Logical Models of Composite Dynamic Objects Control

The questions of designing multicriteria control systems on the basis of logic models of composite dynamic objects are considered.

Structural reliability prediction of a steel bridge element using Dynamic Object Oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

New Architectural Models for Visibly Controllable Computing: The Relevance of Dynamic Object Oriented Architectures and Plan Based Computing Models

Traditionally, we've focussed on the question of how to make a system easy to code the first time, or perhaps on how to ease the system's continued evolution. But if we look at life cycle costs, then we must conclude that the important question is how to make a system easy to operate. To do this we need to make it easy for the operators to see what's going on and to then manipulate the system so that it does what it is supposed to. This is a radically different criterion for success. What makes a computer system visible and controllable? This is a difficult question, but it's clear that today's modern operating systems with nearly 50 million source lines of code are neither. Strikingly, the MIT Lisp Machine and its commercial successors provided almost the same functionality as today's mainstream sytsems, but with only 1 Million lines of code. This paper is a retrospective examination of the features of the Lisp Machine hardware and software system. Our key claim is that by building the Object Abstraction into the lowest tiers of the system, great synergy and clarity were obtained. It is our hope that this is a lesson that can impact tomorrow's designs. We also speculate on how the spirit of the Lisp Machine could be extended to include a comprehensive access control model and how new layers of abstraction could further enrich this model.

Preliminary study on bridge health prediction using Dynamic Objective Oriented Bayesian Network (DOOBN)

The availability of bridges is crucial to people’s daily life and national economy. Bridge health prediction plays an important role in bridge management because maintenance optimization is implemented based on prediction results of bridge deterioration. Conventional bridge deterioration models can be categorised into two groups, namely condition states models and structural reliability models. Optimal maintenance strategy should be carried out based on both condition states and structural reliability of a bridge. However, none of existing deterioration models considers both condition states and structural reliability. This study thus proposes a Dynamic Objective Oriented Bayesian Network (DOOBN) based method to overcome the limitations of the existing methods. This methodology has the ability to act upon as a flexible unifying tool, which can integrate a variety of approaches and information for better bridge deterioration prediction. Two demonstrative case studies are conducted to preliminarily justify the feasibility of the methodology

Animal foraging from an individual perspective: an object orientated model

A model system, HOOFS (Hierarchical Object Orientated Foraging Simulator), has been developed to study foraging by animals in a complex environment. The model is implemented using an individual-based object-orientated structure. Different species of animals inherit their general properties from a generic animal object which inherits from the basic dynamic object class. Each dynamic object is a separate program thread under the control of a central scheduler. The environment is described as a map of small hexagonal patches, each with their own level of resources and a patch-specific rate of resource replenishment. Each group of seven patches (0th order) is grouped into a Ist order super-patch with seven nth order super-patches making up a n + 1th order super-patch for n up to a specified value. At any time each animal is associated with a single patch. Patch choice is made by combining the information on the resources available within different order patches and super-patches along with information on the spatial location of other animals. The degree of sociality of an animal is defined in terms of optimal spacing from other animals and by the weighting of patch choice based on social factors relative to that based on food availability. Information, available to each animal, about patch resources diminishes with distance from that patch. The model has been used to demonstrate that social interactions can constrain patch choice and result in a short-term reduction of intake and a greater degree of variability in the level of resources in patches. We used the model to show that the effect of this variability on the animal's intake depends on the pattern of patch replenishment. (C) 1998 Elsevier Science B.V. All rights reserved.</p>

Object focused simultaneous estimation of optical flow and state dynamics

The framework of differential optical flow has been built upon to enhance the performance of motion estimation from optical flow. By coupling optical flow and object state parameters, an effective procedure for object tracking is implemented with the dasiaSimultaneous Estimation of Optical Flow and Object Statepsila (SEOS) technique. The SEOS method utilizes dynamic object parameter information when calculating optical flow for tracking a moving object within a video stream. Optical flow estimation for the SEOS method requires minimization of an error functional containing object physical parameter data. The convergence of an energy functional to a feasible or optimal solution set is not guaranteed. Convergence criteria is often assumed and not shown explicitly. Convergence of the SEOS method for both the Jacobi and Gauss-Seidel numerical resolution methods is evaluated.

Convergence of object focused simultaneous estimation of optical flow and state dynamics

The purpose of this study is to prove the convergence of the simultaneous estimation of the optical flow and object state (SEOS) method. The SEOS method utilizes dynamic object parameter information when calculating optical flow in tracking a moving object within a video stream. Optical flow estimation for the SEOS method requires the minimization of an error function containing the object's physical parameter data. When this function is discretized, the Euler-Lagrange equations form a system of linear equations. The system is arranged such that its property matrix is positive definite symmetric, proving the convergence of the Gauss-Seidel iterative methods. The system of linear equations produced by SEOS can alternatively be resolved by Jacobi iterative schemes. The positive definite symmetric property is not sufficient for Jacobi convergence. The convergence of SEOS for a block diagonal Jacobi is proved by analysing the Euclidean norm of the Jacobi matrix. In this paper, we also investigate the use of SEOS for tracking individual objects within a video sequence. The illustrations provided show the effectiveness of SEOS for localizing objects within a video sequence and generating optical flow results.

Increasing Realism and Supporting Content Planning for Dynamic Scenes in a Mixed Reality System incorporating a Time-of-Flight Camera

For broadcasting purposes MIXED REALITY, the combination of real and virtual scene content, has become ubiquitous nowadays. Mixed Reality recording still requires expensive studio setups and is often limited to simple color keying. We present a system for Mixed Reality applications which uses depth keying and provides threedimensional mixing of real and artificial content. It features enhanced realism through automatic shadow computation which we consider a core issue to obtain realism and a convincing visual perception, besides the correct alignment of the two modalities and correct occlusion handling. Furthermore we present a possibility to support placement of virtual content in the scene. Core feature of our system is the incorporation of a TIME-OF-FLIGHT (TOF)-camera device. This device delivers real-time depth images of the environment at a reasonable resolution and quality. This camera is used to build a static environment model and it also allows correct handling of mutual occlusions between real and virtual content, shadow computation and enhanced content planning. The presented system is inexpensive, compact, mobile, flexible and provides convenient calibration procedures. Chroma-keying is replaced by depth-keying which is efficiently performed on the GRAPHICS PROCESSING UNIT (GPU) by the usage of an environment model and the current ToF-camera image. Automatic extraction and tracking of dynamic scene content is herewith performed and this information is used for planning and alignment of virtual content. An additional sustainable feature is that depth maps of the mixed content are available in real-time, which makes the approach suitable for future 3DTV productions. The presented paper gives an overview of the whole system approach including camera calibration, environment model generation, real-time keying and mixing of virtual and real content, shadowing for virtual content and dynamic object tracking for content planning.

Impact of urban traffic and climate change on water quality from road runoff

Urban traffic and climate change are two phenomena that have the potential to degrade urban water quality by influencing the build-up and wash-off of pollutants, respectively. However, limited knowledge has made it difficult to establish any link between pollutant buildup and wash-off under such dynamic conditions. In order to safeguard urban water quality, adaptive water quality mitigation measures are required. In this research, pollutant build-up and wash-off have been investigated from a dynamic point of view which incorporated the impacts of changed urban traffic as well as changes in the rainfall characteristics induced by climate change. The study has developed a dynamic object classification system and thereby, conceptualised the study of pollutant build-up and wash-off under future changes in urban traffic and rainfall characteristics. This study has also characterised the buildup and wash-off processes of traffic generated heavy metals, volatile, semi-volatile and non-volatile hydrocarbons under dynamic conditions which enables the development of adaptive mitigation measures for water quality. Additionally, predictive frameworks for the build-up and wash-off of some pollutants have also been developed.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

Contours in Motion

My practice-led research explores and maps workflows for generating experimental creative work involving inertia based motion capture technology. Motion capture has often been used as a way to bridge animation and dance resulting in abstracted visuals outcomes. In early works this process was largely done by rotoscoping, reference footage and mechanical forms of motion capture. With the evolution of technology, optical and inertial forms of motion capture are now more accessible and able to accurately capture a larger range of complex movements. The creative work titled “Contours in Motion” was the first in a series of studies on captured motion data used to generating experimental visual forms that reverberate in space and time. With the source or ‘seed’ comes from using an Xsens MVN - Inertial Motion Capture system to capture spontaneous dance movements, with the visual generation conducted through a customised dynamics simulation. The aim of the creative work was to diverge way from a standard practice of using particle system and/or a simple re-targeting of the motion data to drive a 3d character as a means to produce abstracted visual forms. To facilitate this divergence a virtual dynamic object was tether to a selection of data points from a captured performance. The proprieties of the dynamic object were then adjusted to balance the influences from the human movement data with the influence of computer based randomization. The resulting outcome was a visual form that surpassed simple data visualization to project the intent of the performer’s movements into a visual shape itself. The reported outcomes from this investigation have contributed to a larger study on the use of motion capture in the generative arts, furthering the understanding of and generating theories on practice.

A framework for model integration and holistic modelling of socio-technical systems

This paper presents a layered framework for the purposes of integrating different Socio-Technical Systems (STS) models and perspectives into a whole-of-systems model. Holistic modelling plays a critical role in the engineering of STS due to the interplay between social and technical elements within these systems and resulting emergent behaviour. The framework decomposes STS models into components, where each component is either a static object, dynamic object or behavioural object. Based on existing literature, a classification of the different elements that make up STS, whether it be a social, technical or a natural environment element, is developed; each object can in turn be classified according to the STS elements it represents. Using the proposed framework, it is possible to systematically decompose models to an extent such that points of interface can be identified and the contextual factors required in transforming the component of one model to interface into another is obtained. Using an airport inbound passenger facilitation process as a case study socio-technical system, three different models are analysed: a Business Process Modelling Notation (BPMN) model, Hybrid Queue-based Bayesian Network (HQBN) model and an Agent Based Model (ABM). It is found that the framework enables the modeller to identify non-trivial interface points such as between the spatial interactions of an ABM and the causal reasoning of a HQBN, and between the process activity representation of a BPMN and simulated behavioural performance in a HQBN. Such a framework is a necessary enabler in order to integrate different modelling approaches in understanding and managing STS.

Effect of nanoclay content and compatibilizer on viscoelastic properties of montmorillonite/polypropylene nanocomposites

This paper deals with preparation of nanocomposites using modified nanoclay (organoclay) and polypropylene (PP), with, and without compatibilizer (m-TMI-g-PP) to study the effects of modified nanoclay and compatibilizer on viscoelastic properties. Nanocomposites were prepared in two steps; compounding of master batch of nanoclay, polypropylene and m-TMI-g-PP in a torque rheometer and blending of this master-batch with polypropylene in a twin-screw extruder in the specific proportions to yield 3-9% nanoclay by weight in the composite. Dynamic Mechanical Analysis (DMA) tests were carried out to investigate the viscoelastic behavior of virgin polypropylene and nanocomposites. The dynamic mechanical properties such as storage modulus (E'), loss modulus (E `') and damping coefficient (tand) of PP and nano-composites were investigated with and without compatibilizer in the temperature range of -40 degrees C to 140 degrees C at a step of 5 degrees C and frequency range of 5 Hz to 100 Hz at a step of 10 Hz. Storage modulus and loss modulus of the nano-composites was significantly higher than virgin polypropylene throughout the temperature range. Storage modulus of the composites increased continuously with increasing nano-content from 3% to 9%. Composites prepared with compatibilizer exhibited inferior storage modulus than the composites without compatibilizer. Surface morphology such as dispersion of nanoclay in the composites with and without compatibilizer was analyzed through Atomic Force Microscope (AFM) that explained the differences in viscoelastic behavior of composites. (C) 2011 Elsevier Ltd. All rights reserved.

Rétro ingénierie des modèles d’objets dynamiques pour JavaScript

La compréhension des objets dans les programmes orientés objet est une tâche impor- tante à la compréhension du code. JavaScript (JS) est un langage orienté-objet dyna- mique, et son dynamisme rend la compréhension du code source très difficile. Dans ce mémoire, nous nous intéressons à l’analyse des objets pour les programmes JS. Notre approche construit de façon automatique un graphe d’objets inspiré du diagramme de classes d’UML à partir d’une exécution concrète d’un programme JS. Le graphe résul- tant montre la structure des objets ainsi que les interactions entre eux. Notre approche utilise une transformation du code source afin de produire cette in- formation au cours de l’exécution. Cette transformation permet de recueillir de l’infor- mation complète au sujet des objets crées ainsi que d’intercepter toutes les modifications de ces objets. À partir de cette information, nous appliquons plusieurs abstractions qui visent à produire une représentation des objets plus compacte et intuitive. Cette approche est implémentée dans l’outil JSTI. Afin d’évaluer l’utilité de l’approche, nous avons mesuré sa performance ainsi que le degré de réduction dû aux abstractions. Nous avons utilisé les dix programmes de réfé- rence de V8 pour cette comparaison. Les résultats montrent que JSTI est assez efficace pour être utilisé en pratique, avec un ralentissement moyen de 14x. De plus, pour 9 des 10 programmes, les graphes sont suffisamment compacts pour être visualisés. Nous avons aussi validé l’approche de façon qualitative en inspectant manuellement les graphes gé- nérés. Ces graphes correspondent généralement très bien au résultat attendu. Mots clés: Analyse de programmes, analyse dynamique, JavaScript, profilage.