• Journal of Advanced Technology Convergence
• /
• v.1 no.2
• /
• pp.75-81
• /
• 2022

This study was attempted to understand the relationship between gender sensitivity and rejection sensitivity of nursing students. From August 26 to September 4, 2022, a survey was conducted on 180 people attending nursing universtity in I Metropolitan city, and then analyzed using the SPSS 24.0 Program. As a result of the study, nursing students' gender sensitivity had a high score of openness to gender identity and openness to gender roles, and overreaction was high among the sub-factors of rejection sensitivity. The gender sensitivity of nursing students was statistically significantly higher when female students, under the age of 23, without religion, experienced gender discrimination, and when there were LGBT people among family, relatives, or friends. In addition, rejection sensitivity was statistically significantly higher only when female students. Finally, among the sub-factors of gender sensitivity, there was a positive correlation between introspection and rejection sensitivity. In order to improve the introspection of gender sensitivity for nursing students, it is necessary to include education or programs on rejection sensitivity.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.29 no.12 s.243
• /
• pp.1629-1637
• /
• 2005

Optimum sensitivity analysis (OSA) is the process to find the sensitivity of optimum solution with respect to the parameter in the optimization problem. The prevalent OSA methods calculate the optimum sensitivity as a post-processing. In this research, a simple technique is proposed to obtain optimum sensitivity as a result of the original optimization problem, provided that the optimum sensitivity of objective function is required. The parameters are considered as additional design variables in the original optimization problem. And then, it is endowed with equality constraints to penalize the additional variables. When the optimization problem is solved, the optimum sensitivity of objective function is simultaneously obtained as Lagrange multiplier. Several mathematical and engineering examples are solved to show the applicability and efficiency of the method compared to other OSA ones.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.10a
• /
• pp.464-469
• /
• 2005

Optimum sensitivity analysis (OSA) is the process to find the sensitivity of optimum solution with respect to the parameter in the optimization problem. The prevalent OSA methods calculate the optimum sensitivity as a post-processing. In this research, a simple technique is proposed to obtain optimum sensitivity as a result of the original optimization problem, provided that the optimum sensitivity of objective function is required. The parameters are considered as additional design variables in the original optimization problem. And then, it is endowed with equality constraints to penalize the additional variables. When the optimization problem is solved, the optimum sensitivity of objective function is simultaneously obtained as Lagrange multiplier. Several mathematical and engineering examples are solved to show the applicability and efficiency of the method compared to other OSA ones.

• Journal of Korea Water Resources Association
• /
• v.31 no.3
• /
• pp.243-252
• /
• 1998

In this study, the new dimensionless values were defined and proposed to determine the parameters of urban runoff models based on the relative sensitivity analysis. Also, the sensitivity characteristics of each parameter were investigate. In order to analyze the parameter sensitivities of each model, total runoff ratio, peak runoff ratio, runoff sensitivity ratio, sensitivity ratio of total runoff, and sensitivity ratio of peak runoff were defined. $$Total\;runoff\;ratio(Q_{TR})\;=\;\frac{Total\;runoff\;of\;corresponding\;step}{Maximum\;total\;runoff}$$$$Peak\;runoff\;ratio(Q_{PR})\;=\;\frac{Peak\;runoff\;of\;corresponding\;step}{Maximum\;peak\;runoff}$$$$Runoff\;sensitivity\;ratio(Q_{SR})\;=\;\frac{Q_{TR}}{Q_{PR}}$$ And for estimation of sensitivity ratios based on the scale of basin area, rainfall distributions and rainfall durations in ILLUDAS & SWMM, the reasonable ranges of parameters were proposed.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10b
• /
• pp.1377-1382
• /
• 1990

In Multi-Layer Perceptron (MLP) which realizes continuous mappings, the output errors is directly affected by the weight errors which may be caused by the limited precision of digital or analog hardware in implementations. So, it is important to study the sensitivity due to the perturbation of connection weights between neurons. In this paper, we derive a sensitivity function to the statistical weight perturbations in MLP with differentiable activation functions. This sensitivity function can be regarded as an ensemble average of deterministic sensitivity measures due to the perturbations of weights. Hence, this sensitivity function can be used as the criteria for selecting weights with the minimum sensitivity among possible sets of connection weights in MLP. For the verification of the validity of the proposed sensitivity function, computer simulations have been performed and through the simulations we find good agreement between the theoretical and simulation results.

• Transactions on Electrical and Electronic Materials
• /
• v.15 no.4
• /
• pp.182-188
• /
• 2014

When the windowed DFT algorithm is applied in harmonic measurements, the problem of initial-phase sensitivity will be encountered, this has an effect on harmonic amplitude accuracy. In this paper, the origin of initial-phase sensitivity is analyzed and the main factors that influence the level of initial-phase sensitivity are demonstrated. A method of reducing initial-phase sensitivity is proposed to increase the stability of harmonic measurements. We found that initial-phase sensitivity is determined by the side lobe peak level of the window functions when synchronous deviation is fixed. In addition, increasing the length of the time recorded can be used to remove initial-phase sensitivity. The correctness and validity of our conclusions have been confirmed through numerical results and field tests.

• International Journal of Aeronautical and Space Sciences
• /
• v.2 no.1
• /
• pp.20-27
• /
• 2001

Aerodynamic sensitivity analysis is performed for the Navier-Stokes equations coupled with two-equation turbulence models using a discrete adjoint method and a direct differentiation method respectively. Like the mean flow equations, the turbulence model equations are also hand-differentiated to accurately calculate the sensitivity derivatives of flow quantities with respect to design variables in turbulent viscous flows. The sensitivity codes are then compared with the flow solver in terms of solution accuracy, computing time and computer memory requirements. The sensitivity derivatives obtained from the sensitivity codes with different turbulence models are compared with each other. The capability of the present sensitivity codes to treat complex geometry is successfully demonstrated by analyzing the flows over multi-element airfoils on Chimera overlaid grid systems.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.4
• /
• pp.295-303
• /
• 2004

Previously it has been shown that the all pole systems resulting good time responses can be characterized by so called K-polynomial. The polynomial is defined in terms of the principal characteristic ratio $\alpha_1$ and the generalized time constant $\tau$ . In this paper, Part II presents several sensitivity analyses of such systems with respect to $\alpha_1$ and $\tau$ changes. We first deal with the root sensitivity to the perturbation of $\alpha_1$ . By way of determining the unnormalized function sensitivity, both time response sensitivity and frequency response sensitivity are derived. Finally, the root sensitivity relative to $\tau$ change is also analyzed. These results provide some useful insight and background theory when we select of and l to compose a reference model of which denominator is a K-polynomial, which is illustrated by examples.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1992.10a
• /
• pp.19-24
• /
• 1992

Design sensitivity analysis method for the vibration of vehicle structure is developed using adjoint variable method. A variational approach with complex response method is used to derive sensitivity expression. To evaluate sensitivity, FEM analysis of ship deck and vehicle structure are performed using MSC/NASTRAN on the super computer CRAY2S, and sensitivity computation is carried on PC. The accuracy of sensitivity is verified by the results of finite difference method. When compared to structural analysis time on CRAY2S, sensitivity computation is remarkably economical. The sensitivity of vehicle frame can be used to reduce the vibration responses such as displacement and acceleration of vehicle.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.04a
• /
• pp.508-512
• /
• 1997

Design sensitivity analysis of Eigen Problem give systematic design improvement information for noise and vibration of a system. Based on reliable results form commercial FE code(UAI/NASTRAN), three computational procedures for design sensitivity analysis of eigen problem are suggested. Those methods are finite difference,design sensitivity analysis using external module and design sensitivity analysis running with NASTRAN. To verify the suggested methods, a numerical example is given and these results are compared with the results from UAI/NASTRAN eigen sensitivity option. We can conclude that design sensitivity coefficient of eigen proplems can be computed outside of the FE code as easy as inside of the FE code.