• Title/Summary/Keyword: Class Optimization

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Approximate Optimization with Discrete Variables of Fire Resistance Design of A60 Class Bulkhead Penetration Piece Based on Multi-island Genetic Algorithm (다중 섬 유전자 알고리즘 기반 A60 급 격벽 관통 관의 방화설계에 대한 이산변수 근사최적화)

  • Park, Woo-Chang;Song, Chang Yong
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.20 no.6
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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.

Pareto-Based Multi-Objective Optimization for Two-Block Class-Based Storage Warehouse Design

  • Sooksaksun, Natanaree
    • Industrial Engineering and Management Systems
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    • v.11 no.4
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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.

Triangular units based method for simultaneous optimizations of planar trusses

  • Mortazavi, Ali;Togan, Vedat
    • Advances in Computational Design
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    • v.2 no.3
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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.

Nearest Neighbor Based Prototype Classification Preserving Class Regions

  • Hwang, Doosung;Kim, Daewon
    • Journal of Information Processing Systems
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    • v.13 no.5
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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.

Structure Design Optimization of Small Class Forklift for Idle Vibration Reduction (소형 지게차의 Idle 진동 저감을 위한 차체 구조 최적 설계)

  • Lee, Wontae;Kim, Younghyun
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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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.

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AST Creating and Crosscutting Concern Weaving Mechanism for Class Optimization in .NET Framework (닷넷 프레임워크에서 클래스 최적화를 위한 추상구조트리 생성 및 크로스커팅 위빙 메커니즘)

  • Lee, Seung-Hyung;Park, Je-Yeon;Song, Young-Jae
    • The Journal of the Korea Contents Association
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    • v.10 no.2
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    • pp.89-98
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    • 2010
  • The enterprise system is becoming more complex and larger. With the changes of the times, the system is developing to object-oriented programming method(OOP). However, the same code inserts to the core class repetitiously in the OOP, that causes a decrease in productivity and a trouble of application of another requirement. To solve this weak point, we propose a weaving mechanism what applies to metadata and crosscutting concern. For a class optimization and an integration between different languages, we take the following way. This paper uses three ways, those are, metadata generation using reflection, transformation to Abstract Syntax Tree, and mapping through crosscutting information specified XML. Through the proposed theory, class optimization can be accomplished by solving a functional decentralization and a confusion of codes.

Consideration about Radiological Technology Student's Frequent Workers Exposure Dose Rate (방사선과 재학생의 수시출입자 방사선 피폭선량에 대한 고찰)

  • Park, Hoon-Hee
    • Journal of radiological science and technology
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    • v.41 no.6
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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.

A hybrid method to compose an optimal gene set for multi-class classification using mRMR and modified particle swarm optimization (mRMR과 수정된 입자군집화 방법을 이용한 다범주 분류를 위한 최적유전자집단 구성)

  • Lee, Sunho
    • The Korean Journal of Applied Statistics
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    • v.33 no.6
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    • pp.683-696
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    • 2020
  • The aim of this research is to find an optimal gene set that provides highly accurate multi-class classification with a minimum number of genes. A two-stage procedure is proposed: Based on minimum redundancy and maximum relevance (mRMR) framework, several statistics to rank differential expression genes and K-means clustering to reduce redundancy between genes are used for data filtering procedure. And a particle swarm optimization is modified to select a small subset of informative genes. Two well known multi-class microarray data sets, ALL and SRBCT, are analyzed to indicate the effectiveness of this hybrid method.

Optimization of Harmonic Tuning Circuit vary as Drain Voltage of Class F Power Amplifier (Class F 전력 증폭기의 드레인 전압 변화에 따른 고조파 조정 회로의 최적화)

  • Lee, Chong-Min;Seo, Chul-Hun
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.46 no.1
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    • pp.102-106
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    • 2009
  • This paper presents the design and optimization of output matching network according to envelope for class F power amplifier(PA) which is to apply to envelope elimination and restoration(EER) transmitter. In this paper, to increase the PAE of class F power amplifier which applies to EER transmitter, the varactor diode has been used on output matching network. As envelope changes, it optimizes constitution of harmonic trap that is short circuit in 2nd-harmonic and is open circuit in 3rd-harmonic. When drain voltage changes from 25 V to 30 V, some percentage is improved in the PAE.put the abstract of paper here.

Surrogate Models and Genetic Algorithm Application to Approximate Optimization of Discrete Design for A60 Class Deck Penetration Piece (A60 급 갑판 관통 관의 이산설계 근사최적화를 위한 대리모델과 유전자 알고리즘 응용)

  • Park, Woo Chang;Song, Chang Yong
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.2
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    • pp.377-386
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    • 2021
  • The A60 class deck penetration piece is a fire-resistant system installed on a horizontal compartment to prevent flame spreading and protect lives in fire accidents in ships and offshore plants. This study deals with approximate optimization using discrete variables for the fire resistance design of an A60 class deck penetration piece using different surrogate models and a genetic algorithm. Transient heat transfer analysis was performed to evaluate the fire resistance design of the A60 class deck penetration piece. For the approximate optimization of the piece, the length, diameter, material type, and insulation density were applied to discrete design variables, and temperature, productivity, and cost constraints were considered. The approximate optimum design problem based on the surrogate models was formulated such that the discrete design variables were determined by minimizing the weight of the piece subjected to the constraints. The surrogate models used in the approximate optimization were the response surface model, Kriging model, and radial basis function-based neural network. The approximate optimization results were compared with the actual analysis results in terms of approximate accuracy. The radial basis function-based neural network showed the most accurate optimum design results for the fire resistance design of the A60 class deck penetration piece.