The Impact of Petri Nets on System-of-Systems Engineering

The successful engineering of a large-scale system-of-systems project towards deterministic behaviour depends on integrating autonomous components using international communications standards in accordance with dynamic requirements. To-date, their engineering has been unsuccessful: no combination of top-down and bottom-up engineering perspectives is adopted, and information exchange protocol and interfaces between components are not being precisely specified. Various approaches such as modelling, and architecture frameworks make positive contributions to system-of-systems specification but their successful implementation is still a problem. One of the most popular modelling notations available for specifying systems, UML, is intuitive and graphical but also ambiguous and imprecise. Supplying a range of diagrams to represent a system under development, UML lacks simulation and exhaustive verification capability. This shortfall in UML has received little attention in the context of system-of-systems and there are two major research issues: 1. Where the dynamic, behavioural diagrams of UML can and cannot be used to model and analyse system-of-systems 2. Determining how Petri nets can be used to improve the specification and analysis of the dynamic model of a system-of-systems specified using UML This thesis presents the strengths and weaknesses of Petri nets in relation to the specification of system-of-systems and shows how Petri net models can be used instead of conventional UML Activity Diagrams. The model of the system-of-systems can then be analysed and verified using Petri net theory. The Petri net formalism of behaviour is demonstrated using two case studies from the military domain. The first case study uses Petri nets to specify and analyse a close air support mission. This case study concludes by indicating the strengths, weaknesses, and shortfalls of the proposed formalism in system-of-systems specification. The second case study considers specification of a military exchange network parameters problem and the results are compared with the strengths and weaknesses identified in the first case study. Finally, the results of the research are formulated in the form of a Petri net enhancement to UML (mapping existing activity diagram elements to Petri net elements) to meet the needs of system-of-systems specification, verification and validation

Assessing the key enablers for Industry 4.0 adoption using MICMAC analysis: A case study

Purpose: The aim of this research is to assess the key enablers of Industry 4.0 (I4.0) in the context of the Indian automobile industry. It is done to apprehend their comparative effect on executing I4.0 concepts and technology in manufacturing industries, in a developing country context. The progression to I4.0 grants the opportunity for manufacturers to harness the benefits of this industry generation. Design/methodology/approach: The literature related to I4.0 has been reviewed for the identification of key enablers of I4.0. The enablers were further verified by academic professionals. Additionally, key executive insights had been revealed by using interpretive structural modelling (ISM) model for the vital enablers unique to the Indian scenario. The authors have also applied MICMAC analysis to group the enablers of I4.0. Findings: The analysis of this study’s data from respondents using ISM provided us with seven levels of enabler framework. This study adds to the existing literature on I4.0 enablers and findings highlight the specificities of the territories in India context. The results show that top management is the major enabler to I4.0 implementation. Infact, it occupies the 7th layer of the ISM framework. Subsequently, government policies enable substantial support to develop smart factories in India. Practical implications: The findings of this work provide implementers of I4.0 in the automobile industry in the form of a robust framework. This framework can be followed by the automobile sector in enhancing their competency in the competitive market and ultimately provide a positive outcome for the Indian economic development led by these businesses. Furthermore, this work will guide decision-makers in enabling strategic integration of I4.0, opening doors for the development of new business opportunities as well. Originality/value: The study proposes a framework for Indian automobile industries. The automobile sector was chosen for this study as it covers a large percentage of the market share of the manufacturing industry in India. The existing literature does not address the broader picture of I4.0 and most papers do not provide validation of the data collected. This study thus addresses this research gap

A study of the impact of technological innovations on the social sustainability of facilities management employees in South Africa

This research investigates the impact of technological innovations (TIs) on the social sustainability of facilities management (FM) employees in South Africa. The rationale for the study is that no empirical evidence shows how the adoption of TIs impacts the social sustainability of FM employees. The study adopts the sequential mixed-methodology approach. The quantitative phase makes use of a questionnaire survey which formed the foundation for the qualitative interview phase. The relative importance index (RII) is used to analyse different questions, such as (1) the factors influencing the adoption of TIs in FM organisations (2) the impact of the TIs on FM practice, (3) the localisation of the employee social sustainability factors and (4) the determination of the impact of TIs on the social sustainability of FM employees. An Interpretive Structural Model (ISM) approach is used to determine which social sustainability factor(s) should be prioritised while promoting the social sustainability of the FM employees. The findings of this study show that cloud-based TIs, ICT-based TIs and sensor-based TIs are the most popular in FM organisations in South Africa. Furthermore, the impact of TIs on the core business factors in FM organisations have a mean score of between 3.00 to 3.19 depending on the factor of interest. The RII analysis led to the development of the initial FM employee social sustainability framework which identified “job security”, “remuneration” and “professional status” as the three most important FM employee social sustainability factors. However, the ISM analysis which considered hierarchy, driving power and dependence of the factors identified “organisation policy” as main factor in level five that drives other employee social sustainability factors. Furthermore, “overwork”, “autonomy”, “interpersonal relationship”, “work and home-life balance” and “retirement development plan” were the root factors in level four that must be prioritised by facilities managers to promote employee social sustainability. The study contributes to knowledge by identifying the most popular TIs that are adopted by FM organisations in South Africa, and determining the interrelationship, hierarchical importance and dependences of the various employees’ social sustainability factors in FM organisations. Through the development of the framework for FM employee social sustainability, facilities managers have the knowledge of the factors to prioritise when they need to promote the social sustainability of their employees. The study recommends that FM organisation policies on TI adoption must align with the overall socio-economic wellbeing program to contribute to social sustainability in South Africa

A Memory Usage Comparison Between Jitana and Soot

There are several factors that make analyzing Android apps to address dependability and security concerns challenging. These factors include (i) resource efficiency as analysts need to be able to analyze large code-bases to look for issues that can exist in the application code and underlying platform code; (ii) scalability as today’s cybercriminals deploy attacks that may involve many participating apps; and (iii) in many cases, security analysts often rely on dynamic or hybrid analysis techniques to detect and identify the sources of issues. The underlying principle governing the design of existing program analysis engines is the main cause that prevents them from satisfying these factors. Existing designs operate like compilers, so they only analyze one app at a time using a close-world process that leads to poor efficiency and scalability. Recently, Tsutana et al. introduced Jitana, a Virtual Class-Loader (VCL) based approach to construct program analyses based on the open-world concept. This approach is able to continuously load and analyze code. As such, this approach establishes a new way to make analysis efforts proportional to the code size and provides an infrastructure to construct complex, efficient, and scalable static, dynamic, and hybrid analysis procedures to address emerging dependability and security needs. In this thesis, we attempt to quantify the performance benefit of Jitana through the lens of memory usage. Memory is a very important system-level resource that if not expended efficiently, can result in long execution time and premature termination of a program. Existing program analysis frameworks are notorious for consuming a large amount of memory during an attempt to analyze a large software project. As such, we design an experiment to compare the memory usage between Jitana and Soot, a widely used program analysis and optimization framework for Java. Our evaluation consists of using 18 Android apps, with sizes ranging from 0.02 MB to 80.4 MB. Our empirical evaluations reveal that Jitana requires up to 81% less memory than Soot to analyze an app. At the same time, it can also analyze more components including those belonging to the application and those belonging to the Android framework. Adviser: Witawas Srisa-a

Unveiling the features of successful ebay sellers of smartphones: a data mining sales predictive model

JEL Classification guidelines (M310); (C380).EBay is one of the largest online retailing corporations worldwide, providing numerous ways for customer feedback on registered sellers. In accordance, with the advent of Web 2.0 and online shopping, an immensity of data is collected from manifold devices. This data is often unstructured, which inevitably asks for some form of further treatment that allows classification, discovery of patterns and trends or prediction of outcomes. That treatment implies the usage of increasingly complex and combined statistical tools as the size of datasets builds up. Nowadays, datasets may extend to several exabytes, which can be transformed into knowledge using adequate methods. The aim of the present study is to evaluate and analyse which and in what way seller and product attributes such as feedback ratings and price influence sales of smartphones on eBay using data mining framework and techniques. The methods used include SVM algorithms for modelling the sales of smartphones by eBay sellers combined with 10-fold cross-validation scheme which ensured model robustness and employment of metrics MAE, RAE and NMAE for the sake of gauging prediction accuracy followed by sensitivity analysis in order to assess the influence of individual features on sales. The methods were considered effective for both modelling evaluation and knowledge extraction reaching positive results although with some discrepancies between different prediction accuracy metrics. Lastly, it was discovered that the number of items in auction, average price and the variety of products available from a given seller were the most significant attributes, i.e., the largest contributors for sales.O EBay é uma das plataformas e retalho online de maior dimensão e abarca inúmeras oportunidades de extração de dados de feedback dos consumidores sobre vários vendedores. Em concordância, o advento da Web 2.0 e das compras online está fortemente associado à geração de dados em abundância e à possibilidade da sua respetiva recolha através de variados dispositivos e plataformas. Estes dados encontram-se, frequentemente, desestruturados o que inevitavelmente revela a necessidade da sua normalização e tratamento mais aprofundado de modo a possibilitar tarefas de classificação, descoberta de padrões e tendências ou de previsão. A complexidade dos métodos estatísticos aplicados para executar essas tarefas aumenta ao mesmo tempo que a dimensão das bases de dados. Atualmente, existem bases de dados que atingem vários exabytes e que se constituem como oportunidades para extração de conhecimento dado que métodos apropriados e particularizados sejam utilizados. Pretende-se, então, com o presente estudo quantificar e analisar quais e de que modo as características de vendedores e produtos influenciam as vendas de smartphones no eBay, recorrendo ao enquadramento conceptual e técnicas de mineração de dados. Os métodos utilizados incluem máquinas de vetores de suporte (SVMs) visando a modelação das vendas de smartphones por vendedores do eBay em combinação com validação cruzada 10-fold de modo a assegurar a robustez do modelo e com recurso às métricas de avaliação de desempenho erro absoluto médio (MAE), erro absoluto relativo (RAE) e erro absoluto médio normalizado (NMAE) para garantir a precisão do modelo preditivo. Seguidamente, é implementada a análise de sensibilidade para aferir a contribuição individual de cada atributo para as vendas. Os métodos são considerados eficazes tanto na avaliação do modelo como na extração de conhecimento visto que viabilizam resultados positivos ainda que sejam verificadas discrepâncias entre as estimativas para diferentes métricas de desempenho. Finalmente, foi possível descobrir que número de itens em leilão, o preço médio e a variedade de produtos disponibilizada por cada vendedor foram os atributos mais significantes, i.e., os que mais contribuíram para as vendas

Lifelong learning of concepts in CRAFT

La planification à des niveaux d’abstraction plus élevés est essentielle lorsqu’il s’agit de résoudre des tâches à long horizon avec des complexités hiérarchiques. Pour planifier avec succès à un niveau d’abstraction donné, un agent doit comprendre le fonctionnement de l’environnement à ce niveau particulier. Cette compréhension peut être implicite en termes de politiques, de fonctions de valeur et de modèles, ou elle peut être définie explicitement. Dans ce travail, nous introduisons les concepts comme un moyen de représenter et d’accumuler explicitement des informations sur l’environnement. Les concepts sont définis en termes de transition d’état et des conditions requises pour que cette transition ait lieu. La simplicité de cette définition offre flexibilité et contrôle sur le processus d’apprentissage. Étant donné que les concepts sont de nature hautement interprétable, il est facile d’encoder les connaissances antérieures et d’intervenir au cours du processus d’apprentissage si nécessaire. Cette définition facilite également le transfert de concepts entre différents domaines. Les concepts, à un niveau d’abstraction donné, sont intimement liés aux compétences, ou actions temporellement abstraites. Toutes les transitions d’état suffisamment importantes pour être représentées par un concept se produisent après l’exécution réussie d’une compétence. En exploitant cette relation, nous introduisons un cadre qui facilite l’apprentissage tout au long de la vie et le raffinement des concepts à différents niveaux d’abstraction. Le cadre comporte trois volets: Le sytème 1 segmente un flux d’expérience (par exemple une démonstration) en une séquence de compétences. Cette segmentation peut se faire à différents niveaux d’abstraction. Le sytème 2 analyse ces segments pour affiner et mettre à niveau son ensemble de concepts, lorsqu’applicable. Le sytème 3 utilise les concepts disponibles pour générer un graphe de dépendance de sous-tâches. Ce graphe peut être utilisé pour planifier à différents niveaux d’abstraction. Nous démontrons l’applicabilité de ce cadre dans l’environnement hiérarchique 2D CRAFT. Nous effectuons des expériences pour explorer comment les concepts peuvent être appris de différents flux d’expérience et comment la qualité de la base de concepts affecte l’optimalité du plan général. Dans les tâches avec des dépendances de sous-tâches complexes, où la plupart des algorithmes ne parviennent pas à se généraliser ou prennent un temps impraticable à converger, nous démontrons que les concepts peuvent être utilisés pour simplifier considérablement la planification. Ce cadre peut également être utilisé pour comprendre l’intention d’une démonstration donnée en termes de concepts. Cela permet à l’agent de répliquer facilement la démonstration dans différents environnements. Nous montrons que cette méthode d’imitation est beaucoup plus robuste aux changements de configuration de l’environnement que les méthodes traditionnelles. Dans notre formulation du problème, nous faisons deux hypothèses: 1) que nous avons accès à un ensemble de compétences suffisamment exhaustif, et 2) que notre agent a accès à des environnements de pratique, qui peuvent être utilisés pour affiner les concepts en cas de besoin. L’objectif de ce travail est d’explorer l’aspect pratique des concepts d’apprentissage comme moyen d’améliorer la compréhension de l’environnement. Dans l’ensemble, nous démontrons que les concepts d’apprentissagePlanning at higher levels of abstraction is critical when it comes to solving long horizon tasks with hierarchical complexities. To plan successfully at a given level of abstraction, an agent must have an understanding of how the environment functions at that particular level. This understanding may be implicit in terms of policies, value functions, and world models, or it can be defined explicitly. In this work, we introduce concepts as a means to explicitly represent and accumulate information about the environment. Concepts are defined in terms of a state transition and the conditions required for that transition to take place. The simplicity of this definition offers flexibility and control over the learning process. Since concepts are highly interpretable in nature, it is easy to encode prior knowledge and intervene during the learning process if necessary. This definition also makes it relatively straightforward to transfer concepts across different domains wherever applicable. Concepts, at a given level of abstraction, are intricately linked to skills, or temporally abstracted actions. All the state transitions significant enough to be represented by a concept occur only after the successful execution of a skill. Exploiting this relationship, we introduce a framework that aids in lifelong learning and refining of concepts across different levels of abstraction. The framework has three components: - System 1 segments a stream of experience (e.g. a demonstration) into a sequence of skills. This segmentation can be done at different levels of abstraction. - System 2 analyses these segments to refine and upgrade its set of concepts, whenever applicable. - System 3 utilises the available concepts to generate a sub-task dependency graph. This graph can be used for planning at different levels of abstraction We demonstrate the applicability of this framework in the 2D hierarchical environment CRAFT. We perform experiments to explore how concepts can be learned from different streams of experience, and how the quality of the concept base affects the optimality of the overall plan. In tasks with complex sub-task dependencies, where most algorithms fail to generalise or take an impractical amount of time to converge, we demonstrate that concepts can be used to significantly simplify planning. This framework can also be used to understand the intention of a given demonstration in terms of concepts. This makes it easy for the agent to replicate a demonstration in different environments. We show that this method of imitation is much more robust to changes in the environment configurations than traditional methods. In our problem formulation, we make two assumptions: 1) that we have access to a sufficiently exhaustive set of skills, and 2) that our agent has access to practice environments, which can be used to refine concepts when needed. The objective behind this work is to explore the practicality of learning concepts as a means to improve one’s understanding about the environment. Overall, we demonstrate that learning concepts can be a light-weight yet efficient way to increase the capability of a system

Peripersonal Space in the Humanoid Robot iCub

Developing behaviours for interaction with objects close to the body is a primary goal for any organism to survive in the world. Being able to develop such behaviours will be an essential feature in autonomous humanoid robots in order to improve their integration into human environments. Adaptable spatial abilities will make robots safer and improve their social skills, human-robot and robot-robot collaboration abilities. This work investigated how a humanoid robot can explore and create action-based representations of its peripersonal space, the region immediately surrounding the body where reaching is possible without location displacement. It presents three empirical studies based on peripersonal space findings from psychology, neuroscience and robotics. The experiments used a visual perception system based on active-vision and biologically inspired neural networks. The first study investigated the contribution of binocular vision in a reaching task. Results indicated the signal from vergence is a useful embodied depth estimation cue in the peripersonal space in humanoid robots. The second study explored the influence of morphology and postural experience on confidence levels in reaching assessment. Results showed that a decrease of confidence when assessing targets located farther from the body, possibly in accordance to errors in depth estimation from vergence for longer distances. Additionally, it was found that a proprioceptive arm-length signal extends the robot’s peripersonal space. The last experiment modelled development of the reaching skill by implementing motor synergies that progressively unlock degrees of freedom in the arm. The model was advantageous when compared to one that included no developmental stages. The contribution to knowledge of this work is extending the research on biologically-inspired methods for building robots, presenting new ways to further investigate the robotic properties involved in the dynamical adaptation to body and sensing characteristics, vision-based action, morphology and confidence levels in reaching assessment.CONACyT, Mexico (National Council of Science and Technology

Automated taxiing for unmanned aircraft systems

Over the last few years, the concept of civil Unmanned Aircraft System(s) (UAS) has been realised, with small UASs commonly used in industries such as law enforcement, agriculture and mapping. With increased development in other areas, such as logistics and advertisement, the size and range of civil UAS is likely to grow. Taken to the logical conclusion, it is likely that large scale UAS will be operating in civil airspace within the next decade. Although the airborne operations of civil UAS have already gathered much research attention, work is also required to determine how UAS will function when on the ground. Motivated by the assumption that large UAS will share ground facilities with manned aircraft, this thesis describes the preliminary development of an Automated Taxiing System(ATS) for UAS operating at civil aerodromes. To allow the ATS to function on the majority of UAS without the need for additional hardware, a visual sensing approach has been chosen, with the majority of work focusing on monocular image processing techniques. The purpose of the computer vision system is to provide direct sensor data which can be used to validate the vehicle s position, in addition to detecting potential collision risks. As aerospace regulations require the most robust and reliable algorithms for control, any methods which are not fully definable or explainable will not be suitable for real-world use. Therefore, non-deterministic methods and algorithms with hidden components (such as Artificial Neural Network (ANN)) have not been used. Instead, the visual sensing is achieved through a semantic segmentation, with separate segmentation and classification stages. Segmentation is performed using superpixels and reachability clustering to divide the image into single content clusters. Each cluster is then classified using multiple types of image data, probabilistically fused within a Bayesian network. The data set for testing has been provided by BAE Systems, allowing the system to be trained and tested on real-world aerodrome data. The system has demonstrated good performance on this limited dataset, accurately detecting both collision risks and terrain features for use in navigation