Expert model

Such a system would include a program similar to that of Storer and Comish-Bowden to do equilibrium calculations. A communication-control subprogram would be Hiked to an expert model by using the EXPERT knowledge-base shall (or system-builder) which is advantageous here because it can interact with procedures such as those written in FORTRAN for numerical computation. Additional programs and a small data base, which EXPERT can handle, would keep track of which chemical was what array element, and other requirements mentioned above. 

Evolving from efforts [22] to use the best features of trial-and-error, process model, expert system, and expert model approaches, QPA [23-25] combines KBES traits with online dielectric, pressure, and temperature data to implement autoclave curing control. QPA combines extensive sensor data with KBES rules to determine control actions. These rules determine curing progress based upon process feedback, and implement control action. QPA adjusts production parameters on-line as such—within the limits of its heuristics—QPA can accommodate batch-to-batch prepreg variations. 

To parameterize the reasons for information instability that appear in problems of the remote sounding of the environment, we consider two types of models. The first refers to the class of expert structures, which reflects expert opinion. Expert models are ranged according to their efficiency. Each of these models, M, is characterized by structure M and complexity C M = M, C. The quality of the model M is assessed either by expert criterion r or by objective criterion (. Let A( ) be the multitude of arguments present in the model s structure, the best by criterion (, and A((,t) the multitude of arguments included in the model s structure, the best by both criteria ( and r. Then, the simplest empirical component of knowledge is the parameter ... 

Again, these considerations are more typical for models for regulatory purposes. Expert modelers can explore complex situations using their experience, even if the possibility of applying the procedure in external situations or by other users is limited. 

In a mixture-of-experts model (Jacobs et al., 1991), different expert networks were assigned to tackle sub-tasks of training cases, and an extra gating network was used to decide which of the experts should determine the output. The model discovered a suitable decomposition of the input space as part of the learning process. Later the model was further extended (Jordan Jacobs, 1994) into a hierarchical system with a tree structure. In molecular applications, cascaded networks-where outputs of some networks become the inputs of others-were used to improve performance (Rost Sander, 1994). Multiple neural network modules may run in parallel in order to scale up the system (Wu et al., 1995). More than one network can also be used to extract different (e.g., local vs. global) features (Mahadevan Ghosh, 1994). 

Transparent and inclusive decision-making processes. Environmental health decisions tend to be primarily policy decisions, informed by science and values. A more participative process for decision-making under the precautionary principle could improve the ability of decision-makers to anticipate and prevent harm to ecosystems and human health. Public participation is vital to development of accountable solutions to environmental health problems because nonexperts see problems, issues, and solutions that experts miss, reflect sensitivity to social and political values and common sense not included in expert models. Broader public participation processes may increase the quality, legitimacy and accountability of complex decisions. 

Characterize the expert-modeling models. Present an example of such a model for lake water rehabilitation. 

The following steps are concerned with the design of the compounding step. They are supported by the flow diagram editor and the simulation tool MOREX. The extruder simulation expert models the compounding process as a part of the overall chemical process with the help of the flow diagram editor. The respective part of the flow diagram is used to derive a model for ID simulation in MOREX. 

Implicit in this paradigm is the notion that the cybernetic sequence is embedded in the neural, knowledge base, and learned internal models and can be mapped by defining the differential change in mental, disciplinary structural, and expert models. 

Van den Brink PJ, Brown CD, Dubus IG. 2006b. Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks. Ecol Model 191 106-117. 

To carry out the analysis, we used IBM SPSS Statistics 19, which contains the tool Expert Modeler to time series . Table 7 contains the proposed model for each one of the eight time series. 

One way of using ejqtert judgment is to collect information from ejqterts to build a computable model, a so-called ejqtert model, with which the desired value (e.g. a failure probabihty) can be obtained. Expert models can be an attractive option when is it difficult to have a pool of experts available to assess the desired value. Conversion of the expert knowledge into a computable model also can improve traceability and repeatabhity to the assessment. 

The aim of this paper is to investigate the application of a Bayesian Network (BN) expert model to Hmnan Reliabihty Assessment (HRA), the part of the PSA that deals with analyzing the human performance and quantifying its impact on risk and safety. The specific problem is the assessment of HRA dependencies, which refers to evaluating the influence of the failure of operators to perform one task on the performance of subsequent tasks. In qualitative terms, it can be expected that if two tasks are closely related in some fashion, the fahme of the second task is more likely if the operators have failed in their performance of the first task, than if they have succeeded. At the quantification level, the dependence assessment entails evaluating a conditional HFE probability, conditioned on the fadme of the preceding task. The assessment of such dependence is a difficult task also. 

Recent work by some of the authors has addressed the use of expert models, in particular a Fuzzy Expert System (FES), for the assessment of HRA dependencies. At this stage, the results show the potential of expert models to increase traceability and repeatability of the assessment process (Zio et al., in prep., Podofillini et al., in prep.). 

With respect to the dependence model of Section 1, a BN expert model with ranked nodes has been adopted (Fenton Caballero 2007). This type of nodes is used to represent quahtative variables described by a finite set of states or labels. Each of the m = 4 input factors of the dependence model (named Xk, k = 1,2,..., m) and the only output y are expressed in terms of a BN discrete node characterized by a set of states X, v = 1,2,..., , for the input factors. 

This paper investigated the use of BNs expert model to support HRA dependence assessment. Two methods of input ehcitation are proposed (for discrete and continuous variables) to convert the analyst knowledge into the probabilistic input for the model the focus of the discussion and of the examples is on how the uncertainty in the analyst input is handled. Further, an interface is introduced that converts the output of the BN model into a quantitative form for a clear evaluation of the dependence, suitable for PSA application. 

THE MULTIPLE-ASSUMPTIONS MULTIPLE-EXPERTS MODEL UNCERTAINTY PROBLEM... 

The tutor uses information from the student assessment and from the expert model to generate instructional sequences. At this point, if a student misses a declarative knowledge question, s/ he is transported to the hypertext node (card) that contains that information. If the student misses a structural knowledge question, then the expert model expert system is activated, generating an optimal structural path for the learner. This path is then illustrated for the learner. If the learner misses a procedural knowledge question, practice scenarios are provided with standard practice and feedback. The assumptions and sophistication of this part of the tutor needs development. 

Once the proposed protein structure has been constructed, it usually needs to be refined. For models developed from very similar templates (greater than 85% similarity), the need to refine the structures will likely be minimal, however. The creation and refinement of a protein model is considered by expert modelers to be a single process, but here, for simplicity, we treat... 

Netjasov, F. 2004. Fuzzy expert model for determination of runway in use case study Airport Zurich. Proceedings of the 1st International Conference on Research in Air Transportation ICRAT2004 , Zilina, Slovakia 59 4. 

Two lines of research have addressed the use of BBNs for dependence assessment in HRA. The first one investigates the use of BBNs (Baraldi et al. 2009) as expert models, to increase the repeatability and the transparency of the dependence assessment. The BBN nodes in this study are based on the dependence factors described in THERP (Fig. 4). The lack of traceability in the development of the decision trees used for dependence analysis is one of the main motivations behind the work by Baraldi et al. (2009). The consequence is that the fundamental assumptions behind the influence of factors (e.g. which factors are important and how much each influences the model output) are not directly linkable to the developed model. The work in Baraldi et al. (2009) generally is included in the larger activity of the authors of the present paper to improve trace-ability and transparency of model development in HRA via systematic expert elicitation approaches (cf. Podofillini and Dang, 2010). 

The expert model is an attempt to pool in a systematic manner, everything known, or believed, by the community of experts that is relevant for the risk decisions the audience faces... . Despite the name, the model need not exist in any one expert mind. Even were a single expert to have such comprehensive knowledge, this expert modeling procedure is a reactive one it forces the experts to think more systematically about their beliefs than they might have otherwise. As a result, they may have different beliefs at the end of the interview-process then they had at its beginning. 

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