Abstract: The design of a pattern classifier includes an attempt
to select, among a set of possible features, a minimum subset of
weakly correlated features that better discriminate the pattern classes.
This is usually a difficult task in practice, normally requiring the
application of heuristic knowledge about the specific problem
domain. The selection and quality of the features representing each
pattern have a considerable bearing on the success of subsequent
pattern classification. Feature extraction is the process of deriving
new features from the original features in order to reduce the cost of
feature measurement, increase classifier efficiency, and allow higher
classification accuracy. Many current feature extraction techniques
involve linear transformations of the original pattern vectors to new
vectors of lower dimensionality. While this is useful for data
visualization and increasing classification efficiency, it does not
necessarily reduce the number of features that must be measured
since each new feature may be a linear combination of all of the
features in the original pattern vector. In this paper a new approach is
presented to feature extraction in which feature selection, feature
extraction, and classifier training are performed simultaneously using
a genetic algorithm. In this approach each feature value is first
normalized by a linear equation, then scaled by the associated weight
prior to training, testing, and classification. A knn classifier is used to
evaluate each set of feature weights. The genetic algorithm optimizes
a vector of feature weights, which are used to scale the individual
features in the original pattern vectors in either a linear or a nonlinear
fashion. By this approach, the number of features used in classifying
can be finely reduced.
Abstract: A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.
Abstract: The Corporate Social Responsibility (CSR) performance has garnered significant interest during the last two decades as numerous methodologies are proposed by Social Responsible Investment (SRI) indexes. The weight of each indicator is a crucial component of the CSR measurement procedures. Based on a previous study, the appropriate weight of each proposed indicator for the Greek telecommunication sector is specified using the rank reciprocal weighting. The Kendall-s Coefficient of Concordance and Spearman Correlation Coefficient non-parametric tests are adopted to determine the level of consensus among the experts concerning the importance rank of indicators. The results show that there is no consensus regarding the rank of indicators in most of stakeholders- domains. The equal weight for all indicators could be proposed as a solution for the lack of consensus among the experts. The study recommends three different equations concerning the adopted weight approach.
Abstract: This paper describes a three-dimensional thermal
model of the current path included in the low voltage power circuit
breakers. The model can be used to analyse the thermal behaviour of
the current path during both steady-state and transient conditions.
The current path lengthwise temperature distribution and timecurrent
characteristic of the terminal connections of the power circuit
breaker have been obtained. The influence of the electric current and
voltage drop on main electric contact of the circuit breaker has been
investigated. To validate the three-dimensional thermal model, some
experimental tests have been done. There is a good correlation
between experimental and simulation results.
Abstract: In this Letter, a class of impulsive switched cellular neural networks with time-varying delays is investigated. At the same time, parametric uncertainties assumed to be norm bounded are considered. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions guaranteeing exponential stability for all admissible parametric uncertainties are derived via constructing appropriate Lyapunov functional. One numerical example is provided to illustrate the validity of the main results obtained in this paper.
Abstract: The intention of this study to design the probability optimized sewing sack-s workstation based on ergonomics for productivity improvement and decreasing musculoskeletal disorders. The physical dimensions of two workers were using to design the new workstation. The physical dimensions are (1) sitting height, (2) mid shoulder height sitting, (3) shoulder breadth, (4) knee height, (5) popliteal height, (6) hip breadth and (7) buttock-knee length. The 5th percentile of buttock knee length sitting (51 cm), the 50th percentile of mid shoulder height sitting (62 cm) and the 95th percentile of popliteal height (43 cm) and hip breadth (45 cm) applied to design the workstation for sewing sack-s operator and the others used to adjust the components of this workstation. The risk assessment by RULA before and after using the probability optimized workstation were 7 and 7 scores and REBA scores were 11 and 5, respectively. Body discomfort-abnormal index was used to assess muscle fatigue of operators before adjustment workstation found that neck muscles, arm muscles area, muscles on the back and the lower back muscles fatigue. Therefore, the extension and flexion exercise was applied to relief musculoskeletal stresses. The workers exercised 15 minutes before the beginning and the end of work for 5 days. After that, the capability of flexion and extension muscles- workers were increasing in 3 muscles (arm, leg, and back muscles).
Abstract: An adaptive neural network controller for
autonomous underwater vehicles (AUVs) is presented in this paper.
The AUV model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
In this regards, a nonlinear neural network is used to approximate the
nonlinear uncertainties of AUV dynamics, thus overcoming some
limitations of conventional controllers and ensure good performance.
The uniform ultimate boundedness of AUV tracking errors and the
stability of the proposed control system are guaranteed based on
Lyapunov theory. Numerical simulation studies for motion control of
an AUV are performed to demonstrate the effectiveness of the
proposed controller.
Abstract: The demand for higher performance graphics
continues to grow because of the incessant desire towards realism.
And, rapid advances in fabrication technology have enabled us to
build several processor cores on a single die. Hence, it is important to
develop single chip parallel architectures for such data-intensive
applications. In this paper, we propose an efficient PIM architectures
tailored for computer graphics which requires a large number of
memory accesses. We then address the two important tasks necessary
for maximally exploiting the parallelism provided by the architecture,
namely, partitioning and placement of graphic data, which affect
respectively load balances and communication costs. Under the
constraints of uniform partitioning, we develop approaches for optimal
partitioning and placement, which significantly reduce search space.
We also present heuristics for identifying near-optimal placement,
since the search space for placement is impractically large despite our
optimization. We then demonstrate the effectiveness of our partitioning
and placement approaches via analysis of example scenes; simulation
results show considerable search space reductions, and our heuristics
for placement performs close to optimal – the average ratio of
communication overheads between our heuristics and the optimal was
1.05. Our uniform partitioning showed average load-balance ratio of
1.47 for geometry processing and 1.44 for rasterization, which is
reasonable.
Abstract: Rural villagers in Thailand have unique skill for producing craft using local materials. However, the appearance and function of their products are not suited to the demand of international market. The Thai government policy on sustainable economy emphasises the necessity to incorporate a design strategy that will draw out the unique qualities and add value to the products, while raising the satisfaction of international consumer. As an industrial designer, the author sees opportunities that design can enhance sustainability of Thai local products through the potentials that available in village-based enterprises. This research attempts to address, how best use design to practically solve the problems in the development of Thais product in. The privilege solution is expressed through the design of design strategy that supports sustain economic development of microenterprise in Thailand in the way that aligns with product design development. This consideration integrates together with global business outlook in the development of products from rural communities.
Abstract: Using efficient classification methods is necessary for automatic fingerprint recognition system. This paper introduces a new structural approach to fingerprint classification by using the directional image of fingerprints to increase the number of subclasses. In this method, the directional image of fingerprints is segmented into regions consisting of pixels with the same direction. Afterwards the relational graph to the segmented image is constructed and according to it, the super graph including prominent information of this graph is formed. Ultimately we apply a matching technique to compare obtained graph with the model graphs in order to classify fingerprints by using cost function. Increasing the number of subclasses with acceptable accuracy in classification and faster processing in fingerprints recognition, makes this system superior.
Abstract: In this paper we propose a multi-agent architecture for web information retrieval using fuzzy logic based result fusion mechanism. The model is designed in JADE framework and takes advantage of JXTA agent communication method to allow agent communication through firewalls and network address translators. This approach enables developers to build and deploy P2P applications through a unified medium to manage agent-based document retrieval from multiple sources.
Abstract: The remote diagnosis and remote medical smoked to
part. In China, in accordance with the requirements of different
applications of remote diagnosis and Relates to the technical
difference, which can be divided into special purpose remote diagnosis
and treatment system, the remote will Referral system, remote medical
consultation system, remote rehabilitation technology and remote
operation technology. In this article, will introduce China for the
special purpose of service remote diagnosis and treatment system and
technology, including: China disabled status and virtual reality
technology; China 's domestic family medical care system and China 's
current situation of the development of telemedicine.
Abstract: A cognitive collaborative reinforcement learning
algorithm (CCRL) that incorporates an advisor into the learning
process is developed to improve supervised learning. An autonomous
learner is enabled with a self awareness cognitive skill to decide
when to solicit instructions from the advisor. The learner can also
assess the value of advice, and accept or reject it. The method is
evaluated for robotic motion planning using simulation. Tests are
conducted for advisors with skill levels from expert to novice. The
CCRL algorithm and a combined method integrating its logic with
Clouse-s Introspection Approach, outperformed a base-line fully
autonomous learner, and demonstrated robust performance when
dealing with various advisor skill levels, learning to accept advice
received from an expert, while rejecting that of less skilled
collaborators. Although the CCRL algorithm is based on RL, it fits
other machine learning methods, since advisor-s actions are only
added to the outer layer.
Abstract: This paper presents a model of case based corporate
memory named ReCaRo (REsource, CAse, ROle). The approach
suggested in ReCaRo decomposes the domain to model through a set
of components. These components represent the objects developed by
the company during its activity. They are reused, and sometimes,
while bringing adaptations. These components are enriched by
knowledge after each reuse. ReCaRo builds the corporate memory on
the basis of these components. It models two types of knowledge: 1)
Business Knowledge, which constitutes the main knowledge capital
of the company, refers to its basic skill, thus, directly to the
components and 2) the Experience Knowledge which is a specialised
knowledge and represents the experience gained during the handling
of business knowledge. ReCaRo builds corporate memories which
are made up of five communicating ones.
Abstract: The purpose of this article is to analyze the
degree of concentration in the banking market in EU member
states as well as to determine the impact of the length of EU
membership on the degree of concentration. In that sense
several analysis were conducted, specifically, panel analysis,
calculation of correlation coefficient and regression analysis of
the impact of the length of EU membership on the degree of
concentration. Panel analysis was conducted to determine
whether there is a similar trend of concentration in three
groups of countries - countries with a low, moderate and high
level of concentration. The conducted panel analysis showed
that in EU countries with a moderate level of concentration,
the level of concentration decreases. The calculation of
correlation showed that, to some extent, with other influential
factors, the length of EU membership negatively affects the
market concentration of the banking market. Using the
regression analysis for investigation of the influence of the
length of EU membership on the level of concentration in the
banking sector in a particular country, the results reveal that
there is a negative effect of the length in EU membership on
market concentration, although it is not significantly influential
variable.
Abstract: The presence of a vertical fatigue crack in the web of
a plate girder subjected to pure bending influences the bending
moment capacity of the girder. The growth of the crack may lead to
premature elastic failure due to flange local yielding, flange local
buckling, or web local buckling. Approximate expressions for the
bending moment capacities corresponding to these failure modes
were formulated. Finite element analyses were then used to validate
the expressions. The expressions were employed to assess the effects
of crack length on the capacity. Neglecting brittle fracture, tension
buckling, and ductile failure modes, it was found that typical girders
are governed by the capacity associated with flange local yielding as
influenced by the crack. Concluding, a possible use of the capacity
expressions in girder design was demonstrated.
Abstract: Speckle noise affects all coherent imaging systems
including medical ultrasound. In medical images, noise suppression
is a particularly delicate and difficult task. A tradeoff between noise
reduction and the preservation of actual image features has to be made
in a way that enhances the diagnostically relevant image content.
Even though wavelets have been extensively used for denoising
speckle images, we have found that denoising using contourlets gives
much better performance in terms of SNR, PSNR, MSE, variance and
correlation coefficient. The objective of the paper is to determine the
number of levels of Laplacian pyramidal decomposition, the number
of directional decompositions to perform on each pyramidal level and
thresholding schemes which yields optimal despeckling of medical
ultrasound images, in particular. The proposed method consists of the
log transformed original ultrasound image being subjected to contourlet
transform, to obtain contourlet coefficients. The transformed
image is denoised by applying thresholding techniques on individual
band pass sub bands using a Bayes shrinkage rule. We quantify the
achieved performance improvement.
Abstract: In this paper, a new method of image edge-detection
and characterization is presented. “Parametric Filtering method" uses
a judicious defined filter, which preserves the signal correlation
structure as input in the autocorrelation of the output. This leads,
showing the evolution of the image correlation structure as well as
various distortion measures which quantify the deviation between
two zones of the signal (the two Hamming signals) for the protection
of an image edge.
Abstract: In this paper, a theoretical formula is presented to
predict the instantaneous folding force of the first fold creation in a
square column under axial loading. Calculations are based on analysis
of “Basic Folding Mechanism" introduced by Wierzbicki and
Abramowicz. For this purpose, the sum of dissipated energy rate under
bending around horizontal and inclined hinge lines and dissipated
energy rate under extensional deformations are equated to the work rate
of the external force on the structure. Final formula obtained in this
research, reasonably predicts the instantaneous folding force of the first
fold creation versus folding distance and folding angle and also predicts
the instantaneous folding force instead of the average value. Finally,
according to the calculated theoretical relation, instantaneous folding
force of the first fold creation in a square column was sketched
versus folding distance and was compared to the experimental results
which showed a good correlation.
Abstract: Multimedia distributed systems deal with heterogeneous
data, such as texts, images, graphics, video and audio. The specification
of temporal relations among different data types and distributed
sources is an open research area. This paper proposes a fully
distributed synchronization model to be used in multimedia systems.
One original aspect of the model is that it avoids the use of a common
reference (e.g. wall clock and shared memory). To achieve this, all
possible multimedia temporal relations are specified according to
their causal dependencies.