• Journal of Wetlands Research
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• v.11 no.2
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• pp.89-98
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• 2009

This study determines the stream mean slope, stream slope and longitudinal stream profile based on the concept of informational entropy. Maximizing the entropy will make the probability distribution of longitudinal stream profile as uniform as possible while satisfying the constraints. Using this relationships the mean stream slope, stream slope and longitudinal stream profile formulas were derived. The parameters of the applied streams were estimated by the least square method using the geomorphological factors of Dalchon stream basin obtained from Chungcheong Buk-Do local stream consolidation scheme drawings. The comparative investigation was performed between the observed and simulated mean stream slope and longitudinal stream profile, and are in good agreement with the measured data. It is noted that this results can be used in the estimation of stream mean slope and longitudinal stream profile.

• Journal of the Korean Statistical Society
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• v.8 no.1
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• pp.37-64
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• 1979

Criteria and designs are developed for estimating derivatives of P-variable second order polynomial response surfaces. The basic criterion used is mean square error of the estimated derivative, averaged over all directions and then averaged over a region of interest. A new design concept called slope-rotatability is introduced. A simplex optimization program is used to find minimum mena square error designs for the two variable case for $6 \leq N \leq 12$.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.85-99
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• 2022

Rock discontinuities, as weak interfaces in rock, control mechanical properties of rock mass. Presence of discontinuities complicates the engineering properties of rock, which is the root of anisotropy and heterogeneity that have nonnegligible influences on the rock engineering. Morphological characteristics of discontinuities in natural rock are an important factor influencing the mechanical properties, particularly roughness, of discontinuities. Therefore, the accurate measurement and characterization of morphologies of discontinuities are preconditions for studying mechanical properties of discontinuities. Taking discontinuities in red sandstone as research objects, the research obtained three-dimensional (3D) morphologies of discontinuities in natural rock by carrying out 3D morphological scanning tests. The waviness and roughness were separated from 3D morphologies of rock discontinuities through wavelet transform. In addition, the calculation method for the overall slope root mean square (RMS) as well as slope RMSs of waviness and roughness of 3D morphologies of discontinuities considering the shear direction was proposed. The research finally determined an evaluation method for 3D morphologies of discontinuities by quantitatively characterizing 3D morphologies with the mean value of the three slope RMSs.

• Geomechanics and Engineering
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• v.13 no.2
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• pp.237-254
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• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Smart Structures and Systems
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• v.29 no.4
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• pp.523-533
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• 2022

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.662-666
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• 1996

The variations of the surface topographical parameters for the analysis of the pedestrian slip and fall accidents during the sliding friction between the specially prepared floor specimens and three working shoes were investigated. The profile ordinate data for each flooring specimen were obtained at 1.1 .mu.m intervals using a laser scanning confocal microscope system along to the direction of sliding. A number of surface roughness parameters, that is, the centre line average (c.l.a.) and root mean square (r.m.s.) roughness, maximum height (Rtm), maximum mean peak height (Rpm), maximum mean depth (Rvm), and average asperity slope were calculated using a computer program and compared with the dynamic friction results. The analysis showed that the surface parameters undergo marked variations during the sliding process, but the variations were statistically significant. It was found that amongst various surface parameters, the maximum depth (Rvm) and the average asperity slope of the asperities were the biggest variation during the sliding proceeding. This result confirms the previous study and may suggests a new approach to monitoring the flooring environments with their service as the effort to reduce the pedestrain slip accident.

• Journal for History of Mathematics
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• v.13 no.2
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• pp.105-120
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• 2000

In this study, it is certified that the golden ratio exists in the plants, the animals and human bodies(appearance), and is considered how much the ratio of positive and negative is in the psychological, political, social and cultural aspects. The result of this study shows that the golden sections or golden rectangles of the plants(N=58), the insects(N=44), the animals(N=21) and human bodies(N=260) are equal to $0.620{\pm}0.117$, $0.632{\pm}0.203$, $0.625{\pm}0.138$ and $0.60{\pm}0.169$, respectively. The slope in the regression analysis is equal to 0.627(R-square=0.925, p-value=0.0001). Whereas, for the public opinion poll, the ratios($mean{\pm}st.dev.$) of positive and negative of the public mental phenomena are equal to $0.508{\pm}0.179$, $0.808{\pm}0.216$ and $0.711{\pm}0.128$ in the political, economical, and sociocultural aspects, respectively. The slope in the regression is equal to 0.674(R-square=0.764, p-value=0.016). As results, we show that the golden ratio exists in the plants, the animals and human bodies in nature. This shows that the public mental phenomena has some more negative aspects than positive aspects and explains the shrinkage of the public mental phenomena in the economical field.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.82-85
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• 2012

In this paper, we present the forward link ACM method to improve the link availability and system throughput. Also, we compare the prediction algorithm between slope based prediction and LMS algorithm. The simulation results show that the 99% of predicted values in LMS algorithm is within 3dB and that of predicted values in the slope based prediction method is within 4.5dB.

• Geomechanics and Engineering
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• v.6 no.1
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• pp.1-15
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• 2014

In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the critical factor of safety ($F_s$) of the homogeneous finite slopes subjected to earthquake forces. To achieve this, the values of $F_s$ in 5184 nos. of homogeneous finite slopes having different slope, soil and earthquake parameters were calculated by using the Simplified Bishop method and the minimum (critical) $F_s$ for each of the case was determined and used in the development of the ANN and MR models. The results obtained from both the models were compared with those obtained from the calculations. It is found that the ANN model exhibits more reliable predictions than the MR model. Moreover, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed. Also, the receiver operating curves were drawn, and the areas under the curves (AUC) were calculated to assess the prediction capacity of the ANN and MR models developed. The performance level attained in the ANN model shows that the ANN model developed can be used for predicting the critical $F_s$ of the homogeneous finite slopes subjected to earthquake forces.