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: In general, classical methods such as maximum
likelihood (ML) and least squares (LS) estimation methods are used
to estimate the shape parameters of the Burr XII distribution.
However, these estimators are very sensitive to the outliers. To
overcome this problem we propose alternative robust estimators
based on the M-estimation method for the shape parameters of the
Burr XII distribution. We provide a small simulation study and a real
data example to illustrate the performance of the proposed estimators
over the ML and the LS estimators. The simulation results show that
the proposed robust estimators generally outperform the classical
estimators in terms of bias and root mean square errors when there
are outliers in data.
Abstract: A model was constructed to predict the amount of
solar radiation that will make contact with the surface of the earth in
a given location an hour into the future. This project was supported
by the Southern Company to determine at what specific times during
a given day of the year solar panels could be relied upon to produce
energy in sufficient quantities. Due to their ability as universal
function approximators, an artificial neural network was used to
estimate the nonlinear pattern of solar radiation, which utilized
measurements of weather conditions collected at the Griffin, Georgia
weather station as inputs. A number of network configurations and
training strategies were utilized, though a multilayer perceptron with
a variety of hidden nodes trained with the resilient propagation
algorithm consistently yielded the most accurate predictions. In
addition, a modeled direct normal irradiance field and adjacent
weather station data were used to bolster prediction accuracy. In later
trials, the solar radiation field was preprocessed with a discrete
wavelet transform with the aim of removing noise from the
measurements. The current model provides predictions of solar
radiation with a mean square error of 0.0042, though ongoing efforts
are being made to further improve the model’s accuracy.
Abstract: The arm length, hand length, hand breadth and middle
finger length of 1540 right-handed industrial workers of Haryana
state was used to assess the relationship between the upper limb
dimensions and stature. Initially, the data were analyzed using basic
univariate analysis and independent t-tests; then simple and multiple
linear regression models were used to estimate stature using SPSS
(version 17). There was a positive correlation between upper limb
measurements (hand length, hand breadth, arm length and middle
finger length) and stature (p < 0.01), which was highest for hand
length. The accuracy of stature prediction ranged from ± 54.897 mm
to ± 58.307 mm. The use of multiple regression equations gave better
results than simple regression equations. This study provides new
forensic standards for stature estimation from the upper limb
measurements of male industrial workers of Haryana (India). The
results of this research indicate that stature can be determined using
hand dimensions with accuracy, when only upper limb is available
due to any reasons likewise explosions, train/plane crashes, mutilated
bodies, etc. The regression formula derived in this study will be
useful for anatomists, archaeologists, anthropologists, design
engineers and forensic scientists for fairly prediction of stature using
regression equations.
Abstract: Groundwater inflow to the tunnels is one of the most
important problems in tunneling operation. The objective of this
study is the investigation of model dimension effects on tunnel inflow
assessment in discontinuous rock masses using numerical modeling.
In the numerical simulation, the model dimension has an important
role in prediction of water inflow rate. When the model dimension is
very small, due to low distance to the tunnel border, the model
boundary conditions affect the estimated amount of groundwater flow
into the tunnel and results show a very high inflow to tunnel. Hence,
in this study, the two-dimensional universal distinct element code
(UDEC) used and the impact of different model parameters, such as
tunnel radius, joint spacing, horizontal and vertical model domain
extent has been evaluated. Results show that the model domain extent
is a function of the most significant parameters, which are tunnel
radius and joint spacing.
Abstract: Ionic liquids consisting of a phosphonium cationic
moiety and a saccharinate anion are synthesized and compared with
their precursors, phosphonium chlorides, in reference to their
extraction efficiency towards L-lactic acid. On the base of
measurements of the acid and the water partitioning in the
equilibrium biphasic systems, the molar ratios between acid, water
and ionic liquid are estimated which allows to deduce the lactic acid
extractive pathway. The effect of a salting-out addition that
strengthens hydrophobicity in both phases is studied in view to reveal
the best biphasic system with respect to IL low toxicity and high
extraction efficiency.
Abstract: One of the best examples, in evolution of the public
procurement, from post-soviet countries are reforms carried out in
Georgia, which brought them close to international standards of
procurement. In Georgia, public procurement legislation started
functioning short after gaining independence. The reform has passed
several stages and came in the form as it is today. It should also be
noted, that countries with economy in transition, including Georgia,
implemented all the reforms in public procurement based on
recommendations and support of World Bank, the United Nations
and other international organizations.
The aim of first adopted law was regulation of the procurement
process of budget-organizations, transparency and creation of
competitive environment for private companies to access state funds
legally. The priorities were identified quite clearly in the wording of
the law, but operation/function of this law could not be reached on its
level, because of some objective and subjective reasons. The high
level of corruption in all levels of governance can be considered as a
main obstacle reason and of course, it is natural, that it had direct
impact on the procurement process, as well as on transparency and
rational use of state funds. These circumstances were the reasons that
reforms in this sphere continued, to improve procurement process, in
particular, the first wave of reforms began after several years. Public
procurement agency carried out reform with World Bank with main
purpose of smartening the procurement legislation and its
harmonization with international treaties and agreements. Also with
the support of World Bank various activities were carried out to raise
awareness of participants involved in procurement system.
Further major changes in the legislation were filed bit later, which
was also directed towards the improvement and smarten of the
procurement process. The third wave of the reform more or less
guaranteed the transparency of the procurement process, which later
became the basis for the rational spending of state funds. The reform
of the procurement system completely changed the procedures.
Carried out reform in Georgia resulted in introducing new
electronic tendering system, which benefit the transparency of the
process, after this became the basis for the further development of a
competitive environment, which become a prerequisite for the state
rational spending. Increased number of supplier organizations
participating in the procurement process resulted in reduction of the
estimated cost and the actual cost.
Assessment of the reforms in Georgia in the field of public
procurement can be concluded, that proper regulation of the sector
and relevant policy may proceed to rational and transparent spending
of the budget from country’s state institutions. Also, the business
sector has the opportunity to work in competitive market conditions
and to make a preliminary analysis, which is a prerequisite for future
strategy and development.
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: Emissions of atmospheric pollutants from ships and
harbour activities are a growing concern at international level given
their potential impacts on air quality and climate. These close-to-land
emissions have potential impact on local communities in terms of air
quality and health. Recent studies show that the impact of maritime
traffic to atmospheric particulate matter concentrations in several
coastal urban areas is comparable with the impact of road traffic of a
medium size town. However, several different approaches have been
used for these estimates making difficult a direct comparison of
results. In this work, an integrated approach based on emission
inventories and dedicated measurement campaigns has been applied
to give a comparable estimate of the impact of maritime traffic to
PM2.5 and particle number concentrations in three major harbours of
the Adriatic/Ionian Seas. The influences of local meteorology and of
the logistic layout of the harbours are discussed.
Abstract: The present work analyses different parameters of end
milling to minimize the surface roughness for AISI D2 steel. D2 Steel
is generally used for stamping or forming dies, punches, forming
rolls, knives, slitters, shear blades, tools, scrap choppers, tyre
shredders etc. Surface roughness is one of the main indices that
determines the quality of machined products and is influenced by
various cutting parameters. In machining operations, achieving
desired surface quality by optimization of machining parameters, is a
challenging job. In case of mating components the surface roughness
become more essential and is influenced by the cutting parameters,
because, these quality structures are highly correlated and are
expected to be influenced directly or indirectly by the direct effect of
process parameters or their interactive effects (i.e. on process
environment). In this work, the effects of selected process parameters
on surface roughness and subsequent setting of parameters with the
levels have been accomplished by Taguchi’s parameter design
approach. The experiments have been performed as per the
combination of levels of different process parameters suggested by
L9 orthogonal array. Experimental investigation of the end milling of
AISI D2 steel with carbide tool by varying feed, speed and depth of
cut and the surface roughness has been measured using surface
roughness tester. Analyses of variance have been performed for mean
and signal-to-noise ratio to estimate the contribution of the different
process parameters on the process.
Abstract: This study estimates the seismic demands of tall
buildings with central symmetric setbacks by using nonlinear time
history analysis. Three setback structures, all 60-story high with
setback in three levels, are used for evaluation. The effects of
irregularities occurred by setback are evaluated by determination of
global-drift, story-displacement and story drift. Story-displacement is
modified by roof displacement and first story displacement and story
drift is modified by global drift. All results are calculated at the
center of mass and in x and y direction. Also the absolute values of
these quantities are determined. The results show that increasing of
vertical irregularities increases the global drift of the structure and
enlarges the deformations in the height of the structure. It is also
observed that the effects of geometry irregularity in the seismic
deformations of setback structures are higher than those of mass
irregularity.
Abstract: The aim of this paper is to select the most accurate
forecasting method for predicting the future values of the
unemployment rate in selected European countries. In order to do so,
several forecasting techniques adequate for forecasting time series
with trend component, were selected, namely: double exponential
smoothing (also known as Holt`s method) and Holt-Winters` method
which accounts for trend and seasonality. The results of the empirical
analysis showed that the optimal model for forecasting
unemployment rate in Greece was Holt-Winters` additive method. In
the case of Spain, according to MAPE, the optimal model was double
exponential smoothing model. Furthermore, for Croatia and Italy the
best forecasting model for unemployment rate was Holt-Winters`
multiplicative model, whereas in the case of Portugal the best model
to forecast unemployment rate was Double exponential smoothing
model. Our findings are in line with European Commission
unemployment rate estimates.
Abstract: Zinc oxide (ZnO) is one of the light emitting materials in ultraviolet (UV) region. In addition, ZnO nanostructures are also attracting increasing research interest as buildingblocks for UV optoelectronic applications. We have succeeded in synthesizing vertically-aligned ZnO nanostructures by laser interference patterning, which is catalyst-free and non-contact technique. In this study, vertically-aligned ZnO nanowall arrays were synthesized using two-beam interference. The maximum height and average thickness of the ZnO nanowalls were about 4.5µm and 200 nm, respectively.UV lasing from a piece of the ZnO nanowall was obtained under the third harmonic of a Q-switched Nd:YAG laser excitation, and the estimated threshold power density for lasing was about 150 kW/cm2. Furthermore, UV lasing from the vertically-aligned ZnO nanowall was also achieved. The results indicate that ZnO nanowalls can be applied to random laser.
Abstract: The elastic period has a primary role in the seismic
assessment of buildings. Reliable calculations and/or estimates of the
fundamental frequency of a building and its site are essential during
analysis and design process. Various code formulas based on
empirical data are generally used to estimate the fundamental
frequency of a structure. For existing structures, in addition to code
formulas and available analytical tools such as modal analyses,
various methods of testing including ambient and forced vibration
testing procedures may be used to determine dynamic characteristics.
In this study, the dynamic properties of the 32 buildings located in
the Madinah of Saudi Arabia were identified using ambient motions
recorded at several, spatially-distributed locations within each
building. Ambient vibration measurements of buildings have been
analyzed and the fundamental longitudinal and transverse periods for
all tested buildings are presented. The fundamental mode of vibration
has been compared in plots with codes formulae (Saudi Building
Code, EC8, and UBC1997). The results indicate that measured
periods of existing buildings are shorter than that given by most
empirical code formulas. Recommendations are given based on the
common design and construction practice in Madinah city.
Abstract: The seismic responses of steel buildings with semirigid
post-tensioned connections (PC) are estimated and compared
with those of steel buildings with typical rigid (welded) connections
(RC). The comparison is made in terms of global and local response
parameters. The results indicate that the seismic responses in terms of
interstory shears, roof displacements, axial load and bending
moments are smaller for the buildings with PC connection. The
difference is larger for global than for local parameters, which in turn
varies from one column location to another. The reason for this
improved behavior is that the buildings with PC dissipate more
hysteretic energy than those with RC. In addition, unlike the case of
buildings with WC, for the PC structures the hysteretic energy is
mostly dissipated at the connections, which implies that structural
damage in beams and columns is not significant. According to these
results, steel buildings with PC are a viable option in high seismicity
areas because of their smaller response and self-centering connection
capacity as well as the fact that brittle failure is avoided.
Abstract: In this paper, groundwater seepage into Amirkabir
tunnel has been estimated using analytical and numerical methods for
14 different sections of the tunnel. Site Groundwater Rating (SGR)
method also has been performed for qualitative and quantitative
classification of the tunnel sections. The obtained results of above
mentioned methods were compared together. The study shows
reasonable accordance with results of the all methods unless for two
sections of tunnel. In these two sections there are some significant
discrepancies between numerical and analytical results mainly
originated from model geometry and high overburden. SGR and the
analytical and numerical calculations, confirm high concentration of
seepage inflow in fault zones. Maximum seepage flow into tunnel has
been estimated 0.425 lit/sec/m using analytical method and 0.628
lit/sec/m using numerical method occured in crashed zone. Based on
SGR method, six sections of 14 sections in Amirkabir tunnel axis are
found to be in "No Risk" class that is supported by the analytical and
numerical seepage value of less than 0.04 lit/sec/m.
Abstract: China is currently the world's largest producer and distributor of electric bicycle (e-bike). The increasing number of e-bikes on the road is accompanied by rising injuries and even deaths of e-bike drivers. Therefore, there is a growing need to improve the safety structure of e-bikes. This 3D frictionless contact analysis is a preliminary, but necessary work for further structural design improvement of an e-bike. The contact analysis between e-bike and the ground was carried out as follows: firstly, the Penalty method was illustrated and derived from the simplest spring-mass system. This is one of the most common methods to satisfy the frictionless contact case; secondly, ANSYS static analysis was carried out to verify finite element (FE) models with contact pair (without friction) between e-bike and the ground; finally, ANSYS transient analysis was used to obtain the data of the penetration p(u) of e-bike with respect to the ground. Results obtained from the simulation are as estimated by comparing with that from theoretical method. In the future, protective shell will be designed following the stability criteria and added to the frame of e-bike. Simulation of side falling of the improvedsafety structure of e-bike will be confirmed with experimental data.
Abstract: Development of a method to estimate gene functions is
an important task in bioinformatics. One of the approaches for the
annotation is the identification of the metabolic pathway that genes are
involved in. Since gene expression data reflect various intracellular
phenomena, those data are considered to be related with genes’
functions. However, it has been difficult to estimate the gene function
with high accuracy. It is considered that the low accuracy of the
estimation is caused by the difficulty of accurately measuring a gene
expression. Even though they are measured under the same condition,
the gene expressions will vary usually. In this study, we proposed a
feature extraction method focusing on the variability of gene
expressions to estimate the genes' metabolic pathway accurately. First,
we estimated the distribution of each gene expression from replicate
data. Next, we calculated the similarity between all gene pairs by KL
divergence, which is a method for calculating the similarity between
distributions. Finally, we utilized the similarity vectors as feature
vectors and trained the multiclass SVM for identifying the genes'
metabolic pathway. To evaluate our developed method, we applied the
method to budding yeast and trained the multiclass SVM for
identifying the seven metabolic pathways. As a result, the accuracy
that calculated by our developed method was higher than the one that
calculated from the raw gene expression data. Thus, our developed
method combined with KL divergence is useful for identifying the
genes' metabolic pathway.
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.