• 한국기계가공학회지
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• 제20권6호
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• pp.33-43
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• 2021

A60 class bulkhead penetration piece is a fire resistance system installed on a bulkhead compartment to protect lives and to prevent flame diffusion in a fire accident on a ship and offshore plant. This study focuses on the approximate optimization of the fire resistance design of the A60 class bulkhead penetration piece using a multi-island genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class bulkhead penetration piece. For approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were considered discrete design variables; moreover, temperature, cost, and productivity were considered constraint functions. The approximate optimum design problem based on the meta-model was formulated by determining the discrete design variables by minimizing the weight of the A60 class bulkhead penetration piece subject to the constraint functions. The meta-models used for the approximate optimization were the Kriging model, response surface method, and radial basis function-based neural network. The results from the approximate optimization were compared to the actual results of the analysis to determine approximate accuracy. We conclude that the radial basis function-based neural network among the meta-models used in the approximate optimization generates the most accurate optimum design results for the fire resistance design of the A60 class bulkhead penetration piece.

• Industrial Engineering and Management Systems
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• 제11권4호
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• pp.331-338
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• 2012

This research proposes a Pareto-based multi-objective optimization approach to class-based storage warehouse design, considering a two-block warehouse that operates under the class-based storage policy in a low-level, picker-to-part and narrow aisle warehousing system. A mathematical model is formulated to determine the number of aisles, the length of aisle and the partial length of each pick aisle to allocate to each product class that minimizes the travel distance and maximizes the usable storage space. A solution approach based on multiple objective particle swarm optimization is proposed to find the Pareto front of the problems. Numerical examples are given to show how to apply the proposed algorithm. The results from the examples show that the proposed algorithm can provide design alternatives to conflicting warehouse design decisions.

• Advances in Computational Design
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• 제2권3호
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• pp.195-210
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• 2017

Simultaneous optimization of trusses which concurrently takes into account design variables related to the size, shape and topology of the structure is recognized as highly complex optimization problems. In this class of optimization problems, it is possible to encounter several unstable mechanisms throughout the solution process. However, to obtain a feasible solution, these unstable mechanisms somehow should be rejected from the set of candidate solutions. This study proposes triangular unit based method (TUBM) instead of ground structure method, which is conventionally used in the topology optimization, to decrease the complexity of search space of simultaneous optimization of the planar truss structures. TUBM considers stability of the triangular units for 2 dimensional truss systems. In addition, integrated particle swarm optimizer (iPSO) strengthened with robust technique so called improved fly-back mechanism is employed as the optimizer tool to obtain the solution for these class of problems. The results obtained in this study show the applicability and efficiency of the TUBM combined with iPSO for the simultaneous optimization of planar truss structures.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1345-1357
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• 2017

A prototype selection method chooses a small set of training points from a whole set of class data. As the data size increases, the selected prototypes play a significant role in covering class regions and learning a discriminate rule. This paper discusses the methods for selecting prototypes in a classification framework. We formulate a prototype selection problem into a set covering optimization problem in which the sets are composed with distance metric and predefined classes. The formulation of our problem makes us draw attention only to prototypes per class, not considering the other class points. A training point becomes a prototype by checking the number of neighbors and whether it is preselected. In this setting, we propose a greedy algorithm which chooses the most relevant points for preserving the class dominant regions. The proposed method is simple to implement, does not have parameters to adapt, and achieves better or comparable results on both artificial and real-world problems.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.660-664
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• 2014

A diesel forklift truck under 3-ton class has disadvantages in the vibration transmission path. Because the weight ratio of body structure to powertrain which is source of excitation force is lower th an a mid-class forklift. In addition, the torsional and bending vibration mode frequencies of body structure are within the engine excitation frequency range, then high idle vibration generated by resonance. In this paper vehicle body structure design and optimization technique considering idle vibration reduction are presented. Design sensitivity analysis is applied to search the sensitive of design parameters in body structure. The design parameters such as thickness and pillar cross section were optimized to increase the torsional and bending vibration mode frequencies.

• 한국콘텐츠학회논문지
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• 제10권2호
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• pp.89-98
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• 2010

엔터프라이즈 시스템은 점점 복잡해지고 대형화되고 있다. 시대적 흐름에 따라 재사용에 초점을 맞춘 객체지향 프로그래밍 방법으로 시스템을 개발하고 있다. 하지만, 객체지향 방법에서는 core class에 중복되는 코드가 삽입되기 때문에, 생산성 저하, 새로운 요구사항을 적용하기 어려운 문제가 발생한다. 이 단점을 해결하기 위하여, 메타데이터와 크로스커팅 개념을 적용하는 위빙 메카니즘을 제안한다. 클래스 최적화와 다른 언어사이의 통합을 위하여 다음의 방법을 사용한다. 리플렉션을 이용한 메타데이타 생성, 추상구조트리로의 변환, 그리고 XML로 명세된 크로스커팅 정보를 통한 매핑을 이용한다. 제안하는 방법을 이용하여, 기능의 분산과 코드의 혼란을 해결함으로서 클래스를 최적화 할 수 있다.

• 대한방사선기술학회지:방사선기술과학
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• 제41권6호
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• pp.573-580
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• 2018

The Nuclear Safety Commission amended the Nuclear Safety Act by strengthening the safety management system for the frequent workers to the level of radiation workers. And students entering radiation management zones for testing and practical purposes are subject to frequent workers. It is inevitable that this will incur additional costs. In this paper, the validity of the amendment to the Nuclear Safety Act was to be assessed in terms of radiation protection. Study subjects are from 2014 to 2016, among university students in Seong-nam Korea and comparisons for analyses were made taking into account variables that are differences in annual, practical types, on-class and clinical practice students exposure dose. The analysis showed that exposures between on-class and clinical practice received were less than the annual dose limit of 1 mSv for the public. Then, some alternatives that excluding from frequent workers during on-class practice or mitigating the frequent workers' safety regulation for only on-class frequent workers can be considered. Optimization is how rational is the reduction in exposure dose to the costs required. Therefore, the results are hardly considered for optimization. If the data accumulated, it could be considered that the revision of the act could be evaluated and improved.

• 응용통계연구
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• 제33권6호
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• pp.683-696
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• 2020

표본의 다범주 표현형을 예측하는데 사용되는 최적의 유전자집단이란 적은 수의 유전자로 표현형을 정확히 예측할 수 있는 유전자들의 모임이다. 특이발현유전자를 검색하는 통계량은 이미 여러 가지가 있고, K-평균 군집화를 곁들여 중복성이 적은 특이발현유전자들을 선택 가능하다. 이들을 바탕으로 적은 수로 정확하게 다범주 분류가 가능한 유전자집단을 구성할 수 있도록 수정한 입자최적화 방법을 제안한다. 널리 알려진 ALL 248례와 SRBCT 83례를 이용하여 제안된 방법으로 최적유전자집단을 찾을 수 있음을 보였다.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.1-10
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• 2022

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.

• 해양환경안전학회지
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• 제27권2호
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• pp.377-386
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• 2021

A60 급 갑판 관통 관은 선박과 해양플랜트에서 화재사고가 발생할 경우 화염의 확산을 방지하고 인명을 보호하기 위해 수평구조에 설치되는 방화장치이다. 본 연구에서는 다양한 대리모델과 다중 섬 유전자 알고리즘을 이용하여 A60 급 갑판 관통 관의 방화설계에 대한 이산변수 근사최적화를 수행하였다. A60 급 갑판 관통 관의 방화설계는 과도 열전달해석을 통해 평가하였다. 근사최적화에서 관통관의 길이, 지름, 재질, 그리고 단열재의 밀도는 이산설계변수로 적용하였고, 제한조건은 온도, 생산성 및 가격을 고려하였다. 대리모델 기반의 근사최적설계 문제는 제한조건을 만족하면서 A60 급 갑판 관통 관의 중량을 최소화할 수 있는 이산설계변수를 결정하도록 정식화하였다. 반응표면모델, 크리깅, 그리고 방사기저함수 신경망과 같은 다양한 대리모델이 근사최적화에 사용되었다. 근사최적화의 정확도를 검토하기 위해 최적해의 결과는 실제 계산 결과와 비교하였다. 근사최적화에 사용된 대리모델 중 방사기저함수 신경망 모델이 A60 급 갑판 관통 관의 방화설계에 대해 가장 정확한 최적설계 결과를 나타내었다.