• Journal of the Ergonomics Society of Korea
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• v.22 no.1
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• pp.31-42
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• 2003

This study describes a modification of the technique for human error analysis in nuclear power plants (NPPs) which adopts advanced Man-Machine Interface (MMI) features based on computerized working environment, such as LCOs. Flat Panels. Large Wall Board, and computerized procedures. Firstly, the state of the art on human error analysis methods and efforts were briefly reviewed. Human error analysis method applied to NPP design has been THERP and ASEP mainly utilizing Swain's HRA handbook, which has not been facilitated enough to put the varied characteristics of MMI into HRA process. The basic concepts on human errors and the system safety approach were revisited, and adopted the process of FMEA with the new definition of Error Segment (ESJ. A modified human error analysis process was suggested. Then, the suggested method was applied to the failure of manual pump actuation through LCD touch screen in loss of feed water event in order to verify the applicability of the proposed method in practices. The example showed that the method become more facilitated to consider the concerns of the introduction of advanced MMI devices, and to integrate human error analysis process not only into HRA/PRA but also into the MMI and interface design. Finally, the possible extensions and further efforts required to obtain the applicability of the suggested method were discussed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.5
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• pp.503-510
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• 2004

In this paper, the well-conditioned observer for a stochastic system is designed so that the observer is less sensitive to the ill-conditioning factors in transient and steady-state observer performance. These factors include not only deterministic uncertainties such as unknown initial estimation error, round-off error, modeling error and sensing bias, but also stochastic uncertainties such as disturbance and sensor noise. In deterministic perspectives, a small value in the L$_{2}$ norm condition number of the observer eigenvector matrix guarantees robust estimation performance to the deterministic uncertainties. In stochastic viewpoints, the estimation variance represents the robustness to the stochastic uncertainties and its upper bound can be minimized by reducing the observer gain and increasing the decay rate. Both deterministic and stochastic issues are considered as a weighted sum with a LMI (Linear Matrix Inequality) formulation. The gain in the well-conditioned observer is optimally chosen by the optimization technique. Simulation examples are given to evaluate the estimation performance of the proposed observer.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1617-1620
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• 2005

The current control for interior permanent magnet machines is more complicate than surface permanent magnet machine because of its torque characteristic depending on the reluctance. For high performance torque control, it requires state decoupling between the d-current and q-current dynamics. However the variation of the inductances, which couples the state dynamics of the currents, makes the state decoupling difficult. So some decoupling methods have developed to cope this variations and each current can be regulated independently. This paper presents a novel approach for fully decoupling the states cross-coupling using parameter adaptation. The adaptation method is based on the error between reference currents and the currents with state decoupling which have to follow the references. This method is more object-oriented than the other online parameter estimation methods in IPM machine and other electrical machines

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.1
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• pp.32-36
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• 2011

In this study, authors have proposed the autonomous state estimation which has been composed with IEC61850, GPS time synchronization and objective model design concept. The proposed method is able to supervise/correct measurement and communication error from SCADA. The major advantages of the proposed autonomous state estimation are that it is possible to evaluate integrity of data measured and transferred from SCADA, to reduce human intervention and to expense national-size applications such as EMS (Energy Management System), WAMS (Wide Area Monitoring System) or WAPS (Wide Area Protection System). This study addresses the issues related to the operation of the smart grid and proposes a new automated approach to achieve this goal. Through applying the proposed system to IEEE 14-bus test electric system, we prove the possibility of the proposed idea.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.162-166
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• 1990

This paper proposes a new method of the state reduction in dynamic equations of generators in large electric power system stability analysis. This method assumes study groups whose state trajectories we are interested in, coherency groups whose state trajectories are similar to the other state trajectories of generators in the same coherency group by a certain disturbance. By the weighted sum or the other method, the states of generators in one coherency group can be reduced to the equivalent states of an equivalent generator. This method is shown to be highly efficient in reducing the number of states with small error by the result of case study presented latter part of this paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.171-178
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• 2017

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.6
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• pp.1-6
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• 1985

The reference input is one of causes having an effect upon the steady-state error. This paper dcscribes a design method of a digital controller to remove the stcadyftate error caused by the reference input. According to the types of the reference input, new system equations to remove the reference input term from controlled system equations are derived first. And, using the optimal control theory the control law is obtained to minimize the output of the new system. Based on the state-space approach, the proposed control algo-rithm can be applied to time-invariant linear systems including the unstable systems.

• Bulletin of the Korean Mathematical Society
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• v.57 no.2
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• pp.295-309
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• 2020

Loop transversal codes take an alternative approach to the theory of error-correcting codes, placing emphasis on the set of errors that are to be corrected. Hitherto, the loop transversal code method has been restricted to linear codes. The goal of the current paper is to extend the conceptual framework of loop transversal codes to admit nonlinear codes. We present a natural example of this nonlinearity among perfect single-error correcting codes that exhibit efficient domination in a circulant graph, and contrast it with linear codes in a similar context.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1592-1596
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• 2007

Accuracy of flood forecasting is an important non-structural measure on the flood control and mitigation. Hence, combination of horologic model with real time error correction became an important issue. It is one of the efficient ways to improve the forecasting precision. In this work, an approach based on Kalman Filter (KF) is proposed to continuously revise state estimates to promote the accuracy of flood forecasting results. The case study refers to the Wi River in Korea, with the flood forecasting results of Xinanjiang model. Compared to the results, the corrected results based on the Kalman filter are more accurate. It proved that this method can take good effect on hydrologic forecasting of Wi River, Korea, although there are also flood peak discharge and flood reach time biases. The average determined coefficient and the peak discharge are quite improved, with the determined coefficient exceeding 0.95 for every year.