• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.148-154
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• 2021

With advancements of quay side handling equipment and technologies, congestion in terminal operation has moved to the storage yard side from the quay side. The importance of storage yard management has increased in overall terminal operation. Thus, many studies have been conducted to optimize the storage yard management of container terminals. However, there is no academic work to estimate the change of storage yard occupancy ratio by itself in the future. This paper examines the probability of storage yard occupancy ratio in the container terminal of Busan New port using the Markov chain analysis which explains probability change with passing time. The result shows that it is most likely to have the probability of maintaining a high level of storage yard occupancy ratio in the container terminal of Busan New Port.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2674-2697
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• 2019

Identity authentication is a crucial line of defense for network security, and passwords are still the mainstream of identity authentication. So far trawling password attacking has been extensively studied, but the research related with personal information is always sporadic. Probabilistic context-free grammar (PCFG) and Markov chain-based models perform greatly well in trawling guessing. In this paper we propose a systematic targeted attacking model based on structure partition and string reorganization by migrating the above two models to targeted attacking, denoted as TG-SPSR. In structure partition phase, besides dividing passwords to basic structure similar to PCFG, we additionally define a trajectory-based keyboard pattern in the basic grammar and introduce index bits to accurately characterize the position of special characters. Moreover, we also construct a BiLSTM recurrent neural network classifier to characterize the behavior of password reuse and modification after defining nine kinds of modification rules. Extensive experimental results indicate that in online attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 275%, and respectively outperforms its foremost counterparts, Personal-PCFG, TarGuess-I, by about 70% and 19%; In offline attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 90%, outperforms Personal-PCFG and TarGuess-I by 85% and 30%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.186-205
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• 2019

Intercell interference coordination (ICIC) is considered as a promising technique to increase the spectral efficiency of OFDMA cellular systems. The soft frequency reuse (SFR) and fractional frequency reuse (FFR) are representative and efficient management techniques for ICIC. Herein, to enhance the performance of the SFR scheme, we propose a call admission (CAC) scheme. In this CAC scheme, called Spectrum handoff-SFR(S-SFR), the spectrum handoff technique is applied to the user equipment (UE) located near the cell center. We derive the traffic analysis model to describe the S-SFR. In addition, a two-dimensional (2-D) Markov chain and an outage analysis are used in our analytical model. From the traffic analysis, the significant performance measures are the outage probability, call blocking probability, system throughput and resource utilization. Based on those, the outage probability and system throughput are obtained using resource utilization as an interference pattern. The analytical results are verified with computer simulation results. Finally, we compare our proposed scheme with other ICI schemes.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.2
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• pp.161-167
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• 2019

In general, fatigue analysis is performed by using deterministic model to estimate the optimal parameters. However, the deterministic model is difficult to clearly describe the physical phenomena of fatigue failure that contains many uncertainty factors. With regard to this, efforts have been made in this research to compare with the deterministic model and the stochastic models. Firstly, One deterministic S-N curve was derived from ordinary least squares technique and two P-S-N curves were estimated through Bayesian-linear regression model and Markov-Chain Monte Carlo simulation. Secondly, the distribution of Long-term fatigue damage and fatigue life were predicted by using the parameters obtained from the three methodologies and the long-term stress distribution.

• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.425-445
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• 2021

A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.

• Journal of Korea Planning Association
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• v.54 no.7
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• pp.5-23
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• 2019

The central government has implemented policies to strengthen the competitiveness of small and medium sized cities for balanced development at the national scale. However, since it is often difficult to enhance the competitiveness through partial projects of each jurisdiction, many local governments collaborate at the regional scale. This suggests that a regional approach is important for the management of small and medium sized cities. On the one hand, the concept of network city suggests that various functional networks can affect the growth of small and medium sized cities. Given this background, the purposes of this study are to delineate regional boundaries at national scale and identify their relations of growth by using functional network and Moran's I index. The study uses the Markov-chain model and cluster analysis to delineate the regions, and Moran's I is employed to identify the relations of growth. The results show that interactions between jurisdictions through networks could be crucial factors for growth of small and medium sized cities, while the networks based on passenger travel and freight movement have different implications. The results suggest that policy makers should not only consider local level investments, but also take the characteristics of networks between cities into account for achieving balanced development and developing regeneration policies.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2941-2959
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• 2022

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (µ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of -1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4203-4223
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• 2021

Accelerated by the Internet of Things (IoT), the need for further technical innovations and developments within wireless communications beyond the fifth generation (B5G) networks is up-and-coming in the past few years. High altitude platform station (HAPS) communication is expected to achieve such high levels that, with high data transfer rates and low latency, millions of devices and applications can work seamlessly. The HAPS has emerged as an indispensable component of next-generations of wireless networks, which will therefore play an important role in promoting massive IoT interconnectivity with 6G. The performance of communication and key technology mainly depend on the characteristic of channel, thus we propose an efficient Markov chain based channel model, then analyze the HAPS communication system's uplink capability and swing effect through experiments. According to the simulation results, the efficacy of the proposed scheme is proven to meet the requirements of ubiquitous connectivity in future IoT enabled by 6G.

• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Journal of information and communication convergence engineering
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• v.20 no.2
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• pp.79-89
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• 2022

To compute the mean and variance of component-based reliability software, we focused on path-based reliability analysis. System reliability depends on the transition probabilities of components within a system and reliability of the individual components as basic input parameters. The uncertainty in these parameters is estimated from the test data of the corresponding components and arises from the software architecture, failure behaviors, software growth models etc. Typically, researchers perform Monte Carlo simulations to study uncertainty. Thus, we considered a Markov chain Monte Carlo (MCMC) simulation to calculate uncertainty, as it generates random samples through sequential methods. The MCMC approach determines the input parameters from the probability distribution, and then calculates the average approximate expectations for a reliability estimation. The comparison of different techniques for uncertainty analysis helps in selecting the most suitable technique based on data requirements and reliability measures related to the number of components.