• 대한전자공학회논문지TC
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• 제46권1호
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• pp.102-106
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• 2009

본 논문은 EER(Envelope Elimination and Restoration)에 적용된 class F 전력 증폭기의 드레인 전압의 변화에 따른 출력 정합회로의 최적화에 대하여 연구하였다. EER 구조에 적용된 class F PA의 PAE(Power Added Efficiency)를 개선하기 위해 고조파 조정 회로에 Varactor 다이오드를 사용하였다. 포락선의 변화에 따라 2차 고조파는 단락 시키고 3차 고조파는 개방 시키도록 설계되었으며 본 논문에서 제안된 고조파 조정 회로를 통해 드레인 전압이 25 V에서 30 V까지 변화할 때 수 %의 PAE 개선 효과를 얻을 수 있었다.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3595-3603
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• 2022

Nuclear safety-class DCS is used for nuclear reactor protection function, which is one of the key facilities to ensure nuclear power plant safety, the maintenance for DCS to keep system in a high reliability is significant. In this paper, Nuclear safety-class DCS system developed by the Nuclear Power Institute of China is investigated, the model of reliability estimation considering nuclear power plant emergency trip control process is carried out using Markov transfer process. According to the System-Subgroup-Module hierarchical iteration calculation, the evolution curve of failure probability is established, and the preventive maintenance optimization strategy is constructed combining reliability numerical calculation and periodic overhaul interval of nuclear power plant, which could provide a quantitative basis for the maintenance decision of DCS system.

• 대한수학회논문집
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• 제33권2호
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• pp.671-676
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• 2018

In this paper we prove that the gradient ideal of a Morse polynomial is radical. This gives a generic class of polynomials whose gradient ideals are radical. As a consequence we reclaim a previous result that the unconstrained polynomial optimization problem for Morse polynomials has a finite convergence.

• 대한수학회보
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• 제49권2호
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• pp.249-262
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• 2012

In this paper, under some suitable conditions and in virtue of a selection which depends on a vector-valued function and a feasible set map, the sensitivity analysis of a class of implicit multifunctions is investigated. Moreover, by using the results established, the solution sets of parametric vector optimization problems are studied.

• 대한수학회지
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• 제47권1호
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• pp.63-70
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• 2010

In this paper, we develop a nonmonotone spectral memory gradient method for unconstrained optimization, where the spectral stepsize and a class of memory gradient direction are combined efficiently. The global convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.

• 한국경영과학회지
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• 제18권2호
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• pp.83-103
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• 1993

This paper addresses an optimization problem faced by a multi-server and multi-class customer system in manufacturing facilities and service industries. This paper presents a model of an integrated problem of server allocation and customer type partitioning. We approximate the problem through two types of models to make it tractable. As soution approach, the simulated annealing heuristic is constructed based on the general simulated annealing method. Computational results are presented.

• Structural Engineering and Mechanics
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• 제47권4호
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• pp.495-511
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• 2013

In this paper, a new efficient optimization algorithm called Teaching-Learning-Based Optimization (TLBO) is used for the least weight design of trusses with continuous design variables. The TLBO algorithm is based on the effect of the influence of a teacher on the output of learners in a class. Several truss structures are analyzed to show the efficiency of the TLBO algorithm and the results are compared with those reported in the literature. It is concluded that the TLBO algorithm presented in this study can be effectively used in the weight minimization of truss structures.

• Journal of Mechanical Science and Technology
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• 제19권5호
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• pp.1080-1086
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• 2005

The preliminary design optimization of multi-stage spur gear reduction units has been a subject of considerable interest, since many high-performance power transmission applications (e.g., automotive and aerospace) require high-performance gear reduction units. There are multiple objectives in the optimal design of multi-stage spur gear reduction unit, such as minimizing the volume and maximizing the surface fatigue life. It is reasonable to formulate the design of spur gear reduction unit as a multi-objective optimization problem, and find an appropriate approach to solve it. In this paper an interactive physical programming approach is developed to place physical programming into an interactive framework in a natural way. Class functions, which are used to represent the designer's preferences on design objectives, are fixed during the interactive physical programming procedure. After a Pareto solution is generated, a preference offset is added into the class function of each objective based on whether the designer would like to improve this objective or sacrifice the objective so as to improve other objectives. The preference offsets are adjusted during the interactive physical programming procedure, and an optimal solution that satisfies the designer's preferences is supposed to be obtained by the end of the procedure. An optimization problem of three-stage spur gear reduction unit is given to illustrate the effectiveness of the proposed approach.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권3호
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• pp.83-90
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• 2020

프로토타입 선택은 훈련 데이터로부터 클래스 영역을 대표하는 최소 데이터를 선택하여 낮은 학습 시간 및 저장 공간을 보장하는 장점을 제공한다. 본 논문은 모든 분류 알고리즘에 적용할 수 있는 초월 사각형을 이용한 새로운 훈련 데이터의 생성 방법을 설계한다. 초월 사각형 영역은 서로 다른 클래스 데이터를 포함하지 않으며 클래스 공간을 분할한다. 선택된 초월 사각형 내 데이터의 중간값은 프로토타입이 되어 새로운 훈련 데이터를 구성하고, 초월 사각형의 크기는 클래스 영역의 데이터 분포를 반영하여 조절된다. 전체 훈련 데이터를 대표하는 최소의 프로토타입 집합 선택을 위해 집합 덮개 최적화 알고리즘을 설계했다. 제안하는 방법에서는 탐욕 알고리즘과 곱셈 연산을 포함하지 않은 거리 계산식을 이용하여 집합 덮개 최적화 알고리즘의 다항 시간을 요구하는 시간 복잡도 문제를 해결한다. 실험에서는 분류 성능의 비교를 위해 최근접 이웃 규칙과 의사 결정 트리 알고리즘을 이용하며 제안하는 방법이 초월 구를 이용한 프로토타입 선택 방법보다 우수하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.391-411
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• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.