60,762 research outputs found
A Method of Functional Alignment Verification in Hierarchical Enterprise Models
Enterprise modeling involves multiple domains of expertise: requirements engineering, business process modeling, IT development etc. Our experience has shown that hierarchical enterprise models, made of an assembly of system models, are effective. In these models, two hierarchies exist: an organizational level hierarchy (describing systems' construction) and a functional level hierarchy (describing systems' functionality). Using a uniform hierarchical modeling language, system models at different hierarchical levels can be aligned in the context of the enterprise model. Using an operational semantics, each system model can be translated into executable code for model simulation and testing. The possibility to simulate and test models leads to the alignment verification for all system models across both hierarchies. In this paper we propose a method and tool for functional alignment verification. We use the Abstract State Machine (ASM) and the ASM language (AsmL) to formalize our graphical models for simulation and testing. We illustrate this approach with an example
Abridged Petri Nets
A new graphical framework, Abridged Petri Nets (APNs) is introduced for
bottom-up modeling of complex stochastic systems. APNs are similar to
Stochastic Petri Nets (SPNs) in as much as they both rely on component-based
representation of system state space, in contrast to Markov chains that
explicitly model the states of an entire system. In both frameworks, so-called
tokens (denoted as small circles) represent individual entities comprising the
system; however, SPN graphs contain two distinct types of nodes (called places
and transitions) with transitions serving the purpose of routing tokens among
places. As a result, a pair of place nodes in SPNs can be linked to each other
only via a transient stop, a transition node. In contrast, APN graphs link
place nodes directly by arcs (transitions), similar to state space diagrams for
Markov chains, and separate transition nodes are not needed.
Tokens in APN are distinct and have labels that can assume both discrete
values ("colors") and continuous values ("ages"), both of which can change
during simulation. Component interactions are modeled in APNs using triggers,
which are either inhibitors or enablers (the inhibitors' opposites).
Hierarchical construction of APNs rely on using stacks (layers) of submodels
with automatically matching color policies. As a result, APNs provide at least
the same modeling power as SPNs, but, as demonstrated by means of several
examples, the resulting models are often more compact and transparent,
therefore facilitating more efficient performance evaluation of complex
systems.Comment: 17 figure
Graphical User Interface (GUI) Development for an Optical Communication Simulator
Modeling and simulation tools have been an integral part of engineering world for a long time. Various Electronic Design Automation (EDA) tools have been extensively used in various industries and research to evaluate the performance of electronic systems. The advancement of such design tools also has influenced the optical communication sector such that there has been a continuous progress on the Photonic Design Automation (PDA) tools. Currently, many software for simulating optical communications are available. However, they are very expensive and conceal the information on how components are modeled. To avoid these constraints, we developed our own PDA software for optical communication. This thesis delves into the development of Graphical User Interface (GUI) of our software. The studied GUI software conforms to the feature of standard simulation software and assists the users to perform a system-level simulation of fiber optic communication. The developed GUI allows the users to design their layout, run the simulation and view the results in the form of data or plot. The GUI is explained with respect to the graphical layout and the interactive features of the components. The detailed structure is described along with the Java library used to build them. The interactive aspects of GUI are investigated, for adding the hierarchical feature to our GUI software. In addition, a plotting tool is created for the GUI. The thesis provides comprehensive information on working principle of GUI for simulation software and describes the addition of plotting tool and hierarchical design in detail
Dynamic hybrid simulation of batch processes driven by a scheduling module
Simulation is now a CAPE tool widely used by practicing engineers for process design and control. In particular, it allows various offline analyses to improve system performance such as productivity, energy efficiency, waste reduction, etc. In this framework, we have developed the dynamic hybrid simulation environment PrODHyS whose particularity is to provide general and reusable object-oriented components dedicated to the modeling of devices and operations found in chemical processes. Unlike continuous processes, the dynamic simulation of batch processes requires the execution of control recipes to achieve a set of production orders. For these reasons, PrODHyS is coupled to a scheduling module (ProSched) based on a MILP mathematical model in order to initialize various operational parameters and to ensure a proper completion of the simulation. This paper focuses on the procedure used to generate the simulation model corresponding to the realization of a scenario described through a particular scheduling
High-Dimensional Bayesian Geostatistics
With the growing capabilities of Geographic Information Systems (GIS) and
user-friendly software, statisticians today routinely encounter geographically
referenced data containing observations from a large number of spatial
locations and time points. Over the last decade, hierarchical spatiotemporal
process models have become widely deployed statistical tools for researchers to
better understand the complex nature of spatial and temporal variability.
However, fitting hierarchical spatiotemporal models often involves expensive
matrix computations with complexity increasing in cubic order for the number of
spatial locations and temporal points. This renders such models unfeasible for
large data sets. This article offers a focused review of two methods for
constructing well-defined highly scalable spatiotemporal stochastic processes.
Both these processes can be used as "priors" for spatiotemporal random fields.
The first approach constructs a low-rank process operating on a
lower-dimensional subspace. The second approach constructs a Nearest-Neighbor
Gaussian Process (NNGP) that ensures sparse precision matrices for its finite
realizations. Both processes can be exploited as a scalable prior embedded
within a rich hierarchical modeling framework to deliver full Bayesian
inference. These approaches can be described as model-based solutions for big
spatiotemporal datasets. The models ensure that the algorithmic complexity has
floating point operations (flops), where the number of spatial
locations (per iteration). We compare these methods and provide some insight
into their methodological underpinnings
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
Formal Model Engineering for Embedded Systems Using Real-Time Maude
This paper motivates why Real-Time Maude should be well suited to provide a
formal semantics and formal analysis capabilities to modeling languages for
embedded systems. One can then use the code generation facilities of the tools
for the modeling languages to automatically synthesize Real-Time Maude
verification models from design models, enabling a formal model engineering
process that combines the convenience of modeling using an informal but
intuitive modeling language with formal verification. We give a brief overview
six fairly different modeling formalisms for which Real-Time Maude has provided
the formal semantics and (possibly) formal analysis. These models include
behavioral subsets of the avionics modeling standard AADL, Ptolemy II
discrete-event models, two EMF-based timed model transformation systems, and a
modeling language for handset software.Comment: In Proceedings AMMSE 2011, arXiv:1106.596
Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space
This paper proposes a novel framework for multi-group shape analysis relying
on a hierarchical graphical statistical model on shapes within a population.The
framework represents individual shapes as point setsmodulo translation,
rotation, and scale, following the notion in Kendall shape space.While
individual shapes are derived from their group shape model, each group shape
model is derived from a single population shape model. The hierarchical model
follows the natural organization of population data and the top level in the
hierarchy provides a common frame of reference for multigroup shape analysis,
e.g. classification and hypothesis testing. Unlike typical shape-modeling
approaches, the proposed model is a generative model that defines a joint
distribution of object-boundary data and the shape-model variables.
Furthermore, it naturally enforces optimal correspondences during the process
of model fitting and thereby subsumes the so-called correspondence problem. The
proposed inference scheme employs an expectation maximization (EM) algorithm
that treats the individual and group shape variables as hidden random variables
and integrates them out before estimating the parameters (population mean and
variance and the group variances). The underpinning of the EM algorithm is the
sampling of pointsets, in Kendall shape space, from their posterior
distribution, for which we exploit a highly-efficient scheme based on
Hamiltonian Monte Carlo simulation. Experiments in this paper use the fitted
hierarchical model to perform (1) hypothesis testing for comparison between
pairs of groups using permutation testing and (2) classification for image
retrieval. The paper validates the proposed framework on simulated data and
demonstrates results on real data.Comment: 9 pages, 7 figures, International Conference on Machine Learning 201
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