• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1516-1519
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• 1997

With disseminationof Distributed Control Systems(DCS), the huge amounts of process operation data could have been available and led to figure out process behaviors better on the statistical basis. Until now, the statistical modeling technology has been susally applied to process monitoring and fault diagnosis. however, it has been also thought that these process information, extracted from statistical analysis, might serve a great opportunity for process operation improvements and process improvements. This paper proposed a general methodolgy for process operation improvements including data analysis, backing up the result of analysis based on the methodology, and the mapping physical physical phenomena to the Principal Components(PC) which is the most distinguished feature in the methodology form traditional statistical analyses. The application of the proposed methodology to the Balst Furnace(BF) process has been presented for details. The BF process is one of the complicated processes, due to the highly nonlinear and correlated behaviors, and so the analysis for the process based on the mathematical modeling has been very difficult. So the statisitical analysis has come forward as a alternative way for the useful analysis. Using the proposed methodology, we could interpret the complicated process, the BF, better than any other mathematical methods and find the direction for process operation improvement. The direction of process operationimprovement, in the BF case, is to increase the fludization and the permeability, while decreasing the effect of tapping operation. These guide directions, with those physical meanings, could save fuel cost and process operator's pressure for proper actions, the better set point changes, in addition to the assistance with the better knowledge of the process. Open to set point change, the BF has a variety of steady state modes. In usual almost chemical processes are under the same situation with the BF in the point of multimode steady states. The proposed methodology focused on the application to the multimode steady state process such as the BF, consequently can be applied to any chemical processes set point changing whether operator intervened or not.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.4
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• pp.33-42
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• 2004

The usual practice of placing viscoelastic dampers (VED) in the inter-story of building structures frequently interfere with spatial planning and obstruct internal view. These shortcomings can be overcome by installing VED in seismic joints or in expansion joints which are usually hidden under a cover. This study investigates the effect of installing VED in seismic joints to reduce earthquake-induced dynamic reponses. Parametric studies were conducted using 3-DOF systems connected by VED and subjected to earthquake excitations to investigate the effectiveness of the proposed scheme. Nonlinear dynamic analyses were carried out with five-story structures composed of different structure systems and connected by seismic joints. According to the analysis results the use of VED in seismic joints turned out to be effective as long as the natural frequencies of the connected structures are different enough.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.637-648
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• 2007

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.199-208
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• 2017

Lithium polymer batteries are used in energy storage systems (ESS), electric vehicles (EVs), etc. due to their high safety, fast charging and long lifecycle, and now they are used in agricultural drones. However, when overcharging and overdischarging, the lithium-polymer battery is destroyed in the gap structure in the lithium-ion battery and the battery life is reduced. In order to prevent overcharge and overdischarge, uneven cell voltage Cell balancing system is needed. In this paper, a fuzzy controller suitable for nonlinear systems is proposed by detecting the unbalanced cells by detecting the voltage difference between charging and discharging of each cell, and suggesting the applied cell balancing algorithm. In this paper, we have designed the cell balancing of the battery pack of agricultural drones by fuzzy control and it is designed for equal control between cells. As a final result, we checked whether cell balancing is good, and when there are two cells, Cell balancing was confirmed. We tested whether it could be used for other products. As a result, we confirmed that cell balancing is good regardless of the number of cells used.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.1-6
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• 2015

This paper investigates the longitudinal flight dynamics and stability of flapping-wing micro air vehicles. Periodic external forces and moments due to the flapping motion characterize the dynamics of this system as NLTP (Non Linear Time Periodic). However, the averaging theorem can be applied to an NLTP system to obtain an NLTI (Non Linear Time Invariant) system which allows us to use a standard eigen value analysis to assess the stability of the system with linearization around a reference point. In this paper, we investigate the dynamics and stability of a hawkmoth-scale flapping-wing air vehicle by establishing an LTI (Linear Time Invariant) system model around a hovering condition. Also, a direct time integration of full nonlinear equations of motion of the flapping-wing micro air vehicle is conducted to see how the longitudinal flight dynamics appear in the time domain beyond the reference point, i.e. hovering condition. In the study, the flapping-wing air vehicle exhibited three distinct dynamic modes of motion in the longitudinal plane of motion: two stable subsidence modes and one unstable oscillatory mode. The unstable oscillatory mode is found to be a combination of a pitching velocity state and a forward/backward velocity state.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.569-575
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• 2013

This paper proposes a method for PDR (Pedestrian Dead-Reckoning) using a low cost IMU. Generally, GPS has been widely used for localization of pedestrians. However, GPS is disabled in the indoor environment such as in buildings. To solve this problem, this research suggests the PDR scheme with an IMU attached to the pedestrian's waist. However, despite the fact many methods have been proposed to estimate the pedestrian's position, but their results are not sufficient. One of the most important factors to improve performance is, a new calibration method that has been proposed to obtain the reliable sensor data. In addition to this calibration, the PDR method is also proposed to detect steps, where estimation schemes of step length, attitude, and heading angles are developed. Peak and zero crossings are detected to count the steps from 3-axis acceleration values. For the estimation of step length, a nonlinear step model is adopted to take advantage of using one parameter. Complementary filter and zero angular velocity are utilized to estimate the attitude of the IMU module and to minimize the heading angle drift. To verify the effectiveness of this scheme, a real-time system is implemented and demonstrated. Experimental results show an accuracy of below 1% and below 3% in distance and position errors, respectively, which can be achievable using a high cost IMU.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.283-288
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• 2018

In this paper, it is required that the next generation wireless transmission system can support a large number of users without distortion of transmission signal with high data rate in various different propagation environment while using limited resources as efficiently as possible, and therefore an efficient transmission system is continuously required. Because of the large amount of data to be handled in a limited frequency band, a very complex digital modulation scheme is adopted. the linearity of the power amplifier determines the linearity of the entire communication system, and thus a linear amplifier is required. In cognitive radion systems, there is a power control issue in the relationship between primary and secondary users. This problem is solved by simulating the communication system so as to select the cognitive radio power while power control while overcoming linearity by using feed-forward PA.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.145-154
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• 2006

In the companion paper, the composition and structure of the MATDYMO (Multi-Agent for Traffic Simulation with Vehicle Dynamic Model) were proposed. MATDYMO consists of the road management system, the vehicle motion control system, the driver management system, and the integration control system. Among these systems, the road management system and the integration control system were discussed In the companion paper. In this paper, the vehicle motion control system and the driver management system are discussed. The driver management system constructs the driver agent capable of having different driving styles ranging from slow and careful driving to fast and aggressive driving through the yielding index and passing index. According to these indices, the agents pass or yield their lane for other vehicles; the driver management system constructs the vehicle agents capable of representing the physical vehicle itself. A vehicle agent shows its behavior according to its dynamic characteristics. The vehicle agent contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation is conducted for an interrupted flow model and its results are verified by comparison with the results from a commercial software, TRANSYT-7F. The interrupted flow model simulation is implemented for three cases. The first case analyzes the agents' behaviors in the interrupted flow model and it confirms that the agent's behavior could characterize the diversity of human behavior and vehicle well through every rule and communication frameworks. The second case analyzes the traffic signals changed at different intervals and as the acceleration rate changed. The third case analyzes the effects of the traffic signals and traffic volume. The results of these analyses showed that the change of the traffic state was closely related with the vehicle acceleration rate, traffic volume, and the traffic signal interval between intersections. These simulations confirmed that MATDYMO can represent the real traffic condition of the interrupted flow model. At the current stage of development, MATDYMO shows great promise and has significant implications on future traffic state forecasting research.