• Journal of Environmental Health Sciences
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• v.28 no.3
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• pp.19-25
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• 2002

In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)$_{12}$, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

• Biomedical Science Letters
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• v.19 no.1
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• pp.55-60
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• 2013

This study was to know the effects of massage on the back region in order to reduce stress in middle age women. To investigate the effects of massage to the stress levels of middle aged women, we evaluated blood pressure (BP), heart rate variability (HRV), and ultrasonography before and after back massage. The blood pressure after massage was reduced when compared to that of pre-massage. The HRV spectrum analysis was used Frequency domain analysis such as Mean HRV, normalized low frequency (norm LF), norm high frequency (norm HF), and LF/HF ratio. Post-massage BP tended to be low, but not statistical significant. After Massage, the Mean HRV, norm LF, and LF/HF ratio were significantly reduced, while norm HF was significantly increased as compared with pre-massage. The muscle layer and fat layer were significantly diminished by massage. The study was suggested that massage may be an effective treatment for relief of stress.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.125-130
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• 2003

This Paper describes a Nonlinear adoptive noise canceller using Neural Network for Machine Tools Controller System. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this Paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental results show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary Input is divided by Unit and each divided pan is processed for very short time than all the processed data are unified to whole data.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.345-351
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• 2004

This Paper describes a nonlinear adaptive noise filter using neural network for digital controller system. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental reaults show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary input is divided by unit and each divided part is processed for very short time than all the processed data are unified to whole data.

• The Korean Journal of Ecology
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• v.13 no.1
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• pp.25-32
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• 1990

The vertical distribution and seasonal fluctuation of soil microarthropods in the forests with different flora were investigated in this study. soil micrarthropods were concentrated as much as 71.8% in the first layer subsoil (0-5cm), 22.3% in the second layer subsoil (5-10cm), and 5.9% in the third layer subsoil (10-15cm) in the decreasing order. The population density in the first layer decreased slightly in winter while that of the second layer increased. However, the density in the first layer bounced back in the following spring. Seasonal fluctuations of population density were revealed "Two peak-Two valley type", that is, the densities were high in fall and spring, and low in winter and summer.nd summer.

• Journal of Environmental Science International
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• v.11 no.3
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• pp.201-207
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• 2002

This study was carried out to evaluate the artificial neural network algorithm for water quality forecasting in Chungju lake, north Chungcheong province. Multi-layer perceptron(MLP) was used to train artificial neural networks. MLP was composed of one input layer, two hidden layers and one output layer. Transfer functions of the hidden layer were sigmoid and linear function. The number of node in the hidden layer was decided by trial and error method. It showed that appropriate node number in the hidden layer is 10 for pH training, 15 for DO and BOD, respectively. Reliability index was used to verify for the forecasting power. Considering some outlying data, artificial neural network fitted well between actual water quality data and computed data by artificial neural networks.

• Applied Science and Convergence Technology
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• v.23 no.4
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• pp.169-178
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• 2014

Fluid model based numerical analysis is done to simulate a low damage etch back system for 20 nm scale semiconductor fabrication. Etch back should be done conformally with very high material selectivity. One possible mechanism is three steps: reactive radical generation, adsorption and thermal desorption. In this study, plasma generation and transport steps are analyzed by a commercial plasma modeling software package, CFD-ACE+. Ar + $CF_4$ ICP was used as a model and the effect of reactive gas inlet position was investigated in 2D and 3D. At 200~300 mTorr of gas pressure, separated gas inlet scheme is analyzed to work well and generated higher density of F and $F_2$ radicals in the lower chamber region while suppressing ions reach to the wafer by a double layer conducting barrier.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1041-1044
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• 2000

The enhancement to the back-propagation algorithm presented in this paper has resulted from the need to extract sparsely connected networks from networks employing product terms. The enhancement works in conjunction with the back-propagation weight update process, so that the actions of weight zeroing and weight stimulation enhance each other. It is shown that the error measure, can also be interpreted as rate of weight change (as opposed to ${\Delta}W_{ij}$), and consequently used to determine when weights have reached a stable state. Weights judged to be stable are then compared to a zero weight threshold. Should they fall below this threshold, then the weight in question is zeroed. Simulation of such a system is shown to return improved learning rates and reduce network connection requirements, with respect to the optimal network solution, trained using the normal back-propagation algorithm for Multi-Layer Perceptron (MLP), Higher Order Neural Network (HONN) and Sigma-Pi networks.

• Proceedings of the Korean Magnestics Society Conference
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• 2007.12a
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• pp.68-69
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• 2007

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.9
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• pp.701-706
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• 2013

We have focused on the conversion efficiency of CIGS thin film solar cell prepared by co-evaporation method as well as the optimization of process condition. The total thickness of back electrode was fixed at 1 ${\mu}m$ and the structural, electric and optical properties of CIGS thin film were investigated by varying the thickness of Mo:Na bottom layer from 0 to 500 nm. From the experimental results, the content of Na was appeared as 0.28 atomic percent when the thickness of Mo:Na layer was 300 nm with compactly densified plate-shape surface morphology. From the XRD measurements, (112) plane was the strongest preferential orientation together with secondary (220) and (204) planes affecting to the crystallization. The lowest roughness and resistivity were 2.67 nm and 3.9 ${\Omega}{\cdot}cm$, respectively. In addition, very high carrier density and hole mobility were recorded. From the optimization of Mo:Na layer, we have achieved the conversion efficiency of 9.59 percent.