• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.502-504
• /
• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of FIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate FIR filter parameter using the genetic algorithm.

• Proceedings of the KSME Conference
• /
• 2001.11a
• /
• pp.533-538
• /
• 2001

This paper describes a combinational method to compute the global and local solutions of optimization problems. The present hybrid algorithm uses both a genetic algorithm and a local concentrate search algorithm (e. g simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm but also supplies a more accurate solution. In addition, this algorithm can find the global and local optimum solutions. The present algorithm can be supplied to minimize the resonance response (Q factor) and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraint. In the present work, the shaft diameter, the bearing length, and clearance are used as the design variables.

• Proceedings of the KIEE Conference
• /
• 2001.04a
• /
• pp.473-475
• /
• 2001

This paper proposed a MPPT ( Maximum Power Point Tracking) control algorithm for PV(Photovoltaic) array based on a modified constant voltage control MPPT algorithm at low-insolation. This method which combines a IncCond(Incremental Conductance) and a constant voltage control algorithm. In contrast to the typical conventional MPPT algorithm, the proposed method have been obtained high efficiency and good performance. The proposed algorithm is verified through simulation result. In order to confirm the availability of the scheme, a simulation used PSIM and ACSL software tool.

• The KIPS Transactions:PartC
• /
• v.16C no.2
• /
• pp.267-274
• /
• 2009

For the implementation of a RFID system, an anti-collision algorithm is required to identify multiple tags within the range of a RFID Reader. There are two methods of anti-collision algorithms for the identification of multiple tags, conclusive algorithms based on tree and stochastic algorithms based on slotted ALOHA. In this paper, we propose a Dynamic Framed Slotted ALOHA-Slot Congestion(DFSA-SC) Algorithm. The proposed algorithm improves the efficiency of collision resolution. The performance of the proposed DFSA-SC algorithm is showed by simulation. The identification time of the proposed algorithm is shorter than that of the existing DFSA algorithm. Furthermore, when the bit duplication of the tagID is higher, the proposed algorithm is more efficient than Query Tree algorithm.

• ETRI Journal
• /
• v.43 no.2
• /
• pp.260-271
• /
• 2021

This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies. The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N2).

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.493-496
• /
• 2003

Recently, Ryu et al. proposed a multiple target DOA tracking algorithm, which has good features that it has no data association problem and simple structure. But its performance is seriously degraded in the low signal-to-noise ratio. In this paper, a measurement fusion method is presented based on ML(Maximum Likelihood), and the new DOA tracking algorithm is proposed by incorporating the presented fusion method into Ryu's algorithm. The proposed algorithm has a better tracking performance than that of Ryu's algorithm, and it sustains the good features of Ryu's algorithm.

• Journal of Advanced Marine Engineering and Technology
• /
• v.22 no.4
• /
• pp.498-504
• /
• 1998

This paper presents an optimal control algorithm for the overhead crane. To control the swing motion and the position tracking of the payload of the overhead crane a state feedback control algorithm is applied. by using a hybrid genetic algorithm the feedback gains of the state feedback is optimized to minimize the cost function composed of position errors and payload swing angle under unknown constant disturbances. Computer simulation is performed to demonstrate the effectiveness of the proposed control algorithm.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.7
• /
• pp.634-642
• /
• 2004

Unlike actual vehicles, a vehicle driving simulator is limited in kinematic workspace and bounded on dynamic characteristics. So it is difficult to simulate dynamic motions of a multi-body vehicle model. In order to overcome these problems, a washout algorithm which controls the workspace of the simulator within the kinematic limitation is needed. However, a classical washout algorithm contains several problems such as generation of wrong sensation of motions by filters in tilt coordination, requirement of trial and error method in selecting the proper cut-off frequencies, difficulty in returning the simulator to its origin using only high pass filters and etc. This paper proposes a new tilt coordination method as an algorithm which gives more accurate sensations to drivers. In order to reduce time for returning the simulator to its origin, a new washout algorithm that the proposed algorithm selectively onset mode from high pass filters and return mode from error functions is proposed. As a result of this study, the results of the proposed algorithm are compared with the results of classical washout algorithm through the human perception models. Also, the performance of the suggested algorithm is evaluated by using human perception and sensibility of some drivers through experiments.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.4
• /
• pp.721-726
• /
• 2012

In this paper, we proposed an efficient algorithm based on SPFA(shortest path faster algorithm), which is an improved the Bellman-Ford algorithm. The Bellman-Ford algorithm can be used on graphs with negative edge weights unlike Dijkstra's algorithm. And SPFA algorithm used a queue to store the nodes, to avoid redundancy, though the Bellman-Ford algorithm takes a long time to update the nodes table. In this improved algorithm, an adjacency list is also used to store each vertex of the graph, applying dynamic optimal approach. And a queue is used to store the data. The improved algorithm can find the optimal path by continuous relaxation operation to the new node. Simulations to compare the efficiencies for Dijkstra's algorithm, SPFA algorithm and improved Bellman-Ford were taken. The result shows that Dijkstra's algorithm, SPFA algorithm have almost same efficiency on the random graphs, the improved algorithm, although the improved algorithm is not desirable, on grid maps the proposed algorithm is very efficient. The proposed algorithm has reduced two-third times processing time than SPFA algorithm.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.285-286
• /
• 2008

In this paper, we propose adaptive search channel estimate algorithm. The proposed algorithm is modified LMS algorithm which has a variable step size and parallel convolution. In simulation result, a error estimate accuracy of the proposed algorithm is about -20 dB and general LMS algorithm is about 10 dB. The proposed algorithm is better error estimate accuracy than general LMS algorithm.