• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.9 s.180
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• pp.2283-2291
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• 2000

The neural network method is applied to automatically measure the crack opening load under random loading. The crack opening results obtained are compared with the visual measured results. Fatigue crack growth under random loading is predicted using the crack opening data measured by the neural network method, and the prediction results are compared with experimental ones. It is found that the neural network method can be successfully applied to consistently measure the crack opening load under random loading and also gives some results different from the results by visual measurement.

• ETRI Journal
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• v.34 no.5
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• pp.775-778
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• 2012

We propose integrating random network coding with the Enhanced On-Demand Multicast Routing Protocol (E-ODMRP). With the Network Coded E-ODMRP (NCE-ODMRP), we present a framework that enables a seamless integration of random linear network coding with conventional ad hoc multicast protocols for enhanced reliability. Simulation results show that the NCE-ODMRP achieves a nearly perfect packet delivery ratio while keeping the route maintenance overhead low to a degree similar to that of the E-ODMRP.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.595-601
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• 2011

A community of the social network refers to the subset of nodes linked more densely among them than to others. In this study, we propose a Monte-Carlo method for generating random social unipartite and bipartite networks with two or more communities. Proposed random networks can be used to verify the small world phenomenon of the social networks with several communities.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.543-547
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• 2003

We applied the modified invasion percolation (MIP) model to the migration of DNAPL within a two-dimensional random fracture network. The MIP model was verified against laboratory experiments, which was conducted using a two-dimensional random fracture network model. The results showed that the MIP needs modification. To remove TCE trapped in a random fracture network, the density-surfactant-motivated removal method was applied and found very effective to remove TCE from dead-end fractures.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1510-1532
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• 2017

In smart grid, privacy implications to individuals and their families are an important issue because of the fine-grained usage data collection. Wireless communications are utilized by many utility companies to obtain information. Network coding is exploited in smart grids, to enhance network performance in terms of throughput, delay, robustness, and energy consumption. However, random linear network coding introduces a new challenge for privacy preserving due to the encoding of data and updating of coefficients in forwarder nodes. We propose a distributed privacy preserving scheme for random linear network coding in smart grid that considers the converged flows character of the smart grid and exploits a homomorphic encryption function to decrease the complexities in the forwarder node. It offers a data confidentiality privacy preserving feature, which can efficiently thwart traffic analysis. The data of the packet is encrypted and the tag of the packet is encrypted by a homomorphic encryption function. The forwarder node random linearly codes the encrypted data and directly processes the cryptotext tags based on the homomorphism feature. Extensive security analysis and performance evaluations demonstrate the validity and efficiency of the proposed scheme.

• Journal of Distribution Science
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• v.17 no.3
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• pp.85-95
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• 2019

Purpose - The existing marketing studies using Social Network Analysis have assumed that network structure variables are time-invariant. However, a node's network position can fluctuate considerably over time and the node's network structure can be changed dynamically. Hence, if such a dynamic structural network characteristics are not specified for virtual goods purchase model, estimated parameters can be biased. In this paper, by comparing a time-invariant network structure specification model(base model) and time-varying network specification model(proposed model), the authors intend to prove whether the proposed model is superior to the base model. In addition, the authors also intend to investigate whether coefficients of network structure variables are random over time. Research design, data, and methodology - The data of this study are obtained from a Korean social network provider. The authors construct a monthly panel data by calculating the raw data. To fit the panel data, the authors derive random effects panel tobit model and multi-level mixed effects model. Results - First, the proposed model is better than that of the base model in terms of performance. Second, except for constraint, multi-level mixed effects models with random coefficient of every network structure variable(in-degree, out-degree, in-closeness centrality, out-closeness centrality, clustering coefficient) perform better than not random coefficient specification model. Conclusion - The size and importance of virtual goods market has been dramatically increasing. Notwithstanding such a strategic importance of virtual goods, there is little research on social influential factors which impact the intention of virtual good purchase. Even studies which investigated social influence factors have assumed that social network structure variables are time-invariant. However, the authors show that network structure variables are time-variant and coefficients of network structure variables are random over time. Thus, virtual goods purchase model with dynamic network structure variables performs better than that with static network structure model. Hence, if marketing practitioners intend to use social influences to sell virtual goods in social media, they had better consider time-varying social influences of network members. In addition, this study can be also differentiated from other related researches using survey data in that this study deals with actual field data.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.235-238
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• 1996

In this paper, a new optimization method which is a kind of random searching is presented. The proposed method is called RasVal which is an abbreviation of Random Search Method with Variable Seaxch Length and it can search for a global minimum based on the probability density functions of searching, which can be modified using informations on success or failure of the past searching in order to execute intensified and diversified searching. By applying the proposed method to a nonlinear crane control system which can be controlled by the Universal Learning Network with radial basis function(R.B.P.), it has been proved that RasVal is superior in performance to the commonly used back propagation learning algorithm.

• Transactions on Electrical and Electronic Materials
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• v.7 no.4
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• pp.210-214
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• 2006

In this paper, two sampling methods which are Latin hypercube sampling (LHS) and simple random sampling were. compared to improve the modeling speed of neural network model. Sampling method was used to generate initial weights and bias set. Electrical characteristic data for $HfO_2$ thin film was used as modeling data. 10 initial parameter sets which are initial weights and bias sets were generated using LHS and simple random sampling, respectively. Modeling was performed with generated initial parameters and measured epoch number. The other network parameters were fixed. The iterative 20 minimum epoch numbers for LHS and simple random sampling were analyzed by nonparametric method because of their nonnormality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.816-839
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• 2023

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.