Advances in Computer Science and Engineering

The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Requirements for a global data infrastructure in support of CMIP6

The World Climate Research Programme (WCRP)’s Working Group on Climate Modelling (WGCM) Infrastructure Panel (WIP) was formed in 2014 in response to the explosive growth in size and complexity of Coupled Model Intercomparison Projects (CMIPs) between CMIP3 (2005–2006) and CMIP5 (2011–2012). This article presents the WIP recommendations for the global data infrastruc- ture needed to support CMIP design, future growth, and evolution. Developed in close coordination with those who build and run the existing infrastructure (the Earth System Grid Federation; ESGF), the recommendations are based on several principles beginning with the need to separate requirements, implementation, and operations. Other im- portant principles include the consideration of the diversity of community needs around data – a data ecosystem – the importance of provenance, the need for automation, and the obligation to measure costs and benefits. This paper concentrates on requirements, recognizing the diversity of communities involved (modelers, analysts, soft- ware developers, and downstream users). Such requirements include the need for scientific reproducibility and account- ability alongside the need to record and track data usage. One key element is to generate a dataset-centric rather than system-centric focus, with an aim to making the infrastruc- ture less prone to systemic failure. With these overarching principles and requirements, the WIP has produced a set of position papers, which are summa- rized in the latter pages of this document. They provide spec- ifications for managing and delivering model output, includ- ing strategies for replication and versioning, licensing, data quality assurance, citation, long-term archiving, and dataset tracking. They also describe a new and more formal approach for specifying what data, and associated metadata, should be saved, which enables future data volumes to be estimated, particularly for well-defined projects such as CMIP6. The paper concludes with a future facing consideration of the global data infrastructure evolution that follows from the blurring of boundaries between climate and weather, and the changing nature of published scientific results in the digital age

An elastic, parallel and distributed computing architecture for machine learning

Machine learning is a powerful tool that allows us to make better and faster decisions in a data-driven fashion based on training data. Neural networks are especially popular in the context of supervised learning due to their ability to approximate auxiliary functions. However, building these models is typically computationally intensive, which can take significant time to complete on a conventional CPU-based computer. Such a long turnaround time makes business and research infeasible using these models. This research seeks to accelerate this training process through parallel and distributed computing using High-Performance Computing (HPC) resources. To understand machine learning on HPC platforms, theoretical performance analysis from this thesis summarises four key factors for data-parallel machine learning: convergence, batch size, computational and communication efficiency. It is discovered that a maximum computational speed-up exists through parallel and distributed computing for a fixed experimental setup. This primary focus of this thesis is convolutional neural network applications on the Apache Spark platform. The work presented in this thesis directly addresses the computational and communication inefficiencies associated with the Spark platform with improvements to the Resilient Distributed Dataset (RDD) and the introduction of an elastic non-blocking all-reduce. In addition to implementation optimisations, the computational performance has been further improved by overlapping computation and communication, and the use of large batch sizes through fine-grained control. The impacts of these improvements are more prominent with the rise of massively parallel processors and high-speed networks. With all the techniques combined, it is predicted that training the ResNet50 model on the ImageNet dataset for 100 epochs at an effective batch size of 16K will take under 20 minutes on an NVIDIA Tesla P100 cluster, in contrast to 26 months on a single Intel Xeon E5-2660 v3 2.6 GHz processor. Due to the similarities to scientific computing, the resulting computing model of this thesis serves as an exemplar of the integration of high-performance computing and elastic computing with dynamic workloads, which lays the foundation for future research in emerging computational steering applications, such as interactive physics simulations and data assimilation in weather forecast and research

A Survey of Challenges for Runtime Verification from Advanced Application Domains (Beyond Software)

Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor. Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification

Towards the simulation of cooperative perception applications by leveraging distributed sensing infrastructures

With the rapid development of Automated Vehicles (AV), the boundaries of their function alities are being pushed and new challenges are being imposed. In increasingly complex and dynamic environments, it is fundamental to rely on more powerful onboard sensors and usually AI. However, there are limitations to this approach. As AVs are increasingly being integrated in several industries, expectations regarding their cooperation ability is growing, and vehicle-centric approaches to sensing and reasoning, become hard to integrate. The proposed approach is to extend perception to the environment, i.e. outside of the vehicle, by making it smarter, via the deployment of wireless sensors and actuators. This will vastly improve the perception capabilities in dynamic and unpredictable scenarios and often in a cheaper way, relying mostly in the use of lower cost sensors and embedded devices, which rely on their scale deployment instead of centralized sensing abilities. Consequently, to support the development and deployment of such cooperation actions in a seamless way, we require the usage of co-simulation frameworks, that can encompass multiple perspectives of control and communications for the AVs, the wireless sensors and actuators and other actors in the environment. In this work, we rely on ROS2 and micro-ROS as the underlying technologies for integrating several simulation tools, to construct a framework, capable of supporting the development, test and validation of such smart, cooperative environments. This endeavor was undertaken by building upon an existing simulation framework known as AuNa. We extended its capabilities to facilitate the simulation of cooperative scenarios by incorporat ing external sensors placed within the environment rather than just relying on vehicle-based sensors. Moreover, we devised a cooperative perception approach within this framework, showcasing its substantial potential and effectiveness. This will enable the demonstration of multiple cooperation scenarios and also ease the deployment phase by relying on the same software architecture.Com o rápido desenvolvimento dos Veículos Autónomos (AV), os limites das suas funcional idades estão a ser alcançados e novos desafios estão a surgir. Em ambientes complexos e dinâmicos, é fundamental a utilização de sensores de alta capacidade e, na maioria dos casos, inteligência artificial. Mas existem limitações nesta abordagem. Como os AVs estão a ser integrados em várias indústrias, as expectativas quanto à sua capacidade de cooperação estão a aumentar, e as abordagens de perceção e raciocínio centradas no veículo, tornam-se difíceis de integrar. A abordagem proposta consiste em extender a perceção para o ambiente, isto é, fora do veículo, tornando-a inteligente, através do uso de sensores e atuadores wireless. Isto irá melhorar as capacidades de perceção em cenários dinâmicos e imprevisíveis, reduzindo o custo, pois a abordagem será baseada no uso de sensores low-cost e sistemas embebidos, que dependem da sua implementação em grande escala em vez da capacidade de perceção centralizada. Consequentemente, para apoiar o desenvolvimento e implementação destas ações em cooperação, é necessária a utilização de frameworks de co-simulação, que abranjam múltiplas perspetivas de controlo e comunicação para os AVs, sensores e atuadores wireless, e outros atores no ambiente. Neste trabalho será utilizado ROS2 e micro-ROS como as tecnologias subjacentes para a integração das ferramentas de simulação, de modo a construir uma framework capaz de apoiar o desenvolvimento, teste e validação de ambientes inteligentes e cooperativos. Esta tarefa foi realizada com base numa framework de simulação denominada AuNa. Foram expandidas as suas capacidades para facilitar a simulação de cenários cooperativos através da incorporação de sensores externos colocados no ambiente, em vez de depender apenas de sensores montados nos veículos. Além disso, concebemos uma abordagem de perceção cooperativa usando a framework, demonstrando o seu potencial e eficácia. Isto irá permitir a demonstração de múltiplos cenários de cooperação e também facilitar a fase de implementação, utilizando a mesma arquitetura de software

Real-time scheduling for media processing using conditionally guaranteed budgets

In dit proefschrift behandelen we een planningsprobleem dat haar oorsprong vindt in het kosteneffectief verwerken van verschillende media door software in consumentenapparaten, zoals digitale televisies. De laatste jaren zijn er trends gaande van analoge naar digitale systemen, en van verwerking van digitale signalen door speci??eke, toepassingsgerichte hardware naar verwerking door software. Voor de verwerking van digitale media door software wordt gebruik gemaakt van krachtige programmeerbare processoren. Om te kunnen wedijveren met bestaande oplossingen is het van belang dat deze programeerbare hardware zeer kosteneffectief wordt gebruikt. Daarnaast dienen de bestaande eigenschappen van deze consumenten apparaten, zoals robuustheid, stabiliteit, en voorspelbaarheid, behouden te blijven als er software wordt gebruikt. Verder geldt dat er gelijktijdig meerdere media stromen door een consumenten apparaat verwerkt moeten kunnen worden. Deze uitdaging is binnen de onderzoekslaboratoria van Philips aangegaan in het zogenoemde Video-Quality-of-Service programma, en het werk dat in dit proefschrift beschreven wordt is binnen dat programma ontstaan. De binnen dat programma gekozen aanpak is gebaseerd op schaalbare algoritmen voor de verwerking van media, budgetten voor die algoritmen, en software dat de instelling van die algoritmen en de grootte van de budgetten aanpast tijdens de verwerking van de media. Ten behoeve van het kosteneffectief gebruik van de programmeerbare processoren zijn de budgetten krap bemeten. Dit proefschrift geeft een uitvoerige beschrijving van die aanpak, en van een model van een apparaat dat de haalbaarheid van die aanpak aantoont. Vervolgens laten we zien dat die aanpak leidt tot een probleem wanneer er gelijktijdig meerdere stromen worden verwerkt die verschillende relatieve relevanties hebben voor de gebruiker van het apparaat. Om dit probleem op te lossen stellen we het nieuwe concept van voorwaardelijk gegarandeerde budgetten voor, en beschrijven we hoe dat concept kan worden gerealiseerd. De technieken voor het analyseren van het planningprobleem voor budgetten zijn gebaseerd op bestaande technieken voor slechtste-gevals-analyse voor periodieke real-time taken. We breiden die bestaande technieken uit met technieken voor beste-gevals-analyse zodat we apparaten die gebruik maken van dit nieuwe type budget kunnen analyseren

Sense and Respond

Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes

Simulation optimisation: An expert mechanism approach

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