Abstract: In this paper, a PSO-based approach is proposed to
derive a digital controller for redesigned digital systems having an interval plant based on resemblance of the extremal gain/phase
margins. By combining the interval plant and a controller as an interval system, extremal GM/PM associated with the loop transfer function
can be obtained. The design problem is then formulated as an optimization problem of an aggregated error function revealing the deviation on the extremal GM/PM between the redesigned digital
system and its continuous counterpart, and subsequently optimized by
a proposed PSO to obtain an optimal set of parameters for the digital controller. Computer simulations have shown that frequency
responses of the redesigned digital system having an interval plant bare a better resemblance to its continuous-time counter part by the incorporation of a PSO-derived digital controller in comparison to those obtained using existing open-loop discretization methods.
Abstract: Detection of squirrel cage induction motor (SCIM) broken bars has long been an important but difficult job in the detection area of motor faults. Early detection of this abnormality in the motor would help to avoid costly breakdowns. A new detection method based on particle swarm optimization (PSO) is presented in this paper. Stator current in an induction motor will be measured and characteristic frequency components of faylted rotor will be detected by minimizing a fitness function using pso. Supply frequency and side band frequencies and their amplitudes can be estimated by the proposed method. The proposed method is applied to a faulty motor with one and two broken bars in different loading condition. Experimental results prove that the proposed method is effective and applicable.
Abstract: This paper presents an optimal design of linear phase
digital high pass finite impulse response (FIR) filter using Improved
Particle Swarm Optimization (IPSO). In the design process, the filter
length, pass band and stop band frequencies, feasible pass band and
stop band ripple sizes are specified. FIR filter design is a multi-modal
optimization problem. An iterative method is introduced to find the
optimal solution of FIR filter design problem. Evolutionary
algorithms like real code genetic algorithm (RGA), particle swarm
optimization (PSO), improved particle swarm optimization (IPSO)
have been used in this work for the design of linear phase high pass
FIR filter. IPSO is an improved PSO that proposes a new definition
for the velocity vector and swarm updating and hence the solution
quality is improved. A comparison of simulation results reveals the
optimization efficacy of the algorithm over the prevailing
optimization techniques for the solution of the multimodal, nondifferentiable,
highly non-linear, and constrained FIR filter design
problems.
Abstract: Motion control of flexible arms is more difficult than
that of rigid arms, however utilizing its dynamics enables improved
performance such as a fast motion in short operation time. This paper
investigates a ball throwing robot with one rigid link and one flexible
link. This robot throws a ball at a set speed with a proper control torque.
A mathematical model of this ball throwing robot is derived through
Hamilton’s principle. Several patterns of torque input are designed and
tested through the proposed simulation models. The parameters of
each torque input pattern is optimized and determined by chaos
embedded vector evaluated particle swarm optimization (CEVEPSO).
Then, the residual vibration of the manipulator after throwing is
suppressed with input shaping technique. Finally, a real experiment is
set up for the model checking.
Abstract: In the last decades to supply the various and different
demands of clients, a lot of manufacturers trend to use the mixedmodel
assembly line (MMAL) in their production lines, since this
policy make possible to assemble various and different models of the
equivalent goods on the same line with the MTO approach.
In this article, we determine the sequence of (MMAL) line, with
applying the kitting approach and planning of rest time for general
workers to reduce the wastages, increase the workers effectiveness
and apply the sector of lean production approach.
This Multi-objective sequencing problem solved in small size with
GAMS22.2 and PSO meta heuristic in 10 test problems and compare
their results together and conclude that their results are very similar
together, next we determine the important factors in computing the
cost, which improving them cost reduced. Since this problem, is NPhard
in large size, we use the particle swarm optimization (PSO)
meta-heuristic for solving it. In large size we define some test
problems to survey it-s performance and determine the important
factors in calculating the cost, that by change or improved them
production in minimum cost will be possible.
Abstract: This paper proposes the method combining artificial
neural network (ANN) with particle swarm optimization (PSO) to
implement the maximum power point tracking (MPPT) by controlling
the rotor speed of the wind generator. First, the measurements of wind
speed, rotor speed of wind power generator and output power of wind
power generator are applied to train artificial neural network and to
estimate the wind speed. Second, the method mentioned above is
applied to estimate and control the optimal rotor speed of the wind
turbine so as to output the maximum power. Finally, the result reveals
that the control system discussed in this paper extracts the maximum
output power of wind generator within the short duration even in the
conditions of wind speed and load impedance variation.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: This paper presents a novel approach for tuning unified power flow controller (UPFC) based damping controller in order to enhance the damping of power system low frequency oscillations. The design problem of damping controller is formulated as an optimization problem according to the eigenvalue-based objective function which is solved using iteration particle swarm optimization (IPSO). The effectiveness of the proposed controller is demonstrated through eigenvalue analysis and nonlinear time-domain simulation studies under a wide range of loading conditions. The simulation study shows that the designed controller by IPSO performs better than CPSO in finding the solution. Moreover, the system performance analysis under different operating conditions show that the δE based controller is superior to the mB based controller.
Abstract: This paper deals with the tuning of parameters for Automatic Generation Control (AGC). A two area interconnected hydrothermal system with PI controller is considered. Genetic Algorithm (GA) and Particle Swarm optimization (PSO) algorithms have been applied to optimize the controller parameters. Two objective functions namely Integral Square Error (ISE) and Integral of Time-multiplied Absolute value of the Error (ITAE) are considered for optimization. The effectiveness of an objective function is considered based on the variation in tie line power and change in frequency in both the areas. MATLAB/SIMULINK was used as a simulation tool. Simulation results reveal that ITAE is a better objective function than ISE. Performances of optimization algorithms are also compared and it was found that genetic algorithm gives better results than particle swarm optimization algorithm for the problems of AGC.
Abstract: Efficient preprocessing is very essential for automatic
recognition of handwritten documents. In this paper, techniques on
segmenting words in handwritten Arabic text are presented. Firstly,
connected components (ccs) are extracted, and distances among
different components are analyzed. The statistical distribution of this
distance is then obtained to determine an optimal threshold for words
segmentation. Meanwhile, an improved projection based method is
also employed for baseline detection. The proposed method has been
successfully tested on IFN/ENIT database consisting of 26459
Arabic words handwritten by 411 different writers, and the results
were promising and very encouraging in more accurate detection of
the baseline and segmentation of words for further recognition.
Abstract: The present work encounters the solution of the defect identification problem with the use of an evolutionary algorithm combined with a simplex method. In more details, a Matlab implementation of Genetic Algorithms is combined with a Simplex method in order to lead to the successful identification of the defect. The influence of the location and the orientation of the depressed ellipsoidal flaw was investigated as well as the use of different amount of static data in the cost function. The results were evaluated according to the ability of the simplex method to locate the global optimum in each test case. In this way, a clear impression regarding the performance of the novel combination of the optimization algorithms, and the influence of the geometrical parameters of the flaw in defect identification problems was obtained.
Abstract: In rapidly changing market environment, firms are investing a lot of time and resources into new product development (NPD) projects to make profit and to obtain competitive advantage. However, failure rate of NPD projects is becoming high due to various internal and external risks which hinder successful NPD projects. To reduce the failure rate, it is critical that risks have to be managed effectively and efficiently through good strategy, and treated by optimal responses to minimize risk cost. Four strategies are adopted to handle the risks in this study. The optimal responses are characterized by high reduction of risk costs with high efficiency. This study suggests a framework to decide the optimal responses considering the core risks, risk costs, response efficiency and response costs for successful NPD projects. Both binary particles warm optimization (BPSO) and multi-objective particle swarm optimization (MOPSO) methods are mainly used in the framework. Although several limitations exist in use for real industries, the frame work shows good strength for handling the risks with highly scientific ways through an example.
Abstract: The study of the transport coefficients in electronic
devices is currently carried out by analytical and empirical models.
This study requires several simplifying assumptions, generally
necessary to lead to analytical expressions in order to study the
different characteristics of the electronic silicon-based devices.
Further progress in the development, design and optimization of
Silicon-based devices necessarily requires new theory and modeling
tools. In our study, we use the PSO (Particle Swarm Optimization)
technique as a computational tool to develop analytical approaches in
order to study the transport phenomenon of the electron in crystalline
silicon as function of temperature and doping concentration. Good
agreement between our results and measured data has been found.
The optimized analytical models can also be incorporated into the
circuits simulators to study Si-based devices without impact on the
computational time and data storage.
Abstract: There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson-s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.
Abstract: An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets from various platforms such as LANDSAT 5 MSS and NOAA's AVHRR. The experimental results of the proposed algorithm are encouraging. The influence of different values of the algorithm control parameters on performance is studied. Furthermore, the performance of different versions of PSO is also investigated.
Abstract: This paper present the harmonic elimination of hybrid
multilevel inverters (HMI) which could be increase the number of
output voltage level. Total Harmonic Distortion (THD) is one of the
most important requirements concerning performance indices.
Because of many numbers output levels of HMI, it had numerous
unknown variables of eliminate undesired individual harmonic and
THD nonlinear equations set. Optimized harmonic stepped waveform
(OHSW) is solving switching angles conventional method, but most
complicated for solving as added level. The artificial intelligent
techniques are deliberation to solve this problem. This paper presents
the Particle Swarm Optimization (PSO) technique for solving
switching angles to get minimum THD and eliminate undesired
individual harmonics of 15-levels hybrid multilevel inverters.
Consequently it had many variables and could eliminate numerous
harmonics. Both advantages including high level of inverter and
Particle Swarm Optimization (PSO) are used as powerful tools for
harmonics elimination.
Abstract: Transient shape variation of a rotating liquid dropletis
simulated numerically. The three dimensional Navier-Stokes
equations were solved by using the level set method. The shape
variation from the sphere to the rotating ellipsoid, and to the two-robed
shapeare simulated, and the elongation of the two-robed droplet is
discussed. The two-robed shape after the initial transient is found to be
stable and the elongation is almost the same for the cases with different
initial rotation rate. The relationship between the elongation and the
rotation rate is obtained by averaging the transient shape variation. It is
shown that the elongation of two-robed shape is in good agreement
with the existing experimental data. It is found that the transient
numerical simulation is necessary for analyzing the largely elongated
two-robed shape of rotating droplet.
Abstract: It is known that the heart interacts with and adapts to
its venous and arterial loading conditions. Various experimental
studies and modeling approaches have been developed to investigate
the underlying mechanisms. This paper presents a model of the left
ventricle derived based on nonlinear stress-length myocardial
characteristics integrated over truncated ellipsoidal geometry, and
second-order dynamic mechanism for the excitation-contraction
coupling system. The results of the model presented here describe the
effects of the viscoelastic damping element of the electromechanical
coupling system on the hemodynamic response. Different heart rates
are considered to study the pacing effects on the performance of the
left-ventricle against constant preload and afterload conditions under
various damping conditions. The results indicate that the pacing
process of the left ventricle has to take into account, among other
things, the viscoelastic damping conditions of the myofilament
excitation-contraction process.
Abstract: In the traditional concept of product life cycle management, the activities of design, manufacturing, and assembly are performed in a sequential way. The drawback is that the considerations in design may contradict the considerations in manufacturing and assembly. The different designs of components can lead to different assembly sequences. Therefore, in some cases, a good design may result in a high cost in the downstream assembly activities. In this research, an integrated design evaluation and assembly sequence planning model is presented. Given a product requirement, there may be several design alternative cases to design the components for the same product. If a different design case is selected, the assembly sequence for constructing the product can be different. In this paper, first, the designed components are represented by using graph based models. The graph based models are transformed to assembly precedence constraints and assembly costs. A particle swarm optimization (PSO) approach is presented by encoding a particle using a position matrix defined by the design cases and the assembly sequences. The PSO algorithm simultaneously performs design evaluation and assembly sequence planning with an objective of minimizing the total assembly costs. As a result, the design cases and the assembly sequences can both be optimized. The main contribution lies in the new concept of integrated design evaluation and assembly sequence planning model and the new PSO solution method. The test results show that the presented method is feasible and efficient for solving the integrated design evaluation and assembly planning problem. In this paper, an example product is tested and illustrated.
Abstract: This paper deals with a portfolio selection problem
based on the possibility theory under the assumption that the returns
of assets are LR-type fuzzy numbers. A possibilistic portfolio model
with transaction costs is proposed, in which the possibilistic mean
value of the return is termed measure of investment return, and the
possibilistic variance of the return is termed measure of investment
risk. Due to considering transaction costs, the existing traditional
optimization algorithms usually fail to find the optimal solution
efficiently and heuristic algorithms can be the best method. Therefore,
a particle swarm optimization is designed to solve the corresponding
optimization problem. At last, a numerical example is given to
illustrate our proposed effective means and approaches.