• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1756-1759
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• 2002

In this paper, we report the influence on the visual perception of the stochastic resonance for Japanese, following frank Moss and his group (1997) 〔1〕. We confirm that the ability of recognition is improved by adding appropriately noise as same as that of non-Japanese.

• Steel and Composite Structures
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• v.15 no.1
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• pp.15-39
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• 2013

The sensitivities of a structural response due to variation of its design parameters are prerequisite in the majority of the algorithms used for fundamental problems in engineering as system uncertainties, identification and probabilistic assessments etc. The paper presents the concept of probabilistic sensitivity of suspension bridges with respect to near-fault ground motion. In near field earthquake ground motions, large amplitude spectral accelerations can occur at long periods where many suspension bridges have significant structural response modes. Two different types of suspension bridges, which are Bosporus and Humber bridges, are selected to investigate the near-fault ground motion effects on suspension bridges random response sensitivity analysis. The modulus of elasticity is selected as random design variable. Strong ground motion records of Kocaeli, Northridge and Erzincan earthquakes are selected for the analyses. The stochastic sensitivity displacements and internal forces are determined by using the stochastic sensitivity finite element method and Monte Carlo simulation method. The stochastic sensitivity displacements and responses obtained from the two different suspension bridges subjected to these near-fault strong-ground motions are compared with each other. It is seen from the results that near-fault ground motions have different impacts stochastic sensitivity responses of suspension bridges. The stochastic sensitivity information provides a deeper insight into the structural design and it can be used as a basis for decision-making.

• Journal of the Korea Society for Simulation
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• v.12 no.2
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• pp.1-11
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• 2003

In this paper, we focus an optimal policy focus optimal class of (s, S) inventory control systems. To this end, we use the perturbation analysis and apply a stochastic optimization algorithm to minimize the average cost over a period. We obtain the gradients of objective function with respect to ordering amount S and reorder point s via a combined perturbation method. This method uses the infinitesimal perturbation analysis and the smoothed perturbation analysis alternatively according to occurrences of ordering event changes. Our simulation results indicate that the optimal estimates of s and S obtained from a stochastic optimization algorithm are quite accurate. We consider that this may be due to the estimated gradients of little noise from the regenerative system simulation, and their effect on search procedure when we apply the stochastic optimization algorithm. The directions for future study stemming from this research pertain to extension to the more general inventory system with regard to demand distribution, backlogging policy, lead time, and review period. Another directions involves the efficiency of stochastic optimization algorithm related to searching procedure for an improving point of (s, S).

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.205-216
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• 2019

Diagnostic tests in medical fields detect or diagnose a disease with results measured by continuous or discrete ordinal data. The performance of a diagnostic test is summarized using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The diagnostic test is considered clinically useful if the outcomes in actually-positive cases are higher than actually-negative cases and the ROC curve is concave. In this study, we apply the stochastic ordering method in a Bayesian hierarchical model to estimate the proper ROC curve and AUC when the diagnostic test results are measured in discrete ordinal data. We compare the conventional binormal model and binormal model under stochastic ordering. The simulation results and real data analysis for breast cancer indicate that the binormal model under stochastic ordering can be used to estimate the proper ROC curve with a small bias even though the sample sizes were small or the sample size of actually-negative cases varied from actually-positive cases. Therefore, it is appropriate to consider the binormal model under stochastic ordering in the presence of large differences for a sample size between actually-negative and actually-positive groups.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2450-2469
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• 2018

In this paper, we develop a spatiotemporal model to analysis of cellular user in underlay D2D communication by using stochastic geometry and queuing theory. Firstly, by exploring stochastic geometry to model the user locations, we derive the probability that the SINR of cellular user in a predefined interval, which constrains the corresponding transmission rate of cellular user. Secondly, in contrast to the previous studies with full traffic models, we employ queueing theory to evaluate the performance parameters of dynamic traffic model and formulate the cellular user transmission mechanism as a M/G/1 queuing model. In the derivation, Embedded Markov chain is introduced to depict the stationary distribution of cellular user queue status. Thirdly, the expressions of performance metrics in terms of mean queue length, mean throughput, mean delay and mean dropping probability are obtained, respectively. Simulation results show the validity and rationality of the theoretical analysis under different channel conditions.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1245-1255
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• 2017

The online learning is a process of obtaining the solution for a given objective function where the data is accumulated in real time or in batch units. The stochastic gradient descent method is one of the most widely used for the online learning. This method is not only easy to implement, but also has good properties of the solution under the assumption that the generating model of data is homogeneous. However, the stochastic gradient method could severely mislead the online-learning when the homogeneity is actually violated. We assume that there are two heterogeneous generating models in the observation, and propose the a new stochastic gradient method that mitigate the problem of the heterogeneous models. We introduce a robust mini-batch optimization method using statistical tests and investigate the convergence radius of the solution in the proposed method. Moreover, the theoretical results are confirmed by the numerical simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.64-88
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• 2024

Security and reliability are the utmost importance facts in intelligent networked vehicles. Stochastic Petri Net and Z (SPZN) as an excellent formal verification tool for modeling concurrent systems, can effectively handles concurrent operations within a system, establishes relationships among components, and conducts verification and reasoning to ensure the system's safety and reliability in practical applications. However, the application of a system with numerous nodes to Petri Net often leads to the issue of state explosion. To tackle these challenges, a refinement and abstraction method based on SPZN is proposed in this paper. This approach can not only refine and abstract the Stochastic Petri Net but also establish a corresponding relationship with the Z language. In determining the implementation rate of transitions in Stochastic Petri Net, we employ the interval average and weighted average method, which significantly reduces the time and space complexity compared to alternative techniques and is suitable for expert systems at various levels. This reduction facilitates subsequent comprehensive system analysis and module analysis. Furthermore, by analyzing the properties of Markov Chain isomorphism in the case study, recommendations for minimizing system risks in the application of intelligent parking within the intelligent networked vehicle system can be put forward.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1082-1087
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• 2021

In MANET(mobile ad hoc network), Mobility models vary according to the application-specific goals. The most widely used Random WayPoint Mobility Model(RWPMM) is advantageous because it is simple and easy to implement, but the random characteristic of nodes' movement is not enough to express the mobile characteristics of the entire sensor nodes' movements. The random mobility model is insufficient to express the inherent movement characteristics of the entire sensor nodes' movements. In the proposed Stochastic mobility model, To express the overall nodes movement characteristics of the network, the moving nodes are treated as random variables having a specific probability distribution characteristic. The proposed Stochastic mobility model is more stable and energy-efficient than the existing random mobility model applies to the routing protocol to ensure improved performances in terms of energy efficiency.

• Journal of Internet Computing and Services
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• v.16 no.5
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• pp.59-66
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• 2015

A stochastic modeling approach as a mathematical method for workflow intelligence is widely used for analyzing and simulating workflow models in the literature. In particular, as a resource-centric modeling approach, this paper proposes a stochastic model to represent work-handover relationships between performers in a workflow-supported social network. Calculating probabilities for the work-handover relationships are determined by two types of probabilities. One is the work-transition probability between activities, and the other is the task assignment probability between activities and performers. In this paper, we describe formal definitions of stochastic workflow models and stochastic work-handover relationship models, as well. Then, we propose an algorithm for extracting a stochastic work-handover relationship model from a stochastic workflow model. As a consequence, the proposed model ought to be useful in performing resource-centric workflow simulations and model-log comparison analyses.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.3
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• pp.49-57
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• 2018

In a previous article, for analyzing a stochastic activity network, Cho proposed a method for estimating the moments (mean, variance, skewness, kurtosis) of the project completion time under the assumption that the durations of activities are independently and normally distributed. Developed in the present article is a method for estimating those moments for stochastic activity networks which allow any type of distributions for activity durations. The proposed method uses the moment matching approach to discretize the distribution function of activity duration, and then a discrete inverse-transform method to determine activity durations to be used for calculating the project completion time. The proposed method can be easily applied to large-sized activity networks, and computationally more efficient than Monte Carlo simulation, and its accuracy is comparable to that of Monte Carlo simulation.