• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.718-724
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• 2017

Considerable efforts have been made to expand the boundaries of domestic offshore plant industries, which have focused on the construction of the structures, to the engineering field. On the other hand, time domain analysis, which is one of the most important areas in designing floating offshore plants, relies mainly on the information given by foreign companies. As an early design of the Dynamic Positioning System (DPS) is mostly conducted by several specialized companies, domestic ship builders need to spend time and money to reflect the analysis into the hull shape design. This paper presents the framework required to analyze time domain performance of floating type offshore structures, which are equipped with DPS. To easily perform time domain analysis, framework generates the required input data for the solver, and is modularized to test the control algorithm and performance of a certain DPS. The effectiveness of the developed framework was verified by a simulation with a model ship and the total time for the entire analysis work was reduced by 50% or more.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.227-232
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• 2004

Vehicle dynamics control systems are complex and non-linear, so they have difficulties in developing a controller for the anti-lock braking systems and the auto-traction systems. Currently the fuzzy-logic technique to estimate the absolute vehicle speed supplies good results in normal conditions. But the estimation error in severe braking is discontented In this paper, we estimate the absolute vehicle speed of UCT(Unmanned Container Transporter) by using the wheel speed data from standard anti-lock braking system wheel speed sensors. Radial symmetric basis function of the neural network model is proposed to implement and estimate the absolute vehicle speed, and principal component analysis on input data is used 10 algorithms are verified experimentally to estimate the absolute vehicle speed and one of them is perfectly shown to estimate the vehicle speed within 4％ error during a braking maneuver.

• Journal of IKEEE
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• v.26 no.3
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• pp.349-354
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• 2022

In thermal power plants, boiler models have been used widely in evaluating logic configurations, performing system tuning and applying control theory, etc. Furthermore, proper plant models are needed to design the accurate controllers. Sometimes, mathematical models can not exactly describe a power plant due to time varying, nonlinearity, uncertainties and complexity of the thermal power plants. In this case, a neural network can be a useful method to estimate such systems. In this paper, the models of boiler steam system in a thermal power plant are developed by using a generalized regression neural network(GRNN). The models of the superheater, reheater, attemperator and drum are designed by using GRNN and the models are trained and validate with the real data obtained in 540[MW] power plant. The validation results showed that proposed models agree with actual outputs of the drum boiler well.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.6
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• pp.258-263
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• 2021

ZnO varistor is a semiconductor device which can serve to protect the circuit from surge voltage because its non-linear I-V characteristics by controlling the microstructure of grain and grain boundaries. In order to obtain desired electrical properties, it is important to control microstructure evolution during the sintering process. In this research, we defined a dataset composed of process conditions of sintering and relative permittivity of sintered body, and collected experimental dataset with DOE. Meta-models can predict permittivity were developed by learning the collected experimental dataset on various machine learning algorithms. By utilizing the meta-model, we can derive optimized sintering conditions that could show the maximum permittivity from the numerical-based HMA (Hybrid Metaheuristic Algorithm) optimization algorithm. It is possible to search the optimal process conditions with minimum number of experiments if meta-model-based optimization is applied to ceramic processing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5A
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• pp.467-475
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• 2009

Existing studies have suggested Universal Curve only for supplemental damping by damper. Therefore net damping has been determined by means of arithmetic summation between intrinsic, aero-damping of cable and supplemental damping of damper. However linear combination approach by means of the arithmetic summation is not enough theoretical background. So validity of this approach should be verified in order to design adequate cable-damper system by engineers. This study establishes governing differential equation which can consider intrinsic, aero-damping and supplemental damping as well. And also analysis method is solved by combination of muller method and successive iteration method. Consequently, this study succeeds in verification for validity of linear combination approach. As a result of this study, linear combination approach is limitedly effective in case of low stiffness and optimum damping coefficient of damper, short distance from support to damper, lower vibration mode, low aero-damping, and normal windy environment. Whereas this study will be effective in case of opposite conditions, and existing studies or linear combination approach occur to further error. Meaning of this study presents exact solution for net damping of cable-damper system, and verifies linear combination approach by means of the analysis method. In the future, if monitoring of optimum damping coefficient of a damper against aero-damping is feasible on time, algorithm of this study will be available for control of cable and semi-active damper system such as magneto-rheological damper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2283-2291
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• 2010

The shrinking generator which is one of clock-controlled generator is a very simple generator with good cryptographic properties. A nonlinear sequence generator based on two 90/150 maximum length cellular automata can generate pseudorandom sequences at each cell of cellular automata whose characteristic polynomials are same. The nonlinear sequence generated by cellular automata has a larger period and a higher linear complexity than shrunken sequence generated by LFSRs. In this paper we analyze shrunken-interleaved sequence based on 90/150 maximum length cellular automata. We show that the sequence generated by nonlinear sequence generator based on cellular automata belongs to the class of interleaved sequence. And we give an effective algorithm for reconstructing unknown bits of output sequence based on intercepted keystream bits.

• Journal of Navigation and Port Research
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• v.32 no.5
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• pp.379-385
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• 2008

In this paper, we presents a scheme for the parameter estimation and optimal control scheme for apparatus of container crane system. For parameter estimation, first, we construct the open loop of the container crane system and estimate its parameters based on input-output data, a real-coded genetic algorithm(RCGA) and the model adjustment technique. The RCGA plays an important role in parameter estimation as an adaptive mechanism. For controller design, state feedback gain matrix is searched by another RCGA and the estimated model. The performance of the proposed methods are demonstrated through a set of simulation and experiments of the experimental apparatus.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.2
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• pp.107-114
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• 2007

This paper focuses on the performance evaluation of various control allocation methods applied on DURUMI-II UAV system. In order to implement control allocation scheme to aircraft control system, control system can be designed through two step design procedure. The first step is to design a baseline control system for an aircraft without consideration of control surface failure. The second step is to design a control allocator that maps the total control command on the individual control surfaces. In this paper, several control allocation methods such as Psuedo-Inverse CA method, Direct CA method, and Optimization CA method are implemented and integrated to the baseline flight control system of DURUMI-II UAV. The performance of these control allocation methods is evaluated by nonlinear simulation under the flight scenario of control surface failure.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.599-602
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• 2011

A propulsion controller for one-time flight vehicles should be designed robustly so that it can complete its missions even in case sensor failures. These vehicles improve their fault tolerance by back-up sensors prepared for the failure of major sensors, which raises the total cost. This paper presents the NARX model which substitutes vehicles' velocity sensors, and detects failure of sensor signals by using model based fault detection. The designed NARX model and fault detection algorithm were optimized and installed in TI's TMS320F2812 so that they were linked to HILS instruments in real-time. The designed propulsion controller made the vehicle to have better fault tolerance with fewer sensors and to complete its missions under a lot of complicated failure situations. The controller's applicability was finally confirmed by tests under the HILS environment.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.214-221
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• 1998

A new control approach using neural network for the robust position control of a BRUSHLESS direct drive(BLDD) motor is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust BLDD motor system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system will be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained by error back-propagation at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. In addition, the robustness is also obtained without affecting overall system response.