Abstract: This paper proposes the method combining artificial
neural network (ANN) with particle swarm optimization (PSO) to
implement the maximum power point tracking (MPPT) by controlling
the rotor speed of the wind generator. First, the measurements of wind
speed, rotor speed of wind power generator and output power of wind
power generator are applied to train artificial neural network and to
estimate the wind speed. Second, the method mentioned above is
applied to estimate and control the optimal rotor speed of the wind
turbine so as to output the maximum power. Finally, the result reveals
that the control system discussed in this paper extracts the maximum
output power of wind generator within the short duration even in the
conditions of wind speed and load impedance variation.
Abstract: In this paper an approaches for increasing the
effectiveness of error detection in computer network channels with
Pulse-Amplitude Modulation (PAM) has been proposed. Proposed
approaches are based on consideration of special feature of errors,
which are appearances in line with PAM. The first approach consists
of CRC modification specifically for line with PAM. The second
approach is base of weighted checksums using. The way for
checksum components coding has been developed. It has been shown
that proposed checksum modification ensure superior digital data
control transformation reliability for channels with PAM in compare
to CRC.
Abstract: This paper proposes a set of quasi-static mathematical
model of magnetic fields caused by high voltage conductors of
distribution transformer by using a set of second-order partial
differential equation. The modification for complex magnetic field
analysis and time-harmonic simulation are also utilized. In this
research, transformers were study in both balanced and unbalanced
loading conditions. Computer-based simulation utilizing the threedimensional
finite element method (3-D FEM) is exploited as a tool
for visualizing magnetic fields distribution volume a distribution
transformer. Finite Element Method (FEM) is one among popular
numerical methods that is able to handle problem complexity in
various forms. At present, the FEM has been widely applied in most
engineering fields. Even for problems of magnetic field distribution,
the FEM is able to estimate solutions of Maxwell-s equations
governing the power transmission systems. The computer simulation
based on the use of the FEM has been developed in MATLAB
programming environment.
Abstract: This paper proposes an innovative methodology for
Acceptance Sampling by Variables, which is a particular category of
Statistical Quality Control dealing with the assurance of products
quality. Our contribution lies in the exploitation of machine learning
techniques to address the complexity and remedy the drawbacks of
existing approaches. More specifically, the proposed methodology
exploits Artificial Neural Networks (ANNs) to aid decision making
about the acceptance or rejection of an inspected sample. For any
type of inspection, ANNs are trained by data from corresponding
tables of a standard-s sampling plan schemes. Once trained, ANNs
can give closed-form solutions for any acceptance quality level and
sample size, thus leading to an automation of the reading of the
sampling plan tables, without any need of compromise with the
values of the specific standard chosen each time. The proposed
methodology provides enough flexibility to quality control engineers
during the inspection of their samples, allowing the consideration of
specific needs, while it also reduces the time and the cost required for
these inspections. Its applicability and advantages are demonstrated
through two numerical examples.
Abstract: The indistinctness of the manufacturing processes makes that a parts cannot be realized in an absolutely exact way towards the specifications on the dimensions. It is thus necessary to assume that the effectively realized product has to belong in a very strict way to compatible intervals with a correct functioning of the parts. In this paper we present an approach based on mixing tow different characteristics theories, the fuzzy system and Petri net system. This tool has been proposed to model and control the quality in an assembly system. A robust command of a mechanical assembly process is presented as an application. This command will then have to maintain the specifications interval of parts in front of the variations. It also illustrates how the technique reacts when the product quality is high, medium, or low.
Abstract: Ontologies are broadly used in the context of networked home environments. With ontologies it is possible to define and store context information, as well as to model different kinds of physical environments. Ontologies are central to networked home environments as they carry the meaning. However, ontologies and the OWL language is complex. Several ontology visualization approaches have been developed to enhance the understanding of ontologies. The domain of networked home environments sets some special requirements for the ontology visualization approach. The visualization tool presented here, visualizes ontologies in a domain-specific way. It represents effectively the physical structures and spatial relationships of networked home environments. In addition, it provides extensive interaction possibilities for editing and manipulating the visualization. The tool shortens the gap from beginner to intermediate OWL ontology reader by visualizing instances in their actual locations and making OWL ontologies more interesting and concrete, and above all easier to comprehend.
Abstract: The use of magnetic and magnetic/gold core/shell
nanoparticles in biotechnology or medicine has shown good promise
due to their hybrid nature which possesses superior magnetic and
optical properties. Some of these potential applications include
hyperthermia treatment, bio-separations, diagnostics, drug delivery
and toxin removal. Synthesis refinement to control geometric and
magnetic/optical properties, and finding functional surfactants for
biomolecular attachment, are requirements to meet application
specifics.
Various high-temperature preparative methods were used for the
synthesis of iron oxide and gold-coated iron oxide nanoparticles.
Different surface functionalities, such as 11-aminoundecanoic and
11-mercaptoundecanoic acid, were introduced on the surface of the
particles to facilitate further attachment of biomolecular functionality
and drug-like molecules. Nanoparticle thermal stability, composition,
state of aggregation, size and morphology were investigated and the
results from techniques such as Fourier Transform-Infra Red
spectroscopy (FT-IR), Ultraviolet visible spectroscopy (UV-vis),
Transmission Electron Microscopy (TEM) and thermal analysis are
discussed.
Abstract: This paper presents the DC voltage control design of D-STATCOM when the D-STATCOM is used for load voltage regulation. Although, the DC voltage can be controlled by active current of the D-STATCOM, reactive current still affects the DC voltage. To eliminate this effect, the control strategy with elimination effect of the reactive current is proposed and the results of the control with and without the elimination the effect of the reactive current are compared. For obtaining the proportional and integral gains of the PI controllers, the symmetrical optimum and genetic algorithms methods are applied. The stability margin of these methods are obtained and discussed in detail. In addition, the performance of the DC voltage control based on symmetrical optimum and genetic algorithms methods are compared. Effectiveness of the controllers designed was verified through computer simulation performed by using Power System Tool Block (PSB) in SIMULINK/MATLAB. The simulation results demonstrated that the DC voltage control proposed is effective in regulating DC voltage when the DSTATCOM is used for load voltage regulation.
Abstract: Intelligent schools are those which use IT devices and
technologies as media software, hardware and networks to improve
learning process. On the other hand management improvement is
best described as the process from which managers learn and improve
their skills not only to benefit themselves but also their employing
organizations Here, we present a model Management improvement
System that has been applied on some schools and have made strict
improvement.
Abstract: Three dimensional analysis of thermal model in laser
full penetration welding, Nd:YAG, by transparent mode DP600 alloy
steel 1.25mm of thickness and gap of 0.1mm. Three models studied
the influence of thermal dependent temperature properties, thermal
independent temperature and the effect of peak value of specific heat
at phase transformation temperature, AC1, on the transient
temperature. Another seven models studied the influence of
discretization, meshes on the temperature distribution in weld plate.
It is shown that for the effects of thermal properties, the errors less
4% of maximum temperature in FZ and HAZ have identified. The
minimum value of discretization are at least one third increment per
radius for temporal discretization and the spatial discretization
requires two elements per radius and four elements through thickness
of the assembled plate, which therefore represent the minimum
requirements of modeling for the laser welding in order to get
minimum errors less than 5% compared to the fine mesh.
Abstract: In this paper a novel scheme for watermarking digital
audio during its compression to MPEG-1 Layer III format is
proposed. For this purpose we slightly modify some of the selected
MDCT coefficients, which are used during MPEG audio
compression procedure. Due to the possibility of modifying different
MDCT coefficients, there will be different choices for embedding the
watermark into audio data, considering robustness and transparency
factors. Our proposed method uses a genetic algorithm to select the
best coefficients to embed the watermark. This genetic selection is
done according to the parameters that are extracted from the
perceptual content of the audio to optimize the robustness and
transparency of the watermark. On the other hand the watermark
security is increased due to the random nature of the genetic
selection. The information of the selected MDCT coefficients that
carry the watermark bits, are saves in a database for future extraction
of the watermark. The proposed method is suitable for online MP3
stores to pursue illegal copies of musical artworks. Experimental
results show that the detection ratio of the watermarks at the bitrate
of 128kbps remains above 90% while the inaudibility of the
watermark is preserved.
Abstract: This paper proposes a novel game theoretical
technique to address the problem of data object replication in largescale
distributed computing systems. The proposed technique draws
inspiration from computational economic theory and employs the
extended Vickrey auction. Specifically, players in a non-cooperative
environment compete for server-side scarce memory space to
replicate data objects so as to minimize the total network object
transfer cost, while maintaining object concurrency. Optimization of
such a cost in turn leads to load balancing, fault-tolerance and
reduced user access time. The method is experimentally evaluated
against four well-known techniques from the literature: branch and
bound, greedy, bin-packing and genetic algorithms. The experimental
results reveal that the proposed approach outperforms the four
techniques in both the execution time and solution quality.
Abstract: One of research issues in social network analysis is to
evaluate the position/importance of users in social networks. As the
information diffusion in social network is evolving, it seems difficult
to evaluate the importance of users using traditional approaches. In
this paper, we propose an evaluation approach for user importance
with fractal view in social networks. In this approach, the global importance
(Fractal Importance) and the local importance (Topological
Importance) of nodes are considered. The basic idea is that the bigger
the product of fractal importance and topological importance of a
node is, the more important of the node is. We devise the algorithm
called TFRank corresponding to the proposed approach. Finally, we
evaluate TFRank by experiments. Experimental results demonstrate
our TFRank has the high correlations with PageRank algorithm
and potential ranking algorithm, and it shows the effectiveness and
advantages of our approach.
Abstract: This paper analyzes the patterns of the Monte Carlo
data for a large number of variables and minterms, in order to
characterize the circuit path length behavior. We propose models
that are determined by training process of shortest path length
derived from a wide range of binary decision diagram (BDD)
simulations. The creation of the model was done use of feed forward
neural network (NN) modeling methodology. Experimental results
for ISCAS benchmark circuits show an RMS error of 0.102 for the
shortest path length complexity estimation predicted by the NN
model (NNM). Use of such a model can help reduce the time
complexity of very large scale integrated (VLSI) circuitries and
related computer-aided design (CAD) tools that use BDDs.
Abstract: A novel method using bearing-only SLAM to estimate node positions of a localization network is proposed. A group of simple robots are used to estimate the position of each node. Each node has a unique ID, which it can communicate to a robot close by. Initially the node IDs and positions are unknown. A case example using RFID technology in the localization network is introduced.
Abstract: This paper presents a novel approach for representing
the spatio-temporal topology of the camera network with overlapping
and non-overlapping fields of view (FOVs). The topology is
determined by tracking moving objects and establishing object
correspondence across multiple cameras. To track people successfully
in multiple camera views, we used the Merge-Split (MS) approach for
object occlusion in a single camera and the grid-based approach for
extracting the accurate object feature. In addition, we considered the
appearance of people and the transition time between entry and exit
zones for tracking objects across blind regions of multiple cameras
with non-overlapping FOVs. The main contribution of this paper is to
estimate transition times between various entry and exit zones, and to
graphically represent the camera topology as an undirected weighted
graph using the transition probabilities.
Abstract: Wireless sensor networks (WSNs) consist of number
of tiny, low cost and low power sensor nodes to monitor some physical phenomenon. The major limitation in these networks is the use of non-rechargeable battery having limited power supply. The
main cause of energy consumption in such networks is
communication subsystem. This paper presents an energy efficient
Cluster Cooperative Caching at Sensor (C3S) based upon grid type clustering. Sensor nodes belonging to the same cluster/grid form a
cooperative cache system for the node since the cost for
communication with them is low both in terms of energy
consumption and message exchanges. The proposed scheme uses
cache admission control and utility based data replacement policy to
ensure that more useful data is retained in the local cache of a node.
Simulation results demonstrate that C3S scheme performs better in
various performance metrics than NICoCa which is existing
cooperative caching protocol for WSNs.
Abstract: This paper applies fuzzy clustering algorithm in classifying real estate companies in China according to some general financial indexes, such as income per share, share accumulation fund, net profit margins, weighted net assets yield and shareholders' equity. By constructing and normalizing initial partition matrix, getting fuzzy similar matrix with Minkowski metric and gaining the transitive closure, the dynamic fuzzy clustering analysis for real estate companies is shown clearly that different clustered result change gradually with the threshold reducing, and then, it-s shown there is the similar relationship with the prices of those companies in stock market. In this way, it-s great valuable in contrasting the real estate companies- financial condition in order to grasp some good chances of investment, and so on.
Abstract: Wireless sensor network is formed with the combination of sensor nodes and sink nodes. Recently Wireless sensor network has attracted attention of the research community. The main application of wireless sensor network is security from different attacks both for mass public and military. However securing these networks, by itself is a critical issue due to many constraints like limited energy, computational power and lower memory. Researchers working in this area have proposed a number of security techniques for this purpose. Still, more work needs to be done.In this paper we provide a detailed discussion on security in wireless sensor networks. This paper will help to identify different obstacles and requirements for security of wireless sensor networks as well as highlight weaknesses of existing techniques.
Abstract: We propose a novel graphical technique (SVision) for
intrusion detection, which pictures the network as a community of
hosts independently roaming in a 3D space defined by the set of
services that they use. The aim of SVision is to graphically cluster
the hosts into normal and abnormal ones, highlighting only the ones
that are considered as a threat to the network. Our experimental
results using DARPA 1999 and 2000 intrusion detection and
evaluation datasets show the proposed technique as a good candidate
for the detection of various threats of the network such as vertical
and horizontal scanning, Denial of Service (DoS), and Distributed
DoS (DDoS) attacks.