• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• 한국항행학회논문지
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• 제19권2호
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• pp.116-122
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• 2015

지속적으로 증가하고 있는 항공수요에 따라, 공항운영 측면에서 이동지역 내 항공기 이동에 대한 효율성을 증대할 수 있는 방안의 중요성이 대두되고 있다. 본 논문은 공항 이동지역을 운항하는 항공기에게 최단경로를 적시에 제공하여 공항운영의 효율성을 증대시키기 위한 경로 탐색 알고리즘을 제시하고자 한다. 기존 문헌들에서 여러 알고리즘이 개발되었는데, 대표적으로 Dijkstra 알고리즘 $A^*$ 알고리즘이 있다. Dijkstra 알고리즘은 상대적으로 느린 연산속도로 인해 공항구조가 복합해질 경우 최단경로를 적시에 제공하기 어려울 수 있다는 단점이 있으며, $A^*$ 알고리즘은 최적성을 보장하지 못한다는 단점이 있다. 본 논문에서는 두 알고리즘을 병합하여, 각 알고리즘의 단점을 보완한 새로운 Hybrid $A^*$ 알고리즘을 제시하였다. 성능분석 결과, Hybrid $A^*$ 알고리즘은 경로탐색에 있어 빠른 연산속도와 최적성이 개선됨을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1451-1456
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• 2013

This paper presents the improved zero power levitation control algorithm for a quadruple hybrid EMS (Electromagnetic Suspension) system. Quadruple hybrid EMS system is a united form of four hybrid EMS systems one on each corner coupled with a metal plate. Technical issue in controlling a quadruple hybrid EMS system is the permanent magnet's equilibrium point deviation caused by design tolerance which eventually leads to a limited zero power levitation control that only satisfies the zero power levitation in one or two hybrid EMS system among the four hybrid EMS system. In order to satisfy a complete zero power levitation control of the quadruple hybrid EMS system, the proposed method presented in this paper adds a compensating algorithm which adjusts the gap reference of each individual axe. Later, this paper proves the stability and effectiveness of the proposed control algorithm via experiment and disturbance test.

• 한국수자원학회논문집
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• 제54권7호
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• pp.475-484
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• 2021

상수관망의 최적설계는 절점의 최소 요구 수압을 만족함뿐만 아니라 관로비용의 최소화 등을 목적으로 한다. 상수관망 설계안의 수는 다양한 관의 배치로 인해 기하급수적으로 증가한다. 상수관망 설계에서 최적화된 설계를 제안하기 위해 다양한 최적화 알고리즘들이 적용되었다. 본 연구에서는 상수관망 최적설계에 자가적응형 매개변수를 개선한 Modified Hybrid Vision Correction Algorithm (MHVCA)을 적용하였다. 기존 Hybrid Vision Correction Algorithm (HVCA)의 Hybrid Rate (HR)를 비선형적 HR로 수정하여 성능을 개선하였다. 제안된 MHVCA의 성능을 확인하기 위해 결정변수가 2개 및 30개로 구성된 수학문제와 제약조건이 있는 수학문제에 적용하였다. MHVCA의 적용결과를 검토하기 위해 Harmony Search (HS), Improved Harmony Search (IHS), Vision Correction Algorithm (VCA) 및 HVCA와 비교하였다. 최종적으로 MHVCA를 상수관망 최적설계 문제에 적용하여 결과를 다른 알고리즘들과 비교하였다. 수학문제 및 상수관망 설계 문제에서 MHVCA가 다른 알고리즘들에 비해 좋은 결과를 보여주었다. MHVCA는 본 연구에서 적용한 문제뿐만 아니라 다양한 수자원공학 문제에 적용하여 좋은 결과를 보여줄 수 있을 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3772-3790
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• 2016

It is well known that the constant modulus algorithm (CMA) presents a large steady-state mean-square error (MSE) for high-order quadrature amplitude modulation (QAM) signals. In this paper, we propose a low-complexity hybrid adaptive blind equalization algorithm, which augments the CMA error function with a novel constellation matched error (CME) term. The most attractive advantage of the proposed algorithm is that it is computationally simpler than concurrent CMA and soft decision-directed (SDD) scheme (CMA+SDD), and modified CMA (MCMA), while the approximation of steady-state MSE of the proposed algorithm is same with CMA+SDD, and lower than MCMA. Extensive simulations demonstrate the performance of the proposed algorithm.

• 정보통신설비학회논문지
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• 제10권4호
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• pp.128-134
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• 2011

Note that k-anonymity algorithm has been widely discussed in the area of privacy protection. In this paper, a new search algorithm to achieve k-anonymity for database application is introduced. A lattice is introduced to form a solution space for a k-anonymity problem and then a hybrid search method composed of tabu search and genetic algorithm is proposed. In this algorithm, the tabu search plays the role of mutation in the genetic algorithm. The hybrid method with independent tabu search and genetic algorithm is compared, and the hybrid approach performs the best in average case.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.520-525
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• 2003

The determination of unknown parameters in rotating machinery is a difficult task and optimization techniques represent an alternative technique for parameter identification. The Simulated Annealing(SA) and Genetic Algorithm(GA) are powerful global optimization algorithm. This paper proposes new hybrid algorithm which combined GA with SA and local search algorithm for the purpose of parameter identification. Numerical examples are also presented to verify the efficiency of proposed algorithm. And, this paper presents the general methodology based on hybrid algorithm to identify unknown bearing parameters of flexible rotors using measured unbalance responses. Numerical examples are used to ilustrate the methodology used, which is then validated experimentally.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.225-228
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• 2004

This paper presents a hybrid decimal division algorithm to improve division speed. In a binary number system, non-restoring algorithm has a smaller number of operations than restoring algorithm. In decimal number system, however, the number of operations differs with respect to quotient values. Since one digit ranges 0 to 9 in decimal, the proposed hybrid algorithm employ either non-restoring or restoring algorithm on each digit to reduce iterative operations. The selection of the algorithm is based on the remainder values. The proposed algorithm improves computation speed substantially over conventional algorithms by decreasing the number of operations.

• 한국정보시스템학회지:정보시스템연구
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• 제17권3호
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• pp.39-58
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• 2008

Mixed Chinese Postman Problem (MCPP) is a practical generalization of the classical Chinese Postman Problem (CPP) and it could be applied in many real world. Although MCPP is useful in terms of reality, MCPP has been proved to be a NP-complete problem. To find optimal solutions efficiently in MCPP, we can reduce searching space to be small effective searching space containing optimal solutions. We propose GENIIS methodology, which is a kind of hybrid algorithm combines the approximate algorithms and genetic algorithm. To get good solutions in the effective searching space, GENIIS uses approximate algorithm and genetic algorithm. This paper validates the usefulness of the proposed approach in a simulation. The results of our paper could be utilized to increase the efficiencies of network and transportation in business.

• 인터넷정보학회논문지
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• 제22권1호
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• pp.1-11
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• 2021

Estimation of distribution algorithm (EDA) is a popular stochastic metaheuristic algorithm. EDA has been widely utilized in various optimization problems. However, it has been shown that the diversity of the population gradually decreases during the iterations, which makes EDA easily lead to premature convergence. This article introduces a hybrid estimation of distribution algorithm (EDA) with differential evolution (DE) based on self-adaptive strategy, namely HEDADE-SA. Firstly, an alternative probability model is used in sampling to improve population diversity. Secondly, the proposed algorithm is combined with DE, and a self-adaptive strategy is adopted to improve the convergence speed of the algorithm. Finally, twenty-five benchmark problems are conducted to verify the performance of HEDADE-SA. Experimental results indicate that HEDADE-SA is a feasible and effective algorithm.