• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2008년도 하계학술대회 논문집 Vol.9
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• pp.186-188
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• 2008

The wireless communication revolution has spawned a revival of interest in the design and optimization of radio transceivers. Radio transmit modules continue to shrink in die size and cost, requiring novel approaches for integration of the numerous passive elements of the radio front-end. A 3 dB Wilkinson power divider based on GaAs substrates, for DCS 1710-1880 MHz band was designed and fabricated showing excellent performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권9호
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• pp.2037-2051
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• 2012

Cognitive radio (CR) is envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, the main concern is to reliably sense the presence of primary users (PUs) to attain protection against harmful interference caused by potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA) are proposed to minimize the total sensing decision error at the common soft data fusion (SDF) centre of a structurally-centralized cognitive radio network (CRN). Using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed methods are compared with each other as well as with other conventional deterministic algorithms such as maximal ratio combining (MRC) and equal gain combining (EGC). Computer simulations confirm the superiority of the PSO-based scheme over the GA-based and other conventional MRC and EGC schemes in terms of detection performance. In addition, the PSO-based scheme also shows promising convergence performance as compared to the GA-based scheme. This makes PSO an adequate solution to meet real-time requirements.

• 대한조선학회논문집
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• 제34권1호
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• pp.93-101
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• 1997

본 연구에서는 전통적인 비선형 최적화 기법의 문제점을 극복하기 위하여 유전자알고리즘과 지식베이스의 통합을 통한 새로운 개념의 최적화 기법을 개발하였다. 여기에서는 제한조건이 있는 비선형 최적화 문제를 해결하기 위해 사용되는 전통적인 순차적 선형화 방법과 새로운 유전자 알고리즘의 장단점을 서로 보완한 하이브리드형 최적화 기법을 개발하였다. 여기에 지식베이스를 통한 최적화 지원 기법 및 최적화 모델의 자동생성 모듈을 개발하여 최적화 모텔의 성능을 한층 개선할 수 있었다. 개발된 최적화 기법의 검증을 위하여 수학적 비선형 모델을 이용한 여러가지 기법의 비교 검토를 수행하였다.

• Nuclear Engineering and Technology
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• 제54권3호
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• pp.940-947
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• 2022

Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization (HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry's law of physics. To evaluate the performance of a new algorithm, it must be used in various problems. On the other hand, the optimization of the proportional-integral-derivative (PID) gains for load-following of a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, the power control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model with six groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms, an efficient objective function is required. Therefore, the integral of the time-weighted square error (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrained by a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. The results show that this method provides superior results compared to an empirically tuned PID controller with the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2003년도 제21회 추계학술대회 논문집
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• pp.145-150
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• 2003

The present study aims to optimize the performance of the Two-Stage Gas Gun by using the experimentally obtained data. RSM(Response Surface Method) was adopted in the optimization process to find the operating parameter than can maximize the projectile speed with the minimum number of tests. To decide the test points which results can consist of the response surface, 3$^{k}$ full factorial method was used, and the design variables were chosen with piston mass and 2$^{nd}$ driver fill pressure. The response surface was composed by nine test results and consequently the optimization was done with GENOCOP III, inherently GA code, in order to seek the optimal test point. The optimal test condition from the response surface was verified by the experiment. Results showed that the optimization process with response surface can successfully predict the test results fairly well. This study shows the possibility of performance optimization for the experimental facilities using numerical optimization algorithm.

• 전자공학회논문지A
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• 제33A권7호
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• pp.156-161
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• 1996

Accurate equivalent circuit modeling using multi-circuit optimization has been perfomred for detemining small-signal model of AlGaAs/GaAs HBTs. Three equivalent circuits for a cutoff biasing and two active biasing at different curretns are optimized simultaneously to fit gheir S parameters under the physics-based constrain that current-dependent elements for one of active circuits are connected to those for another circit multiplied by the ratio of two currents. The cutoff mode circuit and the physical constrain give the advantage of extracting physically acceptable parameters, because the number of unknown variables. After this optimization, three ses of optimized model S-parameters agree well with their measured S-parameters from 0.045 GHz to 26.5GHz.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2008년도 춘계학술대회논문집
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• pp.377-380
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• 2008

In this study, an aerodynamic shape optimization system was developed to study the optimal shape of airfoil. The system consists of GA (Genetic Algorithm) and CFD code based on the Navier-Stokes equation. Lift-drag ratio is chosen as the object function and optimization is conducted for PARSEC airfoil with nine design variables, which is very efficient in representing the surface geometry of airfoil.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1679-1691
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• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

• Journal of Magnetics
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• 제16권3호
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• pp.268-270
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• 2011

This study introduces a new topology optimization scheme combing the genetic algorithm (GA) with the on/off sensitivity method for the magnetic actuator core and the armature design. The design process intended to maximize the first eigen-frequency of the armature part and the magnetic actuating force acting on the armature simultaneously. GA based optimal design was carried out to obtain the initial structure and the modified on/off sensitivity method was succeeded to accelerate the design process. Final results show tens of percent improvement in actuating force as well as the first eigen-frequency of the armature.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.