Model-based Viewpoint Research Articles

Generating Petri Net-Based Behavioral Models From Textual Use Cases and Application in Railway Networks

A software system's requirements are often specified by textual use cases due to the latter's concrete and narrative style of expressions. However, they have limitation in the synthesis of the system behavior since they have a poor basis for the formal interpretation. Existing synthesis techniques are either largely manual or focus on the use case interactions. We present a framework from a model-based point of view to automatically synthesize system behavior from textual use cases to a Petri net model. The generated net model can well describe component module interactions and thus can be used to check the requirement properties. The function of Send-Railway-Emergency-Call of European Integrated Railway Radio Enhanced Network is used to show the proposed method. Moreover, the experimental results on a set of examples demonstrate the effectiveness of the proposed method.

Viewpoint planning for freeform surface inspection using plane structured light scanners

This paper proposes an automatic model-based viewpoint planning method, which can achieve high precision and high efficiency for freeform surfaces inspection using plane structured light scanners. The surface model is utilized in stereolithography format, which is widely used as an industrial standard. The proposed method consists of 4 steps: topology reconstruction, mesh refinement, scan direction determination and viewpoint generation. In the first step, the topology structure of the surface model is reconstructed according to a designed data structure, based on which a neighborhood search algorithm is developed. In the second step, big facets in the surface model are segmented into several small ones, which are suitable for viewpoint planning. In the third step, an initial scan region of a viewpoint is grouped by the neighborhood search algorithm combining with total area and normal vector restrictions. Accordingly, the scan direction is determined by the normal vectors of facets in the initial scan region. In the fourth step, the position, the orientation, and the final scan region of the viewpoint are determined by 4 scan constraints, i.e., field of view, working distance range, view angle and overlap. Experimental results verify the effectiveness and advantages of the proposed method.

Inference on finite population categorical response: nonparametric regression-based predictive approach

Suppose that a finite population consists of N distinct units. Associated with the ith unit is a polychotomous response vector, d i , and a vector of auxiliary variable x i . The values x i ’s are known for the entire population but d i ’s are known only for the units selected in the sample. The problem is to estimate the finite population proportion vector P. One of the fundamental questions in finite population sampling is how to make use of the complete auxiliary information effectively at the estimation stage. In this article a predictive estimator is proposed which incorporates the auxiliary information at the estimation stage by invoking a superpopulation model. However, the use of such estimators is often criticized since the working superpopulation model may not be correct. To protect the predictive estimator from the possible model failure, a nonparametric regression model is considered in the superpopulation. The asymptotic properties of the proposed estimator are derived and also a bootstrap-based hybrid re-sampling method for estimating the variance of the proposed estimator is developed. Results of a simulation study are reported on the performances of the predictive estimator and its re-sampling-based variance estimator from the model-based viewpoint. Finally, a data survey related to the opinions of 686 individuals on the cause of addiction is used for an empirical study to investigate the performance of the nonparametric predictive estimator from the design-based viewpoint.

Clarifying Some Issues in the Regression Analysis of Survey Data

The literature offers two distinct reasons for incorporating sample weights into the estimation of linear regression coefficients from a model-based point of view. Either the sample selection is nonignorable or the model is incomplete. The traditional sample-weighted least-squares estimator can be improved upon when the sample selection is nonignorable, but not when the standard linear model fails and needs to be extended. Conceptually, it can be helpful to view the realized sample as the result of a two-phase process. In the first phase, the finite population is drawn from a hypothetical superpopulation via simple random (cluster) sampling. In the second phase, the actual sample is drawn from the finite population. In the extended model, the parameters of this superpopulation are vague. Meansquared-error estimation can become problematic when the primary sampling units are drawn within strata using unequal probability sampling without replacement. This remains true even under the standard model when certain aspects of the sample design are nonignorable.

Model-based cluster analysis

The problem of dot clustering is studied from a model-based viewpoint. A set of “placement” processes is chosen, each of which associates a probability with each location in a discrete space; in other words, a placement is a probability mass function (pmf) on the space. A number of dots is then distributed in accordance with each of these pmfs; the pmf and its associated cardinality define a subpopulation of dots. This model is extremely general; the pmfs are arbitrary. Given a set of dots generated by such a model, maximum a posteriori (MAP) methods are applied to recover the most likely set of placements and cardinalities that could have given rise to the dots. This identification problem is different from the partitioning problem, which asks for the most likely partition of the dot population into subpopulations. It is shown how and why MAP methods are useful in cluster analysis, especially when the placement pmfs are non-Gaussian. It is also shown that although the general identification problem is intractable, there is a polynomial time solution if the number of subpopulations is bounded. It is shown that a similar result holds for the partitioning problem.

Design-Adjusted Parameter Estimation

SUMMARY The problem of adjusting for the effect of sample design in parametric estimation is examined and a general model-based approach to parametric inference with sample survey data is proposed. This approach includes, as a special case, recent results on model-based regression estimation with sample survey data. How does one adjust for the effect of sample design when estimating superpopulation model parameters from survey data? Recent papers by Nathan and Holt (1980), Holt, Smith and Winter (1980) and Holt and Scott (1981) have explored this problem from a model-based viewpoint when estimating regression parameters from sample survey data. The traditional way of dealing with this problem uses a design-based (randomisation theory) argument, which precludes any direct reference to a model. Essentially, in this case, one estimates a finite population parameter, which itself is an estimator of the superpopulation parameter of interest. See Kish and Frankel (1974) and Binder (1983). This paper describes a general approach to model-based inference with survey data which includes, as a special case, the results on model-based regression estimation referred to above. It also embodies a philosophy which turns out to be close to that underlying the design-based ap- proach to survey inference. The perceived differences between the model-based and design- based approaches in this area may not therefore be as wide as is sometimes believed to be the case. The suggested model-based approach is as follows. Suppose a finite population of N elements is generated by a stochastic process which involves a (possibly vector-valued) parameter 0. If observations could be made on all N elements of that population, the optimal estimator of 0 based on all these observations would usually be that value which minimised an appropriately chosen population level loss function. That is, a loss function for 0 defined in terms of data for all N elements in the population. This optimal estimator of 0 is then the same as the finite population parameter which design-based theory aims to estimate. Now suppose a sample can be used to estimate the values of that population loss function. The value which minimises this estimated population loss function can then be taken to be the optimal sample estimate of 0. This sample estimate is clearly also an estimate of the above finite population parameter. So far, the argument is only driving the inference problem one step back - from direct estimation of the superpopulation parameter itself to estimation of the population loss function. But, if the underlying stochastic process involves a matrix Z of known supplementary population values and a parameter A which characterises the conditional distribution of the