Abstract: This paper presents an application of particle swarm
optimization (PSO) to the grounding grid planning which compares to
the application of genetic algorithm (GA). Firstly, based on IEEE
Std.80, the cost function of the grounding grid and the constraints of
ground potential rise, step voltage and touch voltage are constructed
for formulating the optimization problem of grounding grid planning.
Secondly, GA and PSO algorithms for obtaining optimal solution of
grounding grid are developed. Finally, a case of grounding grid
planning is shown the superiority and availability of the PSO
algorithm and proposal planning results of grounding grid in cost and
computational time.
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: This paper presents the communication network for
machine vision system to implement to control systems and logistics
applications in industrial environment. The real-time distributed over
the network is very important for communication among vision node,
image processing and control as well as the distributed I/O node. A
robust implementation both with respect to camera packaging and
data transmission has been accounted. This network consists of a
gigabit Ethernet network and a switch with integrated fire-wall is
used to distribute the data and provide connection to the imaging
control station and IEC-61131 conform signal integration comprising
the Modbus TCP protocol. The real-time and delay time properties
each part on the network were considered and worked out in this
paper.
Abstract: In the present paper, a set of parametric FE stress
analyses is carried out for two-planar welded tubular DKT-joints
under two different axial load cases. Analysis results are used to
present general remarks on the effect of geometrical parameters on
the stress concentration factors (SCFs) at the inner saddle, outer
saddle, toe, and heel positions on the main (outer) brace. Then a new
set of SCF parametric equations is developed through nonlinear
regression analysis for the fatigue design of two-planar DKT-joints.
An assessment study of these equations is conducted against the
experimental data; and the satisfaction of the criteria regarding the
acceptance of parametric equations is checked. Significant effort has
been devoted by researchers to the study of SCFs in various uniplanar
tubular connections. Nevertheless, for multi-planar joints
covering the majority of practical applications, very few
investigations have been reported due to the complexity and high
cost involved.
Abstract: Instead of representing individual cognition only, population cognition is represented using artificial neural networks whilst maintaining individuality. This population network trains continuously, simulating adaptation. An implementation of two coexisting populations is compared to the Lotka-Volterra model of predator-prey interaction. Applications include multi-agent systems such as artificial life or computer games.
Abstract: The decisions made by admission control algorithms are
based on the availability of network resources viz. bandwidth, energy,
memory buffers, etc., without degrading the Quality-of-Service (QoS)
requirement of applications that are admitted. In this paper, we
present an energy-aware admission control (EAAC) scheme which
provides admission control for flows in an ad hoc network based
on the knowledge of the present and future residual energy of the
intermediate nodes along the routing path. The aim of EAAC is to
quantify the energy that the new flow will consume so that it can
be decided whether the future residual energy of the nodes along
the routing path can satisfy the energy requirement. In other words,
this energy-aware routing admits a new flow iff any node in the
routing path does not run out of its energy during the transmission
of packets. The future residual energy of a node is predicted using
the Multi-layer Neural Network (MNN) model. Simulation results
shows that the proposed scheme increases the network lifetime. Also
the performance of the MNN model is presented.
Abstract: Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.
Abstract: The paper discusses the mathematics of pattern
indexing and its applications to recognition of visual patterns that are
found in video clips. It is shown that (a) pattern indexes can be
represented by collections of inverted patterns, (b) solutions to
pattern classification problems can be found as intersections and
histograms of inverted patterns and, thus, matching of original
patterns avoided.
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
Abstract: The purpose of this research is to reduce the amount of incomplete coating of stainless steel washers in the electrodeposition painting process by using an experimental design technique. The surface preparation was found to be a major cause of painted surface quality. The influence of pretreating and painting process parameters, which are cleaning time, chemical concentration and shape of hanger were studied. A 23 factorial design with two replications was performed. The analysis of variance for the designed experiment showed the great influence of cleaning time and shape of hanger. From this study, optimized cleaning time was determined and a newly designed electrical conductive hanger was proved to be superior to the original one. The experimental verification results showed that the amount of incomplete coating defects decreased from 4% to 1.02% and operation cost decreased by 10.5%.
Abstract: In many data mining applications, it is a priori known
that the target function should satisfy certain constraints imposed
by, for example, economic theory or a human-decision maker. In this
paper we consider partially monotone prediction problems, where the
target variable depends monotonically on some of the input variables
but not on all. We propose a novel method to construct prediction
models, where monotone dependences with respect to some of
the input variables are preserved by virtue of construction. Our
method belongs to the class of mixture models. The basic idea is to
convolute monotone neural networks with weight (kernel) functions
to make predictions. By using simulation and real case studies,
we demonstrate the application of our method. To obtain sound
assessment for the performance of our approach, we use standard
neural networks with weight decay and partially monotone linear
models as benchmark methods for comparison. The results show that
our approach outperforms partially monotone linear models in terms
of accuracy. Furthermore, the incorporation of partial monotonicity
constraints not only leads to models that are in accordance with the
decision maker's expertise, but also reduces considerably the model
variance in comparison to standard neural networks with weight
decay.
Abstract: The new idea of this research is application of a new fault detection and isolation (FDI) technique for supervision of sensor networks in transportation system. In measurement systems, it is necessary to detect all types of faults and failures, based on predefined algorithm. Last improvements in artificial neural network studies (ANN) led to using them for some FDI purposes. In this paper, application of new probabilistic neural network features for data approximation and data classification are considered for plausibility check in temperature measurement. For this purpose, two-phase FDI mechanism was considered for residual generation and evaluation.
Abstract: The application of Neural Network for disease
diagnosis has made great progress and is widely used by physicians.
An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which
was the great motivation towards our study. In our work, tachycardia
features obtained are used for the training and testing of a Neural
Network. In this study we are using Fuzzy Probabilistic Neural
Networks as an automatic technique for ECG signal analysis. As
every real signal recorded by the equipment can have different
artifacts, we needed to do some preprocessing steps before feeding it
to our system. Wavelet transform is used for extracting the
morphological parameters of the ECG signal. The outcome of the
approach for the variety of arrhythmias shows the represented
approach is superior than prior presented algorithms with an average
accuracy of about %95 for more than 7 tachy arrhythmias.
Abstract: An improved topology of a voltage-fed quasi-resonant
soft switching LCrCdc series-parallel half bridge inverter with a constant-frequency for electronic ballast applications is proposed in this paper. This new topology introduces a low-cost solution to
reduce switching losses and circuit rating to achieve high-efficiency
ballast. Switching losses effect on ballast efficiency is discussed
through experimental point of view. In this discussion, an improved
topology in which accomplishes soft switching operation over a wide
power regulation range is proposed. The proposed structure uses reverse recovery diode to provide better operation for the ballast system. A symmetrical pulse wide modulation (PWM) control scheme is implemented to regulate a wide range of out-put power.
Simulation results are kindly verified with the experimental
measurements obtained by ballast-lamp laboratory prototype. Different load conditions are provided in order to clarify the
performance of the proposed converter.
Abstract: The H.264/AVC standard is a highly efficient video
codec providing high-quality videos at low bit-rates. As employing
advanced techniques, the computational complexity has been
increased. The complexity brings about the major problem in the
implementation of a real-time encoder and decoder. Parallelism is the
one of approaches which can be implemented by multi-core system.
We analyze macroblock-level parallelism which ensures the same bit
rate with high concurrency of processors. In order to reduce the
encoding time, dynamic data partition based on macroblock region is
proposed. The data partition has the advantages in load balancing and
data communication overhead. Using the data partition, the encoder
obtains more than 3.59x speed-up on a four-processor system. This
work can be applied to other multimedia processing applications.
Abstract: In the past decade, the development of microstrip
sensor application has evolved tremendously. Although cut and trial
method was adopted to develop microstrip sensing applications in the
past, Computer-Aided-Design (CAD) is a more effective as it ensures
less time is consumed and cost saving is achieved in developing
microstrip sensing applications. Therefore microstrip sensing
applications has gained popularity as an effective tool adopted in
continuous sensing of moisture content particularly in products that is
administered mainly by liquid content. In this research, the Cole-Cole
representation of reactive relaxation is applied to assess the
performance of the microstrip sensor devices. The microstrip sensor
application is an effective tool suitable for sensing the moisture
content of dielectric material. Analogous to dielectric relaxation
consideration of Cole-Cole diagrams as applied to dielectric
materials, a “reactive relaxation concept” concept is introduced to
represent the frequency-dependent and moisture content
characteristics of microstrip sensor devices.
Abstract: This paper presents the applications of computational intelligence techniques to economic load dispatch problems. The fuel cost equation of a thermal plant is generally expressed as continuous quadratic equation. In real situations the fuel cost equations can be discontinuous. In view of the above, both continuous and discontinuous fuel cost equations are considered in the present paper. First, genetic algorithm optimization technique is applied to a 6- generator 26-bus test system having continuous fuel cost equations. Results are compared to conventional quadratic programming method to show the superiority of the proposed computational intelligence technique. Further, a 10-generator system each with three fuel options distributed in three areas is considered and particle swarm optimization algorithm is employed to minimize the cost of generation. To show the superiority of the proposed approach, the results are compared with other published methods.
Abstract: As is known, one of the priority directions of research
works of natural sciences is introduction of applied section of
contemporary mathematics as approximate and numerical methods to
solving integral equation into practice. We fare with the solving of
integral equation while studying many phenomena of nature to whose
numerically solving by the methods of quadrature are mainly applied.
Taking into account some deficiency of methods of quadrature for
finding the solution of integral equation some sciences suggested of
the multistep methods with constant coefficients. Unlike these papers,
here we consider application of hybrid methods to the numerical
solution of Volterra integral equation. The efficiency of the suggested
method is proved and a concrete method with accuracy order p = 4
is constructed. This method in more precise than the corresponding
known methods.
Abstract: In this paper we explore the application of a formal proof system to verification problems in cryptography. Cryptographic properties concerning correctness or security of some cryptographic algorithms are of great interest. Beside some basic lemmata, we explore an implementation of a complex function that is used in cryptography. More precisely, we describe formal properties of this implementation that we computer prove. We describe formalized probability distributions (o--algebras, probability spaces and condi¬tional probabilities). These are given in the formal language of the formal proof system Isabelle/HOL. Moreover, we computer prove Bayes' Formula. Besides we describe an application of the presented formalized probability distributions to cryptography. Furthermore, this paper shows that computer proofs of complex cryptographic functions are possible by presenting an implementation of the Miller- Rabin primality test that admits formal verification. Our achievements are a step towards computer verification of cryptographic primitives. They describe a basis for computer verification in cryptography. Computer verification can be applied to further problems in crypto-graphic research, if the corresponding basic mathematical knowledge is available in a database.
Abstract: Grid computing is a high performance computing
environment to solve larger scale computational applications. Grid
computing contains resource management, job scheduling, security
problems, information management and so on. Job scheduling is a
fundamental and important issue in achieving high performance in
grid computing systems. However, it is a big challenge to design an
efficient scheduler and its implementation. In Grid Computing, there
is a need of further improvement in Job Scheduling algorithm to
schedule the light-weight or small jobs into a coarse-grained or
group of jobs, which will reduce the communication time,
processing time and enhance resource utilization. This Grouping
strategy considers the processing power, memory-size and
bandwidth requirements of each job to realize the real grid system.
The experimental results demonstrate that the proposed scheduling
algorithm efficiently reduces the processing time of jobs in
comparison to others.