• Journal of Ocean Engineering and Technology
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• v.20 no.3 s.70
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• pp.54-60
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• 2006

For optimal design of a deep-sea ocean mining collector system, based on self-propelled mining vehicle, it is imperative to develop and validate the dynamic model of a tracked vehicle traveling on soft deep seabed. The purpose of this paper is to evaluate the fidelity of the dynamic simulation model by means of response surface methodology. Various statistical techniques related to response surface methodology, such as outlier analysis, detection of interaction effect, analysis of variance, inference of the significance of design variables, and global sensitivity analysis, are examined. To obtain a plausible response surface model, maximum entropy sampling is adopted. From statistical analysis and prediction for dynamic responses of the tracked vehicle, conclusions will be drawn about the accuracy of the dynamic model and the performance of the response surface model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3043-3067
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• 2022

With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.99-110
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• 1999

This study deals with the adaptability questions of O-D table estimation models. Its objectives are two-fold; (1) to estimate the characteristics of various O-D table estimation models(i.e. linear regression models. entropy models and statistic models) and (2) to find the model which estimates the O-D table with the best accuracy under the various data conditions. In Pursuing the above, this study gives the particular attentions to the test of the models, using the Sioux Falls network and equilibrium assignment method of MINUTP. The major findings are the followings. Firstly. it finds that the statistic models have the most goodness of fat among all models, if the required data are all Prepared. But it Presents that statistic models are the most sensitive against the underspecification and inconsistency problems of link data. Secondly, It shows that the linear regression models have the worst goodness of fat among all models. But the linear regression models are the most insensitive to the underspecification and inconsistency problems. Thirdly, THE/1 model of entropy model is sensitive against the underspecification and incon-sistency problems, but THE/2 model is insensitive. Finally, other informations like total volume, zonal Production and attraction volumes in 0-D table, help models to gain the better goodness of fit. Especially, in the statistic models. both the zonal production and attraction volume data are helpful to estimate the link volumes. It can be expected that the results dive some implications not only to the selection of optimal model under the various given data, but also to the development or modification of model.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.

• Bulletin of the Korean Chemical Society
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• v.35 no.8
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• pp.2471-2476
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• 2014

The aim of the present study is to explore the possibility of utilizing loess for the adsorption of total phosphorous (T-P) and total nitrogen (T-N) in water. Batch adsorption studies were performed to evaluate the influences of various factors like initial concentration, contact time and temperature on the adsorption of T-P and T-N. The adsorption data showed that loess is not effective for the adsorption of T-N. However, loess exhibited much higher adsorption capacity for T-P. At concentration of $1.0mgL^{-1}$, approximately 97% of T-P adsorption was achieved by loess. The equilibrium data were fitted well to the Langmuir isotherm model. The pseudo-second-order kinetic model appeared to be the better-fitting model because it has higher $R^2$ compared with the pseudo-first-order and intra-particle kinetic model. The theoretical adsorption equilibrium $q_{e,cal}$ from pseudo-second-order kinetic model was relatively similar to the experimental adsorption equilibrium $q_{e,exp}$. The thermodynamic parameters such as free energy ${\Delta}G$, the enthalpy ${\Delta}H$ and the entropy ${\Delta}S$ were also calculated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2370-2387
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• 2012

A new security management model based on Rough set and Bayesian learner is proposed in the paper. The model focuses on finding out malicious nodes and getting them under control. The degree of dissatisfaction (DoD) is defined as the probability that a node belongs to the malicious node set. Based on transaction history records local DoD (LDoD) is calculated. And recommended DoD (RDoD) is calculated based on feedbacks on recommendations (FBRs). According to the DoD, nodes are classified and controlled. In order to improve computation accuracy and efficiency of the probability, we employ Rough set combined with Bayesian learner. For the reason that in some cases, the corresponding probability result can be determined according to only one or two attribute values, the Rough set module is used; And in other cases, the probability is computed by Bayesian learner. Compared with the existing trust model, the simulation results demonstrate that the model can obtain higher examination rate of malicious nodes and achieve the higher transaction success rate.

• Journal of The Geomorphological Association of Korea
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• v.24 no.2
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• pp.91-101
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• 2017

Quantitative forecasting methods based on spatial data and geographic information system have been used in predicting the landslide location. This study compared the simulated results of logistic, Bayesian, and maximum entropy models to understand the uncertainties of each model and identify the main factors that influence landslide. The study area is Boeun gun where 388 landslides occurred in the year of 1998. The verification results showed that the AUC of the three models was 0.84. However, the landslide susceptibility distribution of Maxent model was different from those of the other two models. With the same landslide occurrence data, the result of high susceptible area in Maxent model is smaller than Logistic or Bayesian. Maxent model, however, proved to be more efficient in predicting landslide than the other two models. In Maxent's simulations, the responsible factors for landslide susceptibility are timber age class, land cover, timber diameter, crown closure, and soil drainage. The results suggest that it is necessary to consider the possibility of overestimation when using Logistic or Bayesian model, and forest management around the study area can be an effective way to minimize landslide possibility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4508-4530
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• 2021

Today distributed data storage service are being widely used. However lack of proper means of security makes the user data vulnerable. In this work, we propose a Randomized Block Size (RBS) model for secure data storage in distributed environments. The model work with multifold block sizes encrypted with the Chinese Remainder Theorem-based RSA (C-RSA) technique for end-to-end security of multimedia data. The proposed RBS model has a key generation phase (KGP) for constructing asymmetric keys, and a rand generation phase (RGP) for applying optimal asymmetric encryption padding (OAEP) to the original message. The experimental results obtained with text and image files show that the post encryption file size is not much affected, and data is efficiently encrypted while storing at the distributed storage server (DSS). The parameters such as ciphertext size, encryption time, and throughput have been considered for performance evaluation, whereas statistical analysis like similarity measurement, correlation coefficient, histogram, and entropy analysis uses to check image pixels deviation. The number of pixels change rate (NPCR) and unified averaged changed intensity (UACI) were used to check the strength of the proposed encryption technique. The proposed model is robust with high resilience against eavesdropping, insider attack, and chosen-plaintext attack.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.883-890
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• 2020

In this paper, we propose a variational expectation-maximization algorithm that computes posterior probabilities from Latent Dirichlet Allocation (LDA) model. The algorithm approximates the intractable posterior distribution of a document term matrix generated from a corpus made up by 50 papers. It approximates the posterior by searching the local optima using lower bound of the true posterior distribution. Moreover, it maximizes the lower bound of the log-likelihood of the true posterior by minimizing the relative entropy of the prior and the posterior distribution known as KL-Divergence. The experimental results indicate that documents clustered to image classification and segmentation are correlated at 0.79 while those clustered to object detection and image segmentation are highly correlated at 0.96. The proposed variational inference algorithm performs efficiently and faster than Gibbs sampling at a computational time of 0.029s.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.5
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• pp.62-74
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• 2001

In this study, the prediction of performances and emissions of the gasoline engine of a light passenger car has been accomplished. The method of characteristics including friction, heat transfer, area change and entropy gradients was used to analyze the flow in the intake and exhaust systems. For in-cylinder calculation, the single-zone model was adopted for the periods of the intake, exhaust, compression and the expansion of the burnt gas and the 2-zone expansion model was applied to the period of combustion process. The simulation program was verified by comparison with the experimental values both for the naturally aspirated engine and the turbocharged engine showing good agreements. Using the simulation program, multi-valve system and turbocharging were examined as a means of increasing engine Performances.