• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.744-748
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• 1989

A newly developed Expert System (ES) for the tuning of thermal power plant control equipment is described. The system is furnished with the rules for controller tuning which were obtained by analysis and arranging the data and knowledge from the experts or tuning records. Based on these rules, automatic tuning or setting of the control parameters is performed in real-time base. The performance of the test equipment, a combination of ES and a boiler simulator, was examined in the automatic tuning test for steam pressure, steam temperature, and load controllers of a constant-pressure once-through boiler model. It was confirmed from the test results that the system is quite promising for future application to actual plants, since the tuning results obtained by the proposed system were similar to those by tuning experts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3619-3637
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• 2022

Big data analytics offers endless opportunities for operational enhancement by extracting valuable insights from complex voluminous data. Hadoop is a comprehensive technological suite which offers solutions for the large scale storage and computing needs of Big data. The performance of Hadoop is closely tied with its configuration settings which depends on the cluster capacity and the application profile. Since Hadoop has over 190 configuration parameters, tuning them to gain optimal application performance is a daunting challenge. Our approach is to extract a subset of impactful parameters from which the performance enhancing sub-optimal configuration is then narrowed down. This paper presents a statistical model to analyze the significance of the effect of Hadoop parameters on a variety of performance metrics. Our model decomposes the total observed performance variation and ascribes them to the main parameters, their interaction effects and noise factors. The method clearly segregates impactful parameters from the rest. The configuration setting determined by our methodology has reduced the Job completion time by 22%, resource utilization in terms of memory and CPU by 15% and 12% respectively, the number of killed Maps by 50% and Disk spillage by 23%. The proposed technique can be leveraged to ease the configuration tuning task of any Hadoop cluster despite the differences in the underlying infrastructure and the application running on it.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.346-349
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• 1996

In this paper development of a CAD of control systems is introduced which enables us to do not only analysis of control systems, design of controllers but also real-time implementation of controllers. By utilizing this software, the control engineer is able to repeat the procedure of modification of controllers and experiments without recompile to attain better performance. The software also offers the facility to update the parameters of controllers without stopping real-time control, which helps on-line tuning of controllers. If some parameters of the controller is changed on-line, the control input may change discontinuously. It has serious effect on the control systems. A method for on-line tuning of state feedback controller with state observer is proposed and verified through the experiment with an inverted pendulum.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.1
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• pp.116-123
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• 2005

The PID controllers have been widely accepted in many industrial systems due to their robust performance in a wide range of operating conditions and their functional simplicity To implement a PID controller, its three parameters must be determined for the given plant. Conventional tuning methods are mainly based on experience and experiment and are lack of systematic procedure Recently. to overcome drawbacks of conventional tuning methods, genetic algorithms have been used, In this paper a real-coded genetic algorithm is employed to search for the optimal parameters of the PID controller for speed control of marine gas turbine engines. Simulation results show the effectiveness of the proposed scheme.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.87-88
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• 2006

In this paper, tuning rules of the PID controller for load disturbance rejection are proposed incorporating with real-coded genetic algorithms(RCGAs). The optimal parameters sets of the PID controller are obtained based on a first-order plus time delay model and a RCGA. As for assessing the performance of the controller, criteria(ISE, IAE and ITAE) are adopted. Then tuning formulae are derived using the tuned parameters sets, potential tuning rule models and another RCGA. A simulation work is carried out to verify the effectiveness of the proposed rules.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.644-649
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• 2006

In this paper, a methodology for estimating the model parameters of a discrete-time system and tuning a digital PID controller based on the estimated model and a genetic algorithm is presented. To deal with optimization problems regarding parameter estimation and controller tuning, pseudo-parallel genetic algorithms(PPGAs) are used. The parameters of a discrete-time system are estimated using both the model adjustment technique and a PPGA. The digital PID controller is described by the pulse transfer function and then its three gains are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.

• 한국연소학회:학술대회논문집
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• 2012.04a
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• pp.195-196
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• 2012

This paper describes combustion tuning methodology of double-swirl gas turbine combustor using six sigma tools. This methodology is consist of five steps-Define, Identify, Design, Optimize and Verify (DIDOV). First, the NOx reduction target was defined in the step design; second, the current status of the plant was diagnosed in the step of identify; third, the vital few control parameters to achieve the defined target were determined by analyzing the correlation between the control parameters and NOx emissions in the step of design; fourth, the optimum condition was derived from one of the six sigma tools in the step of optimize; finally, the optimum condition was verified by applying the condition to the gas turbine combustor in the step of verify. As a result of the suggested method, averaged NOx emissions were reduced by more than 70% and the standard deviation was improved by more than 60%. Thus, this methodology can be attributed to the efficient reduction of NOx emission with saving combustion tuning time.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.289-300
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• 2005

In this article quick variance change point (VCP) detection problem for time series is considered. For this variance VCP detector equipped with tuning parameters is proposed. A major tool for the detector is moving variance ratio (MVR) which monitors variance change of a given time series. Tuning process of detector is investigated via simulation, which shows that tuning parameters are critical in achieving sensitivity and adaptiveness of detector.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• Journal of KIISE:Databases
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• v.31 no.4
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• pp.373-383
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• 2004

There have been an emerging interests in the importance of database tuning techniques under modem database environments in which very large-scale data should be managed. In Particular. database performance parameters should be tuned to reflect system loads appropriately. This paper presents two parameter tuning strategies, namely throughput-based and response-time-based, which tune each performance parameter accordingly. The proposed techniques are applied to two commercial database systems in the TPC-W benchmark to see the effectiveness of those methods. The results show that they can help improve system performance considerably.