• Title/Summary/Keyword: Mean-variance optimization

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Efficient Robust Design Optimization Using Statistical Moments Based on Multiplicative Decomposition Method (곱분해 기법 기반의 통계 모멘트를 이용한 효율적인 강건 최적설계)

  • Cho, Su-Gil;Lee, Min-Uk;Lee, Tae-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.36 no.10
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    • pp.1109-1114
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    • 2012
  • The performance of a system can be affected by various variables such as manufacturing tolerances, uncertainties of material properties, and environmental factors acting on the system. Robust design optimization has attracted much attention in the design of products because it can find the best design solution that minimizes the variance of the response while considering the distribution of the variables. However, the computational cost and accuracy of optimization have thus far been a challenging problem. In this study, robust design optimization using the multiplicative decomposition method is proposed in order to solve these problems. Because the proposed method calculates the mean and variance of the system directly from the kriging metamodel using the multiplicative decomposition method, it can be used to search for a robust optimum design accurately and efficiently. Several mathematical and engineering examples are used to demonstrate the feasibility of the proposed method.

A Study on the Optimization of Linear Equalizer for Underwater Acoustic Communication (수중음향통신을 위한 선형등화기의 최적화에 관한 연구)

  • Lee, Tae-Jin;Kim, Ki-Man
    • Journal of Navigation and Port Research
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    • v.36 no.8
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    • pp.637-641
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    • 2012
  • In this paper, the method that reduce a computation time by optimizing computation process is proposed to realize low-power underwater acoustic communication system. At first, dependency of decision delay on tap length of linear equalizer was investigated. Variance is calculated based on this result, and the optimal decision delay bound is estimated. In addition to decide optimal tap length with decision delay, we extracted the MSE(Mean Square Error) graph. From the graph, we obtained variance value of the MSE-decision delay, and estimated the optimum decision delay range from the variance value. Also, using the extracted optimal parameters, we performed a simulation. According to the result, the simulation employing optimal tap length, which is only 40% of maximum tap length, showed a satisfactory performance comparable to simulation employing maximum tap length. We verified that the proposed method has 33% lower tap length than maximal tap length via sea trial.

Optimum Progressive-Stress Accelerated Life Test (증가하는 스트레스에서의 최적가속수명시험)

  • Yun, Won-Young;Jung, Sung-Gi
    • Journal of Korean Institute of Industrial Engineers
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    • v.19 no.2
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    • pp.15-21
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    • 1993
  • This paper considers the optimal design of accelerated life test in which the stress is linearly increased. It discusses the special case when the life distribution under constant stress follows an exponential distribution and the accelerated equation satisfies the inverse power law. It is assumed that cumulative damage is linear, that is, the remaining life of test units depends only on the current cumulative fraction failed and current stress(cumulative exposure model). The optimization criterion is the asymptotic variance of the maximum likelihood estimator of the log mean life at a design stress. The optimal increasing rate is obtained to minimize the asymptotic variance. Table of sensitivity analysis is given for the prior estimators of model parameters.

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Modelling Voltage Variation at DC Railway Traction Substation using Recursive Least Square Estimation (순환최소자승법을 이용한 직류도시철도 변전소의 가선전압변동 모델링)

  • Bae, Chang-Han
    • The Transactions of the Korean Institute of Power Electronics
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    • v.20 no.6
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    • pp.534-539
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    • 2015
  • The DC overhead line voltage of an electric railway substation swings depending on the accelerating and regenerative-braking energy of trains, and it deteriorates the energy quality of the electric facility in the DC railway substation and restricts the powering and braking performance of subway trains. Recently, an energy storage system or a regenerative inverter has been introduced into railway traction substations to diminish both the variance of the overhead line voltage and the peak power consumption. In this study, the variance of the overhead line voltage in a DC railway substation is modelled by RC parallel circuits in each feeder, and the RC parameters are estimated using the recursive least mean square (RLMS) scheme. The forgetting factor values for the RLMS are selected using simulated annealing optimization, and the modelling scheme of the overhead line voltage variation is evaluated through raw data measured in a downtown railway substation.

Robust Parameter Design Based on Back Propagation Neural Network (인공신경망을 이용한 로버스트설계에 관한 연구)

  • Arungpadang, Tritiya R.;Kim, Young Jin
    • Korean Management Science Review
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    • v.29 no.3
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    • pp.81-89
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    • 2012
  • Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

Sector Investment Strategy with the Black-Litterman Model (블랙리터만 모형을 이용한 섹터지수 투자 전략)

  • Song, Jung-Min;Lee, Young-Ho;Park, Gi-Gyoung
    • Korean Management Science Review
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    • v.29 no.1
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    • pp.57-71
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    • 2012
  • In this paper, we deal with a sector investment strategy by implementing the black-litterman model that incorporates expert evaluation and sector rotation momentum. Expert evaluation analyzes the relative performance of the industry sector compared with the market, while sector rotation momentum reflects the price impact of significant sector anomaly. In addition, we consider the portfolio impact of sector cardinality and weight constraints within the context of mean-variance portfolio optimization. Finally, we demonstrate the empirical viability of the proposed sector investment strategy with KOSPI 200 data.

A Structural Design of Microgyroscope Using Kriging Approximation Model (크리깅 근사모델을 이용한 마이크로 자이로스코프의 구조설계)

  • Kim, Jong-Kyu;Lee, Kwon-Hee
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.7 no.4
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    • pp.149-154
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    • 2008
  • The concept of robust design was introduced by Dr. G. Taguchi in the late 1940s, and his technique has become commonly known as the Taguchi method or the robust design. In this research, a robust design procedure for microgyroscope is suggested based on the kriging and optimization approaches. The kriging interpolation method is introduced to obtain the surrogate approximation model of true function. Robustness is calculated by the kriging model to reduce real function calculations. For this, objective function is represented by the probability of success, thus facilitating robust optimization. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method.

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Linear programming models using a Dantzig type risk for portfolio optimization (Dantzig 위험을 사용한 포트폴리오 최적화 선형계획법 모형)

  • Ahn, Dayoung;Park, Seyoung
    • The Korean Journal of Applied Statistics
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    • v.35 no.2
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    • pp.229-250
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    • 2022
  • Since the publication of Markowitz's (1952) mean-variance portfolio model, research on portfolio optimization has been conducted in many fields. The existing mean-variance portfolio model forms a nonlinear convex problem. Applying Dantzig's linear programming method, it was converted to a linear form, which can effectively reduce the algorithm computation time. In this paper, we proposed a Dantzig perturbation portfolio model that can reduce management costs and transaction costs by constructing a portfolio with stable and small (sparse) assets. The average return and risk were adjusted according to the purpose by applying a perturbation method in which a certain part is invested in the existing benchmark and the rest is invested in the assets proposed as a portfolio optimization model. For a covariance estimation, we proposed a Gaussian kernel weight covariance that considers time-dependent weights by reflecting time-series data characteristics. The performance of the proposed model was evaluated by comparing it with the benchmark portfolio with 5 real data sets. Empirical results show that the proposed portfolios provide higher expected returns or lower risks than the benchmark. Further, sparse and stable asset selection was obtained in the proposed portfolios.

Tolerance Optimization of Design Variables in Lower Arm by Using Response Surface Model and Process Capability Index (반응표면모델과 공정능력지수를 적용한 로워암 설계변수의 공차최적화)

  • Lee, Kwang Ki;Ro, Yun Cheol;Han, Seung Ho
    • Korean Journal of Computational Design and Engineering
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    • v.18 no.5
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    • pp.359-366
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    • 2013
  • In the lower arm design process, a tolerance optimization of the variance of design variables should be preceded before manufacturing process, since it is very cost-effective compared to a strict management of tolerance of products. In this study, a design of experiment (DOE) based on response surface model (RSM) was carried out to find optimized design variables of the lower arm, which can meet a given requirement of probability constraint for the process capability index (Cpk) of the weight and maximum stress. Then, the design space was explored by using the central composite design method, in which the 2nd order Taylor expansion was applied to predict a standard deviation of the responses. The optimal solutions satisfying the probability constraint of the Cpk were found by considering both of the mean value and the standard deviation of the design variables.

Finding optimal portfolio based on genetic algorithm with generalized Pareto distribution (GPD 기반의 유전자 알고리즘을 이용한 포트폴리오 최적화)

  • Kim, Hyundon;Kim, Hyun Tae
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1479-1494
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    • 2015
  • Since the Markowitz's mean-variance framework for portfolio analysis, the topic of portfolio optimization has been an important topic in finance. Traditional approaches focus on maximizing the expected return of the portfolio while minimizing its variance, assuming that risky asset returns are normally distributed. The normality assumption however has widely been criticized as actual stock price distributions exhibit much heavier tails as well as asymmetry. To this extent, in this paper we employ the genetic algorithm to find the optimal portfolio under the Value-at-Risk (VaR) constraint, where the tail of risky assets are modeled with the generalized Pareto distribution (GPD), the standard distribution for exceedances in extreme value theory. An empirical study using Korean stock prices shows that the performance of the proposed method is efficient and better than alternative methods.