• Journal of Korean Society of Coastal and Ocean Engineers
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• v.4 no.1
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• pp.34-44
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• 1992

A numerical model using Hybrid Element Method(HEM) is presented for the prediction of wave agitations in a harbor which are induced by the intrusion and transformation of incident short-period waves. A linear mild-slope equation including bottom friction is used as the governing equation and a partial absorbing boundary condition is used on solid boundaries. Functional derived in the present paper is based on the Chen and Mei(1974)'s concept which uses finite element net in the inner region and analytical solution of Helmholtz equation in the outer region. Final simultaneous equations are solved using the Gaussian Elimination Method. The model appears to be reasonably good from the comparison of numerical calculation with hydraulic experimental results of short-wave diffraction through a breakwater gap(Pos and Kilner, 1987). The problem of requring large computational memory could be overcome using 8-noded isoparametric elements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.227-247
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• 2018

For intermittently connected wireless sensor networks deployed in hash environments, sensor nodes may fail due to internal or external reasons at any time. In the process of data collection and recovery, we need to speed up as much as possible so that all the sensory data can be restored by accessing as few survivors as possible. In this paper a novel redundant data storage algorithm based on minimum spanning tree and quasi-randomized matrix-QRNCDS is proposed. QRNCDS disseminates k source data packets to n sensor nodes in the network (n>k) according to the minimum spanning tree traversal mechanism. Every node stores only one encoded data packet in its storage which is the XOR result of the received source data packets in accordance with the quasi-randomized matrix theory. The algorithm adopts the minimum spanning tree traversal rule to reduce the complexity of the traversal message of the source packets. In order to solve the problem that some source packets cannot be restored if the random matrix is not full column rank, the semi-randomized network coding method is used in QRNCDS. Each source node only needs to store its own source data packet, and the storage nodes choose to receive or not. In the decoding phase, Gaussian Elimination and Belief Propagation are combined to improve the probability and efficiency of data decoding. As a result, part of the source data can be recovered in the case of semi-random matrix without full column rank. The simulation results show that QRNCDS has lower energy consumption, higher data collection efficiency, higher decoding efficiency, smaller data storage redundancy and larger network fault tolerance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.4
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• pp.15-23
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• 2011

An interleaver rearranges a channel-encoded data in the symbol unit to spread burst errors occurred in channels into random errors. Thus, the interleaving process makes it difficult for a receiver, who does not have information of the interleaver parameters used in the transmitter, to de-interleave an unknown interleaved signal. Recently, various researches on the reconstruction of an unknown interleaved signal have been studied in many places of literature by estimating the interleaver parameters. They, however, have been mainly focused on the estimation of the block interleaver parameters required to reconstruct the de-interleaver. In this paper, as an extension of the previous researches, we estimate the convolutional interleaver parameters, e.g., the number of shift registers, a shift register depth, and a codeword length, required to de-interleave the unknown data stream, and propose the de-interleaving procedure by reconstructing the de-interleaver.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.3
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• pp.185-192
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• 1993

A numerical model has been used for the prediction of wave agitations in a harbor which are induced by the intrusion and transformation of incident waves. Based on linear wave theory a mild-slope equation has been used. A partial absorbing boundary condition has been used on solid boundary. Functional has been derived following Chen and Mei(l974)'s technique based on Hybrid Element Method which uses finite discretisation in the inner region and analytical solution of Helmholtz equation in the outer region. Final simultaneous equation has been solved using the Gaussian Elimination Method. Helmholtz natural period and second peak period of seiche in Donghae Harbor coincide very well with the results from numerical calculation. Computed amplification factors show good agreement, especially when the reflection coefficient on solid boundary is 0.99, with those of measurements.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.6
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• pp.587-594
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• 2009

In the paper the effect of a stern-plate attached to a ship was taken into account. The relationship between the trim angle of a ship and the wave-resistance coefficient induced by the a stern-plate was studied using the potential flow analysis method. Numerical algorithm was described using the panel method and the vortex lattice method(VLM) to simulate the flow phenomena around a ship. The non-linearity of the free surface boundary conditions were considered using the iterative method and the IGE-GMRES(Incomplete Gaussian Elimination-The Generalized Minimal RESidual) algorithm was adopted to solve the linear equation at each iterative step. Numerical calculations were carried out to investigate the validity of the adopted algorithm using KCS(KRISO 3600 TEU Container) hull. Possible cases for attachment of the plate were checked. The results showed that the numerical algorithm could be physically appropriate.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.191-197
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• 2021

To calculate the induced charge of atoms in molecular dynamics, linear equations for the induced charges need to be solved. As induced charges are determined at each time step, the process involves considerable computational costs. Hence, an efficient method for calculating the induced charge distribution is required when analyzing large systems. This paper introduces the Uzawa method for solving saddle point problems, which occur in linear systems, for the solution of the Lagrange equation with constraints. We apply the accelerated Uzawa algorithm, which reduces computational costs noticeably using the Schur complement and preconditioned conjugate gradient methods, in order to overcome the drawback of the Uzawa parameter, which affects the convergence speed, and increase the efficiency of the matrix operation. Numerical models of molecular dynamics in which two gold nanoparticles are placed under external electric fields reveal that the proposed method provides improved results in terms of both convergence and efficiency. The computational cost was reduced by approximately 1/10 compared to that for the Gaussian elimination method, and fast convergence of the conjugate gradient, as compared to the basic Uzawa method, was verified.

• Advances in concrete construction
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• v.14 no.3
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.