30,126 research outputs found
The effect of data preprocessing on the performance of artificial neural networks techniques for classification problems
The artificial neural network (ANN) has recently been applied in many areas, such as
medical, biology, financial, economy, engineering and so on. It is known as an excellent
classifier of nonlinear input and output numerical data. Improving training efficiency of
ANN based algorithm is an active area of research and numerous papers have been
reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained
with back-propagation artificial neural network (BP-ANN) method is highly influenced
by the size of the data-sets and the data-preprocessing techniques used. This work
analyzes the advantages of using pre-processing datasets using different techniques in
order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal
Scaling Normalization preprocessing techniques were evaluated. The simulation results
showed that the computational efficiency of ANN training process is highly enhanced
when coupled with different preprocessing techniques
Nonlinear finite element analysis of reinforced concrete beams strengthened with textile fine grained mortar
Nowadays, there was an increasing need of repairing and upgrading the reinforced concrete (RC) structure due to the deterioration of the structure. The fibre reinforced polymer (FRP) was commonly used for structural retrofitting purposes. However, owing to the debonding of the FRP from the concrete substrate and high cost of epoxy, it was gradually replaced with textile fine grained mortar (TFGM) nowadays. The TFGM system has been widely used in the construction field nowadays to repair the structure. Our study focus on the strain performances of the concrete surface, steel reinforcement and the textile itself. There were many proven experimental results showing that the TFGM was more effective than the other strengthening method such as FRP plate method. The experimental work done by previous researcher on investigation of strain performances of the concrete surface, steel reinforcement and the textile itself which consists of eleven (11) RC beams with dimension 150 x 200 x 2500 mm. The RC beams were strengthened with FGM and TFGM with 4 layers. The investigation continued with the finite element (FE) strain performance analysis with using Advanced Tool for Engineering Nonlinear Analysis (ATENA) software. The strain of the concrete surface, steel reinforcement and the textile were measured at a mid-point of RC beam. Then, the results of the finite element analysis software ATENA compared against the experimental results. The strain performances of the concrete and steel reinforcement improved noticeably when the number of layers of textile reinforcement used increased
Modeling and Real-Time Scheduling of DC Platform Supply Vessel for Fuel Efficient Operation
DC marine architecture integrated with variable speed diesel generators (DGs)
has garnered the attention of the researchers primarily because of its ability
to deliver fuel efficient operation. This paper aims in modeling and to
autonomously perform real-time load scheduling of dc platform supply vessel
(PSV) with an objective to minimize specific fuel oil consumption (SFOC) for
better fuel efficiency. Focus has been on the modeling of various components
and control routines, which are envisaged to be an integral part of dc PSVs.
Integration with photovoltaic-based energy storage system (ESS) has been
considered as an option to cater for the short time load transients. In this
context, this paper proposes a real-time transient simulation scheme, which
comprises of optimized generation scheduling of generators and ESS using dc
optimal power flow algorithm. This framework considers real dynamics of dc PSV
during various marine operations with possible contingency scenarios, such as
outage of generation systems, abrupt load changes, and unavailability of ESS.
The proposed modeling and control routines with real-time transient simulation
scheme have been validated utilizing the real-time marine simulation platform.
The results indicate that the coordinated treatment of renewable based ESS with
DGs operating with optimized speed yields better fuel savings. This has been
observed in improved SFOC operating trajectory for critical marine missions.
Furthermore, SFOC minimization at multiple suboptimal points with its treatment
in the real-time marine system is also highlighted
A Hybrid Segmentation and D-bar Method for Electrical Impedance Tomography
The Regularized D-bar method for Electrical Impedance Tomography provides a
rigorous mathematical approach for solving the full nonlinear inverse problem
directly, i.e. without iterations. It is based on a low-pass filtering in the
(nonlinear) frequency domain. However, the resulting D-bar reconstructions are
inherently smoothed leading to a loss of edge distinction. In this paper, a
novel approach that combines the rigor of the D-bar approach with the
edge-preserving nature of Total Variation regularization is presented. The
method also includes a data-driven contrast adjustment technique guided by the
key functions (CGO solutions) of the D-bar method. The new TV-Enhanced D-bar
Method produces reconstructions with sharper edges and improved contrast while
still solving the full nonlinear problem. This is achieved by using the
TV-induced edges to increase the truncation radius of the scattering data in
the nonlinear frequency domain thereby increasing the radius of the low pass
filter. The algorithm is tested on numerically simulated noisy EIT data and
demonstrates significant improvements in edge preservation and contrast which
can be highly valuable for absolute EIT imaging
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