• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.8
• /
• pp.360-367
• /
• 2002

In this paper, genetic algorithm based adaptive image enhancement filtering scheme is proposed and Implemented on FPGA board. Conventional filtering methods require a priori noise information for image enhancement. In general, if a priori information of noise is not available, heuristic intuition or time consuming recursive calculations are required for image enhancement. Contrary to the conventional filtering methods, the proposed filter system can find optimal combination of filters as well as their sequent order and parameter values adaptively to unknown noise types using structured genetic algorithms. The proposed image enhancement filter system is mainly composed of two blocks. The first block consists of genetic algorithm part and fitness evaluation part. And the second block consists of four types of filters. The first block (genetic algorithms and fitness evaluation blocks) is implemented on host computer using C code, and the second block is implemented on re-configurabe FPGA board. For gray scale control, smoothing and deblurring, four types of filters(median filter, histogram equalization filter, local enhancement filter, and 2D FIR filter) are implemented on FPGA. For evaluation, three types of noises are used and experimental results show that the Proposed scheme can generate optimal set of filters adaptively without a pioi noise information.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.6
• /
• pp.587-595
• /
• 2011

In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1278-1282
• /
• 2003

A new interacting multiple model (IMM) method using intelligent input estimation (IIE) is proposed to track a maneuvering target. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown acceleration input by a fuzzy system using the relation between maneuvering filter residual and non-maneuvering one. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of acceleration input. Then, multiple models are composed of these fuzzy systems, which are optimized for different ranges of acceleration input. In computer simulation for an incoming ballistic missile, the tracking performance of the proposed method is compared with those of the input estimation (IE) technique and the adaptive interacting multiple model (AIMM) method.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.615_616
• /
• 2009

유한요소해석(Finite Element Analysis)을 통한 풍력발전기 최적설계시, 해석 특성상 발생하는 막대한 소요시간의 개선이 필요하다. 본 논문에서는 연간 에너지 생산량(Annual Energy Production : AEP)의 최대화를 목표로 GA(Genetic Algorithm)와 MADS(Mesh Adaptive Direct Search)를 결합한 혼합 알고리즘을 이용하여 최적설계를 수행하였다. 또한, 혼합 알고리즘과 병렬분산 유전알고리즘을 이용한 최적설계의 해석 소요시간을 비교 및 검토하였다.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.663-664
• /
• 2006

This paper presents a reconfigurable digital signal processing(rDSP) architecture that is effective for implementing adaptive digital signal processing in the applications of smart health care system. This rDSP architecture employs an evolution capability of FIR filters using genetic algorithm. Parallel genetic algorithm based rDSP architecture evolves FIR filters to explore optimal configuration of filter combination, associated parameters, and structure of feature space adaptively to noisy environments for an adaptive signal processing. The proposed DSP architecture is implemented using Xilinx Virtex4 FPGA device and SMIC 0.18um CMOS Technology.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.9
• /
• pp.4549-4567
• /
• 2016

To cope with the complex electromagnetic environment of wireless communication systems, anti-jamming decision methods are necessary to keep the reliability of communication. Basing on the rule-reduced genetic algorithm (RRGA), an anti-jamming decision method is proposed in this paper to adapt to the fast channel variations. Firstly, the reduced decision rules are obtained according to the rough set (RS) theory. Secondly, the randomly generated initial population of the genetic algorithm (GA) is screened and the individuals are preserved in accordance with the reduced decision rules. Finally, the initial population after screening is utilized in the genetic algorithm to optimize the communication parameters. In order to remove the dependency on the weights, this paper deploys an anti-jamming decision objective function, which aims at maximizing the normalized transmission rate under the constraints of minimizing the normalized transmitting power with the pre-defined bit error rate (BER). Simulations are carried out to verify the performance of both the traditional genetic algorithm and the adaptive genetic algorithm. Simulation results show that the convergence rates of the two algorithms increase significantly thanks to the initial population determined by the reduced-rules, without losing the accuracy of the decision-making. Meanwhile, the weight-independent objective function makes the algorithm more practical than the traditional methods.

• Journal of Mechanical Science and Technology
• /
• v.17 no.2
• /
• pp.187-196
• /
• 2003

In this paper, a new methodology of evolutionary computations - An Adaptive Evolutionary Algorithm (AEA) is proposed. AEA uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations : global search capability of GA and local search capability of ES. In the reproduction procedure, the proportions of the population by GA and ES are adaptively modulated according to the fitness. AEA is used to. designing fuzzy logic controller (FLC) for a high-angle-of-attack flight system for a super-maneuverable version of F-18 aircraft. AEA is used to determine the membership functions and scaling factors of an FLC. The computer simulation results show that the FLC has met both robustness and performance requirements.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.26 no.12
• /
• pp.1031-1043
• /
• 2015

This paper describes the pattern synthesis method of two kinds of conformal array antenna using the Enhanced Adaptive Genetic Algorithm (EAGA). One is the $1{\times}16$ conformal array antenna on a curved cylindrical metallic surface with quadratic function, and the other is the 18-element conformal arrary antenna on a metallic surface obtained by the rotation of a quadratic function curve around the axis. The active element pattern is utilized in the pattern synthesis. Especially for the case of the rotated-type conformal array antenna the transformed active element pattern obtained from the Euler's angle rotation of the active element pattern of the planar concentric array is utilized, which reduces the synthesis time a lot. To verify the validity of the proposed synthesis method the MATLAB results are compared with the MWS results. Furthermore, for the case of $1{\times}16$ conformal array antenna the measured results are compared with the MATLAB synthesized results.

• Journal of Korea Multimedia Society
• /
• v.16 no.1
• /
• pp.67-74
• /
• 2013

In this paper, we propose the pointing and correction algorithm for optimized performance based on Bluetooth communication. The error from the accelerometer sensor's output must be carefully managed as the accelerometer sensor is more sensitive to data change compared to that of the gyroscope sensor. Thus, we minimize the noise by applying the Kalman filter to data for each axis from the accelerometer. In addition, we can also obtain effect compensating the hand tremor by applying the Kalman filter to the data variation for x and y. In this study, we extract data through the Quaternion mapping process on data from the accelerometer and gyroscope. In turn, we can obtain a tilt compensation by applying a compensation algorithm with acceleration of the gravity of the extracted data. Moreover, in order to correct the inaccuracy on smart sensor due to the rapid movement of a device, we propose a adaptive pointing and correction algorithm using the genetic approach to generate the initial population depending on the user.

• Smart Structures and Systems
• /
• v.5 no.6
• /
• pp.645-662
• /
• 2009

Adaptive wings are capable of properly modifying their shape depending on the current aerodynamic conditions, in order to improve the overall performance of a flying vehicle. In this paper is presented the concept design of a small-scale compliant wing rib whose outline may be distorted in order to switch from an aerodynamic profile to another. The distortion loads are induced by shape memory alloy actuators placed within the frame of a wing section whose elastic response is predicted by the matrix method with beam formulation. Genetic optimization is used to find a wing rib structure (corresponding to the first airfoil) able to properly deforms itself when loaded by the SMA-induced forces, becoming as close as possible to the desired target shape (second airfoil). An experimental validation of the design procedure is also carried out with reference to a simplified structure layout.