• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1759-1766
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• 2003

This paper proposes a neuro­fuzzy controllers for trajectory tracking control of robot manipulators. The computed torque method is an effective means for trajectory tracking control. However, the tracking performance of this method is severely affected by the uncertainties of robot manipulators. Therefore, the proposed controller is used to compensate the uncertainties of robot manipulators. In the neuro­fuzzy controllers, the number of fuzzy rules used forty­nine. The effectiveness of the proposed controllers is demonstrated by computer simulations using two­link robot manipulator, As a result, it is confirmed that the output of the proposed neuro­fuzzy controllers can efficiently decrease the uncertainties of robot manipulator.

• Journal of Drive and Control
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• v.14 no.4
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• pp.37-44
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• 2017

This paper introduces a control algorithm for trajectory control of an electro-hydrostatic rotary actuator. A key feature of this paper is that an adaptive PID based on sliding mode is used to control the nonlinearity and uncertainty factor of single input/output system. Accurate knowledge of rotary actuator angle can result in high-performance and efficiency of electro hydraulic system. First, the position control is formulated using the adaptive PID with sliding mode technique and uncertainties in the hydraulic system. Second, the controller can update the PID gains on-line based on error caused by external disturbance and uncertain factors in the system. Finally, three experimental cases were studied to evaluate the proposed control method.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.117-127
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• 1998

Power systems have uncertain dynamics due to a variety of effects such as lightning, severe storms and equipment failures. The variation of the effective reactance of a transmission line due to a fault is an example of uncertainty in power system dynamics. Hence, a robust controller to cope with these uncertainties is needed. Recently, fuzzy controllers are becoming quite popular for robust control due to its potential of dealing with uncertain systems. Thus in this paper we design an adaptive fuzzy controller based on an input-output linearization approach for the transient stabilization and voltage regulation of a power system under a sudden fault. Also this paper proposes a fuzzy system that estimates the upper bound of uncertain term in the system dynamics to guarantee the Lyapunov stability. Simulation results show that good performance is achieved by the proposed controller.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.606-623
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• 2019

The path following problem of a ship sailing in restricted waters under wind effect is investigated based on Robust $H_{\infty}$ Guaranteed Cost Control (RHGCC). To design the controller, the ship maneuvering motion is modeled as a linear uncertain system with norm-bounded time-varying parametric uncertainty. To counteract the bank and wind effects, the integral of path error is augmented to the original system. Based on the extended linear uncertain system, sufficient conditions for existence of the RHGCC are given. To obtain an optimal robust $H_{\infty}$ guaranteed cost control law, a convex optimization problem with Linear Matrix Inequality (LMI) constraints is formulated, which minimizes the guaranteed cost of the close-loop system and mitigates the effect of external disturbance on the performance output. Numerical simulations have confirmed the effectiveness and robustness of the proposed control strategy for the path following goal of a ship sailing in restricted waters under wind effect.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4181-4190
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• 2023

Nuclear power plants are subjected to various processes during decommissioning, including cutting, decontamination, disposal, and treatment. The cutting of massive bio-shields is a significant step in the decommissioning process. Cutting is performed near the target structure, and during this process, workers are exposed to potential radioactive elements. However, studies considering worker exposure management during such cutting operations are limited. Furthermore, dismantling a nuclear power plant under certain circumstances may result in the unnecessary radiation exposure of workers and an increase in secondary waste generation. In this study, a cutting scenario was formulated considering the bio-shield as a representative structure. The specifications of a standard South Korean radioactive waste disposal drum were used as the basic conditions. Additionally, we explored the hot-to-cold and cold-to-hot methods, with and without the application of polishing during decontamination. For evaluating various scenarios, different cutting time points up to 30 years after permanent shutdown were considered, and cutting speeds of 1-10nullm²/h were applied to account for the variability and uncertainty attributable to the design output and specifications. The obtained results provide fundamental guidelines for establishing cutting methods suitable for large structures.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4607-4616
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• 2023

We implement machine learning regression models to predict peak pressures of primary and secondary systems, a major safety concern in Loss Of Condenser Vacuum (LOCV) accident. We selected the Multi-dimensional Analysis of Reactor Safety-KINS standard (MARS-KS) code to analyze the LOCV accident, and the reference plant is the Korean Optimized Power Reactor 1000MWe (OPR1000). eXtreme Gradient Boosting (XGBoost) is selected as a machine learning tool. The MARS-KS code is used to generate LOCV accident data and the data is applied to train the machine learning model. Hyperparameter optimization is performed using a simulated annealing. The randomly generated combination of initial conditions within the operating range is put into the input of the XGBoost model to predict the peak pressure. These initial conditions that cause peak pressure with MARS-KS generate the results. After such a process, the error between the predicted value and the code output is calculated. Uncertainty about the machine learning model is also calculated to verify the model accuracy. The machine learning model presented in this paper successfully identifies a combination of initial conditions that produce a more conservative peak pressure than the values calculated with existing methodologies.

• Journal of Korea Water Resources Association
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• v.41 no.11
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• pp.1079-1094
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• 2008

This study is to evaluate the impact of varying spatial resolutions on the uncertainty of Soil and Water Assessment Tool (SWAT) predicted streamflow, non-point source (NPS) pollution loads transport in a small agricultural watershed (1.21 $km^2$) for three cases of model input; Case A is the combination of 2 m DEM, QuickBird land use, Case B is the combination of 10 m DEM, 1/25,000 land use, and Case C is the combination of 30 m DEM, Landsat land use, soil data is used 1/25,000 for three cases respectively. The model was calibrated for 2 years (1999-2000) using daily streamflow and monthly water quality records, and verified for another 2 years (2001-2002). The average Nash and Sutcliffe model efficiency was 0.59 for streamflow and RMSE were 2.08, 4.30 and 0.70 tons/yr for sediment, T-N and T-P respectively. The model was run for a small agricultural watershed with three cases of spatial input data. The hydrological results showed that output uncertainty was biggest by spatial resolution of land use. Streamflow increase the watershed average CN value of QucikBird land use was 0.4 and 1.8 higher than those of 1/25,000 and Landsat land use caused increase of streamflow. On the other hand, The NPS loadings from the model prediction showed that the sediment, T-N and T-P of QuickBird land use (Case A) showed 23.7 %, 43.3 % and 48.4 % higher value than 1/25,000 land use (Case B) and 50.6 %, 50.8 % and 56.9 % higher value than Landsat land use (Case C) respectively.

• Progress in Medical Physics
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• v.3 no.2
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• pp.1-12
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• 1992

For intraoperative radiation therapy using electron beams, a cone system to deliver a large dose to the tumor during surgical operation and to save the surrounding normal tissue should be developed and dosimetry for the cone system is necessary to find proper X-ray collimator setting as well as to get useful data for clinical use. We developed a docking type of a cone system consisting of two parts made of aluminum: holder and cone. The cones which range from 4cm to 9cm with 1cm step at 100cm SSD of photon beam are 28cm long circular tubular cylinders. The system has two 26cm long holders: one for the cones larger than or equal to 7cm diamter and another for the smaller ones than 7cm. On the side of the holder is an aperture for insertion of a lamp and mirror to observe treatment field. Depth dose curve. dose profile and output factor at dept of dose maximum. and dose distribution in water for each cone size were measured with a p-type silicone detector controlled by a linear scanner for several extra opening of X-ray collimators. For a combination of electron energy and cone size, the opening of the X-ray collimator was caused to the surface dose, depths of dose maximum and 80%, dose profile and output factor. The variation of the output factor was the most remarkable. The output factors of 9MeV electron, as an example, range from 0.637 to 1.549. The opening of X-ray collimators would cause the quantity of scattered electrons coming to the IORT cone system. which in turn would change the dose distribution as well as the output factor. Dosimetry for an IORT cone system is inevitable to minimize uncertainty in the clinical use.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.59-69
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• 2018

In this paper, we present a robust beamforming design to tackle the weighted sum-rate maximization (WSRM) problem in a multicell multiple-input multiple-output (MIMO) - non-orthogonal multipleaccess (NOMA) downlink system for 5G wireless communications. This work consider the imperfectchannel state information (CSI) at the base station (BS) by adding uncertainties to channel estimation matrices as the worst-case model i.e., singular value uncertainty model (SVUM). With this observation, the WSRM problem is formulated subject to the transmit power constraints at the BS. The objective problem is known as on-deterministic polynomial (NP) problem which is difficult to solve. We propose an robust beam forming design which establishes on majorization minimization (MM) technique to find the optimal transmit beam forming matrix, as well as efficiently solve the objective problem. In addition, we also propose a joint user clustering and power allocation (JUCPA) algorithm in which the best user pair is selected as a cluster to attain a higher sum-rate. Extensive numerical results are provided to show that the proposed robust beamforming design together with the proposed JUCPA algorithm significantly increases the performance in term of sum-rate as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.289-297
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• 2010

Common practice of Soil Water Assessment Tool (SWAT) model validation is to use a single variable (i.e., streamlfow) to calibrate SWAT model due to the paucity of actual hydrological measurement data in Korea. This approach, however, often causes errors in the simulated results because of numerous sources of uncertainty and complexity of SWAT model. We employed multi-variables (i.e., streamflow, evapotranspiration, and soil moisture), which were measured at mixed forest in Seolmacheon catchment ($8.54\;km^2$), in order to assess the performance and reduce the uncertainties of SWAT model output. Meteorological and surface topographical data of the catchment were obtained as basic input variables and SWAT model was calibrated using daily data of streamflow (Jan. - Dec.), evapotranspiration (Sep. - Dec.), and soil moisture (Jun. - Dec.) collected in 2007. The model performance was assessed by comparing its results with the observation (i.e., streamflow of 2003 to 2008 and evapotranspiration and soil moisture of 2008). When the multi-variable measurements were used to calibrate the SWAT model, the model results showed better agreement with the measurements compared to those using a single variable measurement by showing increases in coefficient of determination ($R^2$) from 0.72 to 0.76 for streamflow, from 0.49 to 0.59 for soil moisture, and from 0.52 to 0.59 for evapotranspiration. The findings highlight the importance of reliable and accurate collective observation data for improving performance of SWAT model and promote its facilitation for estimating more realistic hydrological cycles at catchment scale.