• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3866-3873
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• 2000

In this paper, we investigates the long-term memory and the Correct answer rate of the foreign exchange data (Yen/Dollar) that is one of economic time series, There are many cases where two kinds of fractal dimensions exist in time series generated from dynamical systems such as AR models that are typical models having a short terrr memory, The sample interval separating from these two dimensions are denoted by kcrossover. Let the fractal dimension be $D_1$ in K < $k^{crossover}$,and $D_2$ in K > $k^{crossover}$ from the statistics mode. In usual, Statistic models have dimensions D1 and D2 such that $D_1$ < $D_2$ and $D_2\cong2$ But it showed a result contrary to this in the real time series such as NIKKEL The exchange data that is one of real time series have relation of $D_1$ > $D_2$ When the interval between data increases, the correlation between data increases, which is quite a peculiar phenomenon, We predict exchange data by neural networks, We confirm that $\beta$ obrained from prediction errors and D calculated from time series data precisely satisfy the relationship $\beta$ = 2-2D which is provided from a non-linear model having fractal dimension, And We identified that the difference of fractal dimension appeaed in the Correct answer rate.

• Journal of Astronomy and Space Sciences
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• v.22 no.3
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• pp.263-272
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• 2005

For the Communication, Ocean and Meteorological Satellite (COMS) which will be launched in 2008, an algorithm for finding the precise location of the sun-glint point on the ocean surface is studied. The precise locations of the sun-glint are estimated by considering azimuth and elevation angles of Sun-satellite-Earth geometric position and the law of reflection. The obtained nonlinear equations are solved by using the Newton-Raphson method. As a result, when COMS is located at $116.2^{\circ}E$ or $128.2^{\circ}E$ longitude, the sun-glint covers region of ${\pm}10^{\circ}(N-S)$ latitude and $80-150^{\circ}(E-W)$ longitude. The diurnal path of the sun-glint in the southern hemisphere is curved towards the North Pole, and the path in the northern hemisphere is forwards the south pole. The algorithm presented in this paper can be applied to predict the precise location of sun-glint region in any other geostationary satellites.

• Geotechnical Engineering
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• v.1 no.1
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• pp.59-72
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• 1985

This paper deals with the numerical analysis by the (mite element method introducing Biot's theory of consolidation and the modified Cambridge model proposed by Roscoe school of Cambridge University as constitutive equation and using Christian-Boehner's technique. Especially, time interval and division of elements are investigated in vies of stability and economics. In order to check the validity of author's program, the program was tested with one-dimensional consolidation case followed by Terzaghi's exact solution and with the results of the Magnan's analysis for existing banking carried out for study at Cubzac-les-ports in France. The main conclusions obtained are summarized as follows: 1. In the case of one-dimensional consolidation, the more divided the elements are near the surface of the foundation, the higher the accuracy of the numerical analysis is. 2. For the time interval, it is stable to divide 20 times per 1-lg cycle. 3. At the element which has long drain distance, the Mandel-fryer effect appears due to time lag. 4. Lateral displacement at an initial loading stage predicted by author's program, in which the load was assumed as not concentrative. but rather in grid form, is well consistent with the value of observation. 5. The pore water pressure predicted by author's program has a better accordance with the value of observation compared with Magnan's results. 6. Optimum construction control by Matsuo-Kawamura's method is possible with the predicted lateral displacement and settlement by the program.

• Journal of the Korean Geotechnical Society
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• v.19 no.6
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• pp.261-271
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• 2003

A condition evaluation procedure for the pavement foundations using multi-load level Falling Weight Deflectometer(FWD) deflections is presented in this paper. A dynamic finite element program incorporating a stress-dependent material model, was used to generate the synthetic deflection database. Based on this synthetic database, the relationships between surface deflections and critical responses, such as stresses and strains in base and subgrade layers, have been established. FWD deflection data, Dynamic Cone Penetrometer(UP) data, and repeated load resilient modulus testing results used in developing this procedure were collected from the Long Term Pavement Performance (LTPP) and North Carolina Department of Transportation (NCDOT) database. Research effort focused on investigation of the effect of the FWD load level on the condition evaluation procedures. The results indicate that the proposed procedure can estimate the pavement foundation conditions. It is also found that structurally adjusted Base Damage Index (BDI) and Base Curvature Index (BCI) are good indicators for the prediction of stiffness characteristics of aggregate base and subgrade respectively. A FWD test with a load of 66.7 kN or less does not improve the accuracy of this procedure. Results from the study for the nonlinear behavior of a pavement foundations indicate that the deflection ratio obtained from multi-load level deflections can predict the type and quality of the pavement foundation materials.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• Journal of Wetlands Research
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• v.18 no.3
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• pp.313-323
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• 2016

Water temperature affects physical and biological processes in ecologies on river system and is important conditions for growth rate and spawning of fish species. The objective of this study is to compare models for water temperature during the summer season for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada). For this, three different models, which are CEQUEAU, Auto-regressive Moving Average with eXogenous input and Nonlinear Autoregressive with eXogenous input, were applied and compared. Also, future water temperature in the Fourchue river were simulated and analyzed its result based on the CMIP5 climate models, RCP 2.6, 4.5, 8.5 climate change scenarios. As the result of the study, the water temperature in the Fourchue river are actually changed and median water temperature will increase $0.2{\sim}0.7^{\circ}C$ in June and could decrease by $0.2{\sim}1.1^{\circ}C$ in September. Also, the UILT ($24.9^{\circ}C$) for brook trout are also likely to occurred for several days.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.119-133
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• 2014

The study adopted extended Kalman filter technique in an effort to predict Z-R relationship parameter as a stable value in real-time. Toward this end, a parameter estimation model was established based on extended Kalman filter in consideration of non-linearity of Z-R relationship. A state-space model was established based on a study that was conducted by Adamowski and Muir (1989). Two parameters of Z-R relationship were set as state variables of the state-space model. As a result, a stable model where a divergence of Kalman gain and state variables are not generated was established. It is noteworthy that overestimated or underestimated parameters based on a conventional method were filtered and removed. As application of inappropriate parameters might cause physically unrealistic rain rate estimation, it can be more effective in terms of quantitative precipitation estimation. As a result of estimation on radar rainfall based on parameters predicted with the extended Kalman filter, the mean field bias correction factor turned out to be around 1.0 indicating that there was a minor difference from the gauge rain rate without the mean field bias correction. In addition, it turned out that it was possible to conduct more accurate estimation on radar rainfall compared to the conventional method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.21 no.3
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• pp.241-253
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• 2009

This study performs the investigation of a long-term variation of annual maximum surge heights(AMSH) and main characteristics of high surge events, and the statistical evaluation of the AMSH using sea level data at Yeosu and Tongyeong tidal stations over more than 30 years. It is found that the long-term uptrends based on the linear regression in the AMSH are 34.5 cm/34 yr at Yeosu and 33.6 cm/31 yr at Tongyeong, which are relatively much higher than those at Sokcho and Mukho in the Eastern Coast. 71% and 68% of the AMSH occur during typhoon's event in Yeosu and Tongyeong tidal stations, respectively, and the highest surge records are mostly produced by the typhoon. The generalized extreme value distribution taking into account of the time variable is applied to detect time trend in annual maximum surge heights. In addition, Gumbel distribution is checked to find which one is best fitted to the data using likelihood ratio test. The return level and its 90% confidence interval are obtained for the statistical prediction of the future trend. The prevention of the growing storm surge damage by the intensified typhoon requires the steady analysis and prediction of the surge events associated with the climate change.

• Journal of the Korea Concrete Institute
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• v.24 no.2
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• pp.157-164
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• 2012

This study evaluates the shear performance of reinforced concrete beams with recycled coarse aggregates. A total of six specimens with various replacement ratios of recycled coarse aggregates (0%, 50%, and 100%) and different amount of shear reinforcement were cast and tested in this study. A finite element analysis was performed to predict the shear behavior of the specimens with natural or recycled coarse aggregates. The FE analysis was performed using a two-dimensional nonlinear FE analysis program based on the disturbed stress field model (DSFM), which is an extension of the modified compression field theory (MCFT). Experimental results showed that the specimens with 50% and 100% replacement ratios of recycled coarse aggregates had the similar shear strength compared to the specimen with natural aggregates, regardless of the replacement ratios of recycled coarse aggregates and the amount of the shear reinforcement. Furthermore, the comparison between experimental and analytical results showed that the proposed numerical modeling methods and the analytical model, DSFM, can be successfully used to predict the shear behavior of reinforced concrete beams with recycled coarse aggregates.

• Resources Recycling
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• v.29 no.3
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• pp.11-23
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• 2020

This study aims to investigate breakage and liberation characteristics of iron ore from Shinyemi mine, Jeongseon by ball mill. Parameters of breakage functions for three grade samples of iron ore were obtained using single-sized-feed breakage test and back-calculation based on nonlinear programming. The results showed that with the increase in the grade of iron ore, the breakage rate factor decrease whereas the particle size sensitivity decreases. This results from retardation of microcrack-propagation by magnetite grain in the ore. Breakage distribution analysis showed that the breakage mechanism appear to be impact fracture dominant with the increase of grade owing to the stress distribution effect by magnetite grain. Degree of liberation (DOL) increased with the increase in grade and decrease in particle size, respectively. Using the breakage function and size-DOL relationship, a model that can predict time-dependent-DOL is established. When scale-up factors from operating condition are available, the model is expected to be capable of predicting size and DOL with time in actual mining process.