Abstract: Quality control is the crucial step for ISO 9001
Quality System Management Standard for companies. While
measuring the quality level of both raw material and semi
product/product, the calibration of the measuring device is an
essential requirement. Calibration suppliers are in the service sector
and therefore the calibration supplier selection is becoming a worthy
topic for improving service quality.
This study presents the results of a questionnaire about the
selection criteria of a calibration supplier. The questionnaire was
applied to 103 companies and the results are discussed in this paper.
The analysis was made with MINITAB 14.0 statistical programs.
“Competence of documentations" and “technical capability" are
defined as the prerequisites because of the ISO/IEC17025:2005
standard. Also “warranties and complaint policy", “communication",
“service features", “quality" and “performance history" are defined as
very important criteria for calibration supplier selection.
Abstract: Logic based methods for learning from structured data
is limited w.r.t. handling large search spaces, preventing large-sized
substructures from being considered by the resulting classifiers. A
novel approach to learning from structured data is introduced that
employs a structure transformation method, called finger printing, for
addressing these limitations. The method, which generates features
corresponding to arbitrarily complex substructures, is implemented in
a system, called DIFFER. The method is demonstrated to perform
comparably to an existing state-of-art method on some benchmark
data sets without requiring restrictions on the search space.
Furthermore, learning from the union of features generated by finger
printing and the previous method outperforms learning from each
individual set of features on all benchmark data sets, demonstrating
the benefit of developing complementary, rather than competing,
methods for structure classification.
Abstract: Well-developed strategic marketing planning is the essential
prerequisite for establishment of the right and unique competitive
advantage. Typical market, however, is a heterogeneous
and decentralized structure with natural involvement of individual
or group subjectivity and irrationality. These features cannot be
fully expressed with one-shot rigorous formal models based on,
e.g. mathematics, statistics or empirical formulas. We present an
innovative solution, extending the domain of agent based computational
economics towards the concept of hybrid modeling in service
provider and consumer market such as telecommunications. The
behavior of the market is described by two classes of agents -
consumer and service provider agents - whose internal dynamics
are fundamentally different. Customers are rather free multi-state
structures, adjusting behavior and preferences quickly in accordance
with time and changing environment. Producers, on the contrary,
are traditionally structured companies with comparable internal processes
and specific managerial policies. Their business momentum is
higher and immediate reaction possibilities limited. This limitation
underlines importance of proper strategic planning as the main
process advising managers in time whether to continue with more
or less the same business or whether to consider the need for future
structural changes that would ensure retention of existing customers
or acquisition of new ones.
Abstract: Classification of electroencephalogram (EEG) signals
extracted during mental tasks is a technique that is actively pursued
for Brain Computer Interfaces (BCI) designs. In this paper, we
compared the classification performances of univariateautoregressive
(AR) and multivariate autoregressive (MAR) models
for representing EEG signals that were extracted during different
mental tasks. Multilayer Perceptron (MLP) neural network (NN)
trained by the backpropagation (BP) algorithm was used to classify
these features into the different categories representing the mental
tasks. Classification performances were also compared across
different mental task combinations and 2 sets of hidden units (HU): 2
to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different
mental tasks from 4 subjects were used in the experimental study and
combinations of 2 different mental tasks were studied for each
subject. Three different feature extraction methods with 6th order
were used to extract features from these EEG signals: AR
coefficients computed with Burg-s algorithm (ARBG), AR
coefficients computed with stepwise least square algorithm (ARLS)
and MAR coefficients computed with stepwise least square
algorithm. The best results were obtained with 20 to 100 HU using
ARBG. It is concluded that i) it is important to choose the suitable
mental tasks for different individuals for a successful BCI design, ii)
higher HU are more suitable and iii) ARBG is the most suitable
feature extraction method.
Abstract: In this paper, we propose a new method to distinguish
between arousal and relaxation states by using multiple features
acquired from a photoplethysmogram (PPG) and support vector
machine (SVM). To induce arousal and relaxation states in subjects, 2
kinds of sound stimuli are used, and their corresponding biosignals are
obtained using the PPG sensor. Two features–pulse to pulse interval
(PPI) and pulse amplitude (PA)–are extracted from acquired PPG
data, and a nonlinear classification between arousal and relaxation is
performed using SVM.
This methodology has several advantages when compared with
previous similar studies. Firstly, we extracted 2 separate features from
PPG, i.e., PPI and PA. Secondly, in order to improve the classification
accuracy, SVM-based nonlinear classification was performed.
Thirdly, to solve classification problems caused by generalized
features of whole subjects, we defined each threshold according to
individual features.
Experimental results showed that the average classification
accuracy was 74.67%. Also, the proposed method showed the better
identification performance than the single feature based methods.
From this result, we confirmed that arousal and relaxation can be
classified using SVM and PPG features.
Abstract: This article explores the self-identity of the Kazakh
people by way of identifying the roots of self-understanding in
Kazakh culture. Unfortunately, Western methods of ethno
psychology cannot fully capture what is unique about identity in
Kazakh culture. Although Kazakhstan is the ninth largest country in
terms of geographical space, Kazakh cultural identity is not wellknown
in the West. In this article we offer an account of the national
psychological features of the Kazakh people, in order to reveal the
spiritual, mental, ethical dimensions of modern Kazakhs. These
factors play a central role in the revival of forms of identity that are
central to the Kazakh people.
Abstract: The use of 3D computer-aided design (CAD) models
to support construction project planning has been increasing in the
previous year. 3D CAD models reveal more planning ideas by
visually showing the construction site environment in different stages
of the construction process. Using 3D CAD models together with
scheduling software to prepare construction plan can identify errors
in process sequence and spatial arrangement, which is vital to the
success of a construction project. A number of 4D (3D plus time)
CAD tools has been developed and utilized in different construction
projects due to the awareness of their importance. Virtual prototyping
extends the idea of 4D CAD by integrating more features for
simulating real construction process. Virtual prototyping originates
from the manufacturing industry where production of products such
as cars and airplanes are virtually simulated in computer before they
are built in the factory. Virtual prototyping integrates 3D CAD,
simulation engine, analysis tools (like structural analysis and
collision detection), and knowledgebase to streamline the whole
product design and production process. In this paper, we present the
application of a virtual prototyping software which has been used in
a few construction projects in Hong Kong to support construction
project planning. Specifically, the paper presents an implementation
of virtual prototyping in a residential building project in Hong Kong.
The applicability, difficulties and benefits of construction virtual
prototyping are examined based on this project.
Abstract: As various mobile sensing technologies, remote
control and ubiquitous infrastructure are developing and expectations
on quality of life are increasing, a lot of researches and developments
on home network technologies and services are actively on going,
Until now, we have focused on how to provide users with high-level
home network services, while not many researches on home network
security for guaranteeing safety are progressing. So, in this paper, we
propose an access control model specific to home network that
provides various kinds of users with home network services up one-s
characteristics and features, and protects home network systems from
illegal/unnecessary accesses or intrusions.
Abstract: The paper is included within the framework of a
complex research program, which was initiated from the hypothesis
arguing on the existence of a correlation between pineal indolic and
peptide hormones and the somatic development rhythm, including
thus the epithalamium-epiphysis complex involvement. At birds,
pineal gland contains a circadian oscillator, playing a main role in the
temporal organization of the cerebral functions. The secretion of
pineal indolic hormones is characterized by a high endogenous
rhythmic alternation, modulated by the light/darkness (L/D)
succession and by temperature as well. The research has been carried
out using 100 chicken broilers - “Ross" commercial hybrid,
randomly allocated in two experimental batches: Lc batch, reared
under a 12L/12D lighting schedule and Lexp batch, which was photic
pinealectomised through continuous exposition to light (150 lux, 24
hours, 56 days). Chemical and physical features of the meat issued
from breast fillet and thighs muscles have been studied, determining
the dry matter, proteins, fat, collagen, salt content and pH value, as
well. Besides the variations of meat chemical composition in relation
with lighting schedule, other parameters have been studied: live
weight dynamics, feed intake and somatic development degree. The
achieved results became significant since chickens have 7 days of
age, some variations of the studied parameters being registered,
revealing that the pineal gland physiologic activity, in relation with
the lighting schedule, could be interpreted through the monitoring of
the somatic development technological parameters, usually studied
within the chicken broilers rearing aviculture practice.
Abstract: Ethnicity identification of face images is of interest in
many areas of application, but existing methods are few and limited.
This paper presents a fusion scheme that uses block-based uniform
local binary patterns and Haar wavelet transform to combine local
and global features. In particular, the LL subband coefficients of the
whole face are fused with the histograms of uniform local binary
patterns from block partitions of the face. We applied the principal
component analysis on the fused features and managed to reduce the
dimensionality of the feature space from 536 down to around 15
without sacrificing too much accuracy. We have conducted a number
of preliminary experiments using a collection of 746 subject face
images. The test results show good accuracy and demonstrate the
potential of fusing global and local features. The fusion approach is
robust, making it easy to further improve the identification at both
feature and score levels.
Abstract: In the study of chironomids in Armenia several
species of Orthocladiinae subfamily of Cricotopus genus,
Diamesinae subfamily of Diamesa genus, and Chironominae
subfamily of Chironomus genus, have been identified. In the
Cricotopus genus two sibling species were found, not distinguishable
by larval morphological features, but clearly distinct cytogenetically.
Abstract: A prototype of an anomaly detection system was
developed to automate process of recognizing an anomaly of
roentgen image by utilizing fuzzy histogram hyperbolization image
enhancement and back propagation artificial neural network.
The system consists of image acquisition, pre-processor, feature
extractor, response selector and output. Fuzzy Histogram
Hyperbolization is chosen to improve the quality of the roentgen
image. The fuzzy histogram hyperbolization steps consist of
fuzzyfication, modification of values of membership functions and
defuzzyfication. Image features are extracted after the the quality of
the image is improved. The extracted image features are input to the
artificial neural network for detecting anomaly. The number of nodes
in the proposed ANN layers was made small.
Experimental results indicate that the fuzzy histogram
hyperbolization method can be used to improve the quality of the
image. The system is capable to detect the anomaly in the roentgen
image.
Abstract: GFRG(Glass Fiber Reinforced Gypsum) wall is a green product which can erect a building fast in prefabricated method, but its application to high-rise residential buildings is limited for its poor lateral stiffness. This paper has proposed a modification to GFRG walls structure to increase its lateral stiffness, which aiming to erect small high-rise residential buildings as load-bearing walls. The elastic finite element analysis to it has shown the lateral deformation feature and the distributions of the axial force and the shear force. The analysis results show that the new GFRG reinforced concrete wall can be used for small high-rise residential buildings.
Abstract: Image fusion aims to enhance the perception
of a scene by combining important information captured by
different sensors. Dual-Tree Complex Wavelet (DT-CWT) has been
thouroughly investigated for image fusion, since it takes advantages
of approximate shift invariance and direction selectivity. But it can
only handle limited direction information. To allow a more flexible
directional expansion for images, we propose a novel fusion scheme,
referred to as complex contourlet transform (CCT). It successfully
incorporates directional filter banks (DFB) into DT-CWT. As a result
it efficiently deal with images containing contours and textures,
whereas it retains the property of shift invariance. Experimental
results demonstrated that the method features high quality fusion
performance and can facilitate many image processing applications.
Abstract: In this paper a novel method for finding the fault zone
on a Thyristor Controlled Series Capacitor (TCSC) incorporated
transmission line is presented. The method makes use of the Support
Vector Machine (SVM), used in the classification mode to
distinguish between the zones, before or after the TCSC. The use of
Discrete Wavelet Transform is made to prepare the features which
would be given as the input to the SVM. This method was tested on a
400 kV, 50 Hz, 300 Km transmission line and the results were highly
accurate.
Abstract: This paper presents the use of a semi-classical signal
analysis method that has been developed recently for the analysis of
turbomachinery flow unsteadiness. We will focus on the correlation
between theSemi-Classical Signal Analysis parameters and some
physical parameters in relation with turbomachinery features. To
demonstrate the potential of the proposed approach, a static pressure
signal issued from a rotor/stator interaction of a centrifugal pump is
studied. Several configurations of the pump are compared.
Abstract: Increasing growth of information volume in the
internet causes an increasing need to develop new (semi)automatic
methods for retrieval of documents and ranking them according to
their relevance to the user query. In this paper, after a brief review
on ranking models, a new ontology based approach for ranking
HTML documents is proposed and evaluated in various
circumstances. Our approach is a combination of conceptual,
statistical and linguistic methods. This combination reserves the
precision of ranking without loosing the speed. Our approach
exploits natural language processing techniques for extracting
phrases and stemming words. Then an ontology based conceptual
method will be used to annotate documents and expand the query.
To expand a query the spread activation algorithm is improved so
that the expansion can be done in various aspects. The annotated
documents and the expanded query will be processed to compute
the relevance degree exploiting statistical methods. The outstanding
features of our approach are (1) combining conceptual, statistical
and linguistic features of documents, (2) expanding the query with
its related concepts before comparing to documents, (3) extracting
and using both words and phrases to compute relevance degree, (4)
improving the spread activation algorithm to do the expansion based
on weighted combination of different conceptual relationships and
(5) allowing variable document vector dimensions. A ranking
system called ORank is developed to implement and test the
proposed model. The test results will be included at the end of the
paper.
Abstract: This paper presents an automatic feature recognition
method based on center-surround difference detecting and fuzzy logic
that can be applied in ground-penetrating radar (GPR) image
processing. Adopted center-surround difference method, the salient
local image regions are extracted from the GPR images as features of
detected objects. And fuzzy logic strategy is used to match the
detected features and features in template database. This way, the
problem of objects detecting, which is the key problem in GPR image
processing, can be converted into two steps, feature extracting and
matching. The contributions of these skills make the system have the
ability to deal with changes in scale, antenna and noises. The results of
experiments also prove that the system has higher ratio of features
sensing in using GPR to image the subsurface structures.
Abstract: The notion of communicative competence has been deemed fuzzy in communication studies. This fuzziness has led to tensions among engineers across tenures in interpreting what constitutes communicative competence. The study seeks to investigate novice and professional engineers- understanding of the said notion in terms of two main elements of communicative competence: linguistic and rhetorical competence. Novice engineers are final year engineering students, whilst professional engineers represent engineers who have at least 5 years working experience. Novice and professional engineers were interviewed to gauge their perceptions on linguistic and rhetorical features deemed necessary to enhance communicative competence for the profession. Both groups indicated awareness and differences on the importance of the sub-sets of communicative competence, namely, rhetorical explanatory competence, linguistic oral immediacy competence, technical competence and meta-cognitive competence. Such differences, a possible attribute of the learning theory, inadvertently indicate sublime differences in the way novice and professional engineers perceive communicative competence.
Abstract: Serial Analysis of Gene Expression is a powerful
quantification technique for generating cell or tissue gene expression
data. The profile of the gene expression of cell or tissue in several
different states is difficult for biologists to analyze because of the large
number of genes typically involved. However, feature selection in
machine learning can successfully reduce this problem. The method
allows reducing the features (genes) in specific SAGE data, and
determines only relevant genes. In this study, we used a genetic
algorithm to implement feature selection, and evaluate the
classification accuracy of the selected features with the K-nearest
neighbor method. In order to validate the proposed method, we used
two SAGE data sets for testing. The results of this study conclusively
prove that the number of features of the original SAGE data set can be
significantly reduced and higher classification accuracy can be
achieved.