• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• International Journal of Concrete Structures and Materials
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• v.19 no.1E
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.199-208
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• 2008

Prediction the performance of pavement provides proper information to an agency on decision-making process; especially evaluating the pavement performance and prioritizing the work plan. To date, there are a number of approaches to predict the future deterioration of pavements. However, there are some limitation to proper prediction of the pavement service life. In this paper, pavement performance model and pavement condition prediction model are developed in order to improve pavement condition prediction method. The prediction model of pavement condition through the regression analysis of real pavement condition is based on the probability distribution of pavement condition, which set to 5%, 15%, 25% and 50%, by condition of the pavement and traffic volume. The pavement prediction model presented from the behavior of individual pavement condition which are set to 5%, 15%, 25% and 50% of probability distribution. The performance of the prediction model is evaluated from analyzing the average, standard deviation of HPCI, and the percentage of HPCI which is lower than 3.0 of comparable section. In this paper, we will suggest the more rational method to determine the future pavement conditions, including the probabilistic duration and deterministic modeling methods regarding the impact of traffic volume, age, and the type of the pavement.

• Composites Research
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• v.13 no.1
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• pp.33-39
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• 2000

A numerical approach for predicting the ultimate strength of laminate composites has been studied using the Weibull distribution of the strengths of lamina plies. The probabilistic initial failure strengths of laminates were calculated using Tsai-Hill failure criterion. The ultimate strength of the laminate composites has been predicted using progressive failure analysis. The experimental results show that the strength prediction based on the Weibull distribution of ply strength reasonably agrees well with the experimentals better than equal strength assumption.

• Structural Engineering and Mechanics
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• v.60 no.1
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• pp.71-90
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• 2016

In this paper, the normalized variable V=(log N-B)(log ${\Delta}{\sigma}-C$-C), as derived from the probabilistic S-N field of Castillo and Canteli, is taken as a reference for calculation of damage accumulation and probability of failure using the Miner number in scenarios of variable amplitude loading. Alternative damage measures, such as the classical Miner and logarithmic Miner, are also considered for comparison between theoretical lifetime prediction and experimental data. The suitability of this approach is confirmed for it provides safe lifetime prediction when applied to fatigue data obtained for riveted joints made of a puddle iron original from the Fao bridge, as well as for data from experimental programs published elsewhere carried out for different materials (aluminium and concrete specimens) under distinct variable loading histories.

• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.957-960
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• 2006

In order to predict the accumulated damages by cyclic freeze-thaw, a regression analysis by the Response Surface Method (RSM) is used. RSM has merits when the other probabilistic simulation techniques can not guarantee the convergence of probability of occurrence or when the others can not differentiate the derivative terms of limit state functions, which are composed of random design variables in the model of complex system or the system having higher reliability. For composing limit state function, the important parameters for cyclic freeze-thaw-deterioration of concrete structures, such as water to cement ratio, entrained air pores, and the number of cycles of freezing and thawing, are used as input parameters of RSM. The predicted results of relative dynamic modulus and residual strains after 300 cycles of freeze-thaw for specimens show very good agreements with the experimental results. The RSM result can be used to predict the probability of occurrence for designer specified critical values. Therefore, it is possible to evaluate the life cycle management of concrete structures considering the accumulated damages by the cyclic freeze-thaw by the use of proposed prediction method.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.69-71
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• 2004

This paper presents a method to predict failure probability related to aging. To calculate failure probability, the Weibull distribution is used due to age-related reliability. The Weibull distribution has shape and scale parameters. Each estimated parameter is obtained from Data Analytic Method (Type II Censoring) which is relatively simpler and faster than the traditional calculation ways for estimating parameters. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an age-related reliability index.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.373-379
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• 2017

Seismic probabilistic safety assessments are used to help understand the impact potential seismic events can have on the operation of a nuclear power plant. An important component to seismic probabilistic safety assessment is the seismic hazard curve which shows the frequency of seismic events. However, these hazard curves are estimated assuming a normal distribution of the seismic events. This may not be a strong assumption given the number of recorded events at each source-to-site distance. The use of a normal distribution makes the calculations significantly easier but may underestimate or overestimate the more rare events, which is of concern to nuclear power plants. This paper shows a preliminary exploration into the effect of using a distribution that perhaps more represents the distribution of events, such as the t-distribution to describe data. The integration of a probability distribution with potentially larger tails basically pushes the hazard curves outward, suggesting a different range of frequencies for use in seismic probabilistic safety assessments. Therefore the use of a more realistic distribution results in an increase in the frequency calculations suggesting rare events are less rare than thought in terms of seismic probabilistic safety assessment. However, the opposite was observed with the ground motion prediction equation considered.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.1
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• pp.14-26
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• 2014

This paper provides the prediction of ultimate longitudinal strengths of the hull girders of a very large crude carrier considering probabilistic damage extent due to collision and grounding accidents based on IMO Guidelines (2003). The probabilistic density functions of damage extent are expressed as a function of non-dimensional damage variables. The accumulated probabilistic levels of 10%, 30%, 50%, and 70% are taken into account for the estimation of damage extent. The ultimate strengths have been calculated using the in-house software called Ultimate Moment Analysis of Damaged Ships which is based on the progressive collapse method, with a new convergence criterion of force vector equilibrium. Damage indices are provided for several probable heeling angles from $0^{\circ}$ (sagging) to $180^{\circ}$ (hogging) due to collision- and grounding-induced structural failures and consequent flooding of compartments. This paper proves from the residual strength analyses that the second moment of area of a damage section can be a reliable index for the estimation of the residual ultimate strength. A simple polynomial formula is also proposed based on minimum residual ultimate strengths.