Abstract: Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.
Abstract: In this paper, a genetic-neural-network (GNN) based large-signal model for GaN HEMTs is presented along with its parameters extraction procedure. The model is easy to construct and implement in CAD software and requires only DC and S-parameter measurements. An improved decomposition technique is used to model self-heating effect. Two GNN models are constructed to simulate isothermal drain current and power dissipation, respectively. The two model are then composed to simulate the drain current. The modeling procedure was applied to a packaged GaN-on-Si HEMT and the developed model is validated by comparing its large-signal simulation with measured data. A very good agreement between the simulation and measurement is obtained.
Abstract: In this paper, a PSO based fractional order PID (FOPID) controller is proposed for concentration control of an isothermal Continuous Stirred Tank Reactor (CSTR) problem. CSTR is used to carry out chemical reactions in industries, which possesses complex nonlinear dynamic characteristics. Particle Swarm Optimization algorithm technique, which is an evolutionary optimization technique based on the movement and intelligence of swarm is proposed for tuning of the controller for this system. Comparisons of proposed controller with conventional and fuzzy based controller illustrate the superiority of proposed PSO-FOPID controller.
Abstract: Vertex Enumeration Algorithms explore the methods and procedures of generating the vertices of general polyhedra formed by system of equations or inequalities. These problems of enumerating the extreme points (vertices) of general polyhedra are shown to be NP-Hard. This lead to exploring how to count the vertices of general polyhedra without listing them. This is also shown to be #P-Complete. Some fully polynomial randomized approximation schemes (fpras) of counting the vertices of some special classes of polyhedra associated with Down-Sets, Independent Sets, 2-Knapsack problems and 2 x n transportation problems are presented together with some discovered open problems.
Abstract: This paper covers application of an elitist selfadaptive
step-size search (ESASS) to optimum design of steel
skeletal structures. In the ESASS two approaches are considered for
improving the convergence accuracy as well as the computational
efficiency of the original technique namely the so called selfadaptive
step-size search (SASS). Firstly, an additional randomness
is incorporated into the sampling step of the technique to preserve
exploration capability of the algorithm during the optimization.
Moreover, an adaptive sampling scheme is introduced to improve the
quality of final solutions. Secondly, computational efficiency of the
technique is accelerated via avoiding unnecessary analyses during the
optimization process using an upper bound strategy. The numerical
results demonstrate the usefulness of the ESASS in the sizing
optimization problems of steel truss and frame structures.
Abstract: This study was conducted for the investigation of
number of cellulolytic bacteria and their ability in decomposition.
Seven samples surface soil were collected on cellulose Zailiskii
Alatau slopes. Cellulolitic activity of new strains of Bacillus, isolated
from soil is determined. Isolated cellulose degrading bacteria were
screened for determination of the highest cellulose activity by
quantitative assay using Congo red, gravimetric assay and
colorimetric DNS method trough of the determination of the
parameters of sugar reduction. Strains are assigned to: B.subtilis,
B.licheniformis, B. cereus and, В. megaterium. Bacillus strains
consisting of several different types of cellulases have broad substrate
specificity of cellulase complexes formed by them. Cellulolitic
bacteria were recorded to have highest cellulase activity and selected
for optimization of cellulase enzyme production.
Abstract: We present a new subband adaptive filter (R-SAF)
which is robust against impulsive noise in system identification. To
address the vulnerability of adaptive filters based on the L2-norm
optimization criterion against impulsive noise, the R-SAF comes from
the L1-norm optimization criterion with a constraint on the energy
of the weight update. Minimizing L1-norm of the a posteriori error
in each subband with a constraint on minimum disturbance gives
rise to the robustness against the impulsive noise and the capable
convergence performance. Experimental results clearly demonstrate
that the proposed R-SAF outperforms the classical adaptive filtering
algorithms when impulsive noise as well as background noise exist.
Abstract: The new design of heat exchangers utilizing an
annular distributor opens a new gateway for realizing higher energy
optimization. To realize this goal, graphene nanoplatelet-based water
nanofluids with promising thermophysical properties were
synthesized in the presence of covalent and noncovalent
functionalization. Thermal conductivity, density, viscosity and
specific heat capacity were investigated and employed as a raw data
for ANSYS-Fluent to be used in two-phase approach. After
validation of obtained results by analytical equations, two special
parameters of convective heat transfer coefficient and pressure drop
were investigated. The study followed by studying other heat transfer
parameters of annular pass in the presence of graphene nanopletelesbased
water nanofluids at different weight concentrations, input
powers and temperatures. As a result, heat transfer performance and
friction loss are predicted for both synthesized nanofluids.
Abstract: The material selection problem is concerned with the
determination of the right material for a certain product to optimize
certain performance indices in that product such as mass, energy
density, and power-to-weight ratio. This paper is concerned about
optimizing the selection of the manufacturing process along with the
material used in the product under performance indices and
availability constraints. In this paper, the material selection problem
is formulated using binary programming and solved by genetic
algorithm. The objective function of the model is to minimize the
total manufacturing cost under performance indices and material and
manufacturing process availability constraints.
Abstract: In this paper, autonomous performance of a small
manufactured unmanned helicopter is tried to be increased. For this
purpose, a small unmanned helicopter is manufactured in Erciyes
University, Faculty of Aeronautics and Astronautics. It is called as
ZANKA-Heli-I. For performance maximization, autopilot parameters
are determined via minimizing a cost function consisting of flight
performance parameters such as settling time, rise time, overshoot
during trajectory tracking. For this purpose, a stochastic optimization
method named as simultaneous perturbation stochastic approximation
is benefited. Using this approach, considerable autonomous
performance increase (around %23) is obtained.
Abstract: In this paper, it is aimed to improve autonomous flight
performance of a load-carrying (payload: 3 kg and total: 6kg)
unmanned aerial vehicle (UAV) through active wing and horizontal
tail active morphing and also integrated autopilot system parameters
(i.e. P, I, D gains) and UAV parameters (i.e. extension ratios of wing
and horizontal tail during flight) design. For this purpose, a loadcarrying
UAV (i.e. ZANKA-II) is manufactured in Erciyes
University, College of Aviation, Model Aircraft Laboratory is
benefited. Optimum values of UAV parameters and autopilot
parameters are obtained using a stochastic optimization method.
Using this approach autonomous flight performance of UAV is
substantially improved and also in some adverse weather conditions
an opportunity for safe flight is satisfied. Active morphing and
integrated design approach gives confidence, high performance and
easy-utility request of UAV users.
Abstract: With 40% of total world energy consumption,
building systems are developing into technically complex large
energy consumers suitable for application of sophisticated power
management approaches to largely increase the energy efficiency
and even make them active energy market participants. Centralized
control system of building heating and cooling managed by
economically-optimal model predictive control shows promising
results with estimated 30% of energy efficiency increase. The research
is focused on implementation of such a method on a case study
performed on two floors of our faculty building with corresponding
sensors wireless data acquisition, remote heating/cooling units and
central climate controller. Building walls are mathematically modeled
with corresponding material types, surface shapes and sizes. Models
are then exploited to predict thermal characteristics and changes in
different building zones. Exterior influences such as environmental
conditions and weather forecast, people behavior and comfort
demands are all taken into account for deriving price-optimal climate
control. Finally, a DC microgrid with photovoltaics, wind turbine,
supercapacitor, batteries and fuel cell stacks is added to make the
building a unit capable of active participation in a price-varying
energy market. Computational burden of applying model predictive
control on such a complex system is relaxed through a hierarchical
decomposition of the microgrid and climate control, where the
former is designed as higher hierarchical level with pre-calculated
price-optimal power flows control, and latter is designed as lower
level control responsible to ensure thermal comfort and exploit
the optimal supply conditions enabled by microgrid energy flows
management. Such an approach is expected to enable the inclusion
of more complex building subsystems into consideration in order to
further increase the energy efficiency.
Abstract: Ant algorithms are well-known metaheuristics which
have been widely used since two decades. In most of the literature,
an ant is a constructive heuristic able to build a solution from scratch.
However, other types of ant algorithms have recently emerged: the
discussion is thus not limited by the common framework of the
constructive ant algorithms. Generally, at each generation of an ant
algorithm, each ant builds a solution step by step by adding an
element to it. Each choice is based on the greedy force (also called the
visibility, the short term profit or the heuristic information) and the
trail system (central memory which collects historical information of
the search process). Usually, all the ants of the population have the
same characteristics and behaviors. In contrast in this paper, a new
type of ant metaheuristic is proposed, namely SMART (for Solution
Methods with Ants Running by Types). It relies on the use of different
population of ants, where each population has its own personality.
Abstract: Energy consumption data, in particular those involving
public buildings, are impacted by many factors: the building structure,
climate/environmental parameters, construction, system operating
condition, and user behavior patterns. Traditional methods for data
analysis are insufficient. This paper delves into the data mining
technology to determine its application in the analysis of building
energy consumption data including energy consumption prediction,
fault diagnosis, and optimal operation. Recent literature are reviewed
and summarized, the problems faced by data mining technology in the
area of energy consumption data analysis are enumerated, and research
points for future studies are given.
Abstract: The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.
Abstract: The out-of-band impedance environment is considered
to be of paramount importance in engineering the in-band impedance
environment. Presenting the frequency independent and constant outof-
band impedances across the wide modulation bandwidth is
extremely important for reliable device characterization for future
wireless systems. This paper presents an out-of-band impedance
optimization scheme based on simultaneous engineering of
significant baseband components IF1 (twice the modulation
frequency) and IF2 (four times the modulation frequency) and higher
baseband components such as IF3 (six times the modulation
frequency) and IF4 (eight times the modulation frequency) to
engineer the in-band impedance environment. The investigations
were carried out on a 10W GaN HEMT device driven to deliver a
peak envelope power of approximately 40.5dBm under modulated
excitation. The presentation of frequency independent baseband
impedances to all the significant baseband components whilst
maintaining the optimum termination for fundamental tones as well
as reactive termination for 2nd harmonic under class-J mode of
operation has outlined separate optimum impedances for best
intermodulation (IM) linearity.
Abstract: This paper proposes an APPLE scheme that aims at providing absolute and proportional throughput guarantees, and maximizing system throughput simultaneously for wireless LANs with homogeneous and heterogenous traffic. We formulate our objectives as an optimization problem, present its exact and approximate solutions, and prove the existence and uniqueness of the approximate solution. Simulations validate that APPLE scheme is accurate, and the approximate solution can well achieve the desired objectives already.
Abstract: Holistic methods covering the development process as
a whole – e.g. systems engineering – have established themselves in
product design. However, technical product optimization,
representing improvements in efficiency and/or minimization of loss,
usually applies to single components of a system. A holistic approach
is being defined based on a hierarchical point of view of systems
engineering. This is subsequently presented using the example of an
electromechanical flywheel energy storage system for automotive
applications.
Abstract: Genetic algorithm is widely used in optimization
problems for its excellent global search capabilities and highly parallel
processing capabilities; but, it converges prematurely and has a poor
local optimization capability in actual operation. Simulated annealing
algorithm can avoid the search process falling into local optimum. A
hybrid genetic algorithm based on simulated annealing is designed by
combining the advantages of genetic algorithm and simulated
annealing algorithm. The numerical experiment represents the hybrid
genetic algorithm can be applied to solve the function optimization
problems efficiently.
Abstract: This paper sets out a behavioral macro-model of a
Merged PiN and Schottky (MPS) diode based on silicon carbide
(SiC). This model holds good for both static and dynamic electrothermal
simulations for industrial applications. Its parameters have
been worked out from datasheets curves by drawing on the
optimization method: Simulated Annealing (SA) for the SiC MPS
diodes made available in the industry. The model also adopts the
Analog Behavioral Model (ABM) of PSPICE in which it has been
implemented. The thermal behavior of the devices was also taken
into consideration by making use of Foster’ canonical network as
figured out from electro-thermal measurement provided by the
manufacturer of the device.