Abstract: This paper presents a novel integrated hybrid
approach for fault diagnosis (FD) of nonlinear systems. Unlike most
FD techniques, the proposed solution simultaneously accomplishes
fault detection, isolation, and identification (FDII) within a unified
diagnostic module. At the core of this solution is a bank of adaptive
neural parameter estimators (NPE) associated with a set of singleparameter
fault models. The NPEs continuously estimate unknown
fault parameters (FP) that are indicators of faults in the system. Two
NPE structures including series-parallel and parallel are developed
with their exclusive set of desirable attributes. The parallel scheme is
extremely robust to measurement noise and possesses a simpler, yet
more solid, fault isolation logic. On the contrary, the series-parallel
scheme displays short FD delays and is robust to closed-loop system
transients due to changes in control commands. Finally, a fault
tolerant observer (FTO) is designed to extend the capability of the
NPEs to systems with partial-state measurement.
Abstract: Applications of the Hausdorff space and its mappings
into tangent spaces are outlined, including their fractal dimensions
and self-similarities. The paper details this theory set up and further
describes virtualizations and atomization of manufacturing processes.
It demonstrates novel concurrency principles that will guide
manufacturing processes and resources configurations. Moreover,
varying levels of details may be produced by up folding and breaking
down of newly introduced generic models. This choice of layered
generic models for units and systems aspects along specific aspects
allows research work in parallel to other disciplines with the same
focus on all levels of detail. More credit and easier access are granted
to outside disciplines for enriching manufacturing grounds. Specific
mappings and the layers give hints for chances for interdisciplinary
outcomes and may highlight more details for interoperability
standards, as already worked on the international level. The new rules
are described, which require additional properties concerning all
involved entities for defining distributed decision cycles, again on the
base of self-similarity. All properties are further detailed and assigned
to a maturity scale, eventually displaying the smartness maturity of a
total shopfloor or a factory. The paper contributes to the intensive
ongoing discussion in the field of intelligent distributed
manufacturing and promotes solid concepts for implementations of
Cyber Physical Systems and the Internet of Things into
manufacturing industry, like industry 4.0, as discussed in German-speaking
countries.
Abstract: Recently, the green architecture becomes a
significant way to a sustainable future. Green building designs
involve finding the balance between comfortable homebuilding and
sustainable environment. Moreover, the utilization of the new
technologies such as artificial intelligence techniques are used to
complement current practices in creating greener structures to keep
the built environment more sustainable. The most common objectives
in green buildings should be designed to minimize the overall impact
of the built environment that effect on ecosystems in general and in
particularly human health and natural environment. This will lead to
protecting occupant health, improving employee productivity,
reducing pollution and sustaining the environmental. In green
building design, multiple parameters which may be interrelated,
contradicting, vague and of qualitative/quantitative nature are
broaden to use. This paper presents a comprehensive critical state- ofart-
review of current practices based on fuzzy and its combination
techniques. Also, presented how green architecture/building can be
improved using the technologies that been used for analysis to seek
optimal green solutions strategies and models to assist in making the
best possible decision out of different alternatives.
Abstract: The development of allometric models is crucial to
accurate forest biomass/carbon stock assessment. The aim of this
study was to develop a set of biomass prediction models that will
enable the determination of total tree aboveground biomass for
savannah woodland area in Niger State, Nigeria. Based on the data
collected through biometric measurements of 1816 trees and
destructive sampling of 36 trees, five species specific and one site
specific models were developed. The sample size was distributed
equally between the five most dominant species in the study site
(Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa,
Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the
equations were developed for five individual species. Secondly these
five species were mixed and were used to develop an allometric
equation of mixed species. Overall, there was a strong positive
relationship between total tree biomass and the stem diameter. The
coefficient of determination (R2 values) ranging from 0.93 to 0.99 P
< 0.001 were realised for the models; with considerable low standard
error of the estimates (SEE) which confirms that the total tree above
ground biomass has a significant relationship with the dbh. F-test
values for the biomass prediction models were also significant at p
Abstract: Previous studies on financial distress prediction choose
the conventional failing and non-failing dichotomy; however, the
distressed extent differs substantially among different financial
distress events. To solve the problem, “non-distressed”, “slightlydistressed”
and “reorganization and bankruptcy” are used in our article
to approximate the continuum of corporate financial health. This paper
explains different financial distress events using the two-stage method.
First, this investigation adopts firm-specific financial ratios, corporate
governance and market factors to measure the probability of various
financial distress events based on multinomial logit models.
Specifically, the bootstrapping simulation is performed to examine the
difference of estimated misclassifying cost (EMC). Second, this work
further applies macroeconomic factors to establish the credit cycle
index and determines the distressed cut-off indicator of the two-stage
models using such index. Two different models, one-stage and
two-stage prediction models are developed to forecast financial
distress, and the results acquired from different models are compared
with each other, and with the collected data. The findings show that the
one-stage model has the lower misclassification error rate than the
two-stage model. The one-stage model is more accurate than the
two-stage model.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: This paper describes the problem of building secure
computational services for encrypted information in the Cloud
Computing without decrypting the encrypted data; therefore, it meets
the yearning of computational encryption algorithmic aspiration
model that could enhance the security of big data for privacy,
confidentiality, availability of the users. The cryptographic model
applied for the computational process of the encrypted data is the
Fully Homomorphic Encryption Scheme. We contribute a theoretical
presentations in a high-level computational processes that are based
on number theory and algebra that can easily be integrated and
leveraged in the Cloud computing with detail theoretic mathematical
concepts to the fully homomorphic encryption models. This
contribution enhances the full implementation of big data analytics
based cryptographic security algorithm.
Abstract: Unmanned aircraft systems (UAS) are playing
increasingly prominent roles in defense programs and defense
strategies around the world. Technology advancements have
enabled the development of it to do many excellent jobs as
reconnaissance, surveillance, battle fighters, and communications
relays. Simulating a small unmanned aerial vehicle (SUAV)
dynamics and analyzing its behavior at the preflight stage is too
important and more efficient. The first step in the UAV design is
the mathematical modeling of the nonlinear equations of motion. .
In this paper, a survey with a standard method to obtain the full
non-linear equations of motion is utilized, and then the
linearization of the equations according to a steady state flight
condition (trimming) is derived. This modeling technique is
applied to an Ultrastick-25e fixed wing UAV to obtain the valued
linear longitudinal and lateral models. At the end the model is
checked by matching between the behavior of the states of the nonlinear
UAV and the resulted linear model with doublet at the
control surfaces.
Abstract: The Cone Penetration Test (CPT) is a common in-situ
test which generally investigates a much greater volume of soil more
quickly than possible from sampling and laboratory tests. Therefore,
it has the potential to realize both cost savings and assessment of soil
properties rapidly and continuously. The principle objective of this
paper is to demonstrate the feasibility and efficiency of using
artificial neural networks (ANNs) to predict the soil angle of internal
friction (Φ) and the soil modulus of elasticity (E) from CPT results
considering the uncertainties and non-linearities of the soil. In
addition, ANNs are used to study the influence of different
parameters and recommend which parameters should be included as
input parameters to improve the prediction. Neural networks discover
relationships in the input data sets through the iterative presentation
of the data and intrinsic mapping characteristics of neural topologies.
General Regression Neural Network (GRNN) is one of the powerful
neural network architectures which is utilized in this study. A large
amount of field and experimental data including CPT results, plate
load tests, direct shear box, grain size distribution and calculated data
of overburden pressure was obtained from a large project in the
United Arab Emirates. This data was used for the training and the
validation of the neural network. A comparison was made between
the obtained results from the ANN's approach, and some common
traditional correlations that predict Φ and E from CPT results with
respect to the actual results of the collected data. The results show
that the ANN is a very powerful tool. Very good agreement was
obtained between estimated results from ANN and actual measured
results with comparison to other correlations available in the
literature. The study recommends some easily available parameters
that should be included in the estimation of the soil properties to
improve the prediction models. It is shown that the use of friction
ration in the estimation of Φ and the use of fines content in the
estimation of E considerable improve the prediction models.
Abstract: The aim of this study is to develop an anterior lumbar
interbody fusion (ALIF) PEEK cage suitable for Korean people. In this
study, CT images were obtained from Korean male (173cm, 71kg) and
3D Korean lumbar models were reconstructed based on the CT images
to investigate anatomical characteristics. Major design parameters of
anterior lumbar interbody fusion (ALIF) PEEK Cage were selected
using the morphological measurement information of the Korean
Lumbar models. Through finite element analysis and mechanical tests,
the developed ALIFPEEK Cage prototype was compared with the
Fidji Cage (Zimmer. Inc, USA) and it was found that the ALIF
prototype showed similar and/or superior mechanical performance
compared to the FidJi Cage. Also, clinical validation for the ALIF
PEEK Cage prototype was carried out to check predictable troubles in
surgical operations. Finally, it is considered that the convenience and
stability of the prototype was clinically verified.
Abstract: The mechanics of rip currents are complex, involving
interactions between waves, currents, water levels and the bathymetry,
that present particular challenges for numerical models. Here,
the effects of a grid-spacing dependent horizontal mixing on the
wave-current interactions are studied. Near the shore, wave rays
diverge from channels towards bar crests because of refraction by
topography and currents, in a way that depends on the rip current
intensity which is itself modulated by the horizontal mixing. At
low resolution with the grid-spacing dependent horizontal mixing,
the wave motion is the same for both coupling modes because the
wave deviation by the currents is weak. In high resolution case,
however, classical results are found with the stabilizing effect of
the flow by feedback of waves on currents. Lastly, wave-current
interactions and the horizontal mixing strongly affect the intensity
of the three-dimensional rip velocity.
Abstract: The number of Ground Motion Prediction Equations
(GMPEs) used for predicting peak ground acceleration (PGA) and
the number of earthquake recordings that have been used for fitting
these equations has increased in the past decades. The current PF-L
database contains 3550 recordings. Since the GMPEs frequently
model the peak ground acceleration the goal of the present study was
to refit a selection of 44 of the existing equation models for PGA in
light of the latest data. The algorithm Levenberg-Marquardt was used
for fitting the coefficients of the equations and the results are
evaluated both quantitatively by presenting the root mean squared
error (RMSE) and qualitatively by drawing graphs of the five best
fitted equations. The RMSE was found to be as low as 0.08 for the
best equation models. The newly estimated coefficients vary from the
values published in the original works.
Abstract: The absorption power generation cycle based on the
ammonia-water mixture has attracted much attention for efficient
recovery of low-grade energy sources. In this paper a thermodynamic
performance analysis is carried out for a Kalina cycle using
ammonia-water mixture as a working fluid for efficient conversion of
low-temperature heat source in the form of sensible energy. The
effects of the source temperature on the system performance are
extensively investigated by using the thermodynamic models. The
results show that the source temperature as well as the ammonia mass
fraction affects greatly on the thermodynamic performance of the
cycle.
Abstract: An existing RC building in Madinah is seismically
evaluated with and without infill wall. Four model systems have been
considered i.e. model I (no infill), model IIA (strut infill-update from
field test), model IIB (strut infill- ASCE/SEI 41) and model IIC (strut
infill-Soft storey- ASCE/SEI 41). Three dimensional pushover
analyses have been carried out using SAP2000 software
incorporating inelastic material behavior for concrete, steel and infill
walls. Infill wall has been modeled as equivalent strut according to
suggested equation matching field test measurements and to the
ASCE/SEI 41 equation. The effect of building modeling on the
performance point as well as capacity and demand spectra due to EQ
design spectrum function in Madinah area has been investigated. The
response modification factor (R) for the 5 story RC building is
evaluated from capacity and demand spectra (ATC-40) for the
studied models. The results are summarized and discussed.
Abstract: The purpose of this paper is to examine gas transport
behavior of mixed matrix membranes (MMMs) combined with
porous particles. Main existing models are categorized in two main
groups; two-phase (ideal contact) and three-phase (non-ideal contact).
A new coefficient, J, was obtained to express equations for estimating
effect of the particle porosity in two-phase and three-phase models.
Modified models evaluates with existing models and experimental
data using Matlab software. Comparison of gas permeability of
proposed modified models with existing models in different MMMs
shows a better prediction of gas permeability in MMMs.
Abstract: Locating Radio Controlled (RC) devices using their
unintended emissions has a great interest considering security
concerns. Weak nature of these emissions requires near field
localization approach since it is hard to detect these signals in far
field region of array. Instead of only angle estimation, near field
localization also requires range estimation of the source which makes
this method more complicated than far field models. Challenges of
locating such devices in a near field region and real time environment
are analyzed in this paper. An ESPRIT like near field localization
scheme is utilized for both angle and range estimation. 1-D search
with symmetric subarrays is provided. Two 7 element uniform linear
antenna arrays (ULA) are employed for locating RC source.
Experiment results of location estimation for one unintended emitting
walkie-talkie for different positions are given.
Abstract: In this study, the performance analyses of the twenty
five Coal-Fired Power Plants (CFPPs) used for electricity generation
are carried out through various Data Envelopment Analysis (DEA)
models. Three efficiency indices are defined and pursued. During the
calculation of the operational performance, energy and non-energy
variables are used as input, and net electricity produced is used as
desired output (Model-1). CO2 emitted to the environment is used as
the undesired output (Model-2) in the computation of the pure
environmental performance while in Model-3 CO2 emissions is
considered as detrimental input in the calculation of operational and
environmental performance. Empirical results show that most of the
plants are operating in increasing returns to scale region and Mettur
plant is efficient one with regards to energy use and environment.
The result also indicates that the undesirable output effect is
insignificant in the research sample. The present study will provide
clues to plant operators towards raising the operational and
environmental performance of CFPPs.
Abstract: Logistics distributors face the issue of having to
provide increasing service levels while being forced to reduce costs at
the same time. Same-day delivery, quick order processing and rapidly
growing ranges of articles are only some of the prevailing challenges.
One key aspect of the performance of an intra-logistics system is how
often and in which amplitude congestions and dysfunctions affect the
processing operations. By gaining knowledge of the so called
‘performance availability’ of such a system during the planning stage,
oversizing and wasting can be reduced whereas planning
transparency is increased. State of the art for the determination of this
KPI is simulation studies. However, their structure and therefore their
results may vary unforeseeably. This article proposes a concept for
the establishment of ‘certified’ and hence reliable and comparable
simulation models.
Abstract: The research conducted in early seventies apparently
assumed the existence of a universal decision model for union
negotiators and furthermore tended to regard financial information as
a ‘neutral’ input into a rational decision making process. However,
research in the eighties began to question the neutrality of financial
information as an input in collective bargaining rather viewing it as a
potentially effective means for controlling the labour force.
Furthermore, this later research also started challenging the simplistic
assumptions relating particularly to union objectives which have
underpinned the earlier search for universal union decision models.
Despite the above developments there seems to be a dearth of studies
in developing countries concerning the use of financial information in
collective bargaining. This paper seeks to begin to remedy this
deficiency. Utilising a case study approach based on two enterprises,
one in the public sector and the other a multinational, the universal
decision model is rejected and it is argued that the decision whether
or not to use financial information is a contingent one and such a
contingency is largely defined by the context and environment in
which both union and management negotiators work. An attempt is
also made to identify the factors constraining as well as promoting
the use of financial information in collective bargaining, these being
regarded as unique to the organisations within which the case studies
are conducted.
Abstract: Through this paper we present a method for automatic
generation of ontological model from any data source using Model
Driven Architecture (MDA), this generation is dedicated to the
cooperation of the knowledge engineering and software engineering.
Indeed, reverse engineering of a data source generates a software
model (schema of data) that will undergo transformations to generate
the ontological model. This method uses the meta-models to validate
software and ontological models.