Abstract: In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.
Abstract: The trends of design and development of information systems have undergone a variety of ongoing phases and stages. These variations have been evolved due to brisk changes in user requirements and business needs. To meet these requirements and needs, a flexible and agile business solution was required to come up with the latest business trends and styles. Another obstacle in agility of information systems was typically different treatment of same diseases of two patients: business processes and information services. After the emergence of information technology, the business processes and information systems have become counterparts. But these two business halves have been treated under totally different standards. There is need to streamline the boundaries of these both pillars that are equally sharing information system's burdens and liabilities. In last decade, the object orientation has evolved into one of the major solutions for modern business needs and now, SOA is the solution to shift business on ranks of electronic platform. BPM is another modern business solution that assists to regularize optimization of business processes. This paper discusses how object orientation can be conformed to incorporate or embed SOA in BPM for improved information systems.
Abstract: The purpose of this article is to analyze economic and
political tendencies of development of integration processes with
different developing level and speed on the Eurasian space, by considering two organizations at the region – Eurasian Economic
Community and Shanghai Cooperation Organization, by considering the interests of participations in organizations of Russia and China as
a global powers and Kazakhstan as a leader among the Central Asian
countries. This article investigates what certain goals Eurasian
countries (especially Russia, Kazakhstan and China) are waiting from integration within the SCO and the EurAsEC, linking the process
with the theories of regional integration. After European debt crisis it is more topically to research the integration within the specific
region's conditions.
Abstract: Knowledge sharing in general and the contextual
access to knowledge in particular, still represent a key challenge in
the knowledge management framework. Researchers on semantic
web and human machine interface study techniques to enhance this
access. For instance, in semantic web, the information retrieval is
based on domain ontology. In human machine interface, keeping
track of user's activity provides some elements of the context that can
guide the access to information. We suggest an approach based on
these two key guidelines, whilst avoiding some of their weaknesses.
The approach permits a representation of both the context and the
design rationale of a project for an efficient access to knowledge. In
fact, the method consists of an information retrieval environment
that, in the one hand, can infer knowledge, modeled as a semantic
network, and on the other hand, is based on the context and the
objectives of a specific activity (the design). The environment we
defined can also be used to gather similar project elements in order to
build classifications of tasks, problems, arguments, etc. produced in a
company. These classifications can show the evolution of design
strategies in the company.
Abstract: Results of Chilean wine classification based on the
information provided by an electronic nose are reported in this paper.
The classification scheme consists of two parts; in the first stage,
Principal Component Analysis is used as feature extraction method to
reduce the dimensionality of the original information. Then, Radial
Basis Functions Neural Networks is used as pattern recognition
technique to perform the classification. The objective of this study is
to classify different Cabernet Sauvignon, Merlot and Carménère wine
samples from different years, valleys and vineyards of Chile.
Abstract: This study reports the implementation of Good
Manufacturing Practice (GMP) in a polycarbonate film processing
plant. The implementation of GMP took place with the creation of a
multidisciplinary team. It was carried out in four steps: conduct gap
assessment, create gap closure plan, close gaps, and follow up the
GMP implementation. The basis for the gap assessment is the
guideline for GMP for plastic materials and articles intended for Food
Contact Material (FCM), which was edited by Plastic Europe. The
effective results of the GMP implementation in this study showed
100% completion of gap assessment. The key success factors for
implementing GMP in production process are the commitment,
intention and support of top management.
Abstract: This article provides empirical evidence on the effect
of domestic and international factors on the U.S. current account
deficit. Linear dynamic regression and vector autoregression models
are employed to estimate the relationships during the period from 1986
to 2011. The findings of this study suggest that the current and lagged
private saving rate and foreign current account for East Asian
economies have played a vital role in affecting the U.S. current
account. Additionally, using Granger causality tests and variance
decompositions, the change of the productivity growth and foreign
domestic demand are determined to influence significantly the change
of the U.S. current account. To summarize, the empirical relationship
between the U.S. current account deficit and its determinants is
sensitive to alternative regression models and specifications.
Abstract: Previous the 3D model texture generation from multi-view images and mapping algorithms has issues in the texture chart generation which are the self-intersection and the concentration of the texture in texture space. Also we may suffer from some problems due to the occluded areas, such as inside parts of thighs. In this paper we propose a texture mapping technique for 3D models using multi-view images on the GPU. We do texture mapping directly on the GPU fragment shader per pixel without generation of the texture map. And we solve for the occluded area using the 3D model depth information. Our method needs more calculation on the GPU than previous works, but it has shown real-time performance and previously mentioned problems do not occur.
Abstract: The present study was designed to investigate the
cardio protective role of chronic oral administration of alcoholic
extract of Terminalia arjuna in in-vivo ischemic reperfusion injury
and the induction of HSP72. Rabbits, divided into three groups, and
were administered with the alcoholic extract of the bark powder of
Terminalia arjuna (TAAE) by oral gavage [6.75mg/kg: (T1) and
9.75mg/kg: (T2), 6 days /week for 12 weeks]. In open-chest
Ketamine pentobarbitone anaesthetized rabbits, the left anterior
descending coronary artery was occluded for 15 min of ischemia
followed by 60 min of reperfusion. In the vehicle-treated group,
ischemic-reperfusion injury (IRI) was evidenced by depression of
global hemodynamic function (MAP, HR, LVEDP, peak LV (+) & (-
) (dP/dt) along with depletion of HEP compounds. Oxidative stress
in IRI was evidenced by, raised levels of myocardial TBARS and
depletion of endogenous myocardial antioxidants GSH, SOD and
catalase. Western blot analysis showed a single band corresponding
to 72 kDa in homogenates of hearts from rabbits treated with both the
doses. In the alcoholic extract of the bark powder of Terminalia
arjuna treatment groups, both the doses had better recovery of
myocardial hemodynamic function, with significant reduction in
TBARS, and rise in SOD, GSH, catalase were observed. The results
of the present study suggest that the alcoholic extract of the bark
powder of Terminalia arjuna in rabbit induces myocardial HSP 72
and augments myocardial endogenous antioxidants, without causing
any cellular injury and offered better cardioprotection against
oxidative stress associated with myocardial IR injury.
Abstract: This research work is aimed at speech recognition
using scaly neural networks. A small vocabulary of 11 words were
established first, these words are “word, file, open, print, exit, edit,
cut, copy, paste, doc1, doc2". These chosen words involved with
executing some computer functions such as opening a file, print
certain text document, cutting, copying, pasting, editing and exit.
It introduced to the computer then subjected to feature extraction
process using LPC (linear prediction coefficients). These features are
used as input to an artificial neural network in speaker dependent
mode. Half of the words are used for training the artificial neural
network and the other half are used for testing the system; those are
used for information retrieval.
The system components are consist of three parts, speech
processing and feature extraction, training and testing by using neural
networks and information retrieval.
The retrieve process proved to be 79.5-88% successful, which is
quite acceptable, considering the variation to surrounding, state of
the person, and the microphone type.
Abstract: In ad hoc networks, the main issue about designing of protocols is quality of service, so that in wireless sensor networks the main constraint in designing protocols is limited energy of sensors. In fact, protocols which minimize the power consumption in sensors are more considered in wireless sensor networks. One approach of reducing energy consumption in wireless sensor networks is to reduce the number of packages that are transmitted in network. The technique of collecting data that combines related data and prevent transmission of additional packages in network can be effective in the reducing of transmitted packages- number. According to this fact that information processing consumes less power than information transmitting, Data Aggregation has great importance and because of this fact this technique is used in many protocols [5]. One of the Data Aggregation techniques is to use Data Aggregation tree. But finding one optimum Data Aggregation tree to collect data in networks with one sink is a NP-hard problem. In the Data Aggregation technique, related information packages are combined in intermediate nodes and form one package. So the number of packages which are transmitted in network reduces and therefore, less energy will be consumed that at last results in improvement of longevity of network. Heuristic methods are used in order to solve the NP-hard problem that one of these optimization methods is to solve Simulated Annealing problems. In this article, we will propose new method in order to build data collection tree in wireless sensor networks by using Simulated Annealing algorithm and we will evaluate its efficiency whit Genetic Algorithm.
Abstract: This paper considers the effect of heat generation
proportional l to (T - T∞ )p , where T is the local temperature and T∞
is the ambient temperature, in unsteady free convection flow near the
stagnation point region of a three-dimensional body. The fluid is
considered in an ambient fluid under the assumption of a step change
in the surface temperature of the body. The non-linear coupled partial
differential equations governing the free convection flow are solved
numerically using an implicit finite-difference method for different
values of the governing parameters entering these equations. The
results for the flow and heat characteristics when p ≤ 2 show that
the transition from the initial unsteady-state flow to the final steadystate
flow takes place smoothly. The behavior of the flow is seen
strongly depend on the exponent p.
Abstract: Nevertheless the widespread application of finite
mixture models in segmentation, finite mixture model selection is
still an important issue. In fact, the selection of an adequate number
of segments is a key issue in deriving latent segments structures and
it is desirable that the selection criteria used for this end are effective.
In order to select among several information criteria, which may
support the selection of the correct number of segments we conduct a
simulation study. In particular, this study is intended to determine
which information criteria are more appropriate for mixture model
selection when considering data sets with only categorical
segmentation base variables. The generation of mixtures of
multinomial data supports the proposed analysis. As a result, we
establish a relationship between the level of measurement of
segmentation variables and some (eleven) information criteria-s
performance. The criterion AIC3 shows better performance (it
indicates the correct number of the simulated segments- structure
more often) when referring to mixtures of multinomial segmentation
base variables.
Abstract: Image Compression using Artificial Neural Networks
is a topic where research is being carried out in various directions
towards achieving a generalized and economical network.
Feedforward Networks using Back propagation Algorithm adopting
the method of steepest descent for error minimization is popular and
widely adopted and is directly applied to image compression.
Various research works are directed towards achieving quick
convergence of the network without loss of quality of the restored
image. In general the images used for compression are of different
types like dark image, high intensity image etc. When these images
are compressed using Back-propagation Network, it takes longer
time to converge. The reason for this is, the given image may
contain a number of distinct gray levels with narrow difference with
their neighborhood pixels. If the gray levels of the pixels in an image
and their neighbors are mapped in such a way that the difference in
the gray levels of the neighbors with the pixel is minimum, then
compression ratio as well as the convergence of the network can be
improved. To achieve this, a Cumulative distribution function is
estimated for the image and it is used to map the image pixels. When
the mapped image pixels are used, the Back-propagation Neural
Network yields high compression ratio as well as it converges
quickly.
Abstract: This paper presents the region based segmentation method for ultrasound images using local statistics. In this segmentation approach the homogeneous regions depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are to be extracted. This method uses a look up table comprising of the local statistics of every pixel, which are consisting of the homogeneity and similarity bounds according to the kernel size. The shape and size of the growing regions depend on this look up table entries. The algorithms are implemented by using connected seeded region growing procedure where each pixel is taken as seed point. The region merging after the region growing also suppresses the high frequency artifacts. The updated merged regions produce the output in formed of segmented image. This algorithm produces the results that are less sensitive to the pixel location and it also allows a segmentation of the accurate homogeneous regions.
Abstract: This paper present the harmonic elimination of hybrid
multilevel inverters (HMI) which could be increase the number of
output voltage level. Total Harmonic Distortion (THD) is one of the
most important requirements concerning performance indices.
Because of many numbers output levels of HMI, it had numerous
unknown variables of eliminate undesired individual harmonic and
THD nonlinear equations set. Optimized harmonic stepped waveform
(OHSW) is solving switching angles conventional method, but most
complicated for solving as added level. The artificial intelligent
techniques are deliberation to solve this problem. This paper presents
the Particle Swarm Optimization (PSO) technique for solving
switching angles to get minimum THD and eliminate undesired
individual harmonics of 15-levels hybrid multilevel inverters.
Consequently it had many variables and could eliminate numerous
harmonics. Both advantages including high level of inverter and
Particle Swarm Optimization (PSO) are used as powerful tools for
harmonics elimination.
Abstract: Urban disaster risks and vulnerabilities are great problems for Turkey. The annual loss of life and property through disaster in the world-s major metropolitan areas is increasing. Urban concentrations of the poor and less-informed in environmentally fragile locations suffer the impact of disaster disproportionately. Gecekondu (squatter) developments will compound the inherent risks associated with high-density environments, in appropriate technologies, and inadequate infrastructure. On the other hand, there are many geological disadvantages such as sitting on top of active tectonic plate boundaries, and why having avalanche, flood, and landslide and drought prone areas in Turkey. However, this natural formation is inevitable; the only way to survive in such a harsh geography is to be aware of importance of these natural events and to take political and physical measures. The main aim of this research is to bring up the magnitude of natural hazard risks in Izmir built-up zone, not being taken into consideration adequately. Because the dimensions of the peril are not taken seriously enough, the natural hazard risks, which are commonly well known, are not considered important or they are being forgotten after some time passes. Within this research, the magnitude of natural hazard risks for Izmir is being presented in the scope of concrete and local researches over Izmir risky areas.
Abstract: Inorganic nanoparticles filled polymer composites
have extended their multiple functionalities to various applications,
including mechanical reinforcement, gas barrier, dimensional
stability, heat distortion temperature, flame-retardant, and thermal
conductivity. Sodium stearate-modified calcium carbonate (CaCO3)
nanoparticles were prepared using surface modification method. The
results showed that sodium stearate attached to the surface of CaCO3
nanoparticles with the chemical bond. The effect of modified CaCO3
nanoparticles on thermal properties of polypropylene (PP) was
studied by means of differential scanning calorimetry (DSC) and
Thermogravimetric analysis (TGA). It was found that CaCO3
significantly affected the crystallization temperature and
crystallization degree of PP. Effect of the modified CaCO3 content on
mechanical properties of PP/CaCO3 nanocomposites was also
studied. The results showed that the modified CaCO3 can effectively
improve the mechanical properties of PP. In comparison with PP, the
impact strength of PP/CaCO3 nanocomposites increased by about
65% and the hardness increased by about 5%.
Abstract: Conjugate natural convection in a differentially heated
square enclosure containing a polygon shaped object is studied numerically in this article. The effect of various polygon types on the
fluid flow and thermal performance of the enclosure is addressed for
different thermal conductivities. The governing equations are modeled
and solved numerically using the built-in finite element method of COMSOL software. It is found that the heat transfer rate remains
stable by varying the polygon types.
Abstract: Intermetallic Ni3Al – based alloys belong to a group
of advanced materials characterized by good chemical and physical
properties (such as structural stability, corrosion resistance) which
offer advenced technological applications. The paper presents the
study of catalytic properties of Ni3Al foils (thickness approximately
50 &m) in the methanol and hexane decomposition. The egzamined
material posses microcrystalline structure without any additional
catalysts on the surface. The better catalytic activity of Ni3Al foils
with respect to quartz plates in both methanol and hexane
decomposition was confirmed. On thin Ni3Al foils the methanol
conversion reaches approximately 100% above 480 oC while the
hexane conversion reaches approximately 100% (98,5%) at 500 oC.
Deposit formed during the methanol decomposition is built up of
carbon nanofibers decorated with metal-like nanoparticles.