• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1049-1054
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• 1990

We formulate optimal quadratic regulator problems with trajectory sensitivity terms as a optimization problem for a fixed controller structure. Using well-known techniques for parametric LQ problems, we give an algorithm to obtain suboptimal feedback gains by iterative solutions of two Lyapunov equations. A numerical example is given to illustrate the effectiveness of the proposed algorithm.

• Nuclear Engineering and Technology
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• v.30 no.4
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• pp.353-363
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• 1998

The H$_{\infty}$ algorithms of the mixed weight sensitivity is used for the robust design of the reactor power control system. The mixed weight sensitivity method requires the selection of the proper weighting functions for the loop shaping in frequency domain. The complexity of the system equation and the non-convexity of the problem make it very difficult to determine the weighting functions. The genetic algorithm which is improved and hybridized with the simulated annealing is applied to determine the weighting functions. This approach permits an automatic calculation and the resultant system shows good robustness and performance.

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

• Journal of Korean Society of Water and Wastewater
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• v.12 no.2
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• pp.50-58
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• 1998

Genetic Algorithm (GA) consists of selection, reproduction, crossover and mutation processes and many parameters including population size, generation number, the probability of crossover (Pc) and the probability of mutation (Pm). Determining values of the parameters is found critical in the whole optimization process and a sensitivity analysis with them seems mandatory. This paper tries to demonstrate such importance of sensitivity analysis of GA using an example water supply tunnel network of the New York City. For optimization of the network with GA, Pc and Pm vary from 0.5 to 0.9 by an increment of 0.1 and from 0.01 to 0.05 by an increment of 0.01, respectively, while fixing both the population size and the generation number to 100. This sensitivity analysis results in an optimum design of 22.3879 million dollars at the values of 0.8 and 0.01 for Pc and Pm, respectively. In addition, the probability of recombination (Pr) is introduced to check its applicability in the GA optimization of water distribution network. When Pr is 0.05 with the same values of Pc and Pm as above, the optimum design costs 20.9077 million dollars. This is lower than the cost of 22.3879 million dollars for the case of not using Pr by 6.6%. These results indicate that conducting a sensitivity analysis with parameter values and using Pr are useful in the optimization of WDN.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.49-56
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• 2013

In this paper, for constrained optimization problem, one multi-objective optimization algorithm that ensures both performance robustness and constraint feasibility is proposed when uncertainties are involved in design variables. In the proposed algorithm, the gradient index of objective function assisted by design sensitivity with the help of finite element method is applied to evaluate robustness; the reliability calculated by the sensitivity-assisted Monte Carlo simulation method is used to assess the feasibility of constraint function. As a demonstration, the performance and numerical efficiency of the proposed method is investigated through application to the optimal design of TEAM problem 22--a superconducting magnetic energy storage system.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.77-93
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• 2022

In this study, we present an algorithm that analyzes the risk by reflecting regional characteristics for factors affected by direct and indirect damage from heavy-snow. Factors affected by heavy-snow damage by 29 regions are selected as influencing variables, and the concept of sensitivity is derived through the relationship with the amount of damage. A snow damage risk prediction model was developed using a machine learning (XGBoost) algorithm by setting weather conditions (snow cover, humidity, temperature) and sensitivity as independent variables, and setting the risk derived according to changes in the independent variables as dependent variables.

• Journal of Biomedical Engineering Research
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• v.32 no.1
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• pp.7-14
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• 2011

This study suggested an optimized algorithm for detecting the loss of balance(LOB) in the seated position. And the sensitivity analysis was performed in order to identify the role of each design variable in the algorithm. The LOB algorithm consisted of data processing of measured signals, an internal model of the central nervous system and a control error anomaly(CEA) detector. This study optimized design variables of a CEA detector to obtain improved values of the success rate(SR) of detecting the LOB and the margin time(MT) provided for preventing the falling. Nine healthy adult volunteers were involved in the experiments. All the subjects were asked to balance their body in a predescribed seated posture with the rear legs of a four-legged wooden chair. The ground reaction force from the right leg was measured from the force plate while the accelerations of the chair and the head were measured from a couple of piezoelectric accelerometers. The measured data were processed to predict the LOB using a detection algorithm. Variables S2, h2 and hd are related to the detector: S2 represents a data selecting window, h2 a time shift and hd an operating period of the LOB detection algorithm. S2 was varied from 0.1 to 10 sec with an increment of 0.1 sec, and both h2 and hd were varied from 0.01 to 1.0 sec with an increment of 0.01 sec. It was found that the SR and MT were increased by up to 9.7% and 0.497 sec comparing with the previously published case when the values of S2, h2 and hd were set to 4.5, 0.3 and 0.2 sec, respectively. Also the results of sensitivity analysis showed that S2 and h2 had considerable influence on the SR while these variables were not so sensitive to the MT.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.810-819
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• 2013

FCM (Fuzzy C-Means) clustering algorithm, a typical split-based clustering algorithm, has been successfully applied to the various fields. Nonetheless, the FCM clustering algorithm has some problems, such as high sensitivity to noise and local data, the different clustering result from the intuitive grasp, and the setting of initial round and the number of clusters. To address these problems, in this paper, we determine fuzzy numbers which project the FCM clustering result on the axis with the specific attribute. And we propose a model that the fuzzy numbers apply to FDT (Fuzzy Decision Tree). This model improves the two problems of FCM clustering algorithm such as elevated sensitivity to data, and the difference of the clustering result from the intuitional decision. And also, this paper compares the effect of the proposed model and the result of FCM clustering algorithm through the experiment using real traffic and rainfall data. The experimental results indicate that the proposed model provides more reliable results by the sensitivity relief for data. And we can see that it has improved on the concordance of FCM clustering result with the intuitive expectation.

• Imaging Science in Dentistry
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• v.48 no.4
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• pp.245-254
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• 2018

Purpose: To assess the effects of object position in the field of view (FOV) and application of a metal artifact reduction (MAR) algorithm on the diagnostic accuracy of cone-beam computed tomography (CBCT) for the detection of vertical root fractures(VRFs). Materials and Methods: Sixty human single-canal premolars received root canal treatment. VRFs were induced in 30 endodontically treated teeth. The teeth were then divided into 4 groups, with 2 groups receiving metal posts and the remaining 2 only having an empty post space. The roots from different groups were mounted in a phantom made of cow rib bone, and CBCT scans were obtained for the 4 different groups. Three observers evaluated the images independently. Results: The highest frequency of correct diagnoses of VRFs was obtained with the object positioned centrally in the FOV, using the MAR algorithm. Peripheral positioning of the object without the MAR algorithm yielded the highest sensitivity for the first observer (66.7%). For the second and third observers, a central position improved sensitivity, with or without the MAR algorithm. In the presence of metal posts, central positioning of the object in the FOV significantly increased the diagnostic sensitivity and accuracy compared to peripheral positioning. Conclusion: Diagnostic accuracy was higher with central positioning than with peripheral positioning, irrespective of whether the MAR algorithm was applied. However, the effect of the MAR algorithm was more significant with central positioning than with peripheral positioning of the object in the FOV. The clinical experience and expertise of the observers may serve as a confounder in this respect.

• Computational Structural Engineering
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• v.8 no.4
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• pp.99-106
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• 1995

The General conceptual constitution of structural optimization is formulated and the algorithm using the gradient projection method and design sensitivity analysis is discussed. Examples of minimum-weight design for six-story steel plane frame are taken to illustrate the applicability of this algorithm. The advantages of this algorithm such as marginal cost and design sensitivity analysis as well as system analysis are explained.