• Journal of the Korean Geotechnical Society
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• v.16 no.1
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• pp.83-97
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• 2000

In the present study, artificial neural network based on the multi-layer perceptron is used and an optimum model is chosen through the process of efficiency evaluation in order to develop a system predicting maximum displacements of the earth retaining walls at various excavation stages. By analyzing the measured field data collected at various urban excavation sites in Korea, factors influencing on the behaviors of the excavation wall are examined. Among the measured data collected, reliable data are further selected on the basis of the performance ratio and are used as a data base. Data-based measurements are also utilized for both teaming and verifying the artificial neural network model. The learning is carried out by using the back-propagation algorithm based on the steepest descent method. Finally, to verify a validity of the formulated artificial neural network system, both the magnitude and the occurring position of the maximum horizontal displacement are predicted and compared with measured data at real excavation sites not included in the teaming process.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.648-653
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• 2010

The non-metric multidimensional scaling (nMDS) is a method for analyzing the relation among objects by mapping them onto the Euclidean space. The nMDS is useful when it is difficult to use the concept of distance between pairs of objects due to non-metric dissimilarities between objects. The nMDS can be regarded as an optimization problem in which there are many local optima. Since the conventional nMDS algorithm utilizes the steepest descent method, it has a drawback in that the method can hardly find a better solution once it falls into a local optimum. To remedy this problem, in this paper, we applied the simulated annealing to the nMDS and proposed a new optimization algorithm which could search for a global optimum more effectively. We examined the algorithm using benchmarking problems and found that improvement rate of the proposed algorithm against the conventional algorithm ranged from 0.7% to 3.2%. In addition, the statistical hypothesis test also showed that the proposed algorithm outperformed the conventional one.

• Journal of Korea Water Resources Association
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• v.34 no.2
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• pp.131-140
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• 2001

The present study is focused on improving the methodology for the determination of parameters involved in USLE(Universal Soil Loss Equation) based on distributed system concept and investigation of sediment delivery ratio. Generally the distributed system concept consists of grid networks throughout the watershed and sediment can be traced from grid to rid in the direction of the steepest descent. The sediment yield data together with physical data of 10 small irrigation reservoirs in Kyounggi-Do are collected. After the sediment delivery ratio of a grid is defined to be related tothe fraction of forested or covered with delivery proofing area of the grid, the preportionality coefficient(C$_1$) is introduced. The distributed system model is calibrated using the available data for 8 reservoirs and is verified with the data for the ramaining 2 reservoirs, and regression analysis is made to express the proportionality coefficient $C_1$ in terms of watershed physical characteristics. By applying this results the verification of the distributed system model for 2 reservoirs showed a fair result, which justifies the applicability of the proposed method in the present study.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.201-208
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• 2000

In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.

• Progress in Medical Physics
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• v.29 no.4
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• pp.150-156
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• 2018

This study aims to develop an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using anisotropic total variation (ATV) minimization to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The algorithm first applies a filter that integrates the Shepp-Logan filter into a cosine window function on all projections for impulse noise removal. A total variation objective function with anisotropic penalty is then minimized to enhance the difference between the real structure and noise using the steepest gradient descent optimization with adaptive step sizes. The preserving parameter to adjust the separation between the noise-free and noisy areas is determined by calculating the cumulative distribution function of the gradient magnitude of the filtered image obtained by the application of the filtering operation on each projection. With these minimized ATV projections, voxel-driven backprojection is finally performed to generate the reconstructed images. The performance of the proposed algorithm was evaluated with the catphan503 phantom dataset acquired with the use of a low-dose protocol. Qualitative and quantitative analyses showed that the proposed ATV minimization provides enhanced CBCT reconstruction images compared with those generated by the conventional FDK algorithm, with a higher contrast-to-noise ratio (CNR), lower root-mean-square-error, and higher correlation. The proposed algorithm not only leads to a potential imaging dose reduction in repeated CBCT scans via lower mA levels, but also elicits high CNR values by removing noisy corrupted areas and by avoiding the heavy penalization of striking features.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.197-206
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• 2010

For an emotion retrieval system implementation to support pedestrian navigation, coordinating the pedestrian emotion model with the system user's emotion is considered a key component. This study proposes a new method for capturing the user's model that corresponds to the pedestrian emotion model and examines the validity of the method. In the first phase, a database comprising a set of interior images that represent hypothetical destinations was developed. In the second phase, 10 subjects were recruited and asked to evaluate on navigation and satisfaction toward each interior image in five rounds of navigation experiments. In the last phase, the subjects' feedback data was used for of the pedestrian emotion model, which is called ‘learning' in this study. After evaluations by the subjects, the learning effect was analyzed by the following aspects: recall ratio, precision ratio, retrieval ranking, and satisfaction. Findings of the analysis verify that all four aspects significantly were improved after the learning. This study demonstrates the effectiveness of the learning algorithm for the proposed pedestrian emotion model. Furthermore, this study demonstrates the potential of such pedestrian emotion model to be well applicable in the development of various mobile contents service systems dealing with visual images such as commercial interiors in the future.

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.51-56
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• 2008

The waveform inversion for isotropic media has ever been studied since the 1980s, but there has been few studies for anisotropic media. We present a seismic waveform inversion algorithm for 2-D heterogeneous transversely isotropic structures. A cell-based finite difference algorithm for anisotropic media in time domain is adopted. The steepest descent during the non-linear iterative inversion approach is obtained by backpropagating residual errors using a reverse time migration technique. For scaling the gradient of a misfit function, we use the pseudo Hessian matrix which is assumed to neglect the zero-lag auto-correlation terms of impulse responses in the approximate Hessian matrix of the Gauss-Newton method. We demonstrate the use of these waveform inversion algorithm by applying them to a two layer model and the anisotropic Marmousi model data. With numerical examples, we show that it's difficult to converge to the true model when we assumed that anisotropic media are isotropic. Therefore, it is expected that our waveform inversion algorithm for anisotropic media is adequate to interpret real seismic exploration data.

• Journal of the Korean Geographical Society
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• v.46 no.6
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• pp.673-692
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• 2011

Advances in computer technology have enabled us to develop and use numerical landscape evolution models (NLEMs) for exploring the dynamics of geomorphic system from a variety of viewpoints which previously could have not been taken. However, as of yet there have been no trials using or developing NLEMs in Korea. The purpose of this research is to develop a 2 dimensional NLEM on a geological time scale and evaluate its usefulness. The newly developed NLEM (ND-NLEM) treats bedrock weathering as one of the major geomorphic processes and attempts to simulate the thickness of soil. As such it is possible to model the weathering-limited as well as the transport-limited environment on hillslopes. Moreover the ND-NLEM includes not only slow and continuous mass transport like soil creep, but also rapid and discrete mass transport like landslides. Bedrock incision is simulated in the ND-NLEM where fluvial transport capacity is large enough to move all channel bed loads, such that ND-NLEM can model the detachment-limited environment. Furthermore the ND-NLEM adopts the D-infinity algorithm when routing flows in the model domain, so it reduces distortion due to the use of the steepest descent slope flow direction algorithm. In the experiments to evaluate the usefulness of the ND-NLEM, characteristics of the channel network observed from the model results were similar to those of the case study area for comparison, and the hypsometry curve log during the experiment showed rational evidence of landscape evolution. Therefore, the ND-NLEM is shown to be useful for simulating landscape evolution on a geological time scale.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.344-355
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• 2002

Having a variety of good characteristics against other pattern recognition techniques, Multilayer Perceptron (MLP) has been used in wide applications. But, it is known that Error Backpropagation (EBP) algorithm which MLP uses in learning has a defect that requires relatively long leaning time. Because learning data in pattern recognition contain abundant redundancies, in order to increase learning speed it is very effective to use online-based teaming methods, which update parameters of MLP pattern by pattern. Typical online EBP algorithm applies fixed learning rate for each update of parameters. Though a large amount of speedup with online EBP can be obtained by choosing an appropriate fixed rate, fixing the rate leads to the problem that the algorithm cannot respond effectively to different leaning phases as the phases change and the learning pattern areas vary. To solve this problem, this paper defines learning as three phases and proposes a Instant Learning by Varying Rate and Skipping (ILVRS) method to reflect only necessary patterns when learning phases change. The basic concept of ILVRS is as follows. To discriminate and use necessary patterns which change as learning proceeds, (1) ILVRS uses a variable learning rate which is an error calculated from each pattern and is suppressed within a proper range, and (2) ILVRS bypasses unnecessary patterns in loaming phases. In this paper, an experimentation is conducted for speaker verification as an application of pattern recognition, and the results are presented to verify the performance of ILVRS.