Objectives probabilistic methods

While these objectives for method sensitivity may seem ambitious, experience has shown that data from such studies are much more usable for supporting fate and transport models (development and/or validation efforts) that may have to be used when more precise and geographically detailed probabilistic risk assessments become necessary. 

Deterministic/Probabilistic techniques A deterministic method classifies an object in one and only one of the training classes and the degree of reliability of this decision is not measured. Probabilistic methods provide an estimate of the reliability of the classification decision. KNN, MLP, SVM and CAIMAN are deterministic. Other techniques, including some kind of ANN are probabilistic (e.g., PNN where a Bayesian decision is implemented). 

A6. As stated in para. A3, the attaiiunent of the safety objectives for a particular nuclear installation is demonstrated by means of a safety analysis. Ideally, diis safety analysis should include all events, sequences and processes where failures or combi-rratiorts of feiluies could potentially have radiological cortsequences. In practical aj licatiorrs, it is not possible or necessary to achieve this degree of completeness. Whedier the safety analysis is carried out by probabilistic methods or by the conventional methods of detailed engineering analyses (deterministic methods) it will, of necessity, be based on a selected set of scenarios (combinations of events, sequences and processes). The selection must be made in such a way that the major contributors to risk are covered as far as is reasonably achievable. Safety analysis, i.e. the demonstration that the types of risks to be considered have been reduced to tolerable levels, as discussed in para. A3, should be performed using proven methods and with appropriate peer review. 

The combination of deterministic and probabilistic procedures in the evaluation of plant safety has been realized in the "approach oriented to safety objectives". This method verifies plant safety via the achievement of so-called "safety objectives". The standard to be issued by the German Nuclear Technology Committee, "KTA 2000", intends to establish the framework conditions for such an approach oriented to safety objectives. In Bavaria, this approach has already been applied to all nuclear power stations during periodical safety inspections (PSI). 

The SAP contain deterministic (engineering) and probabilistic principles and include numerical criteria. Engineering (deterministic) principles are those good practices which lead to a robust, fault tolerant plant based on sound safety concepts they comprise about 75% of the SAPs. Probabilistic methods are the use of quantitative methods to seek out weakness and to demonstrate the achievement of certain numerical objectives. Deterministic and probabilistic principles complement one another. 

The main objective of the study is the abUily to analyze an identified model in identifying automaton models from observations. We want to take an established method to learn a DFA and apply it to our timed sequences. Our problem could be modelled as a timed-state transition graph, a probabilistic deterministic finite automaton (PDFA) taking into account timed-event. We also have a set of positive timed-strings (or time-stamped event sequences). 

As it can be seen, both the risk objectives and the risk analyses on existing plants are reassuring, but, it is frequently asked, how reliable these analyses are How much the inevitable uncertainties on data and methods can influence the results Is it possible that some accident sequence has been forgotten in performing a probabilistic analysis All the available information, including the analyses made before the Three Mile Island accident and the sequence of events in the accident itself, indicate that a corrective... 

A common approach to deal with model uncertainty is model set expansion (Zio and Apostolakis, 1996). According to this approach, the characteristics of the system under consideration are analyzed and models are created in an attempt to emulate the system based on goodness-of-fit criteria (Reinert and Apostolakis, 2006). The models may use different assumptions and require different inputs. These models are then combined to produce a meta-model of the system. Several methods have been proposed regarding the construction of the meta-model. AU rely on expert opinion. In the Bayesian approach, the combination of the individual models is carried out using Bayes theorem (Droguett and Mosleh, 2008). This method is theoretically very attractive dne to its mathematical rigor and ability to incorporate both objective and subjective information in a probabilistic representation. 

This problem can be overcome with the combined use of established probabilistic design methods developed for brittle structural components, good thermoelastic and thetmomechanical databases of the candidate oxide material comprising the TE device, and iteratively applied design sensitivity analysis. Therefore, the objective of this work is to demonstrate the use of a probabilistic... 

In the last twenty years, various non-deterministic methods have been developed to deal with optimum design under environmental uncertainties. These methods can be classified into two main branches, namely reliability-based methods and robust-based methods. The reliability methods, based on the known probabiUty distribution of the random parameters, estimate the probability distribution of the system s response, and are predominantly used for risk analysis by computing the probability of system failure. However, variation is not minimized in reliability approaches (Siddall, 1984) because they concentrate on rare events at the tail of the probability distribution (Doltsinis and Kang, 2004). The robust design methods are commonly based on multiobjective minimization problems. The are commonly indicated as Multiple Objective Robust Optimization (MORO) and find a set of optimal solutions that optimise a performance index in terms of mean value and, at the same time, minimize its resulting dispersion due to input parameters uncertainty. The final solution is less sensitive to the parameters variation but eventually maintains feasibility with regards probabilistic constraints. This is achieved by the optimization of the design vector in order to make the performance minimally sensitive to the various causes of variation. 

The Rough Set theory or Rough Set Data Analysis (RSDA) is widely used for the determination of non-linear relationships in many di erent areas. Rough set theory is a method of information analysis and especially r uction of data sets, discovery of data patterns, classification of the objects into sets and generation of decision rules, e.g. Pawlak, (1982, 2002). Rough set theory does not need any preliminary information about data like probability distribution (as in probabilistic analysis), basic probability... 

The Bayesian approach is one of the probabilistic central parametric classification methods it is based on the consistent apphcation of the classic Bayes equation (also known as the naive Bayes classifier ) for conditional probabihty [34] to constmct a decision rule a modified algorithm is explained in references [105, 109, 121]. In this approach, a chemical compound C, which can be specified by a set of probability features (Cj,...,c ) whose random values are distributed through all classes of objects, is the object of recognition. The features are interpreted as independent random variables of an /w-dimensional random variable. The classification metric is an a posteriori probability that the object in question belongs to class k. Compound C is assigned to the class where the probability of membership is the highest. 

The local distribution method is one combination method using the geometric local nonparametric method in parallel to a probabilistic central parametric method for decision rale constraction. The algorithm was first deseiibed in [109] and later modified as described in [105]. Two metrics serve as classification metrics the similarity coefficient of the features of the object to be predicted and class k objects in /M-dimensional space, and the probabihty that the object of interest belongs to the subclass of similar objects in class k. Componnd C is assigned to the class with the greatest local probabihty that the componnd belongs to the structurally similar subclass. 

Exploitation research activity is realized during object exploitation in order to determine its current and future operation, technical and reliability condition. In complex and advanced technical objects several research methods are used simultaneously based on data in various form (determined, probabilistic and heuristic signals). 

ABSTRACT In order to incorporate domino effect scenarios, deriving from the projection of fragment (so-called missile), within the standard QRA (Quantitative Risk Analysis), a probabilistic model for the estimation of the impact probabiUty of such fragment was developed by applying a Monte-Carlo method to the analytical solution of the set of equations describing the motion of the missile. The objective of this work is to further extend this probabilistic approach, which was previously developed for cylindrical vessels, to make it apphcable also to spherical tanks. A case study is presented, it is the accident occurred in the refinery of Feyzin (France) the influence of the size and shape of the fragments on the impact probability is assessed. 

The most widely used method for the design of the RCSASs is deterministic, whereby SSCs are designed to comply with guiding rules. This approach is generally complemented with a probabilistic risk assessment whose objective is to verify that the plant as designed does not have any unacceptable vulnerabilities. 

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