Abstract: We consider a two-way relay network where two sources exchange information. A relay helps the two sources exchange information using the decode-and-XOR-forward protocol. We investigate the power minimization problem with minimum rate constraints. The system needs two time slots and in each time slot the required rate pair should be achievable. The power consumption is minimized in each time slot and we obtained the closed form solution. The simulation results confirm that the proposed power allocation scheme consumes lower total power than the conventional schemes.
Abstract: Multi-energy systems will enhance the system
reliability and power quality. This paper presents an integrated
approach for the design and operation of distributed energy resources
(DER) systems, based on energy hub modeling. A multi-objective
optimization model is developed by considering an integrated view of
electricity and natural gas network to analyze the optimal design and
operating condition of DER systems, by considering two conflicting
objectives, namely, minimization of total cost and the minimization
of environmental impact which is assessed in terms of CO2
emissions. The mathematical model considers energy demands of the
site, local climate data, and utility tariff structure, as well as technical
and financial characteristics of the candidate DER technologies. To
provide energy demands, energy systems including photovoltaic, and
co-generation systems, boiler, central power grid are considered. As
an illustrative example, a hotel in Iran demonstrates potential
applications of the proposed method. The results prove that
increasing the satisfaction degree of environmental objective leads to
increased total cost.
Abstract: The article deals with the relation between rainfall in selected months and subsequent weed infestation of spring barley. The field experiment was performed at Mendel University agricultural enterprise in Žabčice, Czech Republic. Weed infestation was measured in spring barley vegetation in years 2004 to 2012. Barley was grown in three tillage variants: conventional tillage technology (CT), minimization tillage technology (MT), and no tillage (NT). Precipitation was recorded in one-day intervals. Monthly precipitation was calculated from the measured values in the months of October through to April. The technique of canonical correspondence analysis was applied for further statistical processing. 41 different species of weeds were found in the course of the 9-year monitoring period. The results clearly show that precipitation affects the incidence of most weed species in the selected months, but acts differently in the monitored variants of tillage technologies.
Abstract: Most of the commonly used blind equalization algorithms are based on the minimization of a nonconvex and nonlinear cost function and a neural network gives smaller residual error as compared to a linear structure. The efficacy of complex valued feedforward neural networks for blind equalization of linear and nonlinear communication channels has been confirmed by many studies. In this paper we present two neural network models for blind equalization of time-varying channels, for M-ary QAM and PSK signals. The complex valued activation functions, suitable for these signal constellations in time-varying environment, are introduced and the learning algorithms based on the CMA cost function are derived. The improved performance of the proposed models is confirmed through computer simulations.
Abstract: We consider optimal channel equalization for MIMO
(multi-input/multi-output) time-varying channels in the sense of
MMSE (minimum mean-squared-error), where the observation noise
can be non-stationary. We show that all ZF (zero-forcing) receivers
can be parameterized in an affine form which eliminates completely
the ISI (inter-symbol-interference), and optimal channel equalizers
can be designed through minimization of the MSE (mean-squarederror)
between the detected signals and the transmitted signals,
among all ZF receivers. We demonstrate that the optimal channel
equalizer is a modified Kalman filter, and show that under the AWGN
(additive white Gaussian noise) assumption, the proposed optimal
channel equalizer minimizes the BER (bit error rate) among all
possible ZF receivers. Our results are applicable to optimal channel
equalization for DWMT (discrete wavelet multitone), multirate transmultiplexers,
OFDM (orthogonal frequency division multiplexing),
and DS (direct sequence) CDMA (code division multiple access)
wireless data communication systems. A design algorithm for optimal
channel equalization is developed, and several simulation examples
are worked out to illustrate the proposed design algorithm.
Abstract: The main goal of the present work is to decrease the
computational burden for optimum design of steel frames with
frequency constraints using a new type of neural networks called
Wavelet Neural Network. It is contested to train a suitable neural
network for frequency approximation work as the analysis program.
The combination of wavelet theory and Neural Networks (NN)
has lead to the development of wavelet neural networks.
Wavelet neural networks are feed-forward networks using
wavelet as activation function. Wavelets are mathematical
functions within suitable inner parameters, which help them to
approximate arbitrary functions. WNN was used to predict the
frequency of the structures. In WNN a RAtional function with
Second order Poles (RASP) wavelet was used as a transfer
function. It is shown that the convergence speed was faster
than other neural networks. Also comparisons of WNN with
the embedded Artificial Neural Network (ANN) and with
approximate techniques and also with analytical solutions are
available in the literature.
Abstract: Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.
Abstract: Multiprocessor task scheduling is a NP-hard problem and Genetic Algorithm (GA) has been revealed as an excellent technique for finding an optimal solution. In the past, several methods have been considered for the solution of this problem based on GAs. But, all these methods consider single criteria and in the present work, minimization of the bi-criteria multiprocessor task scheduling problem has been considered which includes weighted sum of makespan & total completion time. Efficiency and effectiveness of genetic algorithm can be achieved by optimization of its different parameters such as crossover, mutation, crossover probability, selection function etc. The effects of GA parameters on minimization of bi-criteria fitness function and subsequent setting of parameters have been accomplished by central composite design (CCD) approach of response surface methodology (RSM) of Design of Experiments. The experiments have been performed with different levels of GA parameters and analysis of variance has been performed for significant parameters for minimisation of makespan and total completion time simultaneously.
Abstract: In this paper, a few chattering-free Sliding Mode Controllers (SMC) are proposed to stabilize an Active Magnetic Bearing (AMB) system with gyroscopic effect that is proportional to the rotor speed. The improved switching terms of the controller inherited from the saturation-type function and boundary layer control technique is shown to be able to achieve bounded and asymptotic stability, respectively, while the chattering effect in the input is attenuated. This is proven to be advantageous for AMB system since minimization of chattering results in optimized control effort. The performance of each controller is demonstrated via result of simulation in which the measurement of the total consumed energy and maximum control magnitude of each controller illustrates the effectiveness of the proposed controllers.
Abstract: The objective of positioning the fixture elements in
the fixture is to make the workpiece stiff, so that geometric errors in
the manufacturing process can be reduced. Most of the work for
optimal fixture layout used the minimization of the sum of the nodal
deflection normal to the surface as objective function. All deflections
in other direction have been neglected. We propose a new method for
fixture layout optimization in this paper, which uses the element
strain energy. The deformations in all the directions have been
considered in this way. The objective function in this method is to
minimize the sum of square of element strain energy. Strain energy
and stiffness are inversely proportional to each other. The
optimization problem is solved by the sequential quadratic
programming method. Three different kinds of case studies are
presented, and results are compared with the method using nodal
deflections as objective function to verify the propose method.
Abstract: Optimal reactive power flow is an optimization problem
with one or more objective of minimizing the active power losses for
fixed generation schedule. The control variables are generator bus
voltages, transformer tap settings and reactive power output of the
compensating devices placed on different bus bars. Biogeography-
Based Optimization (BBO) technique has been applied to solve
different kinds of optimal reactive power flow problems subject
to operational constraints like power balance constraint, line flow
and bus voltages limits etc. BBO searches for the global optimum
mainly through two steps: Migration and Mutation. In the present
work, BBO has been applied to solve the optimal reactive power
flow problems on IEEE 30-bus and standard IEEE 57-bus power
systems for minimization of active power loss. The superiority of the
proposed method has been demonstrated. Considering the quality of
the solution obtained, the proposed method seems to be a promising
one for solving these problems.
Abstract: In this paper, an Interactive Compromise Approach
with Particle Swarm Optimization(ICA-PSO) is presented to solve the
Economic Emission Dispatch(EED) problem. The cost function and
emission function are modeled as the nonsmooth functions,
respectively. The bi-objective including both the minimization of cost
and emission is formulated in this paper. ICA-PSO is proposed to
solve EED problem for finding a better compromise solution. The
solution methodology can offer a global or near-global solution for
decision-making requirements. The effectiveness and efficiency of
ICA-PSO are demonstrated by a sample test system. Test results can
be shown that the proposed method provide a practical and flexible
framework for power dispatch.
Abstract: This paper presents an exact analytical model for
optimizing stability of thin-walled, composite, functionally graded
pipes conveying fluid. The critical flow velocity at which divergence
occurs is maximized for a specified total structural mass in order to
ensure the economic feasibility of the attained optimum designs. The
composition of the material of construction is optimized by defining
the spatial distribution of volume fractions of the material
constituents using piecewise variations along the pipe length. The
major aim is to tailor the material distribution in the axial direction so
as to avoid the occurrence of divergence instability without the
penalty of increasing structural mass. Three types of boundary
conditions have been examined; namely, Hinged-Hinged, Clamped-
Hinged and Clamped-Clamped pipelines. The resulting optimization
problem has been formulated as a nonlinear mathematical
programming problem solved by invoking the MatLab optimization
toolbox routines, which implement constrained function
minimization routine named “fmincon" interacting with the
associated eigenvalue problem routines. In fact, the proposed
mathematical models have succeeded in maximizing the critical flow
velocity without mass penalty and producing efficient and economic
designs having enhanced stability characteristics as compared with
the baseline designs.
Abstract: Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches to reduce the power loss in the system. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 14, 33, and 119-bus systems and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.
Abstract: Recently studies in area of supply chain network
(SCN) have focused on the disruption issues in distribution systems.
Also this paper extends the previous literature by providing a new biobjective
model for cost minimization of designing a three echelon
SCN across normal and failure scenarios with considering multi
capacity option for manufacturers and distribution centers. Moreover,
in order to solve the problem by means of LINGO software, novel
model will be reformulated through a branch of LP-Metric method
called Min-Max approach.
Abstract: Buildings and associated construction methods have a significant impact on the environment. As construction activity increases in Kuwait, there is a need to create design and construction strategies which will minimize the environmental impact of new buildings. Green construction is a design philosophy intended to improve the sustainability of construction by the minimization of resource depletion and CO2 emissions throughout the life cycle of buildings. This paper presents and discusses the results of a survey that was conducted in Kuwait, with the objective of investigating the awareness of developers and other stakeholders regarding their understanding and use of green construction strategies. The results of the survey demonstrate that whilst there seems to be a reasonable level of awareness amongst the stakeholders, this awareness is not currently well reflected in the design and construction practices actually being applied. It is therefore concluded is there is a pressing need for intervention from Government in order that the use of sustainable green design and construction strategies becomes the norm in Kuwait.
Abstract: The excellent suitability of the externally excited synchronous
machine (EESM) in automotive traction drive applications
is justified by its high efficiency over the whole operation range and
the high availability of materials. Usually, maximum efficiency is
obtained by modelling each single loss and minimizing the sum of all
losses. As a result, the quality of the optimization highly depends on
the precision of the model. Moreover, it requires accurate knowledge
of the saturation dependent machine inductances. Therefore, the
present contribution proposes a method to minimize the overall losses
of a salient pole EESM and its inverter in steady state operation based
on measurement data only. Since this method does not require any
manufacturer data, it is well suited for an automated measurement
data evaluation and inverter parametrization. The field oriented control
(FOC) of an EESM provides three current components resp. three
degrees of freedom (DOF). An analytic minimization of the copper
losses in the stator and the rotor (assuming constant inductances) is
performed and serves as a first approximation of how to choose the
optimal current reference values. After a numeric offline minimization
of the overall losses based on measurement data the results are
compared to a control strategy that satisfies cos (ϕ) = 1.
Abstract: Cheating on standardized tests has been a major
concern as it potentially minimizes measurement precision. One
major way to reduce cheating by collusion is to administer multiple
forms of a test. Even with this approach, potential collusion is still
quite large. A Latin-square treatment structure for distributing
multiple forms is proposed to further reduce the colluding potential.
An index to measure the extent of colluding potential is also
proposed. Finally, with a simple algorithm, the various Latin-squares
were explored to find the best structure to keep the colluding
potential to a minimum.
Abstract: Regenerative Thermal Oxidizer (RTO) is one of the
best solutions for removal of Volatile Organic Compounds (VOC)
from industrial processes. In the RTO, VOC in a raw gas are usually
decomposed at 950-1300 K and the combustion heat of VOC is
recovered by regenerative heat exchangers charged with ceramic
honeycombs. The optimization of the treatment of VOC leads to the
reduction of fuel addition to VOC decomposition, the minimization of
CO2 emission and operating cost as well.
In the present work, the thermal efficiency of the RTO was
investigated experimentally in a pilot-scale RTO unit using toluene as
a typical representative of VOC. As a result, it was recognized that the
radiative heat transfer was dominant in the preheating process of a raw
gas when the gas flow rate was relatively low. Further, it was found
that a minimum heat exchanger volume to achieve self combustion of
toluene without additional heating of the RTO by fuel combustion was
dependent on both the flow rate of a raw gas and the concentration of
toluene. The thermal efficiency calculated from fuel consumption and
the decomposed toluene ratio, was found to have a maximum value of
0.95 at a raw gas mass flow rate of 1810 kg·h-1 and honeycombs height
of 1.5m.
Abstract: The Continuously Adaptive Mean-Shift (CamShift)
algorithm, incorporating scene depth information is combined with
the l1-minimization sparse representation based method to form a
hybrid kernel and state space-based tracking algorithm. We take
advantage of the increased efficiency of the former with the
robustness to occlusion property of the latter. A simple interchange
scheme transfers control between algorithms based upon drift and
occlusion likelihood. It is quantified by the projection of target
candidates onto a depth map of the 2D scene obtained with a low cost
stereo vision webcam. Results are improved tracking in terms of drift
over each algorithm individually, in a challenging practical outdoor
multiple occlusion test case.