• Tunnel and Underground Space
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• v.20 no.2
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• pp.97-104
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• 2010

The materials failure relation $\ddot{\Omega}=A{(\dot{\Omega})}^\alpha$ where $\Omega$ is a measurable quantity such as displacement and the dot superscript is the time derivative, may be used to analyze the accelerating creep of materials. Coefficients, A and $\alpha$, are determined by fitting given data sets. In this study, it is tried to predict the failure time of tunnel using the materials failure relation. Four fitting techniques of applying the materials failure relation are attempted to forecast a failure time. Log velocity versus log acceleration technique, log time versus log velocity technique, inverse velocity technique are based on the linear least squares fits and non-linear least squares technique utilizes the Levenberg-Marquardt algorithm. Since the log velocity versus log acceleration technique utilizes a logarithmic representation of the materials failure relation, it indicates the suitability of the materials failure relation applied to predict a failure time of tunnel. A linear correlation between log velocity and log acceleration appears satisfactory(R=0.84) and this represents that the materials failure relation is a suitable model for predicting a failure time of tunnel. Through comparing the real failure time of tunnel with the predicted failure times from four curve fittings, it is shown that the log time versus log velocity technique results in the best prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.97-103
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• 2000

In this paper band-stop filters that reject the undesired 3th and/or 5th harmonics generated from high frequency generator are analyzed using equivalent circuit concept, and optimized using iterative optimization algorithm. The length of the band-stop filter is setup initially a quarter of a wavelength of the harmonic, but initial filter structure is exactly unable to cutoff the harmonics because of the fringing effect at the discontinuous boundary. To reduce the mismatch between the cutoff frequency and the harmonics, iterative LMA(Levenberg-Marquardt Algorithm) is introduced and band-stop filters in coaxial line are optimized.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.88-95
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• 2015

This paper presents the study of the efficient localization algorithm for non-linear least square estimation. Although non-linear least square(NLS) estimation algorithms are more accurate algorithms than linear least square(LLS) estimation, NLS algorithms have more computation loads because of iterations. This study proposed the efficient algorithm which reduced complexity for small accuracy loss in NLS estimation. Simulation results show the accuracy and complexity of the localization system compared to the proposed algorithm and conventional schemes.