Abstract: One of the robust fault detection filter (RFDF)
designing method is based on sliding-mode theory. The main purpose
of our study is to introduce an innovative simplified reference
residual model generator to formulate the RFDF as a sliding-mode
observer without any manipulation package or transformation matrix,
through which the generated residual signals can be evaluated. So the
proposed design is more explicit and requires less design parameters
in comparison with approaches requiring changing coordinates. To
the best author's knowledge, this is the first time that the sliding
mode technique is applied to detect actuator and sensor faults in a
real boiler. The designing procedure is proposed in a drum boiler in
Synvendska Kraft AB Plant in Malmo, Sweden as a multivariable
and strongly coupled system. It is demonstrated that both sensor and
actuator faults can robustly be detected. Also sensor faults can be
diagnosed and isolated through this method.
Abstract: In this paper, we present a maintenance model of a
two-unit series system with economic dependence. Unit#1 which is
considered to be more expensive and more important, is subject to
condition monitoring (CM) at equidistant, discrete time epochs and
unit#2, which is not subject to CM has a general lifetime distribution.
The multivariate observation vectors obtained through condition
monitoring carry partial information about the hidden state of unit#1,
which can be in a healthy or a warning state while operating. Only the
failure state is assumed to be observable for both units. The objective
is to find an optimal opportunistic maintenance policy minimizing
the long-run expected average cost per unit time. The problem
is formulated and solved in the partially observable semi-Markov
decision process framework. An effective computational algorithm
for finding the optimal policy and the minimum average cost is
developed, illustrated by a numerical example.
Abstract: In order to maximize efficiency of an information management platform and to assist in decision making, the collection, storage and analysis of performance-relevant data has become of fundamental importance. This paper addresses the merits and drawbacks provided by the OLAP paradigm for efficiently navigating large volumes of performance measurement data hierarchically. The system managers or database administrators navigate through adequately (re)structured measurement data aiming to detect performance bottlenecks, identify causes for performance problems or assessing the impact of configuration changes on the system and its representative metrics. Of particular importance is finding the root cause of an imminent problem, threatening availability and performance of an information system. Leveraging OLAP techniques, in contrast to traditional static reporting, this is supposed to be accomplished within moderate amount of time and little processing complexity. It is shown how OLAP techniques can help improve understandability and manageability of measurement data and, hence, improve the whole Performance Analysis process.
Abstract: This paper deals with the application of artificial
neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy
Inference System(ANFIS) approach to Load Frequency Control
(LFC) of multi unequal area hydro-thermal interconnected power
system. The proposed ANFIS controller combines the advantages of
fuzzy controller as well as quick response and adaptability nature of
ANN. Area-1 and area-2 consists of thermal reheat power plant
whereas area-3 and area-4 consists of hydro power plant with electric
governor. Performance evaluation is carried out by using intelligent
controller like ANFIS, ANN and Fuzzy controllers and conventional
PI and PID control approaches. To enhance the performance of
intelligent and conventional controller sliding surface is included.
The performances of the controllers are simulated using
MATLAB/SIMULINK package. A comparison of ANFIS, ANN,
Fuzzy, PI and PID based approaches shows the superiority of
proposed ANFIS over ANN & fuzzy, PI and PID controller for 1%
step load variation.
Abstract: Human genome is not only the evolutionary
summation of all advantageous events, but also houses lesions of
deleterious foot prints. A single gene mutation sometimes may
express multiple consequences in numerous tissues and a linear
relationship of the genotype and the phenotype may often be obscure.
ß Thalassemia minor, a transfusion independent mild anaemia,
coupled with environment among other factors may articulate into
phenotypic pleotropy with Hypocholesterolemia, Vitamin D
deficiency, Tissue hypoxia, Hyper-parathyroidism and Psychological
alterations. Occurrence of Pancreatic insufficiency, resultant
steatorrhoea, Vitamin-D (25-OH) deficiency (13.86 ngm/ml) with
Hypocholesterolemia (85mg/dl) in a 30 years old male ß Thal-minor
patient (Hemoglobin 11mg/dl with Fetal Hemoglobin 2.10%, Hb A2
4.60% and Hb Adult 84.80% and altered Hemogram) with increased
Para thyroid hormone (62 pg/ml) & moderate Serum Ca+2
(9.5mg/ml) indicate towards a cascade of phenotypic pleotropy
where the ß Thalassemia mutation ,be it in the 5’ cap site of the
mRNA , differential splicing etc in heterozygous state is effecting
several metabolic pathways. Compensatory extramedulary
hematopoiesis may not coped up well with the stressful life style of
the young individual and increased erythropoietic stress with high
demand for cholesterol for RBC membrane synthesis may have
resulted in Hypocholesterolemia.Oxidative stress and tissue hypoxia
may have caused the pancreatic insufficiency, leading to Vitamin D
deficiency. This may in turn have caused the secondary
hyperparathyroidism to sustain serum Calcium level. Irritability and
stress intolerance of the patient was a cumulative effect of the vicious
cycle of metabolic compromises. From these findings we propose
that the metabolic deficiencies in the ß Thalassemia mutations may
be considered as the phenotypic display of the pleotropy to explain
the genetic epidemiology.
According to the recommendations from the NIH Workshop on
Gene-Environment Interplay in Common Complex Diseases: Forging
an Integrative Model, study design of observations should be
informed by gene-environment hypotheses and results of a study
(genetic diseases) should be published to inform future hypotheses.
Variety of approaches is needed to capture data on all possible
aspects, each of which is likely to contribute to the etiology of
disease. Speakers also agreed that there is a need for development of
new statistical methods and measurement tools to appraise
information that may be missed out by conventional method where
large sample size is needed to segregate considerable effect.
A meta analytic cohort study in future may bring about significant
insight on to the title comment.
Abstract: This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
Abstract: The objective of this paper is twofold: (1) discuss and
analyze the successful case studies worldwide, and (2) identify the
similarities and differences of case studies worldwide. Design
methodology/approach: The nature of this research is mainly method
qualitative (multi-case studies, literature review). This investigation
uses ten case studies, and the data was mainly collected and
organizational documents from the international countries. Finding:
The finding of this research can help incubator manager, policy
maker and government parties for successful implementation.
Originality/value: This paper contributes to the current literate review
on the best practices worldwide. Additionally, it presents future
perspective for academicians and practitioners.
Abstract: In this paper, a new recursive strategy is proposed for determining $\frac{(n-1)!}{2}$ of $n$th order diagrams. The generalization of $n$th diagram for cross multiplication method were proposed by Pavlovic and Bankier but the specific rule of determining $\frac{(n-1)!}{2}$ of the $n$th order diagrams for square matrix is yet to be discovered. Thus using combinatorial approach, $\frac{(n-1)!}{2}$ of the $n$th order diagrams will be presented as $\frac{(n-1)!}{2}$ starter sets. These starter sets will be generated based on exchanging one element. The advantages of this new strategy are the discarding process was eliminated and the sign of starter set is alternated to each others.
Abstract: A major part of the flow field involves no complicated
turbulent behavior in many turbulent flows. In this research work, in
order to reduce required memory and CPU time, the flow field was
decomposed into several blocks, each block including its special
turbulence. A two dimensional backward facing step was considered
here. Four combinations of the Prandtl mixing length and standard k-
E models were implemented as well. Computer memory and CPU
time consumption in addition to numerical convergence and accuracy
of the obtained results were mainly investigated. Observations
showed that, a suitable combination of turbulence models in different
blocks led to the results with the same accuracy as the high order
turbulence model for all of the blocks, in addition to the reductions in
memory and CPU time consumption.
Abstract: In this article we present a methodology which
enables preschool and primary school unlanguaged children to
remember words, phrases and texts with the help of graphic signs -
letters, syllables and words. Reading for a child becomes a support
for speech development. Teaching is based on the principle "from
simple to complex", "a letter - a syllable - a word - a proposal - a
text." Availability of multi-level texts allows using this methodology
for working with children who have different levels of speech
development.
Abstract: The rotation of starting pitchers is a strategic issue
which has a significant impact on the performance of a professional
team. Choosing an optimal starting pitcher from among many
alternatives is a multi-criteria decision-making (MCDM) problem. In
this study, a model using the Analytic Hierarchy Process (AHP) and
Technique for Order Performance by Similarity to the Ideal Solution
(TOPSIS) is proposed with which to arrange the starting pitcher
rotation for teams of the Chinese Professional Baseball League. The
AHP is used to analyze the structure of the starting pitcher selection
problem and to determine the weights of the criteria, while the
TOPSIS method is used to make the final ranking. An empirical
analysis is conducted to illustrate the utilization of the model for the
starting pitcher rotation problem. The results demonstrate the
effectiveness and feasibility of the proposed model.
Abstract: The aim of this paper is to present current and future
procedures in castings procurement. Differences in procurement are
highlighted. The supplier selection criteria used in practice is
compared to literature findings. Different trends related to supply
chains are presented and it is described how they are reflected in
reality to castings procurement. To fulfil the aim, interviews were
conducted in nine companies using castings. It was found that largest
casting users have the most subcontractor foundries and it is more
typical that they have multiple suppliers for the same parts. Currently
only two companies out of nine purchase castings outside Europe,
but the others are also progressing in the same direction. The main
reason is the need to lower purchasing costs. Another trend is that all
companies want to buy cast components or sub-assemblies instead of
raw castings from foundries. It was found that price is a main
supplier selection criterion. All companies use competitive bidding in
supplier selection.
Abstract: The article analyses historical aspects of the formation
of the Kazakh nation in the conditions of the multicultural society.
The authors underline cultural integration as a significant stage of the
cultural advancement of the Kazakh nation. The transition to the
modern-style houses, the adoption and development of the secular
education gave a rise to the development of the society and culture
on the whole.
Abstract: This paper explores the scalability issues associated
with solving the Named Entity Recognition (NER) problem using
Support Vector Machines (SVM) and high-dimensional features. The
performance results of a set of experiments conducted using binary
and multi-class SVM with increasing training data sizes are
examined. The NER domain chosen for these experiments is the
biomedical publications domain, especially selected due to its
importance and inherent challenges. A simple machine learning
approach is used that eliminates prior language knowledge such as
part-of-speech or noun phrase tagging thereby allowing for its
applicability across languages. No domain-specific knowledge is
included. The accuracy measures achieved are comparable to those
obtained using more complex approaches, which constitutes a
motivation to investigate ways to improve the scalability of multiclass
SVM in order to make the solution more practical and useable.
Improving training time of multi-class SVM would make support
vector machines a more viable and practical machine learning
solution for real-world problems with large datasets. An initial
prototype results in great improvement of the training time at the
expense of memory requirements.
Abstract: The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.
Abstract: This paper presents two different sequential switching hybrid-modulation strategies and implemented for cascaded multilevel inverters. Hybrid modulation strategies represent the combinations of Fundamental-frequency pulse width modulation (FFPWM) and Multilevel sinusoidal-modulation (MSPWM) strategies, and are designed for performance of the well-known Alternative Phase opposition disposition (APOD), Phase shifted carrier (PSC). The main characteristics of these modulations are the reduction of switching losses with good harmonic performance, balanced power loss dissipation among the devices with in a cell, and among the series-connected cells. The feasibility of these modulations is verified through spectral analysis, power loss analysis and simulation.
Abstract: Three similar negative differential resistance (NDR)
profiles with both high peak to valley current density ratio (PVCDR)
value and high peak current density (PCD) value in unity resonant
tunneling electronic circuit (RTEC) element is developed in this paper.
The PCD values and valley current density (VCD) values of the three
NDR curves are all about 3.5 A and 0.8 A, respectively. All PV values
of NDR curves are 0.40 V, 0.82 V, and 1.35 V, respectively. The VV
values are 0.61 V, 1.07 V, and 1.69 V, respectively. All PVCDR
values reach about 4.4 in three NDR curves. The PCD value of 3.5 A
in triple PVCDR RTEC element is better than other resonant
tunneling devices (RTD) elements. The high PVCDR value is
concluded the lower VCD value about 0.8 A. The low VCD value is
achieved by suitable selection of resistors in triple PVCDR RTEC
element. The low PV value less than 1.35 V possesses low power
dispersion in triple PVCDR RTEC element. The designed multiple
value logical level (MVLL) system using triple PVCDR RTEC
element provides equidistant logical level. The logical levels of
MVLL system are about 0.2 V, 0.8 V, 1.5 V, and 2.2 V from low
voltage to high voltage and then 2.2 V, 1.3 V, 0.8 V, and 0.2 V from
high voltage back to low voltage in half cycle of sinusoid wave. The
output level of four levels MVLL system is represented in 0.3 V, 1.1 V,
1.7 V, and 2.6 V, which satisfies the NMP condition of traditional
two-bit system. The remarkable logical characteristic of improved
MVLL system with paralleled capacitor are with four significant
stable logical levels about 220 mV, 223 mV, 228 mV, and 230 mV.
The stability and articulation of logical levels of improved MVLL
system are outstanding. The average holding time of improved MVLL
system is approximately 0.14 μs. The holding time of improved
MVLL system is fourfold than of basic MVLL system. The function of
additional capacitor in the improved MVLL system is successfully
discovered.
Abstract: Ontology-based modelling of multi-formatted
software application content is a challenging area in content
management. When the number of software content unit is huge and
in continuous process of change, content change management is
important. The management of content in this context requires
targeted access and manipulation methods. We present a novel
approach to deal with model-driven content-centric information
systems and access to their content. At the core of our approach is an
ontology-based semantic annotation technique for diversely
formatted content that can improve the accuracy of access and
systems evolution. Domain ontologies represent domain-specific
concepts and conform to metamodels. Different ontologies - from
application domain ontologies to software ontologies - capture and
model the different properties and perspectives on a software content
unit. Interdependencies between domain ontologies, the artifacts and
the content are captured through a trace model. The annotation traces
are formalised and a graph-based system is selected for the
representation of the annotation traces.
Abstract: Regression testing is a maintenance activity applied to
modified software to provide confidence that the changed parts are
correct and that the unchanged parts have not been adversely affected
by the modifications. Regression test selection techniques reduce the
cost of regression testing, by selecting a subset of an existing test
suite to use in retesting modified programs. This paper presents the
first general regression-test-selection technique, which based on code
and allows selecting test cases for any programs written in any
programming language. Then it handles incomplete program. We
also describe RTSDiff, a regression-test-selection system that
implements the proposed technique. The results of the empirical
studied that performed in four programming languages java, C#, Cµ
and Visual basic show that the efficiency and effective in reducing
the size of test suit.
Abstract: One of the most growing areas in the embedded community is multimedia devices. Multimedia devices incorporate a number of complicated functions for their operation, like motion estimation. A multitude of different implementations have been proposed to reduce motion estimation complexity, such as spiral search. We have studied the implementations of spiral search and identified areas of improvement. We propose a modified spiral search algorithm, with lower computational complexity compared to the original spiral search. We have implemented our algorithm on an embedded ARM based architecture, with custom memory hierarchy. The resulting system yields energy consumption reduction up to 64% and performance increase up to 77%, with a small penalty of 2.3 dB, in average, of video quality compared with the original spiral search algorithm.