JDEVS: an implementation of a DEVS based formal framework for environmental modelling

Filippi, Jean Baptiste; Bisgambiglia, Paul-Antoine

doi:10.1016/j.envsoft.2003.08.016

Cited by 36 publications

(23 citation statements)

References 16 publications

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“…In this approach, the models that are developed are just automatically coupled. It implies an efficient tasks distribution, a simplified reuse of existing models and a more targeted reengineering process, see [Filippi and Bisambiglia(2003)] for details about the software. Figure 1 represents the Object Oriented Architecture of the Software.…”

Section: Devs Formalism Adapted To Spatially Explicit Modelsmentioning

confidence: 99%

Simulation of drifting seaweeds in East China Sea

Filippi

Komatsu

Tanaka

2010

Ecological Informatics

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Drifting seaweeds plays a major role in areas where they are present. We describe a computer model, Jeosim that clarifies the roles of those seaweeds in relation to their transport using particle tracking algorithm. The Euler rule with trapezoidal approximation, used to calculate drifting paths, is implemented in JeoSim in discrete events fashion in order to simulate the path and movement of seaweeds. Pre-processing of the ocean currents is done with the Princeton Ocean circulation Model (POM). Simulated current constitutes the data that provides force and directions for the calculations of drifting paths. Along with the implementation of the particle tracking algorithm in discrete event fashion, the originality of the JeoSim software lies in its Object Oriented architecture which makes it especially suited to perform simulation of living, cross scale systems with complex behavior. Behavior of drifting seaweeds of the Sargasso family has been implemented in JeoSim, and experimented in East China Sea following a drifting seaweed collection campaign in may 2002. Despite a relative low resolution ocean currents data, simulated results compares well with the observed distribution.

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Section: Devs Formalism Adapted To Spatially Explicit Modelsmentioning

confidence: 99%

Simulation of drifting seaweeds in East China Sea

Filippi

Komatsu

Tanaka

2010

Ecological Informatics

Self Cite

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“…In Filippi and Bisgambiglia, [26] DEVS-NN model is proposed for detection of atmosperic gas in a plot of land. The DEVS-NN hybrid provides intelligent system timing operations and automatic coordination of the system learning capabilities but lacks the ability to handle imprecise and vague information.…”

Section: Related Workmentioning

confidence: 99%

Discrete event based hybrid framework for petroleum products pipeline activities classification

Udoh

Akinyokun

Inyang

et al. 2017

AIR

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The importance of timely detection, classification and response to anomalies on petroleum products pipeline (PPP) have attracted pragmatic researches in recent times. There is need for efficient monitoring and detection of activities on PPP to guide leak detections and remedy decisions. This paper develops an intelligent hybrid system, driven by discrete event system specification (DEVS) and adaptive neuro-fuzzy inference system (ANFIS) for detection and classification of activities on PPP. A dataset comprising 330 records was used for training, validation and testing of the system. Result of sensitivity test shows that inlet pressure, inlet temperature, inlet volume and outlet volume have cumulative significance of 71.72% on flowrate of PPP. Hybrid learning algorithm was observed to converge faster than the back propagation algorithm in the detection of pipeline activities. ANFIS hybrid learning algorithm with training and testing errors of 0.11980 and 0.010233 yielded a correlation of 0.916 between the computed and the desired output and produced optimal consequent parameters to boost the intelligence of DEVS. A testing error of 0.0303 was observed in the evaluation of DEVS-ANFIS system on 33 test data sample, 32 precise detections were made with one incorrect detection, this gives 96.97% level of confidence in the DEVS-ANFIS model for detection, classification and localization of PPP activities.

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“…To support the ongoing development of different requirements from AURIN, a continuous integration module (CI) has been realised. This performs quality control of code provided both by internal (in Melbourne) and by external collaborators of AURIN 7 . This includes running automated tests and deployment of the whole infrastructure continuously across four different environments: a nightly build, a development version (used predominantly by the software development teams for rapid prototyping), a staging (pre-production system), and the actual live production that is deployed and used by the urban research community.…”

Section: Fig 2 Data Provider Architecturementioning

confidence: 99%

“…Cloud computing-enabled web service frameworks enable potentially thousands of computers to be utilised to share data, applications and computing capacity to achieve a desirable outcome in a manner transparent to the end-user who ideally only interacts with a single entity [5]. The hybrid implementation of HTC, Cloud and the growing demands of domain-specific big data needs are essential to address accessibility and interoperability of e-Science models and big data-driven research endeavours more generally [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Elastic Scaling of e-Infrastructures to Support Data-Intensive Research Collaborations

Nino-Ruiz

Bayliss

Galang

et al. 2014

2014 IEEE 10th International Conference on E-Science

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For many research endeavours, e-Infrastructures need to provide predictable, on-demand access to large-scale computational resources with high data availability. These need to scale with the research communities requirements and use. One example of such an e-Infrastructure is the Australian Urban Research Infrastructure Network (AURIN -www.aurin.org.au) project, which supports Australia-wide research in and across the urban and built environment. This paper describes the architecture of the AURIN infrastructure and its support for access to distributed (federated) and highly heterogeneous data sets from a wide range of providers. We present how this architecture solution leverages the intersection of high throughput computing (HTC), infrastructure as a service (IaaS) Cloud services and big data technologies including use of NoSQL resources. The driving concept in this architecture and the focus of this paper is the ability for scaling up or down depending on resource demands at any given time. This is done automatically and on demand avoiding either under-or over-utilization of resources. This resourceoptimization-driven infrastructure has been designed to ensure that peak loads can be predicted and successfully coped with, as well as avoid wasting resources during non-peak times. This overall management strategy has resulted in an e-Infrastructure that provides a flexible, evolving research environment that scales with research needs, rather than providing a rigid (static) end product. The driving concept in this architecture and the focus of this paper is the ability for scaling up or down depending on resource demands at any given time. This is done automatically and on demand avoiding either under-or over-utilization of resources. This resource-optimization-driven infrastructure has been designed to ensure that peak loads can be predicted and successfully coped with, as well as avoid wasting resources during non-peak times. This overall management strategy has resulted in an e-Infrastructure that provides a flexible, evolving research environment that scales with research needs, rather than providing a rigid (static) end product.

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JDEVS: an implementation of a DEVS based formal framework for environmental modelling

Cited by 36 publications

References 16 publications

Simulation of drifting seaweeds in East China Sea

Simulation of drifting seaweeds in East China Sea

Discrete event based hybrid framework for petroleum products pipeline activities classification

Elastic Scaling of e-Infrastructures to Support Data-Intensive Research Collaborations

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