• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.247-253
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• 1999

In this paper predictive PID control system using neural network (NNPPID) is proposed to control temperature system. NNPPID is composed of neural network predictor forecasts the future output of plant based on the present input and output of plant. Neural self-tuner yields parameters of PID controller. Experiments prove that NNPPID temperature control system has better performance than conventional PID control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.698-707
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• 2017

This paper deals with predictive control methods of mobile robots for reference trajectory tracking control. Predictive control methods using predictive model are known as effective schemes that minimize the future errors between the reference trajectories and system states; however, the amount of real-time computation for the predictive control are huge so that their applications were limited to slow dynamic systems such as chemical processing plants. Lately with high computing power due to advanced computer technologies, the predictive control methods have been applied to fast systems such as mobile robots. These predictive controllers have some control parameters related to control performance. But these parameters have not been optimized. In this paper we employed the genetic algorithm to optimize the control parameters of the predictive controller for mobile robots. The improved performances of the proposed control method are demonstrated by the computer simulation studies.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.215-225
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• 2024

Predictive maintenance has been considered fundamental in the industrial applications in the last few years. It contributes to improve reliability, availability, and maintainability of the systems and to avoid breakdowns. These breakdowns could potentially lead to system shutdowns and to decrease the production efficiency of the manufacturing plants. The present article aims to study how predictive maintenance could be planed into the production scheduling, through a systematic review of literature. . The review includes the research articles published in international journals indexed in the Scopus database. 165 research articles were included in the search using #predictive maintenance# AND #production scheduling#. Press articles, conference and non-English papers are not considered in this study. After careful evaluation of each study for its purpose and scope, 50 research articles are selected for this review by following the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) statement. A benchmarking of predictive maintenance methods was used to understand the parameters that contributed to improve the production scheduling. The results of the comparative analysis highlight that artificial intelligence is a promising tool to anticipate breakdowns. An additional impression of this study is that each equipment has its own parameters that have to be collected, monitored and analyzed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.4
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• pp.467-474
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• 2001

The mathematical model of a air-conditioning system is generally very complex and difficult to apply to controller design. In this paper, simple models applicable to the controller design are obtained by modeling the air-conditioning system by single-input single-output between compressor speed and indoor temperature, and by multi-input single-output between compressor speed, indoor fan speed and indoor temperature. Using these empirical models, model predictive control(MPC) technique was implemented for indoor temperature control of the air-conditioning system. It has been shown from various experiments that the indoor temperature control based on the MPC scheme yields reasonably good tracking performance with smooth changes in plant inputs. this multi-input multi-output MPC approach can be extended to multi air- conditioning systems where the conventional PID control scheme is very difficult to apply.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.1-9
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• 2002

Among the state-space description of discrete vent systems, the max-plus algebra is known as one of the effective approach. This paper proposes a model predictive control (MPC) design method based on the max-plus algebra. Several studies related to these topics have been done so far under the constraints that system parameters are constant. However, in practical systems such as production systems, it is common and sometimes inevitable that system parameters vary by each event. Therefore, it is of worth to design a new MPC controller taking account of adjustable system parameters. In this paper, we formulate system parameters as adjustable ones, and they are solved by a linear programing method. Since MPC determines optimal control input considering future reference signals, the controller can be more robust and the operation cost can be reduced. Finally, the proposed method is applied to a production system with three machines, and the effectiveness of the proposed method is verified through a numerical simulation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.133-142
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• 2023

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.77-83
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• 2017

For efficient predictive analysis, self-healing research is needed that enables the system to recover autonomously by self-cognition and diagnosing system problems. However, software development does not provide formal contextual information analysis and appropriate presentation structure according to external situation. In this paper, we propose a prediction analysis method based on the change contents by applying the extraction rule to the functions that can act, data, and transaction based on the new Goal-scenario. We also evaluated how well the predictive analysis met through the performance indicators for achieving the requirements goal. Compared with the existing methods, the proposed method has a maximum 32.8% higher matching result through performance measurement, resulting in a 28.9% error rate and a 45.8% reduction in the change code. This shows that it can be processed into a serviceable form through rules, and it shows that performance can be expanded through predictive analysis of changes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.602-610
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• 2018

The model predictive control is an effective method to optimize the current control input that predicts the current control state and the future error using the predictive model of the control system when the reference trajectory is known. Since the control input can not have a physically infinitely large value, a predictive controller design with constraints should be considered. In addition, the reference model $A_r$ and the weight matrices Q, R that determine the control performance of the predictive controller are not optimized as arbitrarily designated should be considered in the controller design. In this study, we construct a predictive controller of a mobile robot by transforming it into a quadratic programming problem with constraints, The control performance of the mobile robot can be improved by optimizing the control parameters of the predictive controller that determines the control performance of the mobile robot using genetic algorithm. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.

• Journal of Korean Academy of Nursing
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• v.49 no.1
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• pp.26-35
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• 2019

Purpose: The aim of this study was to identify the predictive validity of the Korean Triage and Acuity Scale (KTAS). Methods: This methodological study used data from National Emergency Department Information System for 2016. The KTAS disposition and emergency treatment results for emergency patients aged 15 years and older were analyzed to evaluate its predictive validity through its sensitivity, specificity, positive predictive value, and negative predictive value. Results: In case of death in the emergency department, or where the intensive care unit admission was considered an emergency, the sensitivity, specificity, positive predictive value, and negative predictive value of the KTAS were 0.916, 0.581, 0.097, and 0.993, respectively. In case of death in the emergency department, or where the intensive or non-intensive care unit admission was considered an emergency, the sensitivity, specificity, and positive predictive value, and negative predictive value were 0.700, 0.642, 0.391, and 0.867, respectively. Conclusion: The results of this study showed that the KTAS had high sensitivity but low specificity. It is necessary to constantly review and revise the KTAS level classification because it still results in a few errors of under and over-triage. Nevertheless, this study is meaningful in that it was an evaluation of the KTAS for the total cases of adult patients who sought help at regional and local emergency medical centers in 2016.