• 제목/요약/키워드: Process Parameter

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데이터베이스 시스템 성능 향상을 위한 데이터베이스 관리 시스템 파라미터 튜닝 프로세스 (Database Management System Parameter Tuning Processes for Improving Database System Performance)

  • 최용락;윤병권;정기원
    • 한국전자거래학회지
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    • 제7권1호
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    • pp.107-127
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    • 2002
  • Database system parameter tuning is one of database system tuning that achieve to improve performance of database system with application program tuning and data model tuning. By parameter tuning adjusts value of entry that is staled in data dictionary's parameter file that is included to database system, it is thing which make relevant database system can display performance of most suitable. And, it is that achievement is one o( possible tuning method immediately without occurrence of additional expense or involved hardware for database system performance elevation and ashes composition of software. But, it is actuality that administration about parameter practical use is not achieved, and is using Default Value of parameter that database management system offers just as it is systematically. So, this paper presents parameter tuning process that can :achieve Parameter tuning of database system that is operating present systematically, and parameter tuning process each activity important input urea and tuning achievement product. And explain about effect and result that happen by sort database system performance and parameters that it is affinity systematically, and grasp relationships between parameter, and change parameter of string database system. And not that parameter uses contents that specify by fixing when establish database administration system, is going to emphasize and explain that must utilize changing continuously during database system operation. It changes parameter entry value how in various kinds different operation environment and present if must apply, and will arrange effect that this parameter enoy value alteration gets in performance liking into account point that is actuality that is using parameter that define database administrators when install the database system just as it is continually without alteration.

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평면연삭조건이 가공탄성계수에 미치는 영향 (Effects of the Surface Grinding Conditions on the Machining Elasticity Parameter)

  • 임관혁;김강
    • 한국정밀공학회지
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    • 제15권8호
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    • pp.26-32
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    • 1998
  • The grinding force generated during the grinding process causes an elastic deformation of the workpiece, grinding wheel, and machine system. Thus, the true depth of cut is always smaller than the apparent depth of cut. This is known as machining elasticity phenomenon. The machining elasticity parameter is defined as a ratio between the true depth of cut and the apparent depth of cut. It is an important factor to understand the material removal mechanism of the grinding process. To increase productivity, the value of this machining elasticity parameter must be large. Therefore, it is essential to know the characteristics of this parameter. The objective of this research is to study the effect of the major grinding conditions, such as table speed and depth of cut, on this parameter experimentally. Through this research, it is found that this parameter value is increasing when the table speed is decreasing or the depth of cut is increasing. Also, this parameter value depends on the grinding mode (up grinding, down grinding).

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Genetic association tests when a nuisance parameter is not identifiable under no association

  • Kim, Wonkuk;Kim, Yeong-Hwa
    • Communications for Statistical Applications and Methods
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    • 제24권6호
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    • pp.663-671
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    • 2017
  • Some genetic association tests include an unidentifiable nuisance parameter under the null hypothesis of no association. When the mode of inheritance (MOI) is not specified in a case-control design, the Cochran-Armitage (CA) trend test contains an unidentifiable nuisance parameter. The transmission disequilibrium test (TDT) in a family-based association study that includes the unaffected also contains an unidentifiable nuisance parameter. The hypothesis tests that include an unidentifiable nuisance parameter are typically performed by taking a supremum of the CA tests or TDT over reasonable values of the parameter. The p-values of the supremum test statistics cannot be obtained by a normal or chi-square distribution. A common method is to use a Davies's upper bound of the p-value instead of an exact asymptotic p-value. In this paper, we provide a unified sine-cosine process expression of the CA trend test that does not specify the MOI and the TDT that includes the unaffected. We also present a closed form expression of the exact asymptotic formulas to calculate the p-values of the supremum tests when the score function can be written as a linear form in an unidentifiable parameter. We illustrate how to use the derived formulas using a pharmacogenetics case-control dataset and an attention deficit hyperactivity disorder family-based example.

Parameter Estimation in a Complex Non-Stationary and Nonlinear Diffusion Process

  • So, Beong-Soo
    • Journal of the Korean Statistical Society
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    • 제29권4호
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    • pp.489-499
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    • 2000
  • We propose a new instrumental variable estimator of the complex parameter of a class of univariate complex-valued diffusion processes defined by the possibly non-stationary and/or nonlinear stochastic differential equations. On the basis of the exact finite sample distribution of the pivotal quantity, we construct the exact confidence intervals and the exact tests for the parameter. Monte-Carlo simulation suggests that the new estimator seems to provide a viable alternative to the maximum likelihood estimator (MLE) for nonlinear and/or non-stationary processes.

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신경 회로망 학습을 통한 모델 선택의 자동화 (Automation of Model Selection through Neural Networks Learning)

  • 류재흥
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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    • pp.313-316
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    • 2004
  • Model selection is the process that sets up the regularization parameter in the support vector machine or regularization network by using the external methods such as general cross validation or L-curve criterion. This paper suggests that the regularization parameter can be obtained simultaneously within the learning process of neural networks without resort to separate selection methods. In this paper, extended kernel method is introduced. The relationship between regularization parameter and the bias term in the extended kernel is established. Experimental results show the effectiveness of the new model selection method.

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Test for Parameter Changes in the AR(1) Process

  • Kim, Soo-Hwa;Cho, Sin-Sup;Park, Young J.
    • Journal of the Korean Statistical Society
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    • 제26권3호
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    • pp.417-427
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    • 1997
  • In this paper the parameter change problem in the stationary time series is considered. We propose a cumulative sum (CUSUM) of squares-type test statistic for detection of parameter changes in the AR(1) process. The proposed test statistic is based on the CUSIM of the squared observations and is shown to converge to a standard Brownian bridge. Simulations are performed to evaluate the performance of the proposed statistic and a real example is provided to illustrate the procedure.

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ON THE LARGE AND SMALL INCREMENTS OF GAUSSIAN RANDOM FIELDS

  • Zhengyan Lin;Park, Yong-Kab
    • 대한수학회지
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    • 제38권3호
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    • pp.577-594
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    • 2001
  • In this paper we establish limit theorems on the large and small increments of a two-parameter Gaussian random process on rectangles in the Euclidean plane via estimating upper bounds of large deviation probabilities on suprema of the two-parameter Gaussian random process.

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Super Juction MOSFET의 공정 설계 최적화에 관한 연구 (Optimal Process Design of Super Junction MOSFET)

  • 강이구
    • 한국전기전자재료학회논문지
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    • 제27권8호
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    • pp.501-504
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    • 2014
  • This paper was developed and described core-process to implement low on resistance which was the most important characteristics of SJ (super junction) MOSFET. Firstly, using process-simulation, SJ MOSFET optimal structure was set and developed its process flow chart by repeated simulation. Following process flow, gate level process was performed. And source and drain level process was similar to genral planar MOSFET, so the process was the same as the general planar MOSFET. And then to develop deep trench process which was main process of the whole process, after finishing photo mask process, we developed deep trench process. We expected that developed process was necessary to develop SJ MOSFET for automobile semiconductor.

분산 섭동법 에 의한 CNC보오링 머시인 의 적응제어 (Adaptive Control of CNC Boring Machine by Application of the Variance Perturbation Method)

  • 이종원
    • 대한기계학회논문집
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    • 제8권1호
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    • pp.65-70
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    • 1984
  • A recursive parameter estimation method is applied to spindle deflection model during boring process. The spindle infeed rate is then determined to preserve the diametral tolerance of bore. This estimation method is further extended to adaptive control by application of the variance perturbation method. The results of computer simulation attest that the proposed method renders the optimal cutting conditions, maintaining the diametral accuracy of bore, regardless of parameter fluctuations. The proposed method necessitating only post-process measurements features that initialization of parameter guess values in simple, a priori knowledge on parameter variations is not needed and the accurate estimation of optimal spindle infeed rate is obtained, even if the parameter estimation may be poor.

AN ADAPTIVE SEQUENTIAL PROBABILITY RATIO TEST IN THE AUTOREGRESSIVE PROCESS

  • Choi, Ki-Heon
    • Journal of applied mathematics & informatics
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    • 제11권1_2호
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    • pp.373-378
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    • 2003
  • consider the problem of sequentially hypotheses about a parameter $\theta$ in the presence of the nuisance parameter $\rho$. and we investigate further to computing the error probabilities and expected sample sizes in the frequentist properties of the adaptive S.P.R.T. for $\theta$.