• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.997-1002
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• 2002

Traditional prediction models have been developed with a fixed equation form based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network (ANN) does not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this paper is to develop the I-PreConS (Intelligent system for PREdiction of CONcrete Strength using ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.446-449
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• 2004

Traditional prediction models have been developed with a fixed equation from based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network dose not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this study is to verify faith and application of prediction system of concrete strength using artificial neural networks through mock-up test.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.195-200
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• 1994

This paper presents a nonlinear adaptive control approach to a 4-point attraction magnetic levitation system using the local coordinates transformation and neural network. Based on local coordinates transformations, the magnetic levitation system can be represented in a state magnetic levitation system can be represented in a state space from of a 4-input 4-output. Neural networks which are defined in the new coordinates are used to learn the nonlinear functions of the system which are defined in the new coordinats also. The parameters of the neural networks are updated in an on-line manner according to an augmented tracking error. The simulation results are reported in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1008-1011
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• 1996

Most control systems of power plants are using classical PID controllers for their process control. In order to get the desired control performances, the correct tuning of PID controllers is very important. Sometimes, it is necessary to retune PID controllers after the change of system operating condition and system design change, etc. Commercial auto-tuning controllers such as relay feedback controller can be used for this purpose. However, using these controllers to the safety-critical systems of nuclear power plants may be cause of unsafe operation, because they are using test signals for tuning. A new system identification auto-tuning method without using test signal has been developed in this paper. This method uses process input/output signals for system identification of unknown control process. From the model information of control process which was obtained from system identification approach, the optimal PID parameters can be calculated. The method can be used in the safety-critical systems because it is not using test signals during system modeling process.

• 전기의세계
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• v.22 no.4
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• pp.17-24
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• 1973

In the view point of practical engineering the identification problem may be considered as a problem to determine the optimal model in the sense of minimizing a given criterion function using the input-output records of the plant. In the system identification the statistical approach has been known to be very effective when the topological structure of the system and the statistical characteristics of the observation noises are known a priori. But in the practical situation there are many cases when the inforhation about the observation noises or the system noises are not available a priori. Here, the authors propose a new identification method which can be used effectively even in the cases when the variances of observation noises are unknown a priori. In the method, the identification of unknown parameters of a linear diserete system is achieved by minimizing the improved quadratic criterion function which is composed of the term of square equation errors and the term to eliminate the affection of observation noises. The method also gives the estimate of noise variance. Numerical computations for several examples show that the proposed procedure gives satisfactory results even when the short time observation data are provided.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.379-384
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• 2003

QFT(Quantitative Feedback Theory) is a very practical design technique that emphasizes the use of feedback for achieving the desired system performance tolerances in despite of plant uncertainties and disturbances. The loop shaping procedure of the QFT method is employed to design the robust controller, until the desired bounds are satisfied. This paper presents a method to estimate the Pm parameters using the loop shaping of the QFT. The proposed method identifies the parameter vector of PID controller from a linear system that develops from rearranging the two dimensional input matrices and output vectors obtained from the QFT bounds. The feasibilities of the suggested algorithm are illustrated with an example.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.95-100
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• 2013

This paper presents a new state feedback control approach for communication networks based control systems in which control input and output observation time-delay natures are generally occurred in practice. We first establish a generic state feedback control framework based on well-known linear system theory. A maximum time-delay value which allows critical stability of whole control system are defined to make a positive definite Lyapunov function which is mathematically composed of controlled system states. We analytically derive its control parameters by using a steepest descent optimization method in order to guarantee a stability condition through Lyapunov theory. Computer simulation is numerically carried out for demonstrating reliability of the proposed NCS algorithm and a comparative study is accomplished to prove its superiority for which the traditional control approach for NCS is made use of under same simulation scenarios.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.7
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• pp.740-748
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• 1990

In this paper, we consider the problem of parameter estimation, i.e., definding the internal structure of a linear distribution parameter system from its input/output data. First, a linear partial differential equation describing the system is double-integrated with respect to two variables and then transformed into an integral equation. Next the Walsh Operation Matrix for Walsh function and their integration are introduced to transform the integral equation into algebraic simultaneous equations. Finally, we develop an algorithm to estimate the parameters of the linear distributed parameter system from the simple linear algebraic simultaneous equations. It is also shown that our algorithm could be effective in real time data processing since it uses the Fast Walsh Transform.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.306-314
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• 2000

In this paper we present a new approach to estimate link travel speed based on the hybrid neuro-fuzzy network. It combines the fuzzy ART algorithm for structure learning and the backpropagation algorithm for parameter adaptation. At first, the fuzzy ART algorithm partitions the input/output space using the training data set in order to construct initial neuro-fuzzy inference network. After the initial network topology is completed, a backpropagation learning scheme is applied to optimize parameters of fuzzy membership functions. An initial neuro-fuzzy network can be applicable to any other link where the probe car data are available. This can be realized by the network adaptation and add/modify module. In the network adaptation module, a CBR(Case-Based Reasoning) approach is used. Various experiments show that proposed methodology has better performance for estimating link travel speed comparing to the existing method.

• IE interfaces
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• v.7 no.3
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• pp.171-180
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• 1994

Defined is a Multi-level Parallel Machine Flow-Shop (MPMFS) which reflects some real world manufacturing situations. Just-In-Time (JIT) philosophy is applied to the MPMFS scheduling in order to achieve lowering work-in-process inventory level as well as meeting due dates. A schedule generating simulator is developed. The latest start time of each operation is determined by a backward simulation followed by another forward simulation to analyze the schedule feasibility and actual inventory level. Reasonable schedules are available through adjusting some parameters for allowance factors such as set-up times of machines and other environmental changes. The SLAMSYSTEM under Window is employed for this processing with some input/output data handling processes devised under DOS.