• Nuclear Engineering and Technology
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• 제49권1호
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• pp.134-140
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• 2017

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

• 한국정밀공학회지
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• 제17권2호
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• pp.114-120
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• 2000

Fossil power plant availability is significantly affected by gradual degradations of equipment as operation of the plant continues. It is quite important to determine whether or not to replace some equipment and when to replace the equipment. Performance calculation and analysis can provide the information. Robustness in the performance calculation can be increased by using verification ＆ validation of measured input data. We suggest new algorithm in which estimation relation for validated measurement can be obtained using correlation between measurements. Input estimation model is obtained using design data and acceptance measurement data of domestic 16 fossil power plant. The model consists of finding mostly correlated state variable in plant state and mapping relation based on the model and current state of power plant.

• 전기학회논문지
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• 제57권7호
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• pp.1247-1253
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• 2008

A design method of a T-S fuzzy model based reduced order nonlinear unknown input observer(NUIO) is presented. The fuzzy NUIO is designed based on the parallel distributed compensation(PDC) concept. It consists of a number of the linear UIOs, each of which is designed for each local linear model in the T-S fuzzy model of a class of nonlinear systems. The fuzzy NUIO provides not only the state estimates insensitive to the unknown inputs, for example, disturbances and faults etc., but also the estimates of the unknown inputs. Therefore, It can be employed in the state feedback control and disturbance rejection control of a class of nonlinear systems with unknown disturbances. It also applied to the robust residual generation for the fault detection and isolation systems and to the design of fault tolerant control systems. As an example, the NUIO is applied to an inverted pendulum system to show the state and disturbance estimation performance and to illustrate the fuzzy reduced order NUIO design method.

• Journal of Power Electronics
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• 제19권2호
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• pp.497-508
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• 2019

The predictive speed control (PSC) strategy can realize the simultaneous control of speed and current by using one cost function. As a model-based control method, the performance of the PSC is vulnerable to model mismatches such as load torque disturbances and parameter uncertainties. To solve this problem, this paper presents a robust predictive speed control (RPSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed RPSC uses extended state observers (ESOs) to estimate the lumped disturbances caused by load torque changes and parameter mismatches. The observer-based prediction model is then compensated by using the estimated disturbances. The introduction of ESOs can achieve robustness against predictive model uncertainties. In addition, a modified cost function is designed to further suppress load torque disturbances. The performance of the proposed RPSC scheme has been corroborated by experimental results under the condition of load torque changes and parameter mismatches.

• Journal of Electrical Engineering and Technology
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• 제7권6호
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• pp.891-897
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• 2012

In this paper, the observer based nonlinear state feedback controller has been developed to control the pressures of the oxygen and the hydrogen in the PEM(Proton Exchange Membrane) fuel cell system. Nonlinear model of the PEM fuel cell system was introduced to study the design problems of the state observer and model based controller. A cascade observer using the filtering technique was used to estimate the pressure derivatives of the cathode and the anode in the system. In order to estimate the pressures of the cathode and the anode, the sliding mode observer was designed by using these pressure derivatives. To estimate the oxygen pressure and the hydrogen pressure in the system, the nonlinear state observer was designed by using the cathode pressure estimates and the anode it. These results will be very useful to design the state feedback controller. The validity of the proposed observers and the controller has been investigated by using the Lyapunov's stability analysis strategy.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2001년도 가을 학술발표회 논문집
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• pp.1109-1114
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• 2001

This paper describes an attempt to develop a new truss model for reinforced concrete beams failing in shear based on a rational behavioral model. The key idea incorporated with truss model is the internal force state factor which is able to express global state of internal force flow in cracked reinforced concrete beams subjected to shear and bending. A new truss model using internal force state factor may provide a comprehensive result of shear strength in reinforced concrete beams without web reinforcement.

• Journal of Information Processing Systems
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• 제19권2호
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• 한국항해항만학회지
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• 제48권2호
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• pp.116-124
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• 2024

Automatic docking of small planing ship is a critical aspect of maritime operations, requiring accurate prediction of motion states to ensure safe and efficient maneuvers. This study investigates the use of Artificial Neural Network (ANN) to predict motion state of a small planing ship to enhance navigation automation in port environments. To achieve this, simulation tests were conducted to control a small planing ship while docking at various heading angles in calm water and in waves. Comprehensive analysis of the ANN-based predictive model was conducted by training and validation using data from various docking situations to improve its ability to accurately capture motion characteristics of a small planing ship. The trained ANN model was used to predict the motion state of the small planning ship based on any initial motion state. Results showed that the small planing ship could dock smoothly in both calm water and waves conditions, confirming the accuracy and reliability of the proposed method for prediction. Moreover, the ANN-based prediction model can adjust the dynamic model of the small planing ship to adapt in real-time and enhance the robustness of an automatic positioning system. This study contributes to the ongoing development of automated navigation systems and facilitates safer and more efficient maritime transport operations.

• Asian Pacific Journal of Cancer Prevention
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• 제15권1호
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• pp.441-447
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• 2014

Background: Multi-state models are appropriate for cancer studies such as gastrectomy which have high mortality statistics. These models can be used to better describe the natural disease process. But reaching that goal requires making assumptions like Markov and homogeneity with time. The present study aims to investigate these hypotheses. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. To assess Markov assumption and time homogeneity in modeling transition rates among states of multi-state model, Cox-Snell residuals, Akaikie information criteria and Schoenfeld residuals were used, respectively. Results: The assessment of Markov assumption based on Cox-Snell residuals and Akaikie information criterion showed that Markov assumption was not held just for transition rate of relapse (state 1 ${\rightarrow}$ state 2) and for other transition rates - death hazard without relapse (state 1 ${\rightarrow}$ state 3) and death hazard with relapse (state 2 ${\rightarrow}$ state 3) - this assumption could also be made. Moreover, the assessment of time homogeneity assumption based on Schoenfeld residuals revealed that this assumption - regarding the general test and each of the variables in the model- was held just for relapse (state 1 ${\rightarrow}$ state 2) and death hazard with a relapse (state 2 ${\rightarrow}$ state 3). Conclusions: Most researchers take account of assumptions such as Markov and time homogeneity in modeling transition rates. These assumptions can make the multi-state model simpler but if these assumptions are not made, they will lead to incorrect inferences and improper fitting.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1995년도 봄 학술발표회 논문집
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• pp.286-291
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• 1995

The deterioration of PC box girder may cause serious effect on the durability of PC structure compared to that of RC structures. In the durability assessment of PC box girder bridges, a quantitive model on crack width is considered as a measure of durability. This study suggests a durability limit state model for PC box girder bridges. This durability limit state model in formulated based on the conventional models on the cracks in concrete. And the allowable crack width is taken as an assumed value established by the design specification or provided by the maintenance authority of the structure.