Abstract: The aim of the paper was to elaborate a novel calculator BasWilCalc, that allows to estimate the actual amount of biomass on the basket willow plantations. The proposed method is based on the results of field experiment conducted during years 2011-2013 on basket willow plantation in the south-western part of Poland. As input data the results of destructive measurements of the diameter, length and weight of willow stems and non-destructive biometric measurements of diameter in the middle of stems and their length during the growing season performed at weekly intervals were used. Performed analysis enabled to develop the algorithm which, due to the fact that energy plantations are of known and constant planting structure, allows to estimate the actual amount of willow basket biomass on the plantation with a given probability and accuracy specified by the model, based on the number of stems measured and the age of the plantation.
Abstract: Mass flow measurement is the basis of most technoeconomic
formulations in the chemical industry. This calls for
reliable and accurate detection of mass flow. Flow measurement
laboratory experiments were conducted using various instruments.
These consisted of orifice plates, various sized rotameters, wet gas
meter and soap bubble meter. This work was aimed at evaluating
appropriate operating conditions and accuracy of the aforementioned
devices. The experimental data collected were compared to
theoretical predictions from Bernoulli’s equation and calibration
curves supplied by the instrument’s manufacturers. The results
obtained showed that rotameters were more reliable for measuring
high and low flow rates; while soap-bubble meters and wet-gas
meters were found to be suitable for measuring low flow rates. The
laboratory procedures and findings of the actual work can assist
engineering students and professionals in conducting their flow
measurement laboratory test work.
Abstract: In this research work, neural networks were applied to
classify two types of hip joint implants based on the relative hip joint
implant side speed and three components of each ground reaction
force. The condition of walking gait at normal velocity was used and
carried out with each of the two hip joint implants assessed. Ground
reaction forces’ kinetic temporal changes were considered in the first
approach followed but discarded in the second one. Ground reaction
force components were obtained from eighteen patients under such
gait condition, half of which had a hip implant type I-II, whilst the
other half had the hip implant, defined as type III by Orthoload®.
After pre-processing raw gait kinetic data and selecting the time
frames needed for the analysis, the ground reaction force components
were used to train a MLP neural network, which learnt to distinguish
the two hip joint implants in the abovementioned condition. Further
to training, unknown hip implant side and ground reaction force
components were presented to the neural networks, which assigned
those features into the right class with a reasonably high accuracy for
the hip implant type I-II and the type III. The results suggest that
neural networks could be successfully applied in the performance
assessment of hip joint implants.
Abstract: The system is designed to show images which are
related to the query image. Extracting color, texture, and shape
features from an image plays a vital role in content-based image
retrieval (CBIR). Initially RGB image is converted into HSV color
space due to its perceptual uniformity. From the HSV image, Color
features are extracted using block color histogram, texture features
using Haar transform and shape feature using Fuzzy C-means
Algorithm. Then, the characteristics of the global and local color
histogram, texture features through co-occurrence matrix and Haar
wavelet transform and shape are compared and analyzed for CBIR.
Finally, the best method of each feature is fused during similarity
measure to improve image retrieval effectiveness and accuracy.
Abstract: This research proposes a novel reconstruction protocol
for restoring missing surfaces and low-quality edges and shapes in
photos of artifacts at historical sites. The protocol starts with the
extraction of a cloud of points. This extraction process is based on
four subordinate algorithms, which differ in the robustness and
amount of resultant. Moreover, they use different -but
complementary- accuracy to some related features and to the way
they build a quality mesh. The performance of our proposed protocol
is compared with other state-of-the-art algorithms and toolkits. The
statistical analysis shows that our algorithm significantly outperforms
its rivals in the resultant quality of its object files used to reconstruct
the desired model.
Abstract: In this study, a comparative analysis of the approaches
associated with the use of neural network algorithms for effective
solution of a complex inverse problem – the problem of identifying
and determining the individual concentrations of inorganic salts in
multicomponent aqueous solutions by the spectra of Raman
scattering of light – is performed. It is shown that application of
artificial neural networks provides the average accuracy of
determination of concentration of each salt no worse than 0.025 M.
The results of comparative analysis of input data compression
methods are presented. It is demonstrated that use of uniform
aggregation of input features allows decreasing the error of
determination of individual concentrations of components by 16-18%
on the average.
Abstract: The article presents the results of the application of
artificial neural networks to separate the fluorescent contribution of
nanodiamonds used as biomarkers, adsorbents and carriers of drugs
in biomedicine, from a fluorescent background of own biological
fluorophores. The principal possibility of solving this problem is
shown. Use of neural network architecture let to detect fluorescence
of nanodiamonds against the background autofluorescence of egg
white with high accuracy - better than 3 ug/ml.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%
accuracy.
Abstract: A generalized vortex lattice method for complex
lifting surfaces with flap and aileron deflection is formulated. The
method is not restricted by the linearized theory assumption and
accounts for all standard geometric lifting surface parameters:
camber, taper, sweep, washout, dihedral, in addition to flap and
aileron deflection. Thickness is not accounted for since the physical
lifting body is replaced by a lattice of panels located on the mean
camber surface. This panel lattice setup and the treatment of different
wake geometries is what distinguish the present work form the
overwhelming majority of previous solutions based on the vortex
lattice method. A MATLAB code implementing the proposed
formulation is developed and validated by comparing our results to
existing experimental and numerical ones and good agreement is
demonstrated. It is then used to study the accuracy of the widely used
classical vortex-lattice method. It is shown that the classical approach
gives good agreement in the clean configuration but is off by as much
as 30% when a flap or aileron deflection of 30° is imposed. This
discrepancy is mainly due the linearized theory assumption
associated with the conventional method. A comparison of the effect
of four different wake geometries on the values of aerodynamic
coefficients was also carried out and it is found that the choice of the
wake shape had very little effect on the results.
Abstract: A blood pressure monitor or sphygmomanometer can
be either manual or automatic, employing respectively either the
auscultatory method or the oscillometric method.
The manual version of the sphygmomanometer involves an
inflatable cuff with a stethoscope adopted to detect the sounds
generated by the arterial walls to measure blood pressure in an artery.
An automatic sphygmomanometer can be effectively used to
monitor blood pressure through a pressure sensor, which detects
vibrations provoked by oscillations of the arterial walls.
The pressure sensor implemented in this device improves the
accuracy of the measurements taken.
Abstract: Recently GPS data is used in a lot of studies to
automatically reconstruct travel patterns for trip survey. The aim is to
minimize the use of questionnaire surveys and travel diaries so as to
reduce their negative effects. In this paper data acquired from GPS and
accelerometer embedded in smart phones is utilized to predict the
mode of transportation used by the phone carrier. For prediction,
Support Vector Machine (SVM) and Adaptive boosting (AdaBoost)
are employed. Moreover a unique method to improve the prediction
results from these algorithms is also proposed. Results suggest that the
prediction accuracy of AdaBoost after improvement is relatively better
than the rest.
Abstract: In remote sensing, shadow causes problems in many
applications such as change detection and classification. It is caused
by objects which are elevated, thus can directly affect the accuracy of
information. For these reasons, it is very important to detect shadows
particularly in urban high spatial resolution imagery which created a
significant problem. This paper focuses on automatic shadow
detection based on a new spectral index for multispectral imagery
known as Shadow Detection Index (SDI). The new spectral index
was tested on different areas of WorldView-2 images and the results
demonstrated that the new spectral index has a massive potential to
extract shadows with accuracy of 94% effectively and automatically.
Furthermore, the new shadow detection index improved road
extraction from 82% to 93%.
Abstract: An extensive amount of work has been done in data
clustering research under the unsupervised learning technique in Data
Mining during the past two decades. Moreover, several approaches
and methods have been emerged focusing on clustering diverse data
types, features of cluster models and similarity rates of clusters.
However, none of the single clustering algorithm exemplifies its best
nature in extracting efficient clusters. Consequently, in order to
rectify this issue, a new challenging technique called Cluster
Ensemble method was bloomed. This new approach tends to be the
alternative method for the cluster analysis problem. The main
objective of the Cluster Ensemble is to aggregate the diverse
clustering solutions in such a way to attain accuracy and also to
improve the eminence the individual clustering algorithms. Due to
the massive and rapid development of new methods in the globe of
data mining, it is highly mandatory to scrutinize a vital analysis of
existing techniques and the future novelty. This paper shows the
comparative analysis of different cluster ensemble methods along
with their methodologies and salient features. Henceforth this
unambiguous analysis will be very useful for the society of clustering
experts and also helps in deciding the most appropriate one to resolve
the problem in hand.
Abstract: Current transformers (CTs) are used to transform large primary currents to a small secondary current. Since most standard equipment’s are not designed to handle large primary currents the CTs have an important part in any electrical system for the purpose of Metering and Protection both of which are integral in Power system. Now a days due to advancement in solid state technology, the operation times of the protective relays have come to a few cycles from few seconds. Thus, in such a scenario it becomes important to study the transient response of the current transformers as it will play a vital role in the operating of the protective devices.
This paper shows the steady state and transient behavior of current transformers and how it changes with change in connected burden. The transient and steady state response will be captured using the data acquisition software LabVIEW. Analysis is done on the real time data gathered using LabVIEW. Variation of current transformer characteristics with changes in burden will be discussed.
Abstract: In this paper, a backward semi-Lagrangian scheme
combined with the second-order backward difference formula
is designed to calculate the numerical solutions of nonlinear
advection-diffusion equations. The primary aims of this paper are
to remove any iteration process and to get an efficient algorithm
with the convergence order of accuracy 2 in time. In order to achieve
these objects, we use the second-order central finite difference and the
B-spline approximations of degree 2 and 3 in order to approximate
the diffusion term and the spatial discretization, respectively. For the
temporal discretization, the second order backward difference formula
is applied. To calculate the numerical solution of the starting point
of the characteristic curves, we use the error correction methodology
developed by the authors recently. The proposed algorithm turns out
to be completely iteration free, which resolves the main weakness
of the conventional backward semi-Lagrangian method. Also, the
adaptability of the proposed method is indicated by numerical
simulations for Burgers’ equations. Throughout these numerical
simulations, it is shown that the numerical results is in good
agreement with the analytic solution and the present scheme offer
better accuracy in comparison with other existing numerical schemes.
Abstract: This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.
Abstract: The sound pressure level (SPL) of the moving-coil
loudspeaker (MCL) is often simulated and analyzed using the lumped
parameter model. However, the SPL of a MCL cannot be simulated
precisely in the high frequency region, because the value of cone
effective area is changed due to the geometry variation in different
mode shapes, it is also related to affect the acoustic radiation mass and
resistance. Herein, the paper presents the inverse method which has a
high ability to measure the value of cone effective area in various
frequency points, also can estimate the MCL electroacoustic
parameters simultaneously. The proposed inverse method comprises
the direct problem, adjoint problem, and sensitivity problem in
collaboration with nonlinear conjugate gradient method. Estimated
values from the inverse method are validated experimentally which
compared with the measured SPL curve result. Results presented in
this paper not only improve the accuracy of lumped parameter model
but also provide the valuable information on loudspeaker cone design.
Abstract: In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.
Abstract: This study applies the inverse method and three- dimensional CFD commercial software in conjunction with the experimental temperature data to investigate the heat transfer and fluid flow characteristics of the plate-fin heat sink in a closed rectangular enclosure for various values of fin height. The inverse method with the finite difference method and the experimental temperature data is applied to determine the heat transfer coefficient. The k-ε turbulence model is used to obtain the heat transfer and fluid flow characteristics within the fins. To validate the accuracy of the results obtained, the comparison of the average heat transfer coefficient is made. The calculated temperature at selected measurement locations on the plate-fin is also compared with experimental data.