• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.81-90
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• 2018

In this paper, reducing lense Vignetting effect and adaptive learning rate method are proposed to complement Scribner's neural network for nuc algorithm which is the effective algorithm in statistic SBNUC algorithm. Proposed reducing vignetting effect method is updated weight and bias each differently using different cost function. Proposed adaptive learning rate for updating weight and bias is using sobel edge detection method, which has good result for boundary condition of image. The ordinary statistic SBNUC algorithm has problem to compensate lense vignetting effect, because statistic algorithm is updated weight and bias by using gradient descent method, so it should not be effective for global weight problem same like, lense vignetting effect. We employ the proposed methods to Scribner's neural network method(NNM) and Torres's reducing ghosting correction for neural network nuc algorithm(improved NNM), and apply it to real-infrared detector image stream. The result of proposed algorithm shows that it has 10dB higher PSNR and 1.5 times faster convergence speed then the improved NNM Algorithm.

• Toxicological Research
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• v.18 no.3
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• pp.293-300
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• 2002

Chemical carcinogen-induced alteration of aldehyde metabolic enzymes were examined in clone 9 cell. Diethylnitrosamine (DENA), N-nitrosoethylurea (NEU) and N-nitrosomorpholine (NNM) were wed as model carcinogens. Changes in enzyme activities by repetitive treatment of DENA, NEU or NNM were analyzed in terms of specific activities and activity stainings of the enzymes on the gel. Upon treatment of DENA, lipid peroxide level increased upto 10 fold, indicating strong oxidative stress state of the cell. Notable enhancement of ADH and ALDH activity occurred after DENA treatment, while glutathione-S-transferase activity was slightly increased. Furthermore, about 2.5 fold higher superoxide dismutase (SOD) activity was detected during deactivation of catalase (CAT) activity by repetitive treatment of DENA. However in NEU-treated cell, about 2.3 fold higher ALDH activity was found while ADH activity was slightly increased. Notable increase CAT and SOD could also be found. In contrast, maximum 3.5 fold higher CAT activity occurred during SOD deactivation in NNM-treated cell. These results suggest that there might be different enzymatic responses in relation to cell protection against DENA, NEU or NNM.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.399-412
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• 2010

The goal of this research is to apply the neural networks models for the temporal disaggregation of the yearly pan evaporation (PE) data, Republic of Korea. The neural networks models consist of multilayer perceptron neural networks model (MLP-NNM) and generalized regression neural networks model (GRNNM), respectively. And, for the performances evaluation of the neural networks models, they are composed of training and test performances, respectively. The three types of data such as the historic, the generated, and the mixed data are used for the training performance. The only historic data, however, is used for the testing performance. From this research, we evaluate the application of MLP-NNM and GRNNM for the temporal disaggregation of nonlinear time series data. We should, furthermore, construct the credible monthly PE data from the temporal disaggregation of the yearly PE data, and can suggest the available data for the evaluation of irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1207-1210
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• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1215-1218
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• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training data consist of the mixed data The mixed data involves the historic data and the generated data using PARMA (1,1). And, the testing data consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1211-1214
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• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training data consist of the generated data using PARMA (1,1). And, the testing data consist of the historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1346-1349
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• 2010

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training and test performances consist of the historic, the generated, and the mixed data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Smart Structures and Systems
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• v.3 no.4
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• pp.423-437
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• 2007

The empirical mode decomposition (EMD) method is well-known for its ability to decompose a multi-component signal into a set of intrinsic mode functions (IMFs). The method uses a sifting process in which local extrema of a signal are identified and followed by a spline fitting approximation for decomposition. This method provides an effective and robust approach for decomposing nonlinear and non-stationary signals. On the other hand, the IMF components do not automatically guarantee a well-defined physical meaning hence it is necessary to validate the IMF components carefully prior to any further processing and interpretation. In this paper, an attempt to use the EMD method to identify properties of nonlinear elastic multi-degree-of-freedom structures is explored. It is first shown that the IMF components of the displacement and velocity responses of a nonlinear elastic structure are numerically close to the nonlinear normal mode (NNM) responses obtained from two-dimensional invariant manifolds. The IMF components can then be used in the context of the NNM method to estimate the properties of the nonlinear elastic structure. A two-degree-of-freedom shear-beam building model is used as an example to illustrate the proposed technique. Numerical results show that combining the EMD and the NNM method provides a possible means for obtaining nonlinear properties in a structure.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1922-1927
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• 2003

Nonlinear normal mode (NNM) vibration, in a nonlinear dual mass Hamiltonian system, which has 6$\^$th/ order homogeneous polynomial as a nonlinear term, is studied in this paper. The existence, bifurcation, and the orbital stability of periodic motions are to be studied in the phase space. In order to find the analytic expression of the invariant curves in the Poincare Map, which is a mapping of a phase trajectory onto 2 dimensional surface in 4 dimensional phase space, Whittaker's Adelphic Integral, instead of the direct integration of the equations of motion or the Birkhoff-Gustavson (B-G) canonical transformation, is derived for small value of energy. It is revealed that the integral of motion by Adelphic Integral is essentially consistent with the one obtained from the B-G transformation method. The resulting expression of the invariant curves can be used for analyzing the behavior of NNM vibration in the Poincare Map.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.6
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• pp.321-327
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• 2019

We performed seismic noise level analysis to access the proper functioning of 11 newly established seismic stations in the southeastern region of Korea. One-hour long segments of seismograms were selected from the continuous data of the 3 elements for 61 days from March 1, 2019. For each segment of data, the power spectral density (PSD) was estimated from the continuous back ground noise data of the 3 elements for periods ranging from 0.02~100 s. The median noise levels (NLs) of the stations were compared with the new noise model (NNM) of USGS and NLs of station TJN installed in a tunnel on a granite basement. We observed that the NLs of the newly installed seismometers were between the upper and lower limit of the NNM. In a comparison with the noise level of station TJN, the new seismometers had their own noteworthy features. The NLs from accelerometers (Epi-sensors) were ~ 40 dB higher than the NLs from velocimeters (STS-sensors) for periods > 10 s, which is because the small and light Epi-sensors are sensitive to environmental changes. Daily and weekly variations in spectral noise level were observed clearly in short periods < 1 s, and these are considered to be related to human activities. The seismometers in boreholes showed ~20 dB weaker NLs in the cultural noise band. The NLs of accelerometers at a depth of 30 m were also much lower by 30 dB for long periods > 10 sec. Overall the functioning of the new velocimeter and accelerometer stations was reliable for periods ranging from 0.02~100 s and 0.02~10 s, respectively.