Abstract: OPEN_EmoRec_II is an open multimodal corpus with
experimentally induced emotions. In the first half of the experiment,
emotions were induced with standardized picture material and in the
second half during a human-computer interaction (HCI), realized
with a wizard-of-oz design. The induced emotions are based on the
dimensional theory of emotions (valence, arousal and dominance).
These emotional sequences - recorded with multimodal data (facial
reactions, speech, audio and physiological reactions) during a
naturalistic-like HCI-environment one can improve classification
methods on a multimodal level.
This database is the result of an HCI-experiment, for which 30
subjects in total agreed to a publication of their data including the
video material for research purposes*. The now available open
corpus contains sensory signal of: video, audio, physiology (SCL,
respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus
Major) and facial reactions annotations.
Abstract: OPEN_EmoRec_II is an open multimodal corpus with
experimentally induced emotions. In the first half of the experiment,
emotions were induced with standardized picture material and in the
second half during a human-computer interaction (HCI), realized
with a wizard-of-oz design. The induced emotions are based on the
dimensional theory of emotions (valence, arousal and dominance).
These emotional sequences - recorded with multimodal data (facial
reactions, speech, audio and physiological reactions) during a
naturalistic-like HCI-environment one can improve classification
methods on a multimodal level.
This database is the result of an HCI-experiment, for which 30
subjects in total agreed to a publication of their data including the
video material for research purposes*. The now available open
corpus contains sensory signal of: video, audio, physiology (SCL,
respiration, BVP, EMG Corrugator supercilii, EMG Zygomaticus
Major) and facial reactions annotations.
Abstract: Polymer Electrolyte Membrane Fuel Cell (PEMFC) is
such a time-vary nonlinear dynamic system. The traditional linear
modeling approach is hard to estimate structure correctly of PEMFC
system. From this reason, this paper presents a nonlinear modeling of
the PEMFC using Neural Network Auto-regressive model with
eXogenous inputs (NNARX) approach. The multilayer perception
(MLP) network is applied to evaluate the structure of the NNARX
model of PEMFC. The validity and accuracy of NNARX model are
tested by one step ahead relating output voltage to input current from
measured experimental of PEMFC. The results show that the obtained
nonlinear NNARX model can efficiently approximate the dynamic
mode of the PEMFC and model output and system measured output
consistently.
Abstract: This work is the first dowel in a rather wide research
activity in collaboration with Euro Mediterranean Center for Climate
Changes, aimed at introducing scalable approaches in Ocean
Circulation Models. We discuss designing and implementation of
a parallel algorithm for solving the Variational Data Assimilation
(DA) problem on Graphics Processing Units (GPUs). The algorithm
is based on the fully scalable 3DVar DA model, previously proposed
by the authors, which uses a Domain Decomposition approach
(we refer to this model as the DD-DA model). We proceed with
an incremental porting process consisting of 3 distinct stages:
requirements and source code analysis, incremental development of
CUDA kernels, testing and optimization. Experiments confirm the
theoretic performance analysis based on the so-called scale up factor
demonstrating that the DD-DA model can be suitably mapped on
GPU architectures.
Abstract: Icons, or pictorial and graphical objects, are
commonly used in human-computer interaction (HCI) fields as the
mediator in order to communicate information to users. Yet there has
been little studies focusing on a majority of the world’s population –
semi-literate communities – in terms of the fundamental knowhow
for designing icons for such population. In this study, two sets of
icons belonging in different icon taxonomy – abstract and concrete –
are designed for a mobile application for semi-literate agricultural
communities. In this paper, we propose a triadic relationship of an
icon, namely meaning, task and mental image, which inherits the
triadic relationship of a sign. User testing with the application and a
post-pilot questionnaire are conducted as the experimental approach
in two rural villages in India. Icons belonging to concrete taxonomy
perform better than abstract icons on the premise that the design of
the icon fulfills the underlying rules of the proposed triadic
relationship.
Abstract: The coaxial transformer-coupled push-pull circuitry
has been used widely in HF and VHF amplifiers for many decades
without significant changes in the topology of the transformers. Basic
changes over the years concerned the construction and turns ratio of
the transformers as has been imposed upon the newer technologies
active devices demands. The balun transmission line transformers
applied in push-pull amplifiers enable input/output impedance
transformation, but are mainly used to convert the balanced output
into unbalanced and the input unbalanced into balanced. A simple
and affordable alternative solution over the traditional coaxial
transformer is the coreless planar balun. A key advantage over the
traditional approach lies in the high specifications repeatability;
simplifying the amplifier construction requirements as the planar
balun constitutes an integrated part of the PCB copper layout. This
paper presents the performance analysis of a planar LDMOS
MRFE6VP5600 Push-Pull amplifier that enables robust operation in
Band III, DVB-T, DVB-T2 standards but functions equally well in
Band II, for DRM+ new generation transmitters.
Abstract: Crosstalk among interconnects and printed-circuit
board (PCB) traces is a major limiting factor of signal quality in highspeed
digital and communication equipments especially when fast
data buses are involved. Such a bus is considered as a planar
multiconductor transmission line. This paper will demonstrate how
the finite difference time domain (FDTD) method provides an exact
solution of the transmission-line equations to analyze the near end
and the far end crosstalk. In addition, this study makes it possible to
analyze the rise time effect on the near and far end voltages of the
victim conductor. The paper also discusses a statistical analysis,
based upon a set of several simulations. Such analysis leads to a
better understanding of the phenomenon and yields useful
information.
Abstract: Organizational tendencies towards computer-based
information processing have been observed noticeably in the
third-world countries. Many enterprises are taking major initiatives
towards computerized working environment because of massive
benefits of computer-based information processing. However,
designing and developing information resource management software
for small and mid-size enterprises under budget costs and strict
deadline is always challenging for software engineers. Therefore, we
introduced an approach to design mid-size enterprise software by
using the Waterfall model, which is one of the SDLC (Software
Development Life Cycles), in a cost effective way. To fulfill research
objectives, in this study, we developed mid-sized enterprise software
named “BSK Management System” that assists enterprise software
clients with information resource management and perform complex
organizational tasks. Waterfall model phases have been applied to
ensure that all functions, user requirements, strategic goals, and
objectives are met. In addition, Rich Picture, Structured English, and
Data Dictionary have been implemented and investigated properly in
engineering manner. Furthermore, an assessment survey with 20
participants has been conducted to investigate the usability and
performance of the proposed software. The survey results indicated
that our system featured simple interfaces, easy operation and
maintenance, quick processing, and reliable and accurate transactions.
Abstract: ABC classification is widely used by managers for
inventory control. The classical ABC classification is based on Pareto
principle and according to the criterion of the annual use value only.
Single criterion classification is often insufficient for a closely
inventory control. Multi-criteria inventory classification models have
been proposed by researchers in order to consider other important
criteria. From these models, we will consider a specific model in
order to make a sensitive analysis on the composite score calculated
for each item. In fact, this score, based on a normalized average
between a good and a bad optimized index, can affect the ABC-item
classification. We will focus on items differently assigned to classes
and then propose a classification compromise.
Abstract: The following article presents Technology Centre of
Ostrava (TCO) in the Czech Republic describing the structure and
main research areas realized by the project ENET - Energy Units for
Utilization of non Traditional Energy Sources. More details are
presented from the research program dealing with transformation,
accumulation and distribution of electric energy. Technology Centre
has its own energy mix consisting of alternative sources of fuel
sources that use of process gases from the storage part and also the
energy from distribution network. The article will be focus on the
properties and application possibilities SiC semiconductor devices for
power semiconductor converter for photovoltaic systems.
Abstract: Behavioral aspects of experience such as will power
are rarely subjected to quantitative study owing to the numerous
complexities involved. Will is a phenomenon that has puzzled
humanity for a long time. It is a belief that will power of an individual
affects the success achieved by them in life. It is also thought that a
person endowed with great will power can overcome even the most
crippling setbacks in life while a person with a weak will cannot make
the most of life even the greatest assets. This study is an attempt
to subject the phenomena of will to the test of an artificial neural
network through a computational model. The claim being tested is
that will power of an individual largely determines success achieved
in life. It is proposed that data pertaining to success of individuals
be obtained from an experiment and the phenomenon of will be
incorporated into the model, through data generated recursively using
a relation between will and success characteristic to the model.
An artificial neural network trained using part of the data, could
subsequently be used to make predictions regarding data points in
the rest of the model. The procedure would be tried for different
models and the model where the networks predictions are found to
be in greatest agreement with the data would be selected; and used
for studying the relation between success and will.
Abstract: In the past few years, the amount of malicious software
increased exponentially and, therefore, machine learning algorithms
became instrumental in identifying clean and malware files through
(semi)-automated classification. When working with very large
datasets, the major challenge is to reach both a very high malware
detection rate and a very low false positive rate. Another challenge
is to minimize the time needed for the machine learning algorithm to
do so. This paper presents a comparative study between different
machine learning techniques such as linear classifiers, ensembles,
decision trees or various hybrids thereof. The training dataset consists
of approximately 2 million clean files and 200.000 infected files,
which is a realistic quantitative mixture. The paper investigates the
above mentioned methods with respect to both their performance
(detection rate and false positive rate) and their practicability.
Abstract: DC motors have been widely used in the past
centuries which are proudly known as the workhorse of industrial
systems until the invention of the AC induction motors which makes
a huge revolution in industries. Since then, the use of DC machines
has been decreased due to enormous factors such as reliability,
robustness and complexity but it lost its fame due to the losses. In this
paper a new methodology is proposed to construct a DC motor
through the simulation in LabVIEW to get an idea about its real time
performances, if a change in parameter might have bigger
improvement in losses and reliability.
Abstract: Proposed paper dealt with the modelling and analysis of induction motor based on the mathematical expression using the graphical programming environment of Laboratory Virtual Instrument Engineering Workbench (LabVIEW). Induction motor modelling with the mathematical expression enables the motor to be simulated with the various required parameters. Owing to the invention of variable speed drives study about the induction motor characteristics became complex. In this simulation motor internal parameter such as stator resistance and reactance, rotor resistance and reactance, phase voltage, frequency and losses will be given as input. By varying the speed of motor corresponding parameters can be obtained they are input power, output power, efficiency, torque induced, slip and current.
Abstract: PhilSHORE is a multi-site, multi-device and multicriteria
decision support tool designed to support the development of
tidal current energy in the Philippines. Its platform is based on
Geographic Information Systems (GIS) which allows for the
collection, storage, processing, analyses and display of geospatial
data. Combining GIS tools with open source web development
applications, PhilSHORE becomes a webGIS-based marine spatial
planning tool. To date, PhilSHORE displays output maps and graphs
of power and energy density, site suitability and site-device analysis.
It enables stakeholders and the public easy access to the results of
tidal current energy resource assessments and site suitability
analyses. Results of the initial development show that PhilSHORE is
a promising decision support tool for ORE project developments.
Abstract: Transmission system performance analysis is vital to
proper planning and operations of power systems in the presence of
deregulation. Key performance indicators (KPIs) are often used as
measure of degree of performance. This paper gives a novel method
to determine the transmission efficiency by evaluating the ratio of
real power losses incurred from a specified transfer direction.
Available Transmission Transfer Efficiency (ATTE) expresses the
percentage of real power received resulting from inter-area available
power transfer. The Tie line (Rated system path) performance is seen
to differ from system wide (Network response) performance and
ATTE values obtained are transfer direction specific. The required
sending end quantities with specified receiving end ATC and the
receiving end power circle diagram are obtained for the tie line
analysis. The amount of real power loss load relative to the available
transfer capability gives a measure of the transmission grid
efficiency.
Abstract: Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFP-growth” that enhancing the FPgrowth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms: Apriori-multiple minimum supports using maximum constraints and FP-growth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy.
Abstract: This work proposes a data-driven multiscale based
quantitative measures to reveal the underlying complexity of
electroencephalogram (EEG), applying to a rodent model of
hypoxic-ischemic brain injury and recovery. Motivated by that real
EEG recording is nonlinear and non-stationary over different
frequencies or scales, there is a need of more suitable approach over
the conventional single scale based tools for analyzing the EEG data.
Here, we present a new framework of complexity measures
considering changing dynamics over multiple oscillatory scales. The
proposed multiscale complexity is obtained by calculating entropies of
the probability distributions of the intrinsic mode functions extracted
by the empirical mode decomposition (EMD) of EEG. To quantify
EEG recording of a rat model of hypoxic-ischemic brain injury
following cardiac arrest, the multiscale version of Tsallis entropy is
examined. To validate the proposed complexity measure, actual EEG
recordings from rats (n=9) experiencing 7 min cardiac arrest followed
by resuscitation were analyzed. Experimental results demonstrate that
the use of the multiscale Tsallis entropy leads to better discrimination
of the injury levels and improved correlations with the neurological
deficit evaluation after 72 hours after cardiac arrest, thus suggesting an
effective metric as a prognostic tool.
Abstract: Moving into a new era of healthcare, new tools and
devices are developed to extend and improve health services, such as
remote patient monitoring and risk prevention. In this concept,
Internet of Things (IoT) and Cloud Computing present great
advantages by providing remote and efficient services, as well as
cooperation between patients, clinicians, researchers and other health
professionals. This paper focuses on patients suffering from bipolar
disorder, a brain disorder that belongs to a group of conditions
called affective disorders, which is characterized by great mood
swings. We exploit the advantages of Semantic Web and Cloud
Technologies to develop a patient monitoring system to support
clinicians. Based on intelligently filtering of evidence-knowledge and
individual-specific information we aim to provide treatment
notifications and recommended function tests at appropriate times or
concluding into alerts for serious mood changes and patient’s nonresponse
to treatment. We propose an architecture as the back-end
part of a cloud platform for IoT, intertwining intelligence devices
with patients’ daily routine and clinicians’ support.
Abstract: Testability modeling is a commonly used method in
testability design and analysis of system. A dependency matrix will be
obtained from testability modeling, and we will give a quantitative
evaluation about fault detection and isolation.
Based on the dependency matrix, we can obtain the diagnosis tree.
The tree provides the procedures of the fault detection and isolation.
But the dependency matrix usually includes built-in test (BIT) and
manual test in fact. BIT runs the test automatically and is not limited
by the procedures. The method above cannot give a more efficient
diagnosis and use the advantages of the BIT.
A Comprehensive method of fault detection and isolation is
proposed. This method combines the advantages of the BIT and
Manual test by splitting the matrix. The result of the case study shows
that the method is effective.