• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.161-172
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• 2017

In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.636-642
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• 2015

This paper presents an application of heuristic harmony search (HS) optimization algorithm for the identification of linear continuous time-delay system from step response. Identification model is first-order plus dead time (FOPDT), which describes a linear monotonic process quite well in most chemical processes and HAVC process and is often sufficient for PID controller tuning. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.5
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• pp.91-98
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• 2017

This study was conducted to optimize the spring constant and the damping coefficient, which are design parameters of the tractor cabin suspension system, to minimize the ride vibration. A 3D tractor MBD (multi-body dynamics) model with a cabin suspension system was developed using a dynamic analysis program (Recurdyn). Using the developed model and optimization algorithm, the spring constant and the damping coefficient, which are the design parameters of the cabin suspension for the tractor, was were optimized so thatto minimize the maximum overshoot for the vertical displacement of the cabin was minimized. The percent maximum overshoot of the tractor cabin was simulated for the 13 initial models, which were obtained using the ISCD-II method, and for the 3 additional SAO models presented in the optimization algorithm software. The model that represents with the smallest percent maximum overshoot among the 16 models was selected as the optimized model. The percent maximum overshoot of the optimized model was about approximately 5% lower than that of the existing model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5955-5977
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• 2018

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.2
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• pp.7-15
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• 2017

Recently, an electricity sensor system has been installed and operated to prevent failures and accidents by identifying whether a transformer is normal in advance of failure. This electricity sensor system is able to both measure and monitor the transformer's power and voltage remotely and send information to a manager when unusual operation is discovered. However, a battery is required to operate power detection devices, and battery systems need ongoing management such as regular replacement. In addition, at a maintenance cost, occasional human resources and worker safety problems arise. Accordingly, we apply a linear electromagnetic generator using vibration energy from a transformer for an electric sensor system's drive in this research and we conduct optimal design to maximize the linear electromagnetic generator's power. We consider design variables using the provided design method from Process Integration, Automation, and Optimization (PIAnO), which is common tool from process integration and design optimization (PIDO). In addition, we analyze the experiment point from the design of the experiments using "MAXWELL," which is a common electromagnet analysis program. We then create an approximate model and conduct accuracy verification. Finally, we determine the optimal model that generates the maximum power using the proven approximate kriging model and evolutionary optimization algorithm, which we then confirm via simulation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1119-1126
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• 1995

The construction of rule-base for a nonlinear time-varying system, becomes much more complicated because of model uncertainty and parameter variations. Furthemore, FLC does not have an ability of adjusting rule- base in responding to some sudden changes of control environments. To cope with these problems, an auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), which is known to be very effective in the optimization problem, will be proposed. The tuning of the proposed system is performed by two tuning processes(the course tuning process and the fine tuning/adaptive learning process). The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.

• Wind and Structures
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• v.29 no.2
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• pp.99-110
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• 2019

This paper provides a simple numerical method to determine the optimal parameters of tuned mass damper (TMD) and viscoelastic dampers (VEDs) in frame structure for wind vibration control considering the soil-structure interation (SSI) effect in frequency domain. Firstly, the numerical model of frame structure equipped with TMD and VEDs considering SSI effect is established in frequency domain. Then, the genetic algorithm (GA) is applied to obtain the optimal parameters of VEDs and TMD. The optimization process is demonstrated by a 20-storey frame structure supported by pile group for different soil conditions. Two wind resistant systems are considered in the analysis, the Structure-TMD system and the Structure-TMD-VEDs system. The example proves that this method can quickly determine the optimal parameters of energy dissipation devices compared with the traditional finite element method, thus is practically valuable.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.4
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• pp.1-11
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• 2018

A system design and integrated design process for a space launch vehicle were established based on system engineering. With the mission design results for a given payload weight and trajectory, it is possible to perform optimal design by integrating each unit such as propulsion, weight estimation, and aerodynamic force after analysis, during in the system design process. The program is finally configured to verify that the designed vehicle can perform its mission through 3-DOF trajectory optimization simulation. Genetic algorithms are used as the optimization method, and the optimal design results of the variables and parameters to be considered during design are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.40.6-40
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• 2001

Fuzzy controls are widely used in industrial fields using experts knowledge base for its high degree of performance. Genetic Algorithm(GA) is one of the numerical method that has an advantage of optimization. In this paper, we present an acquisition method of fuzzy rules using genetic algorithm. Knowledge of the system is the key to generating the control rules. As these rules, a system can be more stable and it reaches the control goal the faster. To get the optimal fuzzy control rules and the membership functions, we use the GA instead of the experts knowledge base. Information of the system is coded the chromosome with suitable phenotype. Then, it is operated by genetic operator, and evaluated by evaluation function. Passing by the decoding process with the fittest chromosome, the genetic algorithm can tune the fuzzy rules and the membership functions automatically ...

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.362-373
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• 2004

In this study, we introduce a noble neurogenetic approach to the design of the fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and neurofuzzy networks (NFN). The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, tuning of the scaling factors of the fuzzy controller is carried out, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based NFN. The developed approach is applied to an inverted pendulum nonlinear system where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.