turbine inlet temperature. There are two basic systems currently
available for inlet cooling. The first and most cost-effective system is
evaporative cooling. Evaporative coolers make use of the evaporation
of water to reduce the gas turbine-s inlet air temperature. The second
system employs various ways to chill the inlet air. In this method, the
cooling medium flows through a heat exchanger located in the inlet
duct to remove heat from the inlet air. However, the evaporative
cooling is limited by wet-bulb temperature while the chilling can cool
the inlet air to temperatures that are lower than the wet bulb
temperature. In the present work, a thermodynamic model of a gas
turbine is built to calculate heat rate, power output and thermal
efficiency at different inlet air temperature conditions. Computational
results are compared with ISO conditions herein called "base-case".
Therefore, the two cooling methods are implemented and solved for
different inlet conditions (inlet temperature and relative humidity).
Evaporative cooler and absorption chiller systems results show that
when the ambient temperature is extremely high with low relative
humidity (requiring a large temperature reduction) the chiller is the
more suitable cooling solution. The net increment in the power output
as a function of the temperature decrease for each cooling method is
also obtained.
Abstract: Human identification at a distance has recently gained
growing interest from computer vision researchers. Gait recognition
aims essentially to address this problem by identifying people based
on the way they walk [1]. Gait recognition has 3 steps. The first step
is preprocessing, the second step is feature extraction and the third
one is classification. This paper focuses on the classification step that
is essential to increase the CCR (Correct Classification Rate).
Multilayer Perceptron (MLP) is used in this work. Neural Networks
imitate the human brain to perform intelligent tasks [3].They can
represent complicated relationships between input and output and
acquire knowledge about these relationships directly from the data
[2]. In this paper we apply MLP NN for 11 views in our database and
compare the CCR values for these views. Experiments are performed
with the NLPR databases, and the effectiveness of the proposed
method for gait recognition is demonstrated.
Abstract: This paper describes how the correct endian mode of
the TMS320C6713 DSK board can be identified. It also explains how
the TMS320C6713 DSK board can be used in the little endian and in
the big endian modes for assembly language programming in
particular and for signal processing in general. Similarly, it discusses
how crucially important it is for a user of the TMS320C6713 DSK
board to identify the mode of operation and then use it correctly
during the development stages of the assembly language
programming; otherwise, it will cause unnecessary confusion and
erroneous results as far as storing data into the memory and loading
data from the memory is concerned. Furthermore, it highlights and
strongly recommends to the users of the TMS320C6713 DSK board
to be aware of the availability and importance of various display
options in the Code Composer Studio (CCS) for correctly
interpreting and displaying the desired data in the memory. The
information presented in this paper will be of great importance and
interest to those practitioners and developers who wants to use the
TMS320C6713 DSK board for assembly language programming as
well as input-output signal processing manipulations. Finally,
examples that clearly illustrate the concept are presented.
Abstract: This paper describes a low-voltage and low-power
channel selection analog front end with continuous-time low pass
filters and highly linear programmable gain amplifier (PGA). The
filters were realized as balanced Gm-C biquadratic filters to achieve a
low current consumption. High linearity and a constant wide
bandwidth are achieved by using a new transconductance (Gm) cell.
The PGA has a voltage gain varying from 0 to 65dB, while
maintaining a constant bandwidth. A filter tuning circuit that requires
an accurate time base but no external components is presented.
With a 1-Vrms differential input and output, the filter achieves
-85dB THD and a 78dB signal-to-noise ratio. Both the filter and PGA
were implemented in a 0.18um 1P6M n-well CMOS process. They
consume 3.2mW from a 1.8V power supply and occupy an area of
0.19mm2.
Abstract: In this paper, we investigate the study of techniques
for scheduling users for resource allocation in the case of multiple
input and multiple output (MIMO) packet transmission systems. In
these systems, transmit antennas are assigned to one user or
dynamically to different users using spatial multiplexing. The
allocation of all transmit antennas to one user cannot take full
advantages of multi-user diversity. Therefore, we developed the case
when resources are allocated dynamically. At each time slot users
have to feed back their channel information on an uplink feedback
channel. Channel information considered available in the schedulers
is the zero forcing (ZF) post detection signal to interference plus
noise ratio. Our analysis study concerns the round robin and the
opportunistic schemes.
In this paper, we present an overview and a complete capacity
analysis of these schemes. The main results in our study are to give
an analytical form of system capacity using the ZF receiver at the
user terminal. Simulations have been carried out to validate all
proposed analytical solutions and to compare the performance of
these schemes.
Abstract: The purpose of this paper is to demonstrate the ability
of a genetic programming (GP) algorithm to evolve a team of data
classification models. The GP algorithm used in this work is
“multigene" in nature, i.e. there are multiple tree structures (genes)
that are used to represent team members. Each team member assigns
a data sample to one of a fixed set of output classes. A majority vote,
determined using the mode (highest occurrence) of classes predicted
by the individual genes, is used to determine the final class
prediction. The algorithm is tested on a binary classification problem.
For the case study investigated, compact classification models are
obtained with comparable accuracy to alternative approaches.
Abstract: A new approach for the improvement of coding gain
in channel coding using Advanced Encryption Standard (AES) and
Maximum A Posteriori (MAP) algorithm is proposed. This new
approach uses the avalanche effect of block cipher algorithm AES
and soft output values of MAP decoding algorithm. The performance
of proposed approach is evaluated in the presence of Additive White
Gaussian Noise (AWGN). For the verification of proposed approach,
computer simulation results are included.
Abstract: Selective harmonic elimination-pulse width modulation techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. Traditional optimization methods suffer from various drawbacks, such as prolonged and tedious computational steps and convergence to local optima; thus, the more the number of harmonics to be eliminated, the larger the computational complexity and time. This paper presents a novel method for output voltage harmonic elimination and voltage control of PWM AC/AC voltage converters using the principle of hybrid Real-Coded Genetic Algorithm-Pattern Search (RGA-PS) method. RGA is the primary optimizer exploiting its global search capabilities, PS is then employed to fine tune the best solution provided by RGA in each evolution. The proposed method enables linear control of the fundamental component of the output voltage and complete elimination of its harmonic contents up to a specified order. Theoretical studies have been carried out to show the effectiveness and robustness of the proposed method of selective harmonic elimination. Theoretical results are validated through simulation studies using PSIM software package.
Abstract: Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.
Abstract: The article presents a new method for detection of
artificial objects and materials from images of the environmental
(non-urban) terrain. Our approach uses the hue and saturation (or Cb
and Cr) components of the image as the input to the segmentation
module that uses the mean shift method. The clusters obtained as the
output of this stage have been processed by the decision-making
module in order to find the regions of the image with the significant
possibility of representing human. Although this method will detect
various non-natural objects, it is primarily intended and optimized for
detection of humans; i.e. for search and rescue purposes in non-urban
terrain where, in normal circumstances, non-natural objects shouldn-t
be present. Real world images are used for the evaluation of the
method.
Abstract: Air bubbles have been detected in human circulation
of end-stage renal disease patients who are treated by hemodialysis.
The consequence of air embolism, air bubbles, is under recognized
and usually overlooked in daily practice. This paper shows results of
a capacitor based detection method that capable of detecting the
presence of air bubbles in the blood stream in different frequencies.
The method is based on a parallel plates capacitor made of platinum
with an area of 1.5 cm2 and a distance between the two plates is 1cm.
The dielectric material used in this capacitor is Dextran70 solution
which mimics blood rheology. Simulations were carried out using
RC circuit at two frequencies 30Hz and 3 kHz and results compared
with experiments and theory. It is observed that by injecting air
bubbles of different diameters into the device, there were significant
changes in the capacitance of the capacitor. Furthermore, it is
observed that the output voltage from the circuit increased with
increasing air bubble diameter. These results demonstrate the
feasibility of this approach in improving air bubble detection in
Hemodialysis.
Abstract: It is difficult to judge ripeness by outward
characteristics such as size or external color. In this paper a nondestructive
method was studied to determine watermelon (Crimson
Sweet) quality. Responses of samples to excitation vibrations were
detected using laser Doppler vibrometry (LDV) technology. Phase
shift between input and output vibrations were extracted overall
frequency range. First and second were derived using frequency
response spectrums. After nondestructive tests, watermelons were
sensory evaluated. So the samples were graded in a range of ripeness
based on overall acceptability (total desired traits consumers).
Regression models were developed to predict quality using obtained
results and sample mass. The determination coefficients of the
calibration and cross validation models were 0.89 and 0.71
respectively. This study demonstrated feasibility of information
which is derived vibration response curves for predicting fruit
quality. The vibration response of watermelon using the LDV method
is measured without direct contact; it is accurate and timely, which
could result in significant advantage for classifying watermelons
based on consumer opinions.
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: This paper presents one of the best applications of wireless sensor network for campus Monitoring. With the help of PIR sensor, temperature sensor and humidity sensor, effective utilization of energy resources has been implemented in one of rooms of Sharda University, Greater Noida, India. The RISC microcontroller is used here for analysis of output of sensors and providing proper control using ZigBee protocol. This wireless sensor module presents a tremendous power saving method for any campus