• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.1
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• pp.42-50
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• 2002

In this paper, we propose an FDI(fault detection and isolation) method using neural network-based multi-fault models to detect and isolate faults in nonlinear systems. When a change in the system occurs, the errors between the system output and the neural network nominal system output cross a threshold, and once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.282-285
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• 2006

This study is to examine bond strength of beam reinforced with GFRP rebar under 4-point bending test by adopting BRITISH STANDARD. The variables were made to have bonding length of 5times$(5d_b)$, 10times$(10d_b)$ and 15times$(15d_b)$ of the nominal diameter of GFRP rebar and were done to analyze the relationship between the bonding strength and the slip. In the result of the test, pull-out failure was dominant in the $5d_b$ and $10d_b$ specimen, both patterns of the pull-out failure and concrete splitting failure appeared in the $10d_b$. On the other hand, the $15d_b$ specimen showed only concrete splitting failure at the end of bonding length. Therefore, it was prove that available bonding length of the GFRP rebar under bending condition on static test is over $15d_b$ then farther research such as fatigue bending test, development of bonding model, FEM parameter study should be performed.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.830-837
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• 2004

Neural networks-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the multilayer neural network-based nominal model output cross a Predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by nultilayer/ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1855-1866
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• 2003

This paper discusses an entire procedure for a robust controller design and its implementation of an AMB (active magnetic bearing) spindle, which is part II of the papers presenting details of system modeling and robust control of an AMB spindle. Since there are various uncertainties in an AMB system and reliability is the most important factor for applications, robust control naturally gains attentions in this field. However, tight evaluations of various uncertainties based on experimental data and appropriate performance weightings for an AMB spindle are still ongoing research topics. In addition, there are few publications on experimental justification of a designed robust controller. In this paper, uncertainties for the AMB spindle are classified and described based on the measurement and identification results of part I, and an appropriate performance weighting scheme for the AMB spindle is developed. Then, a robust control is designed through the mixed ${\mu}$ synthesis based on the validated accurate nominal model of part I, and the robust controller is reduced considering its closed loop performance. The reduced robust controller is implemented and confirmed with measurements of closed-loop responses. The AMB spindle is operated up to 57,600 rpm and performance of the designed controller is compared with a benchmark PID controller through experiments. Experiments show that the robust controller offers higher stiffness and more efficient control of rigid modes than the benchmark PID controller.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.942-947
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• 2001

This paper provides a stability analysis of the transverse vibration of a spinning disk under the torque fluctuation from a permanent magnetic motor. An analytical model has been formulated for a flexible annular disk with its spinning velocity varying harmonically with the same frequency as the cogging torque. A perturbation method based on multiple time scales is applied to perform the stability analysis. Based on expressions for the amplitude and frequency of the parametric excitation, stability boundaries are determined in terms of a nominal spindle velocity, the least common multiple of poles and slots, the magnitude of torque fluctuation and the modal characteristics of. the disk. The stability diagrams predicted by perturbation have been verified numerically using the Floquet theory, which is in good agreement. In conclusion, the fluctuation in spinning velocity is found to affect the stability of the transverse vibration of a rotating disks. The results of this work can be applied to high precision spindle systems such as computer storage systems.

• Molecular & Cellular Toxicology
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• v.3 no.3
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• pp.185-189
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• 2007

Gene expression levels of choriogenin, vitellogenin and estrogen receptor were determined using Reverse transcription (RT)-PCR technique after exposure to estrogenic chemical bisphenol A in the Korean wild medaka (Oryzias sinensis). These genes have been known to be induced in male test fish when the fish are exposed to estrogenic chemicals. Therefore they can be suggested as a possible biomarker of endocrine disruption in fish, however, relatively little has been known about these genes expression by estrogenic chemicals in Korean wild fish. Mature male Oryzias sinensis were treated with bisphenol A at nominal concentrations of 0.02, 0.2 and 2 mg/L for 6 days and total RNA was extracted from the livers of treated fish for RT-PCR. When the five biomarker genes were amplified by RT-PCR in the same condition, mRNA induction level of each gene was elevated with different sensitivities. Conclusively, the results of this work indicated that measurement of vitellogenin and choriogenin using RT-PCR is effective as a simple tool for the screening of estrogenic chemicals and suggested that O. sinensis would be a suitable model fish for the environmental risk assessment of potential endocrine disruptors.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.998-1003
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• 2013

This study proposes an adaptive control algorithm for lateral motion of a UGV (Unmanned Ground Vehicle) using an NN (Neural Networks). The lateral motion of the UGV can be corrupted with various uncertainties such as side slip. In order to compensate the performance degradation of the UGV under various uncertainties, an NN-based adaptive control is designed by utilizing a virtual control concept. Since both the drift and input gain terms are uncertain, the proposed method adapts the whole terms related to the difference between the nominal and real systems. To avoid a singularity problem with the adaptive control, the affine property of the UGV dynamic model is utilized and the overall closed-loop stability is analyzed rigorously. Finally, numerical simulations using Carsim are performed to validate the effectiveness of the proposed scheme.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.1
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• pp.48-54
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• 2002

A part definition must not only provide shape information of a nominal part but also contain non-shape information such as tolerances, surface roughness and material specifications. Although machining features are useful for suitable shape information fur process reasoning in CAPP, they need to be integrated with tolerance information for effective process planning. We develop a tolerance modeler that efficiently integrates the machining features with the tolerance information fur feature-based CAPP. It is based on the association of machining features, tolerance features, and tolerances. The tolerance features in this study, where tolerances are assigned, are classified into two types; one type is a face that is a topological entity on a solid model and the other type is a functional geometry that is not referenced to topological entities. The (unctional geometry is represented by using machining features. All the data fur representing the tolerance information are stored completely and unambiguously in an independent tolerance data structure. The developed tolerance modeler is implemented as a module of a comprehensive feature-based CAPP system.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.4
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• pp.18-29
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• 1998

This study was carried out to test the theory of planned behavior in recreational choices behavior. Lotte World was chosen as study area, and college students were selected by nonprobability sampling for two waves of data collection. The first wave of data were collected one weeks into the spring semester : intention, attitude, subjective norm, and perceived behavioral control were measured. To collect the data of the second wave, the same resondents were asked their behavior, one week data of the second wave, the same respondents were asked their behavior , one week prior to the final examination : whether they visited the Lotte World or not. Polychoric correlation among variables were calculated by the PRELIS because behavior was nominal variable. Then, weighted least square method was utilized to calibrate structural equation model by the LISREL version 7.2. Structural link effect on intention among three determinants : the direct effect on intention was 0.421 and the indirect effect via intention on behavior was 0.145, respectively. However, its effect on behavior was insignificant because actual control over 'visiting of Lotte World' was relatively high. A few comments were sugested on data collection, and inclusion of new variables was discussed for the sufficiency f the theory of planned behavior.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.591-597
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• 1999

When fuzzy logic controllers which are designed based on plant models and intuitive base are applied to real plants, the control systems may not give satisfactory control results due to the modeling error and the lack of knowledge on the plants. In that case. the controller must be retuned by adjusting the control parameters; this retuning process may require a large number of trial-and-error evaluations and thus much time and cost. In order to resolve these problems, we propose a systematic and efficient procedure for designing a fuzzy logic controller using response surface methodology. First wc select the initial optimal conditions of control parameters using a genetic algorithm, in which a nominal plant model with intrinsic modeling errors is used. And then we determine the tinal optimal conditions of the control parameters using response surface methodology. Computer simulations are performed to verify the capability of the proposed method.