A compositional modelling and analysis framework for stochastic hybrid systems

The theory of hybrid systems is well-established as a model for real-world systems consisting of continuous behaviour and discrete control. In practice, the behaviour of such systems is also subject to uncertainties, such as measurement errors, or is controlled by randomised algorithms. These aspects can be modelled and analysed using stochastic hybrid systems. In this paper, we present HModest, an extension to the Modest modelling language—which is originally designed for stochastic timed systems without complex continuous aspects—that adds differential equations and inclusions as an expressive way to describe the continuous system evolution. Modest is a high-level language inspired by classical process algebras, thus compositional modelling is an integral feature. We define the syntax and semantics of HModest and show that it is a conservative extension of Modest that retains the compositional modelling approach. To allow the analysis of HModest models, we report on the implementation of a connection to recently developed tools for the safety verification of stochastic hybrid systems, and illustrate the language and the tool support with a set of small, but instructive case studies.     

Bio-PEPA: A framework for the modelling and analysis of biological systems

In this work we present Bio-PEPA, a process algebra for the modelling and the analysis of biochemical networks. It is a modification of PEPA, originally defined for the performance analysis of computer systems, in order to handle some features of biological models, such as stoichiometry and the use of general kinetic laws. Bio-PEPA may be seen as an intermediate, formal, compositional representation of biological systems, on which different kinds of analyses can be carried out. Bio-PEPA is enriched with some notions of equivalence. Specifically, the isomorphism and strong bisimulation for PEPA have been considered and extended to our language. Finally, we show the translation of a biological model into the new language and we report some analysis results.     

A timeband framework for modelling real-time systems

Complex real-time systems must integrate physical processes with digital control, human operation and organisational structures. New scientific foundations are required for specifying, designing and implementing these systems. One key challenge is to cope with the wide range of time scales and dynamics inherent in such systems. To exploit the unique properties of time, with the aim of producing more dependable computer-based systems, it is desirable to explicitly identify distinct time bands in which the system is situated. Such a framework enables the temporal properties and associated dynamic behaviour of existing systems to be described and the requirements for new or modified systems to be specified. A system model based on a finite set of distinct time bands is motivated and developed in this paper.     

A rewriting logic framework for operational semantics of membrane systems

Existing results in membrane computing refer mainly to P systems’ characterization of Turing computability, also to some polynomial solutions to NP-complete problems by using an exponential workspace created in a “biological way”. In this paper we define an operational semantics of a basic class of P systems, and give two implementations of the operational semantics using rewriting logic. We present some results regarding these implementations, including two operational correspondence results, and discuss why these implementations are relevant in order to take advantage of good features of both structural operational semantics and rewriting logic.     

A framework for knowledge-based temporal abstraction

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge.

The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed.

The knowledge-based temporal abstraction method has been implemented in the RÉSUMÉ system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.     

Assessing the operational analysis robustness of flexible manufacturing systems modelling

An effective tool for modelling complex production systems such as flexible manufacturing systems (FMS) is the queueing network theory. In this framework, operational analysis is a recent model that does not require any stochastic assumption and is based on a set of operational hypotheses related to directly observable properties. Applications of operational analysis to FMS performance prediction involve the assumption of homogeneous service times for all work stations. This is the most restrictive assumption of the operational approach. We present a new technique for obtaining the infimum and the maximum for throughput and utilizations when all work stations arbitrarily deviate from homogeneous service time behaviour. This paper proves that the robustness of resolvent formulae is not an absolute characteristic of the operational model.     

A formal framework for verifying distributed embedded systems based on abstraction methods

This paper presents a formal framework for verifying distributed embedded systems. An embedded system is described as a set of concurrent real time functions which communicate through a network of interconnected switches involving messages queues and routing services.In order to allow requirements verification, such a model is then translated into timed automata. However, the complexity inherent in distributed embedded systems often does not allow to apply model checking techniques. Consequently, the paper presents an abstraction-based verification method which consists in abstracting the communication network by end-to-end timed channels. To prove a given safety property φ requires then (1) to prove a set of proof obligations ensuring the correctness of the abstraction step (i.e. the end-to-end channels correctly abstract the network), and (2) to prove φ at the abstract level. The expected advantage of such a method lies in the ability to overcome the combinatorial explosion frequently met when verifying complex systems. This method is illustrated by an avionic case study.     

A visual analytics framework for spatio-temporal analysis and modelling

To support analysis and modelling of large amounts of spatio-temporal data having the form of spatially referenced time series (TS) of numeric values, we combine interactive visual techniques with computational methods from machine learning and statistics. Clustering methods and interactive techniques are used to group TS by similarity. Statistical methods for TS modelling are then applied to representative TS derived from the groups of similar TS. The framework includes interactive visual interfaces to a library of modelling methods supporting the selection of a suitable method, adjustment of model parameters, and evaluation of the models obtained. The models can be externally stored, communicated, and used for prediction and in further computational analyses. From the visual analytics perspective, the framework suggests a way to externalize spatio-temporal patterns emerging in the mind of the analyst as a result of interactive visual analysis: the patterns are represented in the form of computer-processable and reusable models. From the statistical analysis perspective, the framework demonstrates how TS analysis and modelling can be supported by interactive visual interfaces, particularly, in a case of numerous TS that are hard to analyse individually. From the application perspective, the framework suggests a way to analyse large numbers of spatial TS with the use of well-established statistical methods for TS analysis.     

A framework for systems analysis for decision support systems

Comprehensive and elaborate systems analysis techniques have been developed in the past of routine and operational information systems. Developing support systems for organizational decision-making requires new tools and methodologies. We present a new framework for data collection and decision analysis which is useful for developing decision support systems. This task analysis methodology encompasses (1) event analysis, (2) participant analysis, and (3) decision content analysis. With a proper coding manual, it provides a framework for collecting relevant and detailed information required for decision support design and implementation. Further research is suggested for application and evaluation of the methodology in real-life DSS environments.     

A modelling framework for complementary design of production planning and control systems

In a business era characterized by a dazzling rate of change, the improvement of production planning and control begins to be a main objective for manufacturing industries. This paper postulates four main statements to be considered for the design of production planning and control systems (PPC-systems) comprising human and technical sub-systems. The first is that production models required for the design of PPC-systems (i.e. design models) cannot be identical to production models required for planning and control of production systems (i.e. regulatory models). The design of PPC-systems must primarily focus on the quality of interaction between the regulatory models. This insight supports the second statement, which postulates that the design of PPC-systems requires a complementary design approach. Complementary design means to take explicitly into account that human and technical sub-systems- based on the differences in strengths and weaknesses of both- can achieve through their interaction a new quality, possible neither to human nor technical sub-systems alone. The third statement is that a complementary design of PPC-systems will only be possible if a fundamental change of mind from a static to a dynamic as well as from a technical to a socio-technical perception (i.e. a complete perception) of production systems takes place. Without a complete perception of production systems, designed PPC-systems will not be sufficiently reliable, maintainable and flexible, will be difficult to comprehend, and their elements will not be re-usable for further applications. The fourth statement is that the integral support of the design process requires a dual modelling framework comprising a meta- and an object-model. Considering these fundamental insights that were confirmed by a practical case study, a dual modelling framework for the design of PPC-systems which incorporates criteria for complementary design is outlined.     

An integrated modelling framework for regulated river systems

Management of regulated water systems has become increasingly complex due to rapid socio-economic growth and environmental changes in river basins over recent decades. This paper introduces the Source Integrated Modelling System (IMS), and describes the individual modelling components and how they are integrated within it. It also describes the methods employed for tracking and assessment of uncertainties, as well as presenting outcomes of two case study applications.Traditionally, the mathematical tools for water resources planning and management were generally designed for sectoral applications with, for example, groundwater being modelled separately from surface water. With the increasing complexity of water resources management in the 21st century those tools are becoming outmoded. Water management organisations are increasingly looking for new generation tools that allow integration across domains to assist their decision making processes for short-term operations and long-term planning; not only to meet current needs, but those of the future as well.In response to the need for an integrated tool in the water industry in Australia, the eWater Cooperative Research Centre (CRC) has developed a new generation software package called the Source IMS. The Source IMS is an integrated modelling environment containing algorithms and approaches that allow defensible predictions of water flow and constituents from catchment sources to river outlets at the sea. It is designed and developed to provide a transparent, robust and repeatable approach to underpin a wide range of water planning and management purposes. It can be used to develop water sharing plans and underpin daily river operations, as well as be used for assessments on water quantity and quality due to changes in: i) land-use and climate; ii) demands (irrigation, urban, ecological); iii) infrastructure, such as weirs and reservoirs; iv) management rules that might be associated with these; and v) the impacts of all of the above on various ecological indices. The Source IMS integrates the existing knowledge and modelling capabilities used by different state and federal water agencies across Australia and has additional functionality required for the river system models that will underpin the next round of water sharing plans in the country. It is built in a flexible modelling environment to allow stakeholders to incorporate new scientific knowledge and modelling methods as they evolve, and is designed as a generic tool suitable for use across different jurisdictions. Due to its structure, the platform can be extended/customised for use in other countries and basins, particularly where there are boundary issues.     

An ontological process modelling framework for stochastic systems

Most process modelling techniques exist without any firm theoretical foundation. This results in a lack of model validation, which can be in-terms of model consistency, feasibility and goal compliance. Moreover, these techniques are mostly deterministic in nature and not applicable to stochastic systems. In this paper, we propose an ontology-based stochastic process modelling framework that further provides a specialization to failure and reliability issues. The framework is notation independent, and is primarily rooted in Bunge’s ontology. The well-established theory of reliability constructs are also mapped to facilitate the modelling of failure prone systems.     

On hierarchical modelling of motion for workflow analysis from overhead view

Understanding human behaviour is a high level perceptual problem, one which is often dominated by the contextual knowledge of the environment, and where concerns such as occlusion, scene clutter and high within-class variations are commonplace. Nonetheless, such understanding is highly desirable for automated visual surveillance. We consider this problem in a context of a workflow analysis within an industrial environment. The hierarchical nature of the workflow is exploited to split the problem into ‘activity’ and ‘task’ recognition. In this, sequences of low level activities are examined for instances of a task while the remainder are labelled as background. An initial prediction of activity is obtained using shape and motion based features of the moving blob of interest. A sequence of these activities is further adjusted by a probabilistic analysis of transitions between activities using hidden Markov models (HMMs). In task detection, HMMs are arranged to handle the activities within each task. Two separate HMMs for task and background compete for an incoming sequence of activities. Imagery derived from a camera mounted overhead the target scene has been chosen over the more conventional oblique views (from the side) as this view does not suffer from as much occlusion, and it poses a manageable detection and tracking problem while still retaining powerful cues as to the workflow patterns. We evaluate our approach both in activity and task detection on a challenging dataset of surveillance of human operators in a car manufacturing plant. The experimental results show that our hierarchical approach can automatically segment the timeline and spatially localize a series of predefined tasks that are performed to complete a workflow.     

A multi-level abstraction and modeling in video databases

In this paper, we propose a multi-level abstraction mechanism for capturing the spatial and temporal semantics associated with various objects in an input image or in a sequence of video frames. This abstraction can manifest itself effectively in conceptualizing events and views in multimedia data as perceived by individual users. The objective is to provide an efficient mechanism for handling content-based queries, with the minimum amount of processing performed on raw data during query evaluation. We introduce a multi-level architecture for video data management at different levels of abstraction. The architecture facilitates a multi-level indexing/searching mechanism. At the finest level of granularity, video data can be indexed based on mere appearance of objects and faces. For management of information at higher levels of abstractions, an object-oriented paradigm is proposed which is capable of supporting domain specific views.     

Strong normalization from an unusual point of view

A new complete characterization of β-strong normalization is given, both in the classical and in the lazy λ-calculus, through the notion of potential valuability inside two suitable parametric calculi.     

A framework for a video analysis tool for suspicious event detection

This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video data that exists in today’s world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation of the event classification and detection technique is presented and a future experiment to refine this technique is proposed. These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation scheme proposed in this paper.

Urban 3D segmentation and modelling from street view images and LiDAR point clouds

3D urban maps with semantic labels and metric information are not only essential for the next generation robots such autonomous vehicles and city drones, but also help to visualize and augment local environment in mobile user applications. The machine vision challenge is to generate accurate urban maps from existing data with minimal manual annotation. In this work, we propose a novel methodology that takes GPS registered LiDAR (Light Detection And Ranging) point clouds and street view images as inputs and creates semantic labels for the 3D points clouds using a hybrid of rule-based parsing and learning-based labelling that combine point cloud and photometric features. The rule-based parsing boosts segmentation of simple and large structures such as street surfaces and building facades that span almost 75% of the point cloud data. For more complex structures, such as cars, trees and pedestrians, we adopt boosted decision trees that exploit both structure (LiDAR) and photometric (street view) features. We provide qualitative examples of our methodology in 3D visualization where we construct parametric graphical models from labelled data and in 2D image segmentation where 3D labels are back projected to the street view images. In quantitative evaluation we report classification accuracy and computing times and compare results to competing methods with three popular databases: NAVTEQ True, Paris-Rue-Madame and TLS (terrestrial laser scanned) Velodyne.