• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.2-102
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• 2002

In this paper, the improved Multi-FNN (Fuzzy-Neural Networks) model is identified and optimized using HCM (Hard C-Means) clustering method and optimization algorithms. The proposed Multi-FNN is based on FNN and use simplified and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and genetic algorithms (GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parame...

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.48-54
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• 1994

A recursive on-line algorithm with orthogonal ARMA identification is proposed for linear systems with unkonwn time delay, order, and parameters. The algorithm is based on the Gram-Schmidt orthogonalization of basis functions, and extendedto recursive form by using two dimensional autocorrelations and crosscorrelations of input and output with constant data length. The proposed algorith can cope with slowly time-varying or order-varying delayed system. Various simulations reveal the performance of the algorithm.

• Journal of electromagnetic engineering and science
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• v.17 no.4
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• pp.208-220
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• 2017

This paper proposes an experimental optimization method for a wireless power transfer (WPT) system. The power transfer characteristics of a WPT system with arbitrary loads and various types of coupling and compensation networks can be extracted by frequency domain measurements. The various performance parameters of the WPT system, such as input real/imaginary/apparent power, power factor, efficiency, output power and voltage gain, can be accurately extracted in a frequency domain by a single passive measurement. Subsequently, the design parameters can be efficiently tuned by separating the overall design steps into two parts. The extracted performance parameters of the WPT system were validated with time-domain experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.346-350
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• 1996

This paper presents a Neural Network Identification (NNI) method for modeling of highly complicated nonlinear and time varing human system with a pneumatically driven mock circulation system of Left Ventricular Assist Device(LVD). This system consists of electronic circuits and pneumatic driving circuits. The initation of systole and the pumping duration can be determined by the computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded. System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, Heart Rate(HR), Systole-Diastole Rate(SDR), which can vary state of system, and preload, afterload, which indicate the systemic dynamic characteristics and output parameters are preload, afterload.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.276-280
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• 1992

In the paper, an optimal tuning algorithm is presented to automatically improve the performance of a hybrid controller, using the simplified reasoning method and the proposed complex method. The method estimates automatically the optimal values of the parameters of a hybrid controller, according to the change rate and limitation condition of output, The controller is applied to plants with time-delay. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.47-50
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• 2000

In this paper, we propose PID type direct control method using single neuron neural network. The proposed method has an output error and 2 time-delay as inputs and is designed to have input weights composed of P, I, D parameters to be controlled through teaming. We could verify the better performance of this system than the conventional method through simulations. And the reduced calculation, due to single neuron, makes it possible the real time processing, and the simple implementation.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.71-90
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• 2016

Steel nitriding is a thermo-chemical process leading to surface hardening and improvement in fatigue properties. The process is strongly influenced by many different variables such as steel composition, nitrogen potential, temperature, time, and quenching media. In the present study, the influence of such parameters affecting physic-chemical and mechanical properties of nitride steels was evaluated. The aim was to streamline the process by numerical-experimental analysis allowing defining the optimal conditions for the success of the process. Input parameters-output results correlations were calculated through the employment of a multi-objective optimization software, modeFRONTIER (Esteco). The mechanical and microstructural results belonging to the nitriding process, performed with different processing conditions for various steels, are presented. The data were employed to obtain the analytical equations describing nitriding behavior as a function of nitriding parameters and steel composition. The obtained model was validated, through control designs, and optimized by taking into account physical and processing conditions.

• Journal of Radiation Protection and Research
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• v.16 no.2
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• pp.41-54
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• 1991

This paper aimed to analyse dose sensitivity to the controllable parameters of indoor radon $(^{222}Rn)$ and its decay products (Rn-D) by applying the input~output linear system theory. Physical behaviors of $^{222}Rn\;&\;Rn-D$ were analyzed in terms of $(^{222}Rn)$ gas -generation, -migation and -infiltration to indoor environments, and the performance output-function, i. e. mean dose equivalent to Tracho-Bronchial (TB) lung region, was assessed to the following extented ranges of the controllable paramenters; a) the ventilation rate $constant({\lambda}_v)\;:\;0{\sim}50[h^{-l}].\;b)$ the attachment rate $constant({\lambda}_a)\;:\;0{\sim}500[h^{-l}].\;c)$ the unattached-deposition rate constant (${\lambda}^u_d)\;:\;0-50[h-l]$. A linear input-output model was reconstructed from the original models in literatures, as follows, which was modified into the matrices consisting of 111 nodal equations; a) indoor $^{222}Rn\;&\;Rn-D$ Behaviour; Jacobi-Porstendoerfer-Bruno model.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.298-300
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• 2005

Satellite payload on-board date compression unit are use for saving date storage space and reducing time to transmit payload data to the ground station. The KOMPSAT-3 payload will generate higher data rate than KOMPSAT-2 due to its better ground sample distance capacity. High input data rate and limited output transmission data rate might lead excessive compression and degraded image quality. This paper presents a trade-off study about data storage capacity and compression parameters for estimated KOMPSAT-3 system.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.475-478
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• 2004

The DCSU performs satellite payload data acquisition and storage functions before sending the data to the ground station. While imaging, the DCSU makes a file per each input channel and store into memory stack. For the successful imaging mission, proper DCSU mission parameters should be uploaded before the mission such as file name, file size, output channel for the download transmission and so on. This paper will describe the DCSU recording mechanism and some notices that might be helpful for the ground operators.