• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.301-317
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• 2011

Statistical and probabilistic behaviors of fibers forming fiber webs of all kinds are of great significance in the determination of the uniformity and physical properties of the webs commonly found in many industrial products such as filters, membranes and non-woven fabrics. However, in studying the spatial geometry of the webs the observations must be theoretically as well as experimentally confined within a specified unit area. This paper provides a general theory and framework for computer simulation for quantifying the fiber segments bounded by the unit area in consideration of the "edge effects" resulting from the truncated length segments within the boundary. The probability density function and the first and second moments of the length segments found within the counting region were derived by properly defining the seeding region and counting region.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.313-320
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• 2014

We consider the problem to find economic inventory quantity of a single commodity under stochastic demands and order cancellation. In contrast to the traditional economic production quantity (EPQ) model, we assume that once the amount of inventory reaches to a predetermined level of quantity then the production is not halted but its production speed decreases until the inventory level drops to zero. We establish two probabilistic models representing the behaviors of both the high-production period and low-production period, respectively, and derive the relationship between the level of inventory and costs of production, cancellation, and holding, from which the quantity of economic inventory is obtained.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5826-5841
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• 2019

In our previous research, we proposed a tag emotion-based item recommendation scheme. The ternary associations among users, items, and tags are described as a three-order tensor in order to capture the emotions in tags. The candidates for recommendation are created based on the latent semantics derived by a high-order singular value decomposition technique (HOSVD). However, the tensor is very sparse because the number of tagged items is smaller than the amount of all items. The previous research do not consider the previous behaviors of users and items. To mitigate the problems, in this paper, the item-based collaborative filtering scheme is used to build an extended data. We also apply the probabilistic ranking algorithm considering the user and item profiles to improve the recommendation performance. The proposed method is evaluated based on Movielens dataset, and the results show that our approach improves the performance compared to other methods.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.529-543
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• 2021

Inevitable source-uncertainties in geometry configuration, boundary condition, and material properties may deviate the structural dynamics from its expected responses. This paper aims to examine the influence of these uncertainties on the vibration of functionally graded beams. Finite element procedures are presented for Timoshenko beams and utilized to generate reliable datasets. A prerequisite to the uncertainty quantification of the beam vibration using Monte Carlo simulation is generating large datasets, that require executing the numerical procedure many times leading to high computational cost. Utilizing artificial neural networks to model beam vibration can be a good approach. Initially, the optimal network for each beam configuration can be determined based on numerical performance and probabilistic criteria. Instead of executing thousands of times of the finite element procedure in stochastic analysis, these optimal networks serve as good alternatives to which the convergence of the Monte Carlo simulation, and the sensitivity and probabilistic vibration characteristics of each beam exposed to randomness are investigated. The simple procedure presented here is efficient to quantify the uncertainty of different stochastic behaviors of composite structures.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.285-291
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• 2002

This paper proposes the concept and the general framework of the risk-based seismic design. Because earthquakes and the behaviors of structures are very unpredictable, probabilistic seismic design methods have been proposed after deterministic design methods. Considering these changes, we can find that the important point of seismic design is not the structural behavior itself, but the consequence of structural behavior under possible earthquakes. Risk-based seismic design can tell these consequences under any earthquakes. In this paper, structural confidences are considered by using fragility curve, and risk is modeled by failure probability and consequence-property damage cost, casualty cost.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.62-68
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• 2019

The optoelectronic display units such as TFT-LCD or OLED require many thin optical plastic films and their mass manufacturing processes employ CO2 laser cutting of those thin films in a large quantity. However, laser film cutting could generate fume particles through melt shearing, vaporization, and chemical degradation and those particles could be of great concern for film surface contamination. In order to appreciate the fume particle dispersion behaviors in laser film cutting, this study relies on random particle simulations by probabilistic distributions of particle size, ejection velocity and angles coupled with Basset-Boussinesq-Oseen model of particle trajectory in low Reynolds number flows. Here, up to one million particles of random sampling have been tested to effectively show fume particles dispersed on the film surface. The computational results could show that particular range of fume particle size could easily disperse into the pixel region of processed optical films.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.600-606
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• 2019

Studies on the application of the lead rubber bearing (LRB) isolation system to nuclear power plants are being carried out as one of the measures to improve seismic performance. Nuclear power plants with isolation systems require seismic probabilistic safety assessments, for which the seismic fragility of the structures, systems, and components needs be calculated, including for beyond design basis earthquakes. To this end, seismic response analyses are required, where it can be seen that the behaviors of the isolation system components govern the overall seismic response of an isolated plant. The numerical model of the LRB used in these seismic response analyses plays an important role, but in most cases, the extreme performance of the LRB has not been well studied. The current work therefore develops an extreme nonlinear numerical model that can express the seismic response of the LRB for beyond design basis earthquakes. A full-scale LRB was fabricated and dynamically tested with various input conditions, and test results confirmed that the developed numerical model better represents the behavior of the LRB over previous models. Subsequent seismic response analyses of isolated nuclear power plants using the model developed here are expected to provide more accurate results for seismic probabilistic safety assessments.

• Journal of the Korea Concrete Institute
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• v.28 no.2
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• pp.149-156
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• 2016

The service life in RC (Reinforced Concrete) is very important and it is usually obtained through deterministic method based on Fick's 2nd law and probabilistic method. This paper presents an evaluation of $P_{df}$(durability failure probability) and the related service life considering time-dependent behaviors in chloride diffusion and surface chloride content. For the work, field investigation is performed for RC structures exposed to chloride attack for 3.5~4.5years, focusing tidal zone (6.0 m) and sea shore (9.0 m), respectively. Random variables like cover depth, chloride diffusion coefficient, and surface chloride content are obtained, and $P_{df}$ and the service life are evaluated. Unlike the results from deterministic method using LIFE 365, probabilistic method with time effects on diffusion and surface chloride shows a relatively rapid change in the result, which is a significant reductions of service life in the case with low surface chloride content. For probabilistic evaluation of durability, high surface chloride content over $10.0kg/m^3$ is required and reasonable service life can be derived with consideration of time-dependent diffusion coefficient.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.612-628
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• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Journal of Ocean Engineering and Technology
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• v.21 no.4
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• pp.28-33
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• 2007

The objective of this paper is to investigate the relationship between the short-term creep rupture time and the creep rupture properties at three different elevated temperatures in STS304 stainless steel. Uniaxial constant stress creep rupture tests were performed on the steel to observe the creep rupture behaviors at the elevated temperatures of 600, 650 and 700, according to the testing matrix. It is very important to predict creep life in practical creep design problems. As one of the series of studies on the statistical modelling of probabilistic creep rupture time and the development of creep life prediction techniques, the relationship between applied stress and creep rupture behaviors, such as creep strain rate and rupture time, were investigated. In addition, the Monkman-Grant relationship was observed between the steady-state creep rate and the creep rupture time. The creep rupture surfaces observed by SEM showed up dimple phenomenon at all conditions.