Abstract: Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows drawing conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
Abstract: With a surge of stream processing applications novel
techniques are required for generation and analysis of association
rules in streams. The traditional rule mining solutions cannot handle
streams because they generally require multiple passes over the data
and do not guarantee the results in a predictable, small time. Though
researchers have been proposing algorithms for generation of rules
from streams, there has not been much focus on their analysis.
We propose Association rule profiling, a user centric process for
analyzing association rules and attaching suitable profiles to them
depending on their changing frequency behavior over a previous
snapshot of time in a data stream.
Association rule profiles provide insights into the changing nature
of associations and can be used to characterize the associations. We
discuss importance of characteristics such as predictability of
linkages present in the data and propose metric to quantify it. We
also show how association rule profiles can aid in generation of user
specific, more understandable and actionable rules.
The framework is implemented as SUPAR: System for Usercentric
Profiling of Association Rules in streaming data. The
proposed system offers following capabilities:
i) Continuous monitoring of frequency of streaming item-sets
and detection of significant changes therein for association rule
profiling.
ii) Computation of metrics for quantifying predictability of
associations present in the data.
iii) User-centric control of the characterization process: user
can control the framework through a) constraint specification and b)
non-interesting rule elimination.
Abstract: The Application of e-health solutions has brought superb advancements in the health care industry. E-health solutions have already been embraced in the industrialized countries. In an effort to catch up with the growth, the developing countries have strived to revolutionize the healthcare industry by use of Information technology in different ways. Based on a technology assessment carried out in Kenya – one of the developing countries – and using multiple case studies in Nyanza Province, this work focuses on an investigation on how five rural hospitals are adapting to the technology shift. The issues examined include the ICT infrastructure and e-health technologies in place, the knowledge of participants in terms of benefits gained through the use of ICT and the challenges posing barriers to the use of ICT technologies in these hospitals. The results reveal that the ICT infrastructure in place is inadequate for e-health implementations as a result to various challenges that exist. Consequently, suggestions on how to tackle the various challenges have been addressed in this paper.
Abstract: In this paper, an automatic detecting algorithm for
QRS complex detecting was applied for analyzing ECG recordings
and five criteria for dangerous arrhythmia diagnosing are applied for a
protocol type of automatic arrhythmia diagnosing system. The
automatic detecting algorithm applied in this paper detected the
distribution of QRS complexes in ECG recordings and related
information, such as heart rate and RR interval. In this investigation,
twenty sampled ECG recordings of patients with different pathologic
conditions were collected for off-line analysis. A combinative
application of four digital filters for bettering ECG signals and
promoting detecting rate for QRS complex was proposed as
pre-processing. Both of hardware filters and digital filters were
applied to eliminate different types of noises mixed with ECG
recordings. Then, an automatic detecting algorithm of QRS complex
was applied for verifying the distribution of QRS complex. Finally,
the quantitative clinic criteria for diagnosing arrhythmia were
programmed in a practical application for automatic arrhythmia
diagnosing as a post-processor. The results of diagnoses by automatic
dangerous arrhythmia diagnosing were compared with the results of
off-line diagnoses by experienced clinic physicians. The results of
comparison showed the application of automatic dangerous
arrhythmia diagnosis performed a matching rate of 95% compared
with an experienced physician-s diagnoses.
Abstract: When the failure function is monotone, some monotonic reliability methods are used to gratefully simplify and facilitate the reliability computations. However, these methods often work in a transformed iso-probabilistic space. To this end, a monotonic simulator or transformation is needed in order that the transformed failure function is still monotone. This note proves at first that the output distribution of failure function is invariant under the transformation. And then it presents some conditions under which the transformed function is still monotone in the newly obtained space. These concern the copulas and the dependence concepts. In many engineering applications, the Gaussian copulas are often used to approximate the real word copulas while the available information on the random variables is limited to the set of marginal distributions and the covariances. So this note catches an importance on the conditional monotonicity of the often used transformation from an independent random vector into a dependent random vector with Gaussian copulas.
Abstract: In this paper we propose and examine an Adaptive
Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition
Autoregressive (STAR) modeling. Because STAR models follow
fuzzy logic approach, in the non-linear part fuzzy rules can be
incorporated or other training or computational methods can be
applied as the error backpropagation algorithm instead to nonlinear
squares. Furthermore, additional fuzzy membership functions can be
examined, beside the logistic and exponential, like the triangle,
Gaussian and Generalized Bell functions among others. We examine
two macroeconomic variables of US economy, the inflation rate and
the 6-monthly treasury bills interest rates.
Abstract: Feature selection plays an important role in applications with high dimensional data. The assessment of the stability of feature selection/ranking algorithms becomes an important issue when the dataset is small and the aim is to gain insight into the underlying process by analyzing the most relevant features. In this work, we propose a graphical approach that enables to analyze the similarity between feature ranking techniques as well as their individual stability. Moreover, it works with whatever stability metric (Canberra distance, Spearman's rank correlation coefficient, Kuncheva's stability index,...). We illustrate this visualization technique evaluating the stability of several feature selection techniques on a spectral binary dataset. Experimental results with a neural-based classifier show that stability and ranking quality may not be linked together and both issues have to be studied jointly in order to offer answers to the domain experts.
Abstract: Due to the rise of aging population, effective utilization
of healthcare resources has become an important issue. With the
advance of ICT technology, the application of tele-healthcare service
has received more attention than ever. The main purpose of this
research is to investigate how to conduct innovative design for
tele-healthcare service based on user-s perspectives. First, the
healthcare service blueprint was used to describe the processes
of tele-healthcare service delivery, and then construct PZB service
quality gap model based on the literature and practitioners-
interviews. Next, TRIZ theory is applied to implement service
innovation. We found the proposed service innovation procedures can
effectively improve the quality of service design.
Abstract: The Czech Republic is a country whose economy has
undergone a transformation since 1989. Since joining the EU it has
been striving to reduce the differences in its economic standard and
the quality of its institutional environment in comparison with
developed countries. According to an assessment carried out by the
World Bank, the Czech Republic was long classed as a country
whose institutional development was seen as problematic. For many
years one of the things it was rated most poorly on was its bankruptcy
law. The new Insolvency Act, which is a modern law in terms of its
treatment of bankruptcy, was first adopted in the Czech Republic in
2006. This law, together with other regulatory measures, offers debtridden
Czech economic subjects legal instruments which are well
established and in common practice in developed market economies.
Since then, analyses performed by the World Bank and the London
EBRD have shown that there have been significant steps forward in
the quality of Czech bankruptcy law. The Czech Republic still lacks
an analytical apparatus which can offer a structured characterisation
of the general and specific conditions of Czech company and
household debt which is subject to current changes in the global
economy. This area has so far not been given the attention it
deserves. The lack of research is particularly clear as regards analysis
of household debt and householders- ability to settle their debts in a
reasonable manner using legal and other state means of regulation.
We assume that Czech households have recourse to a modern
insolvency law, yet the effective application of this law is hampered
by the inconsistencies in the formal and informal institutions
involved in resolving debt. This in turn is based on the assumption
that this lack of consistency is more marked in cases of personal
bankruptcy. Our aim is to identify the symptoms which indicate that
for some time the effective application of bankruptcy law in the
Czech Republic will be hindered by factors originating in
householders- relative inability to identify the risks of falling into
debt.
Abstract: In the course of the present work, plain (nonencapsulated)
and microencapsulated polyphenols were produced
using olive mill wastewater (OMW) as raw material, in order to be
used for enrichment of yogurt and dairy products. The OMW was
first clarified by using membrane technology and subsequently the
contained poly-phenols were isolated by adsorption-desorption
technique using selective macro-porous resins and finally recovered
in dry form after been processed by RO membrane technique
followed by freeze drying. Moreover, the polyphenols were
encapsulated in modified starch by freeze drying in order to mask the
color and bitterness effect and improve their functionality. The two
products were used successfully as additives in yogurt preparations
and the produced products were acceptable by the consumers and
presented with certain advantage to the plain yogurt. For the herein
proposed production scheme a patent application was already
submitted.
Abstract: In this paper the application of rule mining in order to
review the effective factors on supplier selection is reviewed in the
following three sections 1) criteria selecting and information
gathering 2) performing association rule mining 3) validation and
constituting rule base. Afterwards a few of applications of rule base
is explained. Then, a numerical example is presented and analyzed
by Clementine software. Some of extracted rules as well as the
results are presented at the end.
Abstract: The log periodogram regression is widely used in empirical
applications because of its simplicity, since only a least squares
regression is required to estimate the memory parameter, d, its good
asymptotic properties and its robustness to misspecification of the
short term behavior of the series. However, the asymptotic distribution
is a poor approximation of the (unknown) finite sample distribution
if the sample size is small. Here the finite sample performance of different
nonparametric residual bootstrap procedures is analyzed when
applied to construct confidence intervals. In particular, in addition to
the basic residual bootstrap, the local and block bootstrap that might
adequately replicate the structure that may arise in the errors of the
regression are considered when the series shows weak dependence in
addition to the long memory component. Bias correcting bootstrap
to adjust the bias caused by that structure is also considered. Finally,
the performance of the bootstrap in log periodogram regression based
confidence intervals is assessed in different type of models and how
its performance changes as sample size increases.
Abstract: Efficient modulo 2n+1 adders are important for
several applications including residue number system, digital signal
processors and cryptography algorithms. In this paper we present a
novel modulo 2n+1 addition algorithm for a recently represented
number system. The proposed approach is introduced for the
reduction of the power dissipated. In a conventional modulo 2n+1
adder, all operands have (n+1)-bit length. To avoid using (n+1)-bit
circuits, the diminished-1 and carry save diminished-1 number
systems can be effectively used in applications. In the paper, we also
derive two new architectures for designing modulo 2n+1 adder, based
on n-bit ripple-carry adder. The first architecture is a faster design
whereas the second one uses less hardware. In the proposed method,
the special treatment required for zero operands in Diminished-1
number system is removed. In the fastest modulo 2n+1 adders in
normal binary system, there are 3-operand adders. This problem is
also resolved in this paper. The proposed architectures are compared
with some efficient adders based on ripple-carry adder and highspeed
adder. It is shown that the hardware overhead and power
consumption will be reduced. As well as power reduction, in some
cases, power-delay product will be also reduced.
Abstract: Wireless Sensor Network is Multi hop Self-configuring
Wireless Network consisting of sensor nodes. The deployment of
wireless sensor networks in many application areas, e.g., aggregation
services, requires self-organization of the network nodes into clusters.
Efficient way to enhance the lifetime of the system is to partition the
network into distinct clusters with a high energy node as cluster head.
The different methods of node clustering techniques have appeared in
the literature, and roughly fall into two families; those based on the
construction of a dominating set and those which are based solely on
energy considerations. Energy optimized cluster formation for a set
of randomly scattered wireless sensors is presented. Sensors within a
cluster are expected to be communicating with cluster head only. The
energy constraint and limited computing resources of the sensor nodes
present the major challenges in gathering the data. In this paper we
propose a framework to study how partially correlated data affect the
performance of clustering algorithms. The total energy consumption
and network lifetime can be analyzed by combining random geometry
techniques and rate distortion theory. We also present the relation
between compression distortion and data correlation.
Abstract: Value engineering is an efficacious contraption for
administrators to make up their minds. Value perusals proffer the
gaffers a suitable instrument to decrease the expenditures of the life
span, quality amelioration, structural improvement, curtailment of the
construction schedule, longevity prolongation or a merging of the
aforementioned cases. Subjecting organizers to pressures on one
hand and their accountability towards their pertinent fields together
with inherent risks and ambiguities of other options on the other hand
set some comptrollers in a dilemma utilization of risk management
and the value engineering in projects manipulation with regard to
complexities of implementing projects can be wielded as a
contraption to identify and efface each item which wreaks
unnecessary expenses and time squandering sans inflicting any
damages upon the essential project applications. Of course It should
be noted that implementation of risk management and value
engineering with regard to the betterment of efficiency and functions
may lead to the project implementation timing elongation. Here time
revamping does not refer to time diminishing in the whole cases. his
article deals with risk and value engineering conceptualizations at
first. The germane reverberations effectuated due to its execution in
Iran Khodro Corporation are regarded together with the joint features
and amalgamation of the aforesaid entia; hence the proposed
blueprint is submitted to be taken advantage of in engineering and
industrial projects including Iran Khodro Corporation.
Abstract: In this paper, we describe a rule-based message passing method to support developing collaborative applications, in which multiple users share resources in distributed environments. Message communications of applications in collaborative environments tend to be very complex because of the necessity to manage context situations such as sharing events, access controlling of users, and network places. In this paper, we propose a message communications method based on unification of artificial intelligence and logic programming for defining rules of such context information in a procedural object-oriented programming language. We also present an implementation of the method as java classes.
Abstract: Text similarity measurement is a fundamental issue in
many textual applications such as document clustering, classification,
summarization and question answering. However, prevailing approaches
based on Vector Space Model (VSM) more or less suffer
from the limitation of Bag of Words (BOW), which ignores the semantic
relationship among words. Enriching document representation
with background knowledge from Wikipedia is proven to be an effective
way to solve this problem, but most existing methods still
cannot avoid similar flaws of BOW in a new vector space. In this
paper, we propose a novel text similarity measurement which goes
beyond VSM and can find semantic affinity between documents.
Specifically, it is a unified graph model that exploits Wikipedia as
background knowledge and synthesizes both document representation
and similarity computation. The experimental results on two different
datasets show that our approach significantly improves VSM-based
methods in both text clustering and classification.
Abstract: Breast cancer is one of the most frequent occurring cancers in women throughout the world including U.K. The grading of this cancer plays a vital role in the prognosis of the disease. In this paper we present an overview of the use of advanced computational method of fuzzy inference system as a tool for the automation of breast cancer grading. A new spectral data set obtained from Fourier Transform Infrared Spectroscopy (FTIR) of cancer patients has been used for this study. The future work outlines the potential areas of fuzzy systems that can be used for the automation of breast cancer grading.
Abstract: Using strength Pulse Electrical Field (PEF) in food
industries is a non-thermal process that can deactivate
microorganisms and increase penetration in plant and animals tissues
without serious impact on food taste and quality. In this paper designing and fabricating of a PEF generator has been presented. Pulse generation methods have been surveyed and the best of them
selected. The equipment by controller set can generate square pulse with adjustable parameters such as amplitude 1-5kV, frequency 0.1-10Hz, pulse width 10-100s, and duty cycle 0-100%. Setting the number of pulses, and presenting the output voltage and current
waveforms on the oscilloscope screen are another advantages of this
equipment. Finally, some food samples were tested that yielded the satisfactory results. PEF applying had considerable effects on potato, banana and purple cabbage. It caused increase Brix factor from 0.05
to 0.15 in potato solution. It is also so effective in extraction color material from purple cabbage. In the last experiment effects of PEF
voltages on color extraction of saffron scum were surveyed (about 6% increasing yield).
Abstract: One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an Early Warning System (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7,853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.