• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.630-637
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• 2003

This paper proposes a new modeling scheme to describe the hysteresis and the dynamic characteristics of piezoelectric actuators in the inchworm and develops a control algorithm for the precision motion control. From the analysis of piezoelectric actuator behaviors, the hysteresis can be described by the functions of a maximum input voltage. The dynamic characteristics are also identified by the frequency domain modeling technique based on the experimental data. For the motion control, the hysteresis behavior is compensated by the inverse hysteresis model. The dynamic stiffness of an inchworm is generally low compared to its driving condition, so mechanical vibration may degenerate the motion accuracy of the inchworm. Therefore, the sliding mode control and the Kalman filter are developed for the precision motion control of the inch-warm. To demonstrate the effectiveness of the proposed modeling schemes and control algorithm, experiment validations are performed.

• Biotechnology and Bioprocess Engineering:BBE
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• v.11 no.1
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• pp.67-71
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• 2006

This paper compares regression and neural network modeling approaches to predict competitive biosorption equilibrium data. The regression approach is based on the fitting of modified Langmuir-type isotherm models to experimental data. Neural networks, on the other hand, are non-parametric statistical estimators capable of identifying patterns in data and correlations between input and output. Our results show that the neural network approach outperforms traditional regression-based modeling in correlating and predicting the simultaneous uptake of copper and cadmium by a microbial biosorbent. The neural network is capable of accurately predicting unseen data when provided with limited amounts of data for training. Because neural networks are purely data-driven models, they are more suitable for obtaining accurate predictions than for probing the physical nature of the biosorption process.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.302-305
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• 1996

In this paper, an optimal identification method using fuzzy-neural networks is proposed for modeling of nonlinear complex systems. The proposed fuzzy-neural modeling implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks(NNs) from input and output data of processes. Inference type for this fuzzy-neural modeling is presented as simplified inference. To obtain optimal model, the learning rates and momentum coefficients of fuzz-neural networks(FNNs) and parameters of membership function are tuned using genetic algorithm(GAs). For the purpose of its application to nonlinear processes, data for route choice of traffic problems and those for activated sludge process of sewage treatment system are used for the purpose of evaluating the performance of the proposed fuzzy-neural network modeling. The show that the proposed method can produce the intelligence model w th higher accuracy than other works achieved previously.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.4_1
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• pp.513-519
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• 2022

In this paper, we describe performances of P-PD controllers in the quadrotor system with steady-state error compensation by adding a corrective term to the system input. A decentralized control system using P-PD controllers was successfully implemented on a quadrotor platform. We also presented the results of a mathematical modeling analysis for control the quadrotor and experimental results for each response performance according to the heading reference value in accordance with the mathematical modeling and P-PD controller design. A control experiment with the real system was implemented for the test platform, and the results were evaluated and compared.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.2
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• pp.83-89
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• 2008

This paper presents the dynamic analysis and input shaping control of a positioning stage. Vibration characteristics of the positioning stage are affected not only by the structural dynamics but also by the servo actuators that consist of the mechanism; driving motor and controller. This paper proposes an integrated dynamic model to accommodate both the structural dynamics and the servo actuators. Theoretical modal analysis with a commercial finite element code is carried out to investigate the dynamic characteristics of the experimental positioning stage. Experiments are performed to validate the theoretical modal analysis and estimate the equivalent stiffness due to the servo actuators. This paper deals with an input shaping scheme to suppress vibration of the positioning stage. Input shapers are systematically implemented for the positioning stage in consideration of its dynamics. The effects of servo control gain are also investigated. The experiments show that input shaping effectively removes residual vibrations and then improves the performance of positioning stage.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• Spatial Information Research
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• v.21 no.6
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• pp.91-98
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• 2013

Government introduces a Total Maximum Daily Loads(TMDL) which can be implemented for total pollutant amounts in 2004. Normally, the local governments have been calculated the amounts of pollutant discharge of each watershed using a water quality model. However, among the input data to use the water quality model, creating a schematic diagram of the stream or the modeling usually requires considerable amount of time and efforts due to the manual work. Therefore, this study tried to develop an algorithm which automates the creation of a schematic diagram for water quality modeling using the Korean Reach File capable of river network analysis. Further, this study creates a schematic diagram with the shape of a stream utilizing GIS capabilities. The diagram can be easily analyzed with overlapping various spatial information such as pollution sources and discharge points. This study mainly has automated element segmentation algorithm to divide streamflows into equal distance using line graphic data of Koran Reach File. Also, automated attribute input algorithm has also been developed to enable to insert element order and type into elements using point graphic data of Korean Reach File. For the verification of the developed algorithm, the algorithm was applied to kyungan stream basin to see the acceptable results. To conclude, it was possible to automate generating of schematic diagram of water quality model and it is expected to be able to save time and cost required for the water modeling. In future study, it is necessary to develop an automatic creation system of various types of input data for water quality modeling and this will lead to relatively easier and simple water quality modeling.

• The Journal of Society for e-Business Studies
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• v.2 no.2
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• pp.89-113
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• 1997

Modeling methodology has been widely used for analysis and design of a information system. Specially, under the CALS environments, modeling approach is more important because the enterprise functions are inter-related and information sharing speeds up the business. In this paper, we suggest functional requirements about modeling methodology for CALS by surveying the IDEF0 and ARIS. The former is FIPS 183 and the latter is basic methodology of SAP/R3 which is world-wide ERP system. The proposed functional requirements include all semantics of IDEF0 and adds some features. The first is adding modeling components which are semantic representations. In addition to ICOMs, we add the time and cost component which is required to execute the function. The second is tracing mechanism. When we need some information, we drive the functions related with the information by reverse tracing of the function which produces the information as a output and input. Through the tracing, we find out the bottleneck process or high cost process. This approach guarantees the integrity of data by designating the data ownership. Finally, we suggest the final decomposition level. We call the final decomposed function into unit function which has only one output data. We can combine and reconstruct some of functions such as 'lego block' combination.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.627-640
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• 2018

Submetering electricity consumption data enables more detail input of end use components, such as lighting, plug, HVAC, and occupancy in building energy modeling. However, such an modeling efforts and results are rarely tried and published in terms of the estimation accuracy of electricity demand. In this research, actual submetering data obtained from a university building is analyzed and provided for building energy modeling practice. As alternative modeling cases, conventional modeling method (Case-1), using reference schedule per building usage, and main metering data based modeling method (Case-2) are established. Detail efforts are added to derive prototypical schedules from the metered data by introducing variability index. The simulation results revealed that Case-1 showed the largest error as we can expect. And Case-2 showed comparative error relative to Case-3 in terms of total electricity estimation. But Case-2 showed about two times larger error in CV (RMSE) in lighting energy demand due to lack of End Use consumption information.