Abstract: This paper presents an approach which is based on the
use of supervised feed forward neural network, namely multilayer
perceptron (MLP) neural network and finite element method (FEM)
to solve the inverse problem of parameters identification. The
approach is used to identify unknown parameters of ferromagnetic
materials. The methodology used in this study consists in the
simulation of a large number of parameters in a material under test,
using the finite element method (FEM). Both variations in relative
magnetic permeability and electrical conductivity of the material
under test are considered. Then, the obtained results are used to
generate a set of vectors for the training of MLP neural network.
Finally, the obtained neural network is used to evaluate a group of
new materials, simulated by the FEM, but not belonging to the
original dataset. Noisy data, added to the probe measurements is used
to enhance the robustness of the method. The reached results
demonstrate the efficiency of the proposed approach, and encourage
future works on this subject.
Abstract: This paper discusses the development of a qualitative
simulator (abbreviated QRiOM) for predicting the behaviour of
organic chemical reactions. The simulation technique is based on the
qualitative process theory (QPT) ontology. The modelling constructs
of QPT embody notions of causality which can be used to explain the
behaviour of a chemical system. The major theme of this work is
that, in a qualitative simulation environment, students are able to
articulate his/her knowledge through the inspection of explanations
generated by software. The implementation languages are Java and
Prolog. The software produces explanation in various forms that
stresses on the causal theories in the chemical system which can be
effectively used to support learning.
Abstract: This research was conducted to develop a correlation
between microstructure of HSLA steel and the mechanical properties
that occur as a result of both laser and mechanical forming processes
of the metal. The technique of forming flat metals by applying laser
beams is a relatively new concept in the manufacturing industry.
However, the effects of laser energy on the stability of metal alloy
phases have not yet been elucidated in terms of phase
transformations and microhardness. In this work, CO2 laser source
was used to irradiate the surface of a flat metal then the
microstructure and microhardness of the metal were studied on the
formed specimen. The extent to which the microstructure changed
depended on the heat inputs of up to 1000 J/cm2 with cooling rates of
about 4.8E+02 K/s. Experimental results revealed that the irradiated
surface of a HSLA steel had transformed to austenitic structure
during the heating process.
Abstract: Gas hydrates form when a number of factors co-exist:
free water, hydrocarbon gas, cold temperatures and high pressures are typical of the near mud-line conditions in a deepwater drilling
operation. Subsequently, when drilling with water based muds, particularly on exploration wells, the risk of hydrate formation
associated with a gas influx is high. The consequences of gas hydrate
formation while drilling are severe, and as such, every effort should be made to ensure the risk of hydrate formation is either eliminated
or significantly reduced. Thermodynamic inhibitors are used to reduce the free water content of a drilling mud, and thus suppress the
hydrate formation temperature. Very little experimental work has
been performed by oil and gas research companies on the evaluation
of gas hydrate formation in a water-based drilling mud. The main
objective of this paper is to investigate the experimental gas hydrate
formation for a mixture of methane, carbon dioxide & nitrogen in a
water-based drilling mud with or without presence of different
concentrations of thermodynamic inhibitors including pure salt and a
combination of salt with methanol or ethylene glycol at different
concentrations in a static loop apparatus. The experiments were
performed using a static loop apparatus consisting of a 2.4307 cm
inside diameter and 800 cm long pipe. All experiments were conducted at 2200 psia. The temperature in the loop was decreased at
a rate of 3.33 °F/h from initial temperature of 80 °F.
Abstract: In this paper, we study the cooperative communications where multiple cognitive radio (CR) transmit-receive pairs competitive maximize their own throughputs. In CR networks, the influences of primary users and the spectrum availability are usually different among CR users. Due to the existence of multiple relay nodes and the different spectrum availability, each CR transmit-receive pair should not only select the relay node but also choose the appropriate channel. For this distributed problem, we propose a game theoretic framework to formulate this problem and we apply a regret-matching learning algorithm which is leading to correlated equilibrium. We further formulate a modified regret-matching learning algorithm which is fully distributed and only use the local information of each CR transmit-receive pair. This modified algorithm is more practical and suitable for the cooperative communications in CR network. Simulation results show the algorithm convergence and the modified learning algorithm can achieve comparable performance to the original regretmatching learning algorithm.
Abstract: This paper proposes the study of a robust control of
the doubly fed induction generator (DFIG) used in a wind energy
production. The proposed control is based on the linear active
disturbance rejection control (ADRC) and it is applied to the control
currents rotor of the DFIG, the DC bus voltage and active and
reactive power exchanged between the DFIG and the network. The
system under study and the proposed control are simulated using
MATLAB/SIMULINK.
Abstract: Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.
Abstract: Multicast Network Technology has pervaded our
lives-a few examples of the Networking Techniques and also for the
improvement of various routing devices we use. As we know the
Multicast Data is a technology offers many applications to the user
such as high speed voice, high speed data services, which is presently
dominated by the Normal networking and the cable system and
digital subscriber line (DSL) technologies. Advantages of Multi cast
Broadcast such as over other routing techniques. Usually QoS
(Quality of Service) Guarantees are required in most of Multicast
applications. The bandwidth-delay constrained optimization and we
use a multi objective model and routing approach based on genetic
algorithm that optimizes multiple QoS parameters simultaneously.
The proposed approach is non-dominated routes and the performance
with high efficiency of GA. Its betterment and high optimization has
been verified. We have also introduced and correlate the result of
multicast GA with the Broadband wireless to minimize the delay in
the path.
Abstract: The paper presents the influence of the conventional
ploughing tillage technology in comparison with the minimum
tillage, upon the soil properties, weed control and yield in the case of
maize (Zea mays L.), soya-bean (Glycine hispida L.) and winter
wheat (Triticum aestivum L.) in a three years crop rotation. A
research has been conducted at the University of Agricultural
Sciences and Veterinary Medicine Cluj-Napoca, Romania. The use of
minimum soil tillage systems within a three years rotation: maize,
soya-bean, wheat favorites the rise of the aggregates hydro stability
with 5.6-7.5% on a 0-20 cm depth and 5-11% on 20-30 cm depth.
The minimum soil tillage systems – paraplow, chisel or rotary grape
– are polyvalent alternatives for basic preparation, germination bed
preparation and sowing, for fields and crops with moderate loose
requirements being optimized technologies for: soil natural fertility
activation and rationalization, reduction of erosion, increasing the
accumulation capacity for water and realization of sowing in the
optimal period. The soil tillage system influences the productivity
elements of cultivated species and finally the productions thus
obtained. Thus, related to conventional working system, the
productions registered in minimum tillage working represented 89-
97% in maize, 103-112% in soya-bean, 93-99% in winter-wheat. The
results of investigations showed that the yield is a conclusion soil
tillage systems influence on soil properties, plant density assurance
and on weed control. Under minimum tillage systems in the case of
winter weat as an option for replacing classic ploughing, the best
results in terms of quality indices were obtained from version worked
with paraplow, followed by rotary harrow and chisel. At variants
worked with paraplow were obtained quality indices close to those of
the variant worked with plow, and protein and gluten content was
even higher. At Ariesan variety, highest protein content, 12.50% and
gluten, 28.6% was obtained for the variant paraplow.
Abstract: The customary practice of identifying industrial sickness is a set traditional techniques which rely upon a range of manual monitoring and compilation of financial records. It makes the process tedious, time consuming and often are susceptible to manipulation. Therefore, certain readily available tools are required which can deal with such uncertain situations arising out of industrial sickness. It is more significant for a country like India where the fruits of development are rarely equally distributed. In this paper, we propose an approach based on Artificial Neural Network (ANN) to deal with industrial sickness with specific focus on a few such units taken from a less developed north-east (NE) Indian state like Assam. The proposed system provides decision regarding industrial sickness using eight different parameters which are directly related to the stages of sickness of such units. The mechanism primarily uses certain signals and symptoms of industrial health to decide upon the state of a unit. Specifically, we formulate an ANN based block with data obtained from a few selected units of Assam so that required decisions related to industrial health could be taken. The system thus formulated could become an important part of planning and development. It can also contribute towards computerization of decision support systems related to industrial health and help in better management.
Abstract: In the present work we investigate both the elastic and
electric properties of a chiral material. We consider a composite
structure made from a polymer matrix and anisotropic inclusions of
GaAs taking into account piezoelectric and dielectric properties of
the composite material. The principal task of the work is the
estimation of the functional properties of the composite material.
Abstract: In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients' changes. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-bystage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.
Abstract: This paper presents a critical study about the
application of Neural Networks to ion-exchange process. Ionexchange
is a complex non-linear process involving many factors
influencing the ions uptake mechanisms from the pregnant solution.
The following step includes the elution. Published data presents
empirical isotherm equations with definite shortcomings resulting in
unreliable predictions. Although Neural Network simulation
technique encounters a number of disadvantages including its “black
box", and a limited ability to explicitly identify possible causal
relationships, it has the advantage to implicitly handle complex
nonlinear relationships between dependent and independent
variables. In the present paper, the Neural Network model based on
the back-propagation algorithm Levenberg-Marquardt was developed
using a three layer approach with a tangent sigmoid transfer function
(tansig) at hidden layer with 11 neurons and linear transfer function
(purelin) at out layer. The above mentioned approach has been used
to test the effectiveness in simulating ion exchange processes. The
modeling results showed that there is an excellent agreement between
the experimental data and the predicted values of copper ions
removed from aqueous solutions.
Abstract: This article is an extension and a practical application
approach of Wheeler-s NEBIC theory (Net Enabled Business
Innovation Cycle). NEBIC theory is a new approach in IS research
and can be used for dynamic environment related to new technology.
Firms can follow the market changes rapidly with support of the IT
resources. Flexible firms adapt their market strategies, and respond
more quickly to customers changing behaviors. When every leading
firm in an industry has access to the same IT resources, the way that
these IT resources are managed will determine the competitive
advantages or disadvantages of firm. From Dynamic Capabilities
Perspective and from newly introduced NEBIC theory by Wheeler,
we know that only IT resources cannot deliver customer value but
good configuration of those resources can guarantee customer value
by choosing the right emerging technology, grasping the economic
opportunities through business innovation and growth. We found
evidences in literature that SOA (Service Oriented Architecture) is a
promising emerging technology which can deliver the desired
economic opportunity through modularity, flexibility and loosecoupling.
SOA can also help firms to connect in network which can
open a new window of opportunity to collaborate in innovation and
right kind of outsourcing
Abstract: In this paper, a class of recurrent neural networks (RNNs) with variable delays are studied on almost periodic time scales, some sufficient conditions are established for the existence and global exponential stability of the almost periodic solution. These results have important leading significance in designs and applications of RNNs. Finally, two examples and numerical simulations are presented to illustrate the feasibility and effectiveness of the results.
Abstract: To explore pipelines is one of various bio-mimetic
robot applications. The robot may work in common buildings such as
between ceilings and ducts, in addition to complicated and massive
pipeline systems of large industrial plants. The bio-mimetic robot finds
any troubled area or malfunction and then reports its data. Importantly,
it can not only prepare for but also react to any abnormal routes in the
pipeline. The pipeline monitoring tasks require special types of mobile
robots. For an effective movement along a pipeline, the movement of
the robot will be similar to that of insects or crawling animals. During
its movement along the pipelines, a pipeline monitoring robot has an
important task of finding the shapes of the approaching path on the
pipes. In this paper we propose an effective solution to the pipeline
pattern recognition, based on the fuzzy classification rules for the
measured IR distance data.
Abstract: This study explores how the mechanics of learning
paves the way to engineering innovation. Theories related to learning
in the new product/service innovation are reviewed from an
organizational perspective, behavioral perspective, and engineering
perspective. From this, an engineering team-s external interactions
for knowledge brokering and internal composition for skill balance
are examined from a learning and innovation viewpoints. As a result,
an integrated learning model is developed by reconciling the
theoretical perspectives as well as developing propositions that
emphasize the centrality of learning, and its drivers, in the
engineering product/service development. The paper also provides a
review and partial validation of the propositions using the results of a
previously published field study in the aerospace industry.
Abstract: We present in this paper a new approach for specific JPEG steganalysis and propose studying statistics of the compressed DCT coefficients. Traditionally, steganographic algorithms try to preserve statistics of the DCT and of the spatial domain, but they cannot preserve both and also control the alteration of the compressed data. We have noticed a deviation of the entropy of the compressed data after a first embedding. This deviation is greater when the image is a cover medium than when the image is a stego image. To observe this deviation, we pointed out new statistic features and combined them with the Multiple Embedding Method. This approach is motivated by the Avalanche Criterion of the JPEG lossless compression step. This criterion makes possible the design of detectors whose detection rates are independent of the payload. Finally, we designed a Fisher discriminant based classifier for well known steganographic algorithms, Outguess, F5 and Hide and Seek. The experiemental results we obtained show the efficiency of our classifier for these algorithms. Moreover, it is also designed to work with low embedding rates (< 10-5) and according to the avalanche criterion of RLE and Huffman compression step, its efficiency is independent of the quantity of hidden information.
Abstract: In this paper the direct kinematic model of a multiple
applications three degrees of freedom industrial manipulator, was
developed using the homogeneous transformation matrices and the
Denavit - Hartenberg parameters, likewise the inverse kinematic
model was developed using the same method, verifying that in the
workload border the inverse kinematic presents considerable errors,
therefore a genetic algorithm was implemented to optimize the model
improving greatly the efficiency of the model.
Abstract: In this paper we present an enhanced noise reduction method for robust speech recognition using Adaptive Gain Equalizer with Non linear Spectral Subtraction. In Adaptive Gain Equalizer method (AGE), the input signal is divided into a number of subbands that are individually weighed in time domain, in accordance to the short time Signal-to-Noise Ratio (SNR) in each subband estimation at every time instant. Instead of focusing on suppression the noise on speech enhancement is focused. When analysis was done under various noise conditions for speech recognition, it was found that Adaptive Gain Equalizer method algorithm has an obvious failing point for a SNR of -5 dB, with inadequate levels of noise suppression for SNR less than this point. This work proposes the implementation of AGE when coupled with Non linear Spectral Subtraction (AGE-NSS) for robust speech recognition. The experimental result shows that out AGE-NSS performs the AGE when SNR drops below -5db level.