• Title/Summary/Keyword: parameter function

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Varying skill prameter based on error signal and its effect

  • Hidaka, Koichi
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1741-1744
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    • 2005
  • In this paper, we proposed an adaptive skill element based on error signal. We assume that human progress their skills of actions based on errors, then an inverse dynamic of human motion have to changes. Human controller consists from feedback element (FB) and feed forward element (FF) and their elements cooperate to control actions. Under the assumption, we vary the connection of FF and FB by error signal. We propose the index function for change of a skill parameter. From results of the numerical simulations for the varying skill parameter with index function, we consider that the position error given by our vision changes the skill element and we confirm that the position error is the one of the estimate function for the improvement in our skill.

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Robust Design Using Desirability Function in Product-Array

  • Kwon, Yong-Man
    • Journal of Integrative Natural Science
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    • v.11 no.2
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    • pp.76-81
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    • 2018
  • Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Product array approach which is used in the Taguchi parameter design has a number of advantages by considering the noise factor. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. In this paper we propose a substantially simpler optimization procedure for robust design using desirability function without resorting to SN.

Testing of hypotheses and detection of known signals under the integrated power the integrated power criterion (적분검파력 결정 기준에서의 가설 검정과 알려진 신호 검파)

  • 김선용;송익호;장태주;김광순
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.21 no.3
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    • pp.721-730
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    • 1996
  • In this paper, a new test criterion for binary decision problems is proposed. The integrated power flunction over a parameter interval is first itroduced as an extension of the power function. The concept of the most integrated powerful (MIP) test based on the integrated power function is then introduced. The MIP criterion is to masimize the value of the integrated power function in any paricular parameter interval. As an applicationof the MIP test, the known signal detection problem is considered. The test statistic of the MIP detector for known signals is obtained and an approximation to the MIP test statistic is also considered.

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Multiresponse Optimization Through A New Desirability Function Considering Process Parameter Fluctuation (공정변수의 변동을 고려한 만족도 함수를 통한 다중반응표면 최적화)

  • Gwon Jun-Beom;Lee Jong-Seok;Lee Sang-Ho;Jeon Chi-Hyeok;Kim Gwang-Jae
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2004.10a
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    • pp.39-44
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    • 2004
  • A desirability function approach to a multiresponse problem is proposed considering process parameter fluctuation as well as distance-to-target of response and response variance. The variation of process parameters amplifies the variance of responses. It is called POE (propagation of error), which is defined as the standard deviation of the transmitted variability in the response as a function of process parameters. In order to obtain more robust process parameters, this variability should be considered in the optimization problem. The proposed method is illustrated using a rubber product case.

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Kernel Poisson Regression for Longitudinal Data

  • Shim, Joo-Yong;Seok, Kyung-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.4
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    • pp.1353-1360
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    • 2008
  • An estimating procedure is introduced for the nonlinear mixed-effect Poisson regression, for longitudinal study, where data from different subjects are independent whereas data from same subject are correlated. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation function is introduced to choose optimal hyper-parameters in the procedure. Experimental results are then presented, which indicate the performance of the proposed estimating procedure.

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Nonparametric Estimation of Univariate Binary Regression Function

  • Jung, Shin Ae;Kang, Kee-Hoon
    • International Journal of Advanced Culture Technology
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    • v.10 no.1
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    • pp.236-241
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    • 2022
  • We consider methods of estimating a binary regression function using a nonparametric kernel estimation when there is only one covariate. For this, the Nadaraya-Watson estimation method using single and double bandwidths are used. For choosing a proper smoothing amount, the cross-validation and plug-in methods are compared. In the real data analysis for case study, German credit data and heart disease data are used. We examine whether the nonparametric estimation for binary regression function is successful with the smoothing parameter using the above two approaches, and the performance is compared.

Function Approximation Based on a Network with Kernel Functions of Bounds and Locality : an Approach of Non-Parametric Estimation

  • Kil, Rhee-M.
    • ETRI Journal
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    • v.15 no.2
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    • pp.35-51
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    • 1993
  • This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

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Performance Improvement Method of Convolutional Neural Network Using Agile Activation Function (민첩한 활성함수를 이용한 합성곱 신경망의 성능 향상)

  • Kong, Na Young;Ko, Young Min;Ko, Sun Woo
    • KIPS Transactions on Software and Data Engineering
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    • v.9 no.7
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    • pp.213-220
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    • 2020
  • The convolutional neural network is composed of convolutional layers and fully connected layers. The nonlinear activation function is used in each layer of the convolutional layer and the fully connected layer. The activation function being used in a neural network is a function that simulates the method of transmitting information in a neuron that can transmit a signal and not send a signal if the input signal is above a certain criterion when transmitting a signal between neurons. The conventional activation function does not have a relationship with the loss function, so the process of finding the optimal solution is slow. In order to improve this, an agile activation function that generalizes the activation function is proposed. The agile activation function can improve the performance of the deep neural network in a way that selects the optimal agile parameter through the learning process using the primary differential coefficient of the loss function for the agile parameter in the backpropagation process. Through the MNIST classification problem, we have identified that agile activation functions have superior performance over conventional activation functions.

Estimation and Analysis of Wave Spectrum Parameter using HeMOSU-2 Observation Data (HeMOSU-2 관측 자료를 이용한 파랑 스펙트럼 매개변수 추정 및 분석)

  • Lee, Uk-Jae;Ko, Dong-Hui;Kim, Ji-Young;Cho, Hong-Yeon
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.33 no.6
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    • pp.217-225
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    • 2021
  • In this study, wave spectrum data were calculated using the water surface elevation data observed at 5Hz intervals from the HeMOSU-2 meteorological tower installed on the west coast of Korea, and wave parameters were estimated using wave spectrum data. For all significant wave height ranges, the peak enhancement parameter (γopt) of the JONSWAP spectrum and the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated based on the observed spectrum, and the distribution of each parameter was confirmed. As a result of the analysis, the peak enhancement parameter (γopt) of the JONSWAP spectrum was calculated to be 1.27, which is very low compared to the previously proposed 3.3. And in the range of all significant wave heights, the distribution of the peak enhancement parameter (γopt) was shown as a combined distribution of probability mass function (PMF) and probability density function (PDF). In addition, the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated to be [0.245, -1.278], which are lower than the existing [0.300, -1.098], and the result of the linear correlation analysis between the two parameters was β = -3.86α.

Determination of Parameter Value in Constraint of Sparse Spectrum Fitting DOA Estimation Algorithm (희소성 스펙트럼 피팅 도래각 추정 알고리즘의 제한조건에 포함된 상수 결정법)

  • Cho, Yunseung;Paik, Ji-Woong;Lee, Joon-Ho
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.8
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    • pp.917-920
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    • 2016
  • SpSF algorithm is direction-of-arrival estimation algorithm based on sparse representation of incident signlas. Cost function to be optimized for DOA estimation is multi-dimensional nonlinear function, which is hard to handle for optimization. After some manipulation, the problem can be cast into convex optimiztion problem. Convex optimization problem tuns out to be constrained optimization problem, where the parameter in the constraint has to be determined. The solution of the convex optimization problem is dependent on the specific parameter value in the constraint. In this paper, we propose a rule-of-thumb for determining the parameter value in the constraint. Based on the fact that the noise in the array elements is complex Gaussian distributed with zero mean, the average of the Frobenius norm of the matrix in the constraint can be rigorously derived. The parameter in the constrint is set to be two times the average of the Frobenius norm of the matrix in the constraint. It is shown that the SpSF algorithm actually works with the parameter value set by the method proposed in this paper.