Abstract: In this paper, the detection of a fault in the Global Positioning System (GPS) measurement is addressed. The class of faults considered is a bias in the GPS pseudorange measurements. This bias is modeled as an unknown constant. The fault could be the result of a receiver fault or signal fault such as multipath error. A bias bank is constructed based on set of possible fault hypotheses. Initially, there is equal probability of occurrence for any of the biases in the bank. Subsequently, as the measurements are processed, the probability of occurrence for each of the biases is sequentially updated. The fault with a probability approaching unity will be declared as the current fault in the GPS measurement. The residual formed from the GPS and Inertial Measurement Unit (IMU) measurements is used to update the probability of each fault. Results will be presented to show the performance of the presented algorithm.
Abstract: Many real-world optimization problems involve multiple conflicting objectives and the use of evolutionary algorithms to solve the problems has attracted much attention recently. This paper investigates the application of multi-objective optimization technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the performance of a power system. The design objective is to improve both rotor angle stability and system voltage profile. A Genetic Algorithm (GA) based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimisation problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented to show the effectiveness and robustness of the proposed approach.
Abstract: In literature, there are metrics for identifying the
quality of reusable components but the framework that makes use of
these metrics to precisely predict reusability of software components
is still need to be worked out. These reusability metrics if identified
in the design phase or even in the coding phase can help us to reduce
the rework by improving quality of reuse of the software component
and hence improve the productivity due to probabilistic increase in
the reuse level. As CK metric suit is most widely used metrics for
extraction of structural features of an object oriented (OO) software;
So, in this study, tuned CK metric suit i.e. WMC, DIT, NOC, CBO
and LCOM, is used to obtain the structural analysis of OO-based
software components. An algorithm has been proposed in which the
inputs can be given to K-Means Clustering system in form of
tuned values of the OO software component and decision tree is
formed for the 10-fold cross validation of data to evaluate the in
terms of linguistic reusability value of the component. The developed
reusability model has produced high precision results as desired.
Abstract: This paper presents and evaluates a new classification
method that aims to improve classifiers performances and speed up
their training process. The proposed approach, called labeled
classification, seeks to improve convergence of the BP (Back
propagation) algorithm through the addition of an extra feature
(labels) to all training examples. To classify every new example, tests
will be carried out each label. The simplicity of implementation is the
main advantage of this approach because no modifications are
required in the training algorithms. Therefore, it can be used with
others techniques of acceleration and stabilization. In this work, two
models of the labeled classification are proposed: the LMLP
(Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro
Fuzzy Classifier). These models are tested using Iris, wine, texture
and human thigh databases to evaluate their performances.
Abstract: Although automotive industry has brought different beneficiaries to human life, it is being pointed out as one of the major cause of global air pollution which resulted in climate change, smog, green house gases (GHGs), and human diseases by many reasons. Since auto industry is one of the largest consumers of fossil fuels, the realization of green innovations is becoming a crucial choice to meet the challenges towards sustainable development. Recently, many auto manufacturers have embarked on green technology initiatives to gain a competitive advantage in the global market; however, innovative manufacturing systems and technologies can enhance operational performance only if the human resource management is in place to elicit the motivation of the employees and develop their organizational expertise. No organization can perform at peak levels unless each employee is committed to the company goals and works as an effective team member. Strategic human resource practices are the primary means by which firms can shape the skills, attitudes, and behavior of individuals to align with the business strategic objectives. This study investigates on the comprehensive approach of multiple advanced technology innovations and human resource management at Toyota Motor Corporation as the market leader of full hybrid technology in the automotive industry. Then, HRM framework of the company is described and three sets of human resource practices that support the innovation-oriented HR system, presented. Finally, a conceptual framework for innovativeness in green technology in automotive industry by applying a deliberate strategic HR management system and knowledge management with the intervening factors of organizational culture, knowledge application and knowledge sharing is proposed.
Abstract: Safety Critical hard Real-Time Systems are ever
present in the avionics industry. The Model Driven Architecture
(MDA) offers different levels of model abstraction and generation.
This paper discusses our concerns relating to model development and
generation when using the MDA approach in the avionics industry.
These concerns are based on our experience when looking into
adopting the MDA as part of avionics systems development. We
place emphasis on transformations between model types and discuss
possible benefits of adopting an MDA approach as part of the
software development life cycle.
Abstract: The logistical requirements placed on industrial manufacturing companies are steadily increasing. In order to meet those requirements, a consistent and efficient concept is necessary for production control. Set up properly, production control offers considerable potential with respect to achieving the logistical targets. As experience with the many production control methods already in existence and their compatibility is, however, often inadequate, this article describes a systematic approach to the configuration of production control based on the Lödding model. This model enables production control to be set up individually to suit a company and the requirements. It therefore permits today-s demands regarding logistical performance to be met.
Abstract: With the development of ubiquitous computing,
current user interaction approaches with keyboard, mouse and pen
are not sufficient. Due to the limitation of these devices the useable
command set is also limited. Direct use of hands as an input device is
an attractive method for providing natural Human Computer
Interaction which has evolved from text-based interfaces through 2D
graphical-based interfaces, multimedia-supported interfaces, to fully
fledged multi-participant Virtual Environment (VE) systems.
Imagine the human-computer interaction of the future: A 3Dapplication
where you can move and rotate objects simply by moving
and rotating your hand - all without touching any input device. In this
paper a review of vision based hand gesture recognition is presented.
The existing approaches are categorized into 3D model based
approaches and appearance based approaches, highlighting their
advantages and shortcomings and identifying the open issues.
Abstract: this paper aims to provide an approach to predict the
performance of the product produced after multi-stages of
manufacturing processes, as well as the assembly. Such approach
aims to control and subsequently identify the relationship between
the process inputs and outputs so that a process engineer can more
accurately predict how the process output shall perform based on the
system inputs. The approach is guided by a six-sigma methodology to
obtain improved performance.
In this paper a case study of the manufacture of a hermetic
reciprocating compressor is presented. The application of artificial
neural networks (ANNs) technique is introduced to improve
performance prediction within this manufacturing environment. The
results demonstrate that the approach predicts accurately and
effectively.
Abstract: Texture information plays increasingly an important
role in remotely sensed imagery classification and many pattern
recognition applications. However, the selection of relevant textural
features to improve this classification accuracy is not a straightforward
task. This work investigates the effectiveness of two Mutual
Information Feature Selector (MIFS) algorithms to select salient
textural features that contain highly discriminatory information for
multispectral imagery classification. The input candidate features are
extracted from a SPOT High Resolution Visible(HRV) image using
Wavelet Transform (WT) at levels (l = 1,2).
The experimental results show that the selected textural features
according to MIFS algorithms make the largest contribution to
improve the classification accuracy than classical approaches such
as Principal Components Analysis (PCA) and Linear Discriminant
Analysis (LDA).
Abstract: This paper presents strategies for dynamically creating, managing and removing mesh cells during computations in the context of the Material Point Method (MPM). The dynamic meshing approach has been developed to help address problems involving motion of a finite size body in unbounded domains in which the extent of material travel and deformation is unknown a priori, such as in the case of landslides and debris flows. The key idea is to efficiently instantiate and search only cells that contain material points, thereby avoiding unneeded storage and computation. Mechanisms for doing this efficiently are presented, and example problems are used to demonstrate the effectiveness of dynamic mesh management relative to alternative approaches.
Abstract: This paper presents the design, fabrication and
evaluation of magneto-rheological damper. Semi-active control
devices have received significant attention in recent years because
they offer the adaptability of active control devices without requiring
the associated large power sources. Magneto-Rheological (MR)
dampers are semi- active control devices that use MR fluids to
produce controllable dampers. They potentially offer highly reliable
operation and can be viewed as fail-safe in that they become passive
dampers if the control hardware malfunction. The advantage of MR
dampers over conventional dampers are that they are simple in
construction, compromise between high frequency isolation and
natural frequency isolation, they offer semi-active control, use very
little power, have very quick response, has few moving parts, have a
relax tolerances and direct interfacing with electronics. Magneto-
Rheological (MR) fluids are Controllable fluids belonging to the
class of active materials that have the unique ability to change
dynamic yield stress when acted upon by an electric or magnetic
field, while maintaining viscosity relatively constant. This property
can be utilized in MR damper where the damping force is changed by
changing the rheological properties of the fluid magnetically. MR
fluids have a dynamic yield stress over Electro-Rheological fluids
(ER) and a broader operational temperature range. The objective of
this papert was to study the application of an MR damper to vibration
control, design the vibration damper using MR fluids, test and
evaluate its performance. In this paper the Rheology and the theory
behind MR fluids and their use on vibration control were studied.
Then a MR vibration damper suitable for vehicle suspension was
designed and fabricated using the MR fluid. The MR damper was
tested using a dynamic test rig and the results were obtained in the
form of force vs velocity and the force vs displacement plots. The
results were encouraging and greatly inspire further research on the
topic.
Abstract: In metal cutting industries, mathematical/statistical
models are typically used to predict tool replacement time. These
off-line methods usually result in less than optimum replacement
time thereby either wasting resources or causing quality problems.
The few online real-time methods proposed use indirect measurement
techniques and are prone to similar errors. Our idea is based on
identifying the optimal replacement time using an electronic nose to
detect the airborne compounds released when the tool wear reaches
to a chemical substrate doped into tool material during the
fabrication. The study investigates the feasibility of the idea, possible
doping materials and methods along with data stream mining
techniques for detection and monitoring different phases of tool
wear.
Abstract: The Petri net tool INA is a well known tool by the
Petri net community. However, it lacks a graphical environment to
cerate and analyse INA models. Building a modelling tool for the
design and analysis from scratch (for INA tool for example) is
generally a prohibitive task. Meta-Modelling approach is useful to
deal with such problems since it allows the modelling of the
formalisms themselves. In this paper, we propose an approach based
on the combined use of Meta-modelling and Graph Grammars to
automatically generate a visual modelling tool for INA for analysis
purposes. In our approach, the UML Class diagram formalism is
used to define a meta-model of INA models. The meta-modelling
tool ATOM3 is used to generate a visual modelling tool according to
the proposed INA meta-model. We have also proposed a graph
grammar to automatically generate INA description of the
graphically specified Petri net models. This allows the user to avoid
the errors when this description is done manually. Then the INA tool
is used to perform the simulation and the analysis of the resulted INA
description. Our environment is illustrated through an example.
Abstract: The paper presents a method for multivariate time
series forecasting using Independent Component Analysis (ICA), as a preprocessing tool. The idea of this approach is to do the forecasting in the space of independent components (sources), and then to transform back the results to the original time series
space. The forecasting can be done separately and with a different
method for each component, depending on its time structure. The
paper gives also a review of the main algorithms for independent component analysis in the case of instantaneous mixture models, using second and high-order statistics. The method has been applied in simulation to an artificial multivariate time series
with five components, generated from three sources and a mixing matrix, randomly generated.
Abstract: In this paper the Analytic Network Process (ANP) is
applied to the selection of photovoltaic (PV) solar power projects.
These projects follow a long management and execution process
from plant site selection to plant start-up. As a consequence, there are
many risks of time delays and even of project stoppage.
In the case study presented in this paper a top manager of an
important Spanish company that operates in the power market has to
decide on the best PV project (from four alternative projects) to
invest based on risk minimization. The manager identified 50 project
execution delay and/or stoppage risks.
The influences among elements of the network (groups of risks
and alternatives) were identified and analyzed using the ANP
multicriteria decision analysis method. After analyzing the results the
main conclusion is that the network model can manage all the
information of the real-world problem and thus it is a decision
analysis model recommended by the authors. The strengths and
weaknesses ANP as a multicriteria decision analysis tool are also
described in the paper.
Abstract: MRAM technology provides a combination of fast
access time, non-volatility, data retention and endurance. While a
growing interest is given to two-terminal Magnetic Tunnel Junctions
(MTJ) based on Spin-Transfer Torque (STT) switching as the
potential candidate for a universal memory, its reliability is
dramatically decreased because of the common writing/reading path.
Three-terminal MTJ based on Spin-Orbit Torque (SOT) approach
revitalizes the hope of an ideal MRAM. It can overcome the
reliability barrier encountered in current two-terminal MTJs by
separating the reading and the writing path. In this paper, we study
two possible writing schemes for the SOT-MTJ device based on
recently fabricated samples. While the first is based on precessional
switching, the second requires the presence of permanent magnetic
field. Based on an accurate Verilog-A model, we simulate the two
writing techniques and we highlight advantages and drawbacks of
each one. Using the second technique, pioneering logic circuits based
on the three-terminal architecture of the SOT-MTJ described in this
work are under development with preliminary attractive results.
Abstract: The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.
Abstract: During recent years, the traditional learning
approaches have undergone fundamental changes due to the
emergence of new technologies such as multimedia, hypermedia and
telecommunication. E-learning is a modern world phenomenon that
has come into existence in the information age and in a knowledgebased
society. E-learning has developed significantly within a short
period of time. Thus it is of a great significant to secure information,
allow a confident access and prevent unauthorized accesses. Making
use of individuals- physiologic or behavioral (biometric) properties is
a confident method to make the information secure. Among the
biometrics, fingerprint is more acceptable and most countries use it as
an efficient methods of identification. This article provides a new
method to compare the fingerprint comparison by pattern recognition
and image processing techniques. To verify fingerprint, the shortest
distance method is used together with perceptronic multilayer neural
network functioning based on minutiae. This method is highly
accurate in the extraction of minutiae and it accelerates comparisons
due to elimination of false minutiae and is more reliable compared
with methods that merely use directional images.
Abstract: Abdominal aortic aneurysms rupture (AAAs) is one of the main causes of death in the world. This is a very complex phenomenon that usually occurs “without previous warning". Currently, criteria to assess the aneurysm rupture risk (peak diameter and growth rate) can not be considered as reliable indicators. In a first approach, the main geometric parameters of aneurysms have been linked into five biomechanical factors. These are combined to obtain a dimensionless rupture risk index, RI(t), which has been validated preliminarily with a clinical case and others from literature. This quantitative indicator is easy to understand, it allows estimating the aneurysms rupture risks and it is expected to be able to identify the one in aneurysm whose peak diameter is less than the threshold value. Based on initial results, a broader study has begun with twelve patients from the Clinic Hospital of Valladolid-Spain, which are submitted to periodic follow-up examinations.