Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 985

Search results for: probabilistic relevance

985 Trace Network: A Probabilistic Relevant Pattern Recognition Approach to Attribution Trace Analysis

Authors: Jian Xu, Xiaochun Yun, Yongzheng Zhang, Yafei Sang, Zhenyu Cheng


Network attack prevention is a critical research area of information security. Network attack would be oppressed if attribution techniques are capable to trace back to the attackers after the hacking event. Therefore attributing these attacks to a particular identification becomes one of the important tasks when analysts attempt to differentiate and profile the attacker behind a piece of attack trace. To assist analysts in expose attackers behind the scenes, this paper researches on the connections between attribution traces and proposes probabilistic relevance based attribution patterns. This method facilitates the evaluation of the plausibility relevance between different traceable identifications. Furthermore, through analyzing the connections among traces, it could confirm the existence probability of a certain organization as well as discover its affinitive partners by the means of drawing relevance matrix from attribution traces.

Keywords: attribution trace, probabilistic relevance, network attack, attacker identification

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984 Solutions to Probabilistic Constrained Optimal Control Problems Using Concentration Inequalities

Authors: Tomoaki Hashimoto


Recently, optimal control problems subject to probabilistic constraints have attracted much attention in many research field. Although probabilistic constraints are generally intractable in optimization problems, several methods haven been proposed to deal with probabilistic constraints. In most methods, probabilistic constraints are transformed to deterministic constraints that are tractable in optimization problems. This paper examines a method for transforming probabilistic constraints into deterministic constraints for a class of probabilistic constrained optimal control problems.

Keywords: optimal control, stochastic systems, discrete-time systems, probabilistic constraints

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983 Conservativeness of Probabilistic Constrained Optimal Control Method for Unknown Probability Distribution

Authors: Tomoaki Hashimoto


In recent decades, probabilistic constrained optimal control problems have attracted much attention in many research field. Although probabilistic constraints are generally intractable in an optimization problem, several tractable methods haven been proposed to handle probabilistic constraints. In most methods, probabilistic constraints are reduced to deterministic constraints that are tractable in an optimization problem. However, there is a gap between the transformed deterministic constraints in case of known and unknown probability distribution. This paper examines the conservativeness of probabilistic constrained optimization method with the unknown probability distribution. The objective of this paper is to provide a quantitative assessment of the conservatism for tractable constraints in probabilistic constrained optimization with the unknown probability distribution.

Keywords: optimal control, stochastic systems, discrete time systems, probabilistic constraints

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982 Constructing a Probabilistic Ontology from a DBLP Data

Authors: Emna Hlel, Salma Jamousi, Abdelmajid Ben Hamadou


Every model for knowledge representation to model real-world applications must be able to cope with the effects of uncertain phenomena. One of main defects of classical ontology is its inability to represent and reason with uncertainty. To remedy this defect, we try to propose a method to construct probabilistic ontology for integrating uncertain information in an ontology modeling a set of basic publications DBLP (Digital Bibliography & Library Project) using a probabilistic model.

Keywords: classical ontology, probabilistic ontology, uncertainty, Bayesian network

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981 A Joint Possibilistic-Probabilistic Tool for Load Flow Uncertainty Assessment-Part I: Formulation

Authors: Morteza Aien, Masoud Rashidinejad, Mahmud Fotuhi-Firuzabad


As energetic and environmental issues are getting more and more attention all around the world, the penetration of distributed energy resources (DERs) mainly those harvesting renewable energies (REs) ascends with an unprecedented rate. This matter causes more uncertainties to appear in the power system context; ergo, the uncertainty analysis of the system performance is an obligation. The uncertainties of any system can be represented probabilistically or possibilistically. Since sufficient historical data about all the system variables is not available, therefore, they do not have a probability density function (PDF) and must be represented possibilistiacally. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution is appealed. The first of this two-paper series formulates a new possibilistic-probabilistic tool for the load flow uncertainty assessment. The proposed methodology is based on the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. This possibilistic- probabilistic formulation is solved in the second companion paper in an uncertain load flow (ULF) study problem.

Keywords: probabilistic uncertainty modeling, possibilistic uncertainty modeling, uncertain load flow, wind turbine generator

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980 Probabilistic Approach to Contrast Theoretical Predictions from a Public Corruption Game Using Bayesian Networks

Authors: Jaime E. Fernandez, Pablo J. Valverde


This paper presents a methodological approach that aims to contrast/validate theoretical results from a corruption network game through probabilistic analysis of simulated microdata using Bayesian Networks (BNs). The research develops a public corruption model in a game theory framework. Theoretical results suggest a series of 'optimal settings' of model's exogenous parameters that boost the emergence of corruption. The paper contrasts these outcomes with probabilistic inference results based on BNs adjusted over simulated microdata. Principal findings indicate that probabilistic reasoning based on BNs significantly improves parameter specification and causal analysis in a public corruption game.

Keywords: Bayesian networks, probabilistic reasoning, public corruption, theoretical games

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979 The Lexicographic Serial Rule

Authors: Thi Thao Nguyen, Andrew McLennan, Shino Takayama


We study the probabilistic allocation of finitely many indivisible objects to finitely many agents. Well known allocation rules for this problem include random priority, the market mechanism proposed by Hylland and Zeckhauser [1979], and the probabilistic serial rule of Bogomolnaia and Moulin [2001]. We propose a new allocation rule, which we call the lexico-graphic (serial) rule, that is tailored for situations in which each agent's primary concern is to maximize the probability of receiving her favourite object. Three axioms, lex efficiency, lex envy freeness and fairness, are proposed and fully characterize the lexicographic serial rule. We also discuss how our axioms and the lexicographic rule are related to other allocation rules, particularly the probabilistic serial rule.

Keywords: Efficiency, Envy free, Lexicographic, Probabilistic Serial Rule

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978 A Study of Chinese-specific Terms in Government Work Report(2017-2019) from the Perspective of Relevance Theory

Authors: Shi Jiaxin


The Government Work Report is an essential form of document in the government of the People’s Republic of China. It covers all aspects of Chinese society and reflects China’s development strategy and trend. There are countless special terms in Government Work Report. Only by understanding Chinese-specific terms can we understand the content of the Government Work Report. Only by accurately translating the Chinese-specific terms can people come from all across the world know the Chinese government work report and understand China. Relevance theory is a popular theory of cognitive pragmatics. Relevance Translation Theory, which is closely related to Relevance Theory, has crucial and major guiding significance for the translation of Chinese-specific. Through studying Relevance Theory and researching the translation techniques, strategies and applications in the process of translating Chinese-specific terms from the perspective of Relevance Theory, we can understand the meaning and connotation of Chinese-specific terms, then solve various problems in the process of C-E translation, and strengthen our translation ability.

Keywords: government work report, Chinese-specific terms, relevance theory, translation

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977 Applications of Analytical Probabilistic Approach in Urban Stormwater Modeling in New Zealand

Authors: Asaad Y. Shamseldin


Analytical probabilistic approach is an innovative approach for urban stormwater modeling. It can provide information about the long-term performance of a stormwater management facility without being computationally very demanding. This paper explores the application of the analytical probabilistic approach in New Zealand. The paper presents the results of a case study aimed at development of an objective way of identifying what constitutes a rainfall storm event and the estimation of the corresponding statistical properties of storms using two selected automatic rainfall stations located in the Auckland region in New Zealand. The storm identification and the estimation of the storm statistical properties are regarded as the first step in the development of the analytical probabilistic models. The paper provides a recommendation about the definition of the storm inter-event time to be used in conjunction with the analytical probabilistic approach.

Keywords: hydrology, rainfall storm, storm inter-event time, New Zealand, stormwater management

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976 Efficient Sampling of Probabilistic Program for Biological Systems

Authors: Keerthi S. Shetty, Annappa Basava


In recent years, modelling of biological systems represented by biochemical reactions has become increasingly important in Systems Biology. Biological systems represented by biochemical reactions are highly stochastic in nature. Probabilistic model is often used to describe such systems. One of the main challenges in Systems biology is to combine absolute experimental data into probabilistic model. This challenge arises because (1) some molecules may be present in relatively small quantities, (2) there is a switching between individual elements present in the system, and (3) the process is inherently stochastic on the level at which observations are made. In this paper, we describe a novel idea of combining absolute experimental data into probabilistic model using tool R2. Through a case study of the Transcription Process in Prokaryotes we explain how biological systems can be written as probabilistic program to combine experimental data into the model. The model developed is then analysed in terms of intrinsic noise and exact sampling of switching times between individual elements in the system. We have mainly concentrated on inferring number of genes in ON and OFF states from experimental data.

Keywords: systems biology, probabilistic model, inference, biology, model

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975 Probabilistic Simulation of Triaxial Undrained Cyclic Behavior of Soils

Authors: Arezoo Sadrinezhad, Kallol Sett, S. I. Hariharan


In this paper, a probabilistic framework based on Fokker-Planck-Kolmogorov (FPK) approach has been applied to simulate triaxial cyclic constitutive behavior of uncertain soils. The framework builds upon previous work of the writers, and it has been extended for cyclic probabilistic simulation of triaxial undrained behavior of soils. von Mises elastic-perfectly plastic material model is considered. It is shown that by using probabilistic framework, some of the most important aspects of soil behavior under cyclic loading can be captured even with a simple elastic-perfectly plastic constitutive model.

Keywords: elasto-plasticity, uncertainty, soils, fokker-planck equation, fourier spectral method, finite difference method

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974 The Valuation of Equity Book Value and Net Income of Financial Firms in Times of Financial Crisis

Authors: Sami Adwan, Alaa Alhaj Ismail, Claudia Girardone


This paper examines the changes in the value relevance of book value of equity and net income of financial firms over the crisis period. It also examines how these changes vary with three variables, namely, fair value exposure, ownership concentration, and regulatory capital ratios. Using a sample of financial firms operating in the European Economic Area over 2005-2011, our findings suggest that the value relevance of book value of equity increases while that of net income decreases during the financial crisis. We find that more exposure to fair value accounting mitigates the impact of the crisis on the value relevance of book value of equity and net income. We also find that more concentrated ownership appears to have a mitigating impact on the changes in the value relevance of both book value of equity and net income in times of financial crisis. Finally, we find evidence that the level of regulatory capital ratios tends to have an attenuating effect on the changes in the value relevance of net income (but not book value of equity) in times of financial crisis.

Keywords: value relevance, financial crisis, financial firms, fair value, ownership concentration, regulatory capital

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973 Optimizing the Probabilistic Neural Network Training Algorithm for Multi-Class Identification

Authors: Abdelhadi Lotfi, Abdelkader Benyettou


In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: classification, probabilistic neural networks, network optimization, pattern recognition

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972 Comparison between Deterministic and Probabilistic Stability Analysis, Featuring Consequent Risk Assessment

Authors: Isabela Moreira Queiroz


Slope stability analyses are largely carried out by deterministic methods and evaluated through a single security factor. Although it is known that the geotechnical parameters can present great dispersal, such analyses are considered fixed and known. The probabilistic methods, in turn, incorporate the variability of input key parameters (random variables), resulting in a range of values of safety factors, thus enabling the determination of the probability of failure, which is an essential parameter in the calculation of the risk (probability multiplied by the consequence of the event). Among the probabilistic methods, there are three frequently used methods in geotechnical society: FOSM (First-Order, Second-Moment), Rosenblueth (Point Estimates) and Monte Carlo. This paper presents a comparison between the results from deterministic and probabilistic analyses (FOSM method, Monte Carlo and Rosenblueth) applied to a hypothetical slope. The end was held to evaluate the behavior of the slope and consequent risk analysis, which is used to calculate the risk and analyze their mitigation and control solutions. It can be observed that the results obtained by the three probabilistic methods were quite close. It should be noticed that the calculation of the risk makes it possible to list the priority to the implementation of mitigation measures. Therefore, it is recommended to do a good assessment of the geological-geotechnical model incorporating the uncertainty in viability, design, construction, operation and closure by means of risk management. 

Keywords: probabilistic methods, risk assessment, risk management, slope stability

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971 Probabilistic and Stochastic Analysis of a Retaining Wall for C-Φ Soil Backfill

Authors: André Luís Brasil Cavalcante, Juan Felix Rodriguez Rebolledo, Lucas Parreira de Faria Borges


A methodology for the probabilistic analysis of active earth pressure on retaining wall for c-Φ soil backfill is described in this paper. The Rosenblueth point estimate method is used to measure the failure probability of a gravity retaining wall. The basic principle of this methodology is to use two point estimates, i.e., the standard deviation and the mean value, to examine a variable in the safety analysis. The simplicity of this framework assures to its wide application. For the calculation is required 2ⁿ repetitions during the analysis, since the system is governed by n variables. In this study, a probabilistic model based on the Rosenblueth approach for the computation of the overturning probability of failure of a retaining wall is presented. The obtained results have shown the advantages of this kind of models in comparison with the deterministic solution. In a relatively easy way, the uncertainty on the wall and fill parameters are taken into account, and some practical results can be obtained for the retaining structure design.

Keywords: retaining wall, active earth pressure, backfill, probabilistic analysis

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970 Quick Similarity Measurement of Binary Images via Probabilistic Pixel Mapping

Authors: Adnan A. Y. Mustafa


In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented.

Keywords: big images, binary images, image matching, image similarity

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969 A Joint Possibilistic-Probabilistic Tool for Load Flow Uncertainty Assessment-Part II: Case Studies

Authors: Morteza Aien, Masoud Rashidinejad, Mahmud Fotuhi-Firuzabad


Power systems are innately uncertain systems. To face with such uncertain systems, robust uncertainty assessment tools are appealed. This paper inspects the uncertainty assessment formulation of the load flow (LF) problem considering different kinds of uncertainties, developed in its companion paper through some case studies. The proposed methodology is based on the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. The load and wind power generation are considered as probabilistic uncertain variables and the electric vehicles (EVs) and gas turbine distributed generation (DG) units are considered as possibilistic uncertain variables. The cumulative distribution function (CDF) of the system output parameters obtained by the pure probabilistic method lies within the belief and plausibility functions obtained by the joint propagation approach. Furthermore, the imprecision in the DG parameters is explicitly reflected by the gap between the belief and plausibility functions. This gap, due to the epistemic uncertainty on the DG resources parameters grows as the penetration level increases.

Keywords: electric vehicles, joint possibilistic- probabilistic uncertainty modeling, uncertain load flow, wind turbine generator

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968 Does Operating Cash Flow Really Matter in Value Relevance? A Recent Empirical Analysis on the Largest European Companies

Authors: Francesco Paolone


This paper investigates the role of Operating Cash Flow (OCF) and accruals in firm valuation analyzing financial statement information from the largest European companies and evaluating their relation to firm market value. Using a dataset of 500 largest European companies in 2018, the study investigates the relative value-relevance of equity, net income and operating cash flow (OCF). Findings show that the cash flow measure has the same explanatory power and intensity as equity and earnings to explain the market value. This study contributes to the debate on the value relevance of OCF incremental to book value and earnings. It also extends the literature, showing that OCF has information content (value relevance) superior to earnings and book value in the main European markets (Bepari et al., 2013). Finally, the study provides a support that accounting method choice may confuse investors, who have reduced confidence in accounting earnings and book value; in other words, nowadays European investors rely more on cash flows instead of accruals numbers.

Keywords: Cash Flow Statement, Value Relevance, Accounting, Financial Statement Analysis

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967 Probabilistic Graphical Model for the Web

Authors: M. Nekri, A. Khelladi


The world wide web network is a network with a complex topology, the main properties of which are the distribution of degrees in power law, A low clustering coefficient and a weak average distance. Modeling the web as a graph allows locating the information in little time and consequently offering a help in the construction of the research engine. Here, we present a model based on the already existing probabilistic graphs with all the aforesaid characteristics. This work will consist in studying the web in order to know its structuring thus it will enable us to modelize it more easily and propose a possible algorithm for its exploration.

Keywords: clustering coefficient, preferential attachment, small world, web community

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966 Stochastic Richelieu River Flood Modeling and Comparison of Flood Propagation Models: WMS (1D) and SRH (2D)

Authors: Maryam Safrai, Tewfik Mahdi


This article presents the stochastic modeling of the Richelieu River flood in Quebec, Canada, occurred in the spring of 2011. With the aid of the one-dimensional Watershed Modeling System (WMS (v.10.1) and HEC-RAS (v.4.1) as a flood simulator, the delineation of the probabilistic flooded areas was considered. Based on the Monte Carlo method, WMS (v.10.1) delineated the probabilistic flooded areas with corresponding occurrence percentages. Furthermore, results of this one-dimensional model were compared with the results of two-dimensional model (SRH-2D) for the evaluation of efficiency and precision of each applied model. Based on this comparison, computational process in two-dimensional model is longer and more complicated versus brief one-dimensional one. Although, two-dimensional models are more accurate than one-dimensional method, but according to existing modellers, delineation of probabilistic flooded areas based on Monte Carlo method is achievable via one-dimensional modeler. The applied software in this case study greatly responded to verify the research objectives. As a result, flood risk maps of the Richelieu River with the two applied models (1d, 2d) could elucidate the flood risk factors in hydrological, hydraulic, and managerial terms.

Keywords: flood modeling, HEC-RAS, model comparison, Monte Carlo simulation, probabilistic flooded area, SRH-2D, WMS

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965 Probabilistic Safety Assessment of Koeberg Spent Fuel Pool

Authors: Sibongiseni Thabethe, Ian Korir


The effective management of spent fuel pool (SFP) safety has been raised as one of the emerging issues to further enhance nuclear installation safety after the Fukushima accident on March 11, 2011. Before then, SFP safety-related issues have been mainly focused on (a) controlling the configuration of the fuel assemblies in the pool with no loss of pool coolants and (b) ensuring adequate pool storage space to prevent fuel criticality owing to chain reactions of the fission products and the ability for neutron absorption to keep the fuel cool. A probabilistic safety (PSA) assessment was performed using the systems analysis program for hands-on integrated reliability evaluations (SAPHIRE) computer code. Event and fault tree analysis was done to develop a PSA model for the Koeberg SFP. We present preliminary PSA results of events that lead to boiling and cause fuel uncovering, resulting in possible fuel damage in the Koeberg SFP.

Keywords: computer code, fuel assemblies, probabilistic risk assessment, spent fuel pool

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964 Value Relevance of Accounting Information: A Study of Steel Sector in India

Authors: Pradyumna Mohanty


The paper aims to explore whether accounting information of Indian companies in the Steel sector are value relevant or not. Ohlson’s model which usually takes into consideration book value per share (BV) and earnings per share (EARN) has been used and the same has been expanded to include two more variables such as cash flow from operations (CFO) and return on equity (ROE). The data were collected from CMIE-Prowess data base in respect of BSE-listed steel companies and the time frame spans from 2010 to 2014. OLS regression has been used to test the value relevance of these accounting numbers. Results indicate that both CFO and BV are having significant influence on the stock price in two out of five years of study. But, BV is emerging as the most significant and highly value relevant of all the four variables during the entire period of study.

Keywords: value relevance, accounting information, book value per share, earnings per share

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963 Using Probabilistic Neural Network (PNN) for Extracting Acoustic Microwaves (Bulk Acoustic Waves) in Piezoelectric Material

Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel


In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

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962 International Financial Reporting Standard Adoption and Value Relevance of Earnings in Listed Consumer Goods Companies in Nigerian

Authors: Muktar Haruna


This research work examines the International Financial Reporting Standard (IFRS) adoption and value relevance of earnings of listed consumer goods companies in the Nigerian. The population of the study comprises 22 listed consumer goods companies, out of which 15 were selected as sample size of the study. The scope of the study is a 12-year period covering from 2006 to 2018. Secondary data from the annual report of sampled companies were used, which consists of earnings per share (EPS), the book value of equity per share (BVE) as independent variables; firm size (FSZ) as a control variable, and market share price of sampled companies from Nigerian stock exchange as dependent variable. Multiple regressions were used to analyze the data. The results of the study showed that IFRS did not improve the value relevance of earnings after the adoption, which translates to a decrease in value relevance of accounting numbers in the post-adoption period. The major recommendation is that the Nigerian Reporting Council should ensure full compliance to all provisions of IFRS and provide uniformity in the presentation of non-current assets in the statement of financial position, where some present only net current assets leaving individual figures for current assets and liabilities invisible.

Keywords: IFRS, adoption, value relevance, earning per share, book value of equity per share

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961 Resistance and Sub-Resistances of RC Beams Subjected to Multiple Failure Modes

Authors: F. Sangiorgio, J. Silfwerbrand, G. Mancini


Geometric and mechanical properties all influence the resistance of RC structures and may, in certain combination of property values, increase the risk of a brittle failure of the whole system. This paper presents a statistical and probabilistic investigation on the resistance of RC beams designed according to Eurocodes 2 and 8, and subjected to multiple failure modes, under both the natural variation of material properties and the uncertainty associated with cross-section and transverse reinforcement geometry. A full probabilistic model based on JCSS Probabilistic Model Code is derived. Different beams are studied through material nonlinear analysis via Monte Carlo simulations. The resistance model is consistent with Eurocode 2. Both a multivariate statistical evaluation and the data clustering analysis of outcomes are then performed. Results show that the ultimate load behaviour of RC beams subjected to flexural and shear failure modes seems to be mainly influenced by the combination of the mechanical properties of both longitudinal reinforcement and stirrups, and the tensile strength of concrete, of which the latter appears to affect the overall response of the system in a nonlinear way. The model uncertainty of the resistance model used in the analysis plays undoubtedly an important role in interpreting results.

Keywords: modelling, Monte Carlo simulations, probabilistic models, data clustering, reinforced concrete members, structural design

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960 Probabilistic Fracture Evaluation of Reactor Pressure Vessel Subjected to Pressurized Thermal Shock

Authors: Jianguo Chen, Fenggang Zang, Yu Yang, Liangang Zheng


Reactor Pressure Vessel (RPV) is an important security barrier in nuclear power plant. Crack like defects may be produced on RPV during the whole operation lifetime due to the harsh operation condition and irradiation embrittlement. During the severe loss of coolant accident, thermal shock happened as the injection of emergency cooling water into RPV, which results in re-pressurization of the vessel and very high tension stress on the vessel wall, this event called Pressurized Thermal Shock (PTS). Crack on the vessel wall may propagate even penetrate the vessel, so the safety of the RPV would undergo great challenge. Many assumptions in structure integrity evaluation make the result of deterministic fracture mechanics very conservative, which affect the operation lifetime of the plant. Actually, many parameters in the evaluation process, such as fracture toughness and nil-ductility transition temperature, have statistical distribution characteristics. So it is necessary to assess the structural integrity of RPV subjected to PTS event by means of Probabilistic Fracture Mechanics (PFM). Structure integrity evaluation methods of RPV subjected to PTS event are summarized firstly, then evaluation method based on probabilistic fracture mechanics are presented by considering the probabilistic characteristics of material and structure parameters. A comprehensive analysis example is carried out at last. The results show that the probability of crack penetrates through wall increases gradually with the growth of fast neutron irradiation flux. The results give advice for reactor life extension.

Keywords: fracture toughness, integrity evaluation, pressurized thermal shock, probabilistic fracture mechanics, reactor pressure vessel

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959 Integrating Deterministic and Probabilistic Safety Assessment to Decrease Risk & Energy Consumption in a Typical PWR

Authors: Ebrahim Ghanbari, Mohammad Reza Nematollahi


Integrating deterministic and probabilistic safety assessment (IDPSA) is one of the most commonly used issues in the field of safety analysis of power plant accident. It has also been recognized today that the role of human error in creating these accidents is not less than systemic errors, so the human interference and system errors in fault and event sequences are necessary. The integration of these analytical topics will be reflected in the frequency of core damage and also the study of the use of water resources in an accident such as the loss of all electrical power of the plant. In this regard, the SBO accident was simulated for the pressurized water reactor in the deterministic analysis issue, and by analyzing the operator's behavior in controlling the accident, the results of the combination of deterministic and probabilistic assessment were identified. The results showed that the best performance of the plant operator would reduce the risk of an accident by 10%, as well as a decrease of 6.82 liters/second of the water sources of the plant.

Keywords: IDPSA, human error, SBO, risk

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958 Influence of Maximum Fatigue Load on Probabilistic Aspect of Fatigue Crack Propagation Life at Specified Grown Crack in Magnesium Alloys

Authors: Seon Soon Choi


The principal purpose of this paper is to find the influence of maximum fatigue load on the probabilistic aspect of fatigue crack propagation life at a specified grown crack in magnesium alloys. The experiments of fatigue crack propagation are carried out in laboratory air under different conditions of the maximum fatigue loads to obtain the fatigue crack propagation data for the statistical analysis. In order to analyze the probabilistic aspect of fatigue crack propagation life, the goodness-of fit test for probability distribution of the fatigue crack propagation life at a specified grown crack is implemented through Anderson-Darling test. The good probability distribution of the fatigue crack propagation life is also verified under the conditions of the maximum fatigue loads.

Keywords: fatigue crack propagation life, magnesium alloys, maximum fatigue load, probability

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957 A Hierarchical Method for Multi-Class Probabilistic Classification Vector Machines

Authors: P. Byrnes, F. A. DiazDelaO


The Support Vector Machine (SVM) has become widely recognised as one of the leading algorithms in machine learning for both regression and binary classification. It expresses predictions in terms of a linear combination of kernel functions, referred to as support vectors. Despite its popularity amongst practitioners, SVM has some limitations, with the most significant being the generation of point prediction as opposed to predictive distributions. Stemming from this issue, a probabilistic model namely, Probabilistic Classification Vector Machines (PCVM), has been proposed which respects the original functional form of SVM whilst also providing a predictive distribution. As physical system designs become more complex, an increasing number of classification tasks involving industrial applications consist of more than two classes. Consequently, this research proposes a framework which allows for the extension of PCVM to a multi class setting. Additionally, the original PCVM framework relies on the use of type II maximum likelihood to provide estimates for both the kernel hyperparameters and model evidence. In a high dimensional multi class setting, however, this approach has been shown to be ineffective due to bad scaling as the number of classes increases. Accordingly, we propose the application of Markov Chain Monte Carlo (MCMC) based methods to provide a posterior distribution over both parameters and hyperparameters. The proposed framework will be validated against current multi class classifiers through synthetic and real life implementations.

Keywords: probabilistic classification vector machines, multi class classification, MCMC, support vector machines

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956 Probabilistic Gathering of Agents with Simple Sensors: Distributed Algorithm for Aggregation of Robots Equipped with Binary On-Board Detectors

Authors: Ariel Barel, Rotem Manor, Alfred M. Bruckstein


We present a probabilistic gathering algorithm for agents that can only detect the presence of other agents in front of or behind them. The agents act in the plane and are identical and indistinguishable, oblivious, and lack any means of direct communication. They do not have a common frame of reference in the plane and choose their orientation (direction of possible motion) at random. The analysis of the gathering process assumes that the agents act synchronously in selecting random orientations that remain fixed during each unit time-interval. Two algorithms are discussed. The first one assumes discrete jumps based on the sensing results given the randomly selected motion direction, and in this case, extensive experimental results exhibit probabilistic clustering into a circular region with radius equal to the step-size in time proportional to the number of agents. The second algorithm assumes agents with continuous sensing and motion, and in this case, we can prove gathering into a very small circular region in finite expected time.

Keywords: control, decentralized, gathering, multi-agent, simple sensors

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