• Title/Summary/Keyword: binary optimization

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An Application of Surrogate and Resampling for the Optimization of Success Probability from Binary-Response Type Simulation (이항 반응 시뮬레이션의 성공확률 최적화를 위한 대체모델 및 리샘플링을 이용한 유전 알고리즘 응용)

  • Lee, Donghoon;Hwang, Kunchul;Lee, Sangil;Yun, Won-young
    • Journal of the Korea Institute of Military Science and Technology
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    • v.25 no.4
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    • pp.412-424
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    • 2022
  • Since traditional derivative-based optimization for noisy simulation shows bad performance, evolutionary algorithms are considered as substitutes. Especially in case when outputs are binary, more simulation trials are needed to get near-optimal solution since the outputs are discrete and have high and heterogeneous variance. In this paper, we propose a genetic algorithm called SARAGA which adopts dynamic resampling and fitness approximation using surrogate. SARAGA reduces unnecessary numbers of expensive simulations to estimate success probabilities estimated from binary simulation outputs. SARAGA allocates number of samples to each solution dynamically and sometimes approximates the fitness without additional expensive experiments. Experimental results show that this novel approach is effective and proper hyper parameter choice of surrogate and resampling can improve the performance of algorithm.

The Prediction of Lower Flash Points by Optimization Method

  • Ha, Dong-Myeong;Lee, Sung-Jin
    • International Journal of Safety
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    • v.8 no.2
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    • pp.15-19
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    • 2009
  • The flash point is the most widely used flammability property for the evaluation of the flammability hazard of combustible liquid mixtures. In this paper, the reported flash points for the the binary systems, ethylbenzene+n-butanol and ethylbenzene+n-hexanol were correlated by the optimization method. The optimization method based on the van Laar and Wilson equations were compared with the Raoult's law. The calculated values based on the optimization method were found to be better than those based on the Raoult's law.

Flash Points of the Binary Solutions Using Cleveland Open Cup Tester (클리브랜드 개방식 장치를 이용한 2성분계의 인화점)

  • Ha, Dong-Myeong;Lee, Sung-Jin
    • Fire Science and Engineering
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    • v.25 no.1
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    • pp.57-62
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    • 2011
  • The flash point is one of the most significant combustion properties of flammable liquids in industrial processes when evaluation process safety, In this paper, Cleveland open cup tester is used to measure the flash points for the two binary systems (n-propanol + formic acid and acetic acid + propionic acid). The measured flash points were compared with the values calculated by the Raoult's law and the optimization method using van Laar and Wilson equations. The calculated values based on the optimization method were found to be better than those based on the Raoult's law.

A New Distance Measure for a Variable-Sized Acoustic Model Based on MDL Technique

  • Cho, Hoon-Young;Kim, Sang-Hun
    • ETRI Journal
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    • v.32 no.5
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    • pp.795-800
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    • 2010
  • Embedding a large vocabulary speech recognition system in mobile devices requires a reduced acoustic model obtained by eliminating redundant model parameters. In conventional optimization methods based on the minimum description length (MDL) criterion, a binary Gaussian tree is built at each state of a hidden Markov model by iteratively finding and merging similar mixture components. An optimal subset of the tree nodes is then selected to generate a downsized acoustic model. To obtain a better binary Gaussian tree by improving the process of finding the most similar Gaussian components, this paper proposes a new distance measure that exploits the difference in likelihood values for cases before and after two components are combined. The mixture weight of Gaussian components is also introduced in the component merging step. Experimental results show that the proposed method outperforms MDL-based optimization using either a Kullback-Leibler (KL) divergence or weighted KL divergence measure. The proposed method could also reduce the acoustic model size by 50% with less than a 1.5% increase in error rate compared to a baseline system.

Global Optimization of Clusters in Gene Expression Data of DNA Microarrays by Deterministic Annealing

  • Lee, Kwon Moo;Chung, Tae Su;Kim, Ju Han
    • Genomics & Informatics
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    • v.1 no.1
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    • pp.20-24
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    • 2003
  • The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non­zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.

The Measurement and Prediction of Minimum Flash Point Behaviour for Flammable Binarry Solution Using Pensky-Martens Closed Cup Tester

  • Ha, Dong-Myeong;Choi, Yong-Chan;Lee, Sung-Jin
    • International Journal of Safety
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    • v.9 no.2
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    • pp.6-10
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    • 2010
  • The flash point of liquid solution is one of the most important flammability properties that used in hazard and risk assessments. Minimum flash point behaviour (MFPB) is showed when the flash point of a liquid mixture is below the flash points of the individual components. In this paper, the lower flash points for the flammable binary system, n-decane+n-octanol, were measured by Pensky-Martens closed cup tester. This binary mixture exhibited MFPB. The measured flash points were compared with the values calculated by the Raoult's law and the optimization method using van Laar and UNIQUAC equations. The optimization method were found to be better than those based on the Raoult's law, and successfully estimated MFPB. The opimization method based on the van Laar equation described the experimentally-derived data more effectively than was the case when the prediction model was based upon the UNIQUAC.

The Measurement and Calculation of the Lower Flash Points using of Binary Systems Using Cleveland Open Cup Tester (클리브랜드 개방식 장치를 이용한 이성분계 하부인화점 측정 및 계산)

  • Lee, Sung-Jin;Ha, Dong-Myeong
    • Journal of the Korean Society of Safety
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    • v.23 no.5
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    • pp.67-72
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    • 2008
  • The lower flash points for the flammable binary systems, 2-propanol+formic acid and 2-propanol+n-butyric acid, were measured by Cleveland open cup tester. The optimization method using van Laar equation and the Raoult's law were used to estimate the lower flash points and were compared with experimentally-derived data. The calculated values based on the optimization method were found to be better than those based on the Raoult's law.

Optimal EEG Channel Selection by Genetic Algorithm and Binary PSO based on a Support Vector Machine (Support Vector Machine 기반 Genetic Algorithm과 Binary PSO를 이용한 최적의 EEG 채널 선택 기법)

  • Kim, Jun Yeup;Park, Seung-Min;Ko, Kwang-Eun;Sim, Kwee-Bo
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.6
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    • pp.527-533
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    • 2013
  • BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.