• Transactions of The Korea Fluid Power Systems Society
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• v.6 no.2
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• pp.22-30
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• 2009

An in-process method of diagnosing the spool wear of hydraulic servovalves was explored. The diagnostic method discussed in this paper is for force-control hydraulic servo systems. The key principle used is that pressure sensitivity of a servovalve drops as the valve spool wears out so that it is possible to determine the spool condition by monitoring pressure sensitivity. A diagnostic algorithm was developed and evaluated through numerical simulation and experiments. Two major steps of diagnosis are the evaluation of null bias of the servovalve and the approximation of pressure sensitivity, both of which could be successfully done during normal operation of a servo system. The difference between a new servovalve and a worn valve could be clearly detected in-process, and the diagnostic test was found to be repeatable.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.141-147
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• 1998

Design sensitivity analysis of a vehicle system is an essential tool for design optimization and trade-off studies. Most optimization algorithms require the derivatives of cost and constraint function with respect to design in order to calculate the next improved design. This paper presents an efficient algorithm application for the design sensitivity analysis, using the direct differentiation method. A mounting area of suspension that welded on chassis frame is analyzed to show the validity and the efficiency of the proposed method. A mounting area of suspension that welded on chassis frame is analyzed to show the validity and the efficiency of the proposed method.

• Journal of Magnetics
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• v.16 no.1
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• pp.77-82
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• 2011

This paper presents a new self-adjoint material sensitivity formulation for optimal designs and inverse problems in the high frequency domain. The proposed method is based on the continuum approach using the augmented Lagrangian method. Using the self-adjoint formulation, there is no need to solve the adjoint system additionally when the goal function is a function of the S-parameter. In addition, the algorithm is more general than most previous approaches because it is independent of specific analysis methods or gridding techniques, thereby enabling the use of commercial EM simulators and various custom solvers. For verification, the method was applied to the several numerical examples of dielectric material reconstruction problems in the high frequency domain, and the results were compared with those calculated using the conventional method.

• Geomechanics and Engineering
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• v.13 no.3
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• pp.447-458
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• 2017

This study proposes a sensitivity analysis method for slope stability based on the least squares support vector machine (LS-SVM) to examine the influencing factors of slope stability. The method uses LS-SVM as an algorithm for machine learning. An appropriate training dataset is established according to the slope characteristics, and a testing dataset is designed orthogonally. Results of the testing data in the experiment design are calculated after training using the LS-SVM model. The sensitivity of the slope stability of each factor is examined via gray correlation analysis. The results are consistent with those of the traditional Bishop analysis and can be used as a reference for optimizing slope design.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.2
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• pp.120-128
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• 1997

In this paper, A design strategy of the Surface Mounting Device for accurate and better performance is studied. Analytical modeling, sensitivity analysis, and optimization are being conducted. The ANSYS software and experimental method are used for the verification of the analytical equations with boundary conditions. Through the sensitivity analysis, the most dominant design parameter can be detected. The optimum design parameters for improving the given performances are selected by using the optimiza- tion algorithm. The design tool based on the design strategy for the analysis, modeling, and optimization will be useful for are-design and better improving of the SMD.

• Journal of KSNVE
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• v.9 no.1
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• pp.163-173
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• 1999

Precise and reasonable modelling is necessary and indispensable to the analysis of dynamic characteristics of mechanical structures. Also. the effective prediction of the change of modal properties due to the variation of design parameters is required especially for the application of finite element method to the structural dynamics problems. To meet those necessity and requirement, three model updating algorithms are proposed for finite element methods. Those algorithms are based on sensitivity analysis of the modal data obtained from experimental modal analysis(EMA) and analytical modal analysis(AMA). The adapted sensitivity analysis methods of the algorithms are 1)eigensensitivity(EGNS) method. 2)frequency response function sensitivity(FRFS) method. 3)sensitivity based element-by-element method (SBEEM), Singular value decomposition(SVD) is used for performing eigenanalysis and parameter estimation in the updating process. Those algorithms are applied to finite element of a plate and the updating capability of each algorithm is compared in terms of accuracy. reliability and stability of the updating process. It is shown that the model updating method using frequency response function is superior to the other methods in view of various updating capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3086-3101
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• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.968-976
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• 2019

This paper presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.3
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• pp.409-422
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• 2016

A parametric study based on an unsteady mathematical model of a pyrotechnically actuated device was performed for design optimization. The model simulates time histories for the chamber pressure, temperature, mass transfer and pin motion. It is validated through a comparison with experimentally measured pressure and pin displacement. Parametric analyses were conducted to observe the detailed effects of the design parameters using a validated performance analysis code. The detailed effects of the design variables on the performance were evaluated using the one-at-a-time (OAT) method, while the scatter plot method was used to evaluate relative sensitivity. Finally, the design optimization was conducted by employing a genetic algorithm (GA). Six major design parameters for the GA were chosen based on the results of the sensitivity analysis. A fitness function was suggested, which included the following targets: minimum explosive mass for the uniform ignition (small deviation), light casing weight, short operational time, allowable pyrotechnic shock force and finally the designated pin kinetic energy. The propellant mass and cross-sectional area were the first and the second most sensitive parameters, which significantly affected the pin's kinetic energy. Even though the peak chamber pressure decreased, the pin kinetic energy maintained its designated value because the widened pin cross-sectional area induced enough force at low pressure.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.20-27
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• 2007

Generally, the TFT-LCD image signal have nonuniform brightness and are composed of largely varying background signal, noise signal and abruptly changing Mura signal within Mura region. In this paper, Mura region enhancing algorithms using the proposed modified-MTF, which describes how human-visual-system's sensitivity varies in frequency domain, is proposed. The validity of the proposed algorithm was demonstrated ideal 1-dimensional signal and also then it was also tested TFT-LCD image. By the experimental results, the proposed algorithm is very effective in TFT-LCD image Mura enhancement.