Abstract: In this paper the optimality of the solution of an
existing real word assignment problem known as the seat assignment
problem using Seat Assignment Method (SAM) is discussed. SAM is
the newly driven method from three existing methods, Hungarian
Method, Northwest Corner Method and Least Cost Method in a
special way that produces the easiness & fairness among all methods
that solve the seat assignment problem.
Abstract: This paper presents a procedure for modeling and tuning the parameters of Thyristor Controlled Series Compensation (TCSC) controller in a multi-machine power system to improve transient stability. First a simple transfer function model of TCSC controller for stability improvement is developed and the parameters of the proposed controller are optimally tuned. Genetic algorithm (GA) is employed for the optimization of the parameter-constrained nonlinear optimization problem implemented in a simulation environment. By minimizing an objective function in which the oscillatory rotor angle deviations of the generators are involved, transient stability performance of the system is improved. The proposed TCSC controller is tested on a multi-machine system and the simulation results are presented. The nonlinear simulation results validate the effectiveness of proposed approach for transient stability improvement in a multimachine power system installed with a TCSC. The simulation results also show that the proposed TCSC controller is also effective in damping low frequency oscillations.
Abstract: Graph partitioning is a NP-hard problem with multiple
conflicting objectives. The graph partitioning should minimize the
inter-partition relationship while maximizing the intra-partition
relationship. Furthermore, the partition load should be evenly
distributed over the respective partitions. Therefore this is a multiobjective
optimization problem (MOO). One of the approaches to
MOO is Pareto optimization which has been used in this paper. The
proposed methods of this paper used to improve the performance are
injecting best solutions of previous runs into the first generation of
next runs and also storing the non-dominated set of previous
generations to combine with later generation's non-dominated set.
These improvements prevent the GA from getting stuck in the local
optima and increase the probability of finding more optimal
solutions. Finally, a simulation research is carried out to investigate
the effectiveness of the proposed algorithm. The simulation results
confirm the effectiveness of the proposed method.
Abstract: Customarily, the LMTD correction factor, FT, is used
to screen alternative designs for a heat exchanger. Designs with
unacceptably low FT values are discarded. In this paper, authors have
proposed a more fundamental criterion, based on feasibility of a
multipass exchanger as the only criteria, followed by economic
optimization. This criterion, coupled with asymptotic energy targets,
provide the complete optimization space in a heat exchanger network
(HEN), where cost-optimization of HEN can be performed with only
Heat Recovery Approach temperature (HRAT) and number-of-shells
as variables.
Abstract: Both the minimum energy consumption and
smoothness, which is quantified as a function of jerk, are generally
needed in many dynamic systems such as the automobile and the
pick-and-place robot manipulator that handles fragile equipments.
Nevertheless, many researchers come up with either solely
concerning on the minimum energy consumption or minimum jerk
trajectory. This research paper considers the indirect minimum Jerk
method for higher order differential equation in dynamics
optimization proposes a simple yet very interesting indirect jerks
approaches in designing the time-dependent system yielding an
alternative optimal solution. Extremal solutions for the cost functions
of indirect jerks are found using the dynamic optimization methods
together with the numerical approximation. This case considers the
linear equation of a simple system, for instance, mass, spring and
damping. The simple system uses two mass connected together by
springs. The boundary initial is defined the fix end time and end
point. The higher differential order is solved by Galerkin-s methods
weight residual. As the result, the 6th higher differential order shows
the faster solving time.
Abstract: In this paper we present the deep study about the Bio-
Medical Images and tag it with some basic extracting features (e.g.
color, pixel value etc). The classification is done by using a nearest
neighbor classifier with various distance measures as well as the
automatic combination of classifier results. This process selects a
subset of relevant features from a group of features of the image. It
also helps to acquire better understanding about the image by
describing which the important features are. The accuracy can be
improved by increasing the number of features selected. Various
types of classifications were evolved for the medical images like
Support Vector Machine (SVM) which is used for classifying the
Bacterial types. Ant Colony Optimization method is used for optimal
results. It has high approximation capability and much faster
convergence, Texture feature extraction method based on Gabor
wavelets etc..
Abstract: Adaptive observers used in sensorless control of induction motors suffer from instability especally in regenerating mode. In this paper, an optimal feed back gain design is proposed, it can reduce the instability region in the torque speed plane .
Abstract: Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.
Abstract: This study investigated the removal efficiency of electrokinetic remediation of copper-contaminated soil at different combinations of enhancement reagents used as anolyte and catholyte. Sodium hydroxide (at 0.1, 0.5, and 1.0 M concentrations) and distilled water were used as anolyte, while lactic acid (at 0.01, 0.1, and 0.5 M concentrations), ammonium citrate (also at 0.01, 0.1, and 0.5 M concentrations) and distilled water were used as catholyte. A continuous voltage application (1.0 VDC/cm) was employed for 240 hours for each experiment. The copper content of the catholyte was determined at the end of the 240-hour period. Optimization was carried out with a Response Surface Methodology - Optimal Design, including F test, and multiple comparison method, to determine which pair of anolyte-catholyte was the most significant for the removal efficiency. "1.0 M NaOH" was found to be the most significant anolyte while it was established that lactic acid was the most significant type of catholyte to be used for the most successful electrokinetic experiments. Concentrations of lactic acid should be at the range of 0.1 M to 0.5 M to achieve maximum percent removal values.
Abstract: This paper presents the results related to the
interference reduction technique in multistage multiuser detector for
asynchronous DS-CDMA system. To meet the real-time
requirements for asynchronous multiuser detection, a bit streaming,
cascade architecture is used. An asynchronous multiuser detection
involves block-based computations and matrix inversions. The paper
covers iterative-based suboptimal schemes that have been studied to
decrease the computational complexity, eliminate the need for matrix
inversions, decreases the execution time, reduces the memory
requirements and uses joint estimation and detection process that
gives better performance than the independent parameter estimation
method. The stages of the iteration use cascaded and bits processed
in a streaming fashion. The simulation has been carried out for
asynchronous DS-CDMA system by varying one parameter, i.e.,
number of users. The simulation result exhibits that system gives
optimum bit error rate (BER) at 3rd stage for 15-users.
Abstract: A modified Genetic Algorithm (GA) based optimal selection of parameters for Automatic Generation Control (AGC) of multi-area electric energy systems is proposed in this paper. Simulations on multi-area reheat thermal system with and without consideration of nonlinearity like governor dead band followed by 1% step load perturbation is performed to exemplify the optimum parameter search. In this proposed method, a modified Genetic Algorithm is proposed where one point crossover with modification is employed. Positional dependency in respect of crossing site helps to maintain diversity of search point as well as exploitation of already known optimum value. This makes a trade-off between exploration and exploitation of search space to find global optimum in less number of generations. The proposed GA along with decomposition technique as developed has been used to obtain the optimum megawatt frequency control of multi-area electric energy systems. Time-domain simulations are conducted with trapezoidal integration along with decomposition technique. The superiority of the proposed method over existing one is verified from simulations and comparisons.
Abstract: We present a theory for optimal filtering of infinite sets of random signals. There are several new distinctive features of the proposed approach. First, we provide a single optimal filter for processing any signal from a given infinite signal set. Second, the filter is presented in the special form of a sum with p terms where each term is represented as a combination of three operations. Each operation is a special stage of the filtering aimed at facilitating the associated numerical work. Third, an iterative scheme is implemented into the filter structure to provide an improvement in the filter performance at each step of the scheme. The final step of the concerns signal compression and decompression. This step is based on the solution of a new rank-constrained matrix approximation problem. The solution to the matrix problem is described in this paper. A rigorous error analysis is given for the new filter.
Abstract: As the use of registration packages spreads, the number of the aligned image pairs in image databases (either by manual or automatic methods) increases dramatically. These image pairs can serve as a set of training data. Correspondingly, the images that are to be registered serve as testing data. In this paper, a novel medical image registration method is proposed which is based on the a priori knowledge of the expected joint intensity distribution estimated from pre-aligned training images. The goal of the registration is to find the optimal transformation such that the distance between the observed joint intensity distribution obtained from the testing image pair and the expected joint intensity distribution obtained from the corresponding training image pair is minimized. The distance is measured using the divergence measure based on Tsallis entropy. Experimental results show that, compared with the widely-used Shannon mutual information as well as Tsallis mutual information, the proposed method is computationally more efficient without sacrificing registration accuracy.
Abstract: In this paper we propose a Particle Swarm heuristic
optimized Multi-Antenna (MA) system. Efficient MA systems
detection is performed using a robust stochastic evolutionary
computation algorithm based on movement and intelligence of
swarms. This iterative particle swarm optimized (PSO) detector
significantly reduces the computational complexity of conventional
Maximum Likelihood (ML) detection technique. The simulation
results achieved with this proposed MA-PSO detection algorithm
show near optimal performance when compared with ML-MA
receiver. The performance of proposed detector is convincingly
better for higher order modulation schemes and large number of
antennas where conventional ML detector becomes non-practical.
Abstract: This paper presents a hybrid algorithm for solving a timetabling problem, which is commonly encountered in many universities. The problem combines both teacher assignment and course scheduling problems simultaneously, and is presented as a mathematical programming model. However, this problem becomes intractable and it is unlikely that a proven optimal solution can be obtained by an integer programming approach, especially for large problem instances. A hybrid algorithm that combines an integer programming approach, a greedy heuristic and a modified simulated annealing algorithm collaboratively is proposed to solve the problem. Several randomly generated data sets of sizes comparable to that of an institution in Indonesia are solved using the proposed algorithm. Computational results indicate that the algorithm can overcome difficulties of large problem sizes encountered in previous related works.
Abstract: This work presents a recursive identification algorithm. This algorithm relates to the identification of closed loop system with Variable Structure Controller. The approach suggested includes two stages. In the first stage a genetic algorithm is used to obtain the parameters of switching function which gives a control signal rich in commutations (i.e. a control signal whose spectral characteristics are closest possible to those of a white noise signal). The second stage consists in the identification of the system parameters by the instrumental variable method and using the optimal switching function parameters obtained with the genetic algorithm. In order to test the validity of this algorithm a simulation example is presented.
Abstract: In this paper the optimal control strategy for
Permanent Magnet Synchronous Motor (PMSM) based drive system
is presented. The designed full optimal control is available for speed
operating range up to base speed. The optimal voltage space-vector
assures input energy reduction and stator loss minimization,
maintaining the output energy in the same limits with the
conventional PMSM electrical drive. The optimal control with three
components is based on the energetically criteria and it is applicable
in numerical version, being a nonrecursive solution. The simulation
results confirm the increased efficiency of the optimal PMSM drive.
The properties of the optimal voltage space vector are shown.
Abstract: In this paper, based on steady-state models of Flexible
AC Transmission System (FACTS) devices, the sizing of static
synchronous series compensator (SSSC) controllers in transmission
network is formed as an optimization problem. The objective of this
problem is to reduce the transmission losses in the network. The
optimization problem is solved using particle swarm optimization
(PSO) technique. The Newton-Raphson load flow algorithm is
modified to consider the insertion of the SSSC devices in the
network. A numerical example, illustrating the effectiveness of the
proposed algorithm, is introduced. In addition, a novel model of a 3-
phase voltage source converter (VSC) that is suitable for series
connected FACTS a controller is introduced. The model is verified
by simulation using Power System Blockset (PSB) and Simulink
software.
Abstract: Crude oil blending is an important unit operation in
petroleum refining industry. A good model for the blending system is
beneficial for supervision operation, prediction of the export
petroleum quality and realizing model-based optimal control. Since
the blending cannot follow the ideal mixing rule in practice, we
propose a static neural network to approximate the blending
properties. By the dead-zone approach, we propose a new robust
learning algorithm and give theoretical analysis. Real data of crude
oil blending is applied to illustrate the neuro modeling approach.
Abstract: This paper presents an efficient algorithm for
optimization of radial distribution systems by a network
reconfiguration to balance feeder loads and eliminate overload
conditions. The system load-balancing index is used to determine the
loading conditions of the system and maximum system loading
capacity. The index value has to be minimum in the optimal network
reconfiguration of load balancing. A method based on Tabu search
algorithm, The Tabu search algorithm is employed to search for the
optimal network reconfiguration. The basic idea behind the search is
a move from a current solution to its neighborhood by effectively
utilizing a memory to provide an efficient search for optimality. It
presents low computational effort and is able to find good quality
configurations. Simulation results for a radial 69-bus system with
distributed generations and capacitors placement. The study results
show that the optimal on/off patterns of the switches can be identified
to give the best network reconfiguration involving balancing of
feeder loads while respecting all the constraints.