• Journal of Aerospace System Engineering
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• v.17 no.1
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• pp.79-87
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• 2023

To design a control law of an aircraft, an accurate aircraft dynamic model is required. To obtain an aerodynamic database (DB) to build a dynamic model, a large number of wind tunnel tests are typically required. However, when flight test data of target aircraft exist such as in the process of unmanned conversion of a manned aircraft, an aircraft dynamic model can be obtained through a parameter estimation method and a DB tuning procedure. This paper describes a nonlinear model construction process and a verification method for KC-100 OPV aircraft. Flight data compatibility analysis was performed to determine suitability of the estimation method application. Linear model estimation was performed using the maximum likelihood estimation method. Results of aerodynamic DB tuning process and verification applying the FFS standard to the nonlinear model constructed are presented.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.470-477
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• 2002

Possibility of passive piezoelectric damping based on a new shunting parameter estimation method is studied using finite element analysis. The adopted tuning method is based electrical impedance that is found at piezoelectric device and the optimal criterion for maximizing dissipated energy at the shunt circuit. Full three dimensional finite element model is used for piezoelectric devices with cantilever plate structure and shunt electronic circuit is taken into account in the model. Electrical impedance is calculated at the piezoelectric device, which represents the structural behavior in terms of electrical field, and equivalent electrical circuit parameters for the first mode are extracted using PRAP (Piezoelectric Resonance Analysis Program). After the shunt circuit is connected to the equivalent circuit for the first mode, the shunt parameters are optimally decided based on the maximizing dissipated energy criterion. Since this tuning method is based on electrical impedance calculated at piezoelectric device, multi-mode passive piezoelectric damping can be implemented for arbitrary shaped structures.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1195-1198
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• 1993

We investigate a systematic design procedure of automated rule generation of fuzzy logic based controller for uncertain dynamic systems such as an engine dynamic model.“Automated Tuning”means autonomous clustering or collection of such meaningful transitional relations in the state-space. Optimal control strategies are included in the design procedures, such as minimum squared error, minimum time, minimum energy or combined performance criteria. Fuzzy feedback control systems designed by the cell-state transition method have the properties of closed-loop stability, robustness under parameter variabtions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller design to a highly nonlinear model of engine idle speed contr l.

• International Journal of Aeronautical and Space Sciences
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• v.3 no.1
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• pp.86-94
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• 2002

The initial alignment of inertial platform for navigation system was considered. An adaptive filtering technique is developed for the system with unknown and varying sway disturbance. It is assumed that the random sway motion is the second order ARMA(Auto Regressive Moving Average) model and performed parameter identification for unknown parameters. Designed adaptive filter contain both a Kalman filter and a self-tuning filter. This filtering system can automatically adapt to varying environmental conditions. To verify the robustness of the filtering system, the computer simulation was performed with unknown and varying sway disturbance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.298-298
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• 2000

This paper presents the optimization technique to analyze the effect of the design parameters of rapid prototyping system for human brain model fabrication. The optimization method considers the functional relationships among the design parameters such as thickness gap, shrink rate, and laser speed that govern the operation of fabrication system. This paper applies a discrete optimization technique as the optimization method to determine the dominant parameter values. Additional study includes manner of complement surface image of ellipse which approximates the brain model using the adaptive slicing and the offset contour. According to the parameters tuning and interaction of effect, more suitable parameter values can be obtained by enhanced 3D brain model fabrication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3416-3435
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• 2023

Task offloading in vehicular networks is hot topic in the development of autonomous driving. In these scenarios, due to the role of vehicles and pedestrians, task characteristics are changing constantly. The classical deep learning algorithm always uses a pre-trained neural network to optimize task offloading, which leads to system performance degradation. Therefore, this paper proposes a neural network fine-tuning task offloading algorithm, combining with location prediction for pedestrians and vehicles by the Payne model of fluid dynamics and the car-following model, respectively. After the locations are predicted, characteristics of tasks can be obtained and the neural network will be fine-tuned. Finally, the proposed algorithm continuously predicts task characteristics and fine-tunes a neural network to maintain high system performance and meet low delay requirements. From the simulation results, compared with other algorithms, the proposed algorithm still guarantees a lower task offloading delay, especially when congestion occurs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.830-837
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• 2011

A turbine control system which has been operated for years in a nuclear power plant was retrofitted with a newly developed digital control system. After completion of the retrofit, turbine valve tests were performed to ensure the integrity of each valve's control function. The sequence of each valve test is composed of a closing process and a reopening process. To minimize megawatt variation which normally occurs during the test sequence, we employed a kind of compensator algorithm in the new digital control system which also have been used in the old system. There were difficulties finding optimal parameter settings for our new compensator algorithm because the power plant didn't allow us to perform necessary tuning procedures while the turbine is on load operation. Therefore an alternative measure for the compensator tuning which is independent of the turbine actual operation had to be implemented. So, a process model for the test was required to overcome this situation. We analyzed the operation data of the test and implemented the process model by use of input and output variable relations. Also we verified the process model by use of another condition's operating data. The result shows that the output of model is similar to the actual operation data.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.31-38
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• 2021

In this paper, in order to obtain the optimization of the RNN model used for sentiment analysis, the correlation of each model was studied by observing the trend of loss and accuracy according to hyperparameter tuning. As a research method, after configuring the hidden layer with LSTM and the embedding layer that are most optimized to process sequential data, the loss and accuracy of each model were measured by tuning the unit, batch-size, and embedding size of the LSTM. As a result of the measurement, the loss was 41.9% and the accuracy was 11.4%, and the trend of the optimization model showed a consistently stable graph, confirming that the tuning of the hyperparameter had a profound effect on the model. In addition, it was confirmed that the decision of the embedding size among the three hyperparameters had the greatest influence on the model. In the future, this research will be continued, and research on an algorithm that allows the model to directly find the optimal hyperparameter will continue.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.671-674
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• 2005

The series parasitic resistances ($R_s$, $R_g$, $R_d$, $R_{sub}$) of BSIM3v3 RF MOSFET macro model were directly extracted from measured S-parameters in the GHz region by using simple 2-port parameter equations. Also, overlap capacitance and junction capacitance parameters were extracted by tuning $S_{11}$, $S_{12}$, and $S_{22}$ respectively while DC-parameters and all parasitic resistances are fixed at previously extracted values. These data are verified to be accurate by observing good correspondence between modeled and measured S-parameters up to 10GHz.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.314-320
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• 2005

In this paper, a methodology for estimating the parameters of a discrete-time system and designing a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems occurring regarding parameter estimation and controller design, a pseudo parallel genetic algorithm (PPGA) is used. The parameters of a discrete-time system are estimated using both the model technique and a PPGA. The digital PID controller is described by the pulse transfer function and its parameters are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.