• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1495-1504
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• 2021

An ultra-compact binary power generation plant converts thermal energy into electric power using temperature difference between heat source and cooling source. In the actual power generation environment, the characteristic value of the plant changes due to any negative effects such as environmental condition or corrosion of related equipment. If the characteristic value of the plant changes, it may lead to unstable output of the turbine in a conventional PID control system with fixed PID parameters. A Neuro PID control system based on Neural Network adaptively to adjust the PID parameters according to the change in the characteristic value of the plant is proposed in this paper. Discrete-time transfer function models to represent the dynamic characteristics near the operating point of the investigated plant are deduced, and a design strategy of the proposed control system is described. The proposed Neuro PID control system is compared with the conventional PID control system, and its effectiveness is demonstrated through the simulation results.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.451-461
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• 2015

In megavoltage (MV) radiotherapy, delivering the dose to the target volume is important while protecting the surrounding normal tissue. The purpose of this study was to evaluate the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE) using an edge block in megavoltage X-ray imaging (MVI). We used an edge block, which consists of tungsten with dimensions of 19 (thickness) ${\times}$ 10 (length) ${\times}$ 1 (width) $cm^3$ and measured the pre-sampling MTF at 6 MV energy. Various radiation therapy (RT) devices such as TrueBeam$^{TM}$ (Varian), BEAMVIEW$^{PLUS}$ (Siemens), iViewGT (Elekta) and Clinac$^{(R)}$iX (Varian) were used. As for MTF results, TrueBeam$^{TM}$(Varian) flattening filter free(FFF) showed the highest values of $0.46mm^{-1}$ and $1.40mm^{-1}$ for MTF 0.5 and 0.1. In NPS, iViewGT (Elekta) showed the lowest noise distribution. In DQE, iViewGT (Elekta) showed the best efficiency at a peak DQE and $1mm^{-1}DQE$ of 0.0026 and 0.00014, respectively. This study could be used not only for traditional QA imaging but also for quantitative MTF, NPS, and DQE measurement for development of an electronic portal imaging device (EPID).

• Journal of the Korean Geotechnical Society
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• v.21 no.8
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• pp.73-84
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• 2005

A three layered neural network model was developed using back propagation algorithm to estimate the UU undrained shear strength of Korean soft soil based on the database of actual undrained shear strengths and piezocone measurements compiled from 8 sites over the Korea. The developed model was validated by comparing model predictions with measured values about new piezocone data, which were not previously employed during development of model. Performance of the neural network model was also compared with conventional empirical methods. It was found that the number of neuron in hidden layer is different for the different combination of transfer functions of neural network models. However, all piezocone neural network models are successful in inferring a complex relationship between piezocone measurements and the undrained shear strength of Korean soft soils, which give relatively high coefficients of determination ranging from 0.69 to 0.72. Since neural network model has been generalized by self-learning from database of piezocone measurements and undrained shear strength over the various sites, the developed neural network models give more precise and generally reliable undrained shear strengths than empirical approaches which still need site specific calibration.