Cross-layer system reliability assessment framework for hardware faults

System reliability estimation during early design phases facilitates informed decisions for the integration of effective protection mechanisms against different classes of hardware faults. When not all system abstraction layers (technology, circuit, microarchitecture, software) are factored in such an estimation model, the delivered reliability reports must be excessively pessimistic and thus lead to unacceptably expensive, over-designed systems. We propose a scalable, cross-layer methodology and supporting suite of tools for accurate but fast estimations of computing systems reliability. The backbone of the methodology is a component-based Bayesian model, which effectively calculates system reliability based on the masking probabilities of individual hardware and software components considering their complex interactions. Our detailed experimental evaluation for different technologies, microarchitectures, and benchmarks demonstrates that the proposed model delivers very accurate reliability estimations (FIT rates) compared to statistically significant but slow fault injection campaigns at the microarchitecture level.Peer ReviewedPostprint (author's final draft

Artificial Intelligence in the Path Planning Optimization of Mobile Agent Navigation

AbstractMany difficult problem solving require computational intelligence. One of the major directions in artificial intelligence consists in the development of efficient computational intelligence algorithms, like: evolutionary algorithms, and neural networks. Systems, that operate in isolation or cooperate with each other, like mobile robots could use computational intelligence algorithms for different problems/tasks solving, however in their behavior could emerge an intelligence called system's intelligence, intelligence of a system. The traveling salesman problem TSP has a large application area. It is a well-known business problem. Maximum benefits TSP, price collecting TSP have a large number of economic applications. TSP is also used in the transport logic Raja, 2012. It also has a wide range of applicability in the mobile robotic agent path planning optimization. In this paper a mobile robotic agent's path planning will be discussed, using unsupervised neural networks for the TSP solving, and from the TSP results the finding of a closely optimal path between two points in the agent's working area. In the paper a modification of the criteria function of the winner neuron selection will also be presented. At the end of the paper measurement results will be presented

Dynamic structure identification of Bayesian network model for fault diagnosis of FMS

International audienceThis paper proposes an approach to accurately localize the origin of product quality drifts, in a flexible manufacturing system (FMS). The logical diagnosis model is used to reduce the search space of suspected equipment in the production flow; however, it does not help in accurately localizing the faulty equipment. In the proposed approach, we model this reduced search space as a Bayesian network that uses historical data to compute conditional probabilities for each suspected equipment. This approach helps in making accurate decisions on localizing the cause for product quality drifts as either one of the equipment in production flow or product itself

An Integrated Approach for Supplier Evaluation and Selection using the Delphi Method and Analytic Hierarchy Process (AHP): A New Framework

Supplier selection is one of the most critical processes in supply chain management (SCM). Most small and medium enterprises (SMEs) face difficulties choosing the best supplier using conventional methods. A hybrid multi-criteria decision-making (MCDM) approach is proposed in supplier selection. This proposed framework integrates the Delphi technique as a data-gathering tool and Analytic Hierarchy Process (AHP) as the MCDM methodology for data analysis; both were used to select an effective supplier. This project applies the Delphi technique, allows experts to select the main criteria, and compares the trade-offs between the available alternatives depending on the main criteria. The criteria selected were price, delivery time, online ranking, rejection rate, and flexibility. Using the AHP approach, the criteria's weights were then assigned. The highest was for the price (43.84%), followed by the rejection rate (21.81%), online ranking (19.27%), delivery time (9.44%), and flexibility (5.64%). Lastly, a new framework was suggested using the weighted criteria collection for supplier selection

Machine Learning: um estudo sobre conceitos, tarefas e algoritmos relacionados com predição e recomendação

A atual abundância de dados, por um lado, e a complexidade da sua análise e processamento, por outro lado, tornam indispensáveis os sistemas de tratamento automático de dados, para apoio nas mais diversas tarefas, incluindo a tomada de decisão. Este documento procura apresentar e descrever alguma da terminologia referida em publicações cientı́ficas e outros textos noticiosos, a propósito de trabalhos que envolvem análise preditiva e recomendação. Distinguem-se várias atividades de Data Mining que envolvem Machine Learning, designadamente Classificação, Regressão e Clustering, e são ainda enumerados alguns métodos ou algoritmos para cada uma delas, juntamente com as métricas de avaliação de desempenho mais comuns. Este levantamento termina com a apresentação das principais abordagens para um motor de recomendação.Programa Operacional Regional do Alentejo 2014/202

Uma nova abordagem para a implementação de um sistema multiagente para a configuração e o monitoramento da produção de pequenas séries

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2014A Produção de Pequenas Séries (PPS) é um tipo de manufatura caracterizado pela alta diversidade de produtos a serem produzidos associada a tamanhos de lotes reduzidos - possivelmente unitários. Neste sistema de produção as tecnologias empregadas para configuração e monitoramento do processo devem priorizar a produção sem defeitos, inclusive no primeiro item do lote, que pode ser o único. Falhas neste sistema de produção ou defeitos inseridos nos produtos facilmente inviabilizam economicamente todo o lote. Neste contexto, a linha de produção deve ser capaz de efetuar uma troca rápida de configuração para produzir um novo lote em um cenário de vários lotes de tamanho reduzido. É preciso também garantir completo monitoramento da produção do lote, sem falhas, ou quando ocorrer uma falha, ações corretivas devem ser executadas imediatamente. As pesquisas recentes demonstram que sistemas baseados em agentes é uma abordagem promissora para o cenário da PPS. Diante disso, a contribuição desta tese é a apresentação de uma nova Arquitetura de Referência para a implementação de Sistemas Multiagente na configuração e monitoramento da Produção de Pequenas Séries. A abordagem propõe uma Arquitetura de Referência, chamada MAS4SSP, baseada em uma solução unificada, sinérgica e com alto nível de abstração. Para garantir isto, a abordagem emprega como Modelo de Referência o framework JaCaMo que segue o paradigma orientado a multiagente (MAOP). A integração com a linha de produção é realizada com o emprego da tecnologia de comunicação Web Service que é utilizada pelo Sistema Multiagente (cliente) e por um sistema SCADA baseado em aplicação Web, o ScadaBR (servidor). A interface com o usuário pode ser desenvolvida como um recurso adicional na plataforma JaCaMo, ou pode ser realizada uma integração com sistemas legados de produção (como ERP, PCP, MRP) utilizando também a tecnologia de Web Service. Além da Arquitetura de Referência a tese apresenta um Modelo Genérico de Modelagem e Implementação que serve como guia para o desenvolvedor. Esta abordagem foi instanciada em um experimento simulado no contexto de uma PPS de Placas de Circuito Impresso (PCI). Por fim, os resultados e as conclusões sobre a Arquitetura de Referência e o Modelo Genérico de Modelagem e Implementação são apresentados em conjunto com sugestões de trabalhos futuros.Abstract: Small Series Production (SSP) is a type of manufacturing characterized by a high diversity of products to be produced associated with a reduced batch sizes - possibly unitary. In this production system, the technologies employed for process setup and monitoring must prioritize production without defects, including the first item of the lot, which may be the one. Faults in this production system or defects in the product easily become the whole batch economically unfeasible. In this context, the production line should be able to monitor the process and make a quick change of the configuration to produce a new batch in a scenario of several batches with small sizes. The system should also ensure the batch production without failure or when a fault occurs, the corrective actions must be executed immediately. Recent research shows that agent-based systems are a promising approach for the SSP. The contribution of this thesis is the presentation of a new Reference Architecture for the implementation of Multi-Agent Systems to setup and monitoring Small Series Production. The approach proposes a Reference Architecture, called MAS4SSP, based on a unified, synergistic and high level of abstraction solution. To ensure this, the approach employs as a Reference Model the JaCaMo framework that follows the Multi-Agent Oriented Paradigm (MAOP). The integration with the production line is realized with the use of Web Service as communication technology between the Multi-Agent System (client) and a SCADA system - ScadaBR (server). The user interface can be developed as an additional resource in JaCaMo platform or can be an integration system with legacy production systems (such as ERP, MES, MRP) also using Web Service. Beyond the Reference Architecture, this thesis presents a Generic Modeling and Implementation Model which serves as a guide for the developer and that was instantiated in a controlled experiment - a SSP line of Printed Circuit Boards (PCB). Finally, the conclusions and perspectives about the Reference Architecture and the Generic Modeling and Implementation Model are presented together with suggestions for future works

Bridging the training needs of cybersecurity professionals in Mauritius through the use of smart learning environments.

Doctoral Degree. University of KwaZulu-Natal, Durban.Teaching and Learning confined to within the four walls of a classroom or even online Learning through Massive Online Courses (MOOCs) and other Learning Content Management Systems (LCMS) are no longer seen as the optimal approach for competency and skills development, especially for working professionals. Each of these busy learners have their own training needs and prior knowledge. Adopting the one-size-fits-all teaching approach is definitely not effective, motivating and encouraging. This is why this research presents the use of SMART Learning Environment that makes use of Intelligent Techniques to personalise the learning materials for each learner. It has been observed that on one hand the country is not able to provide the required number of IT professionals with the desired skills and on the other hand, the number of unemployed graduates in areas other than IT is increasing. This mismatch in skills is becoming a pressing issue and is having a direct impact on the ICT Sector, which is one of the pillars of the Mauritian Economy. An in-depth Literature Review was carried out to understand the training needs of these Cybersecurity professionals and also to understand the different Intelligent Techniques that can be used to provide personalisation of learning materials. Data was collected during three phases, namely an Expert Reference Group Discussion, a pre-test questionnaire and a survey questionnaire. The Expert Reference Group Discussion was carried out to further shed light on the research question set and to further understand the training needs and expectations of Cybersecurity professionals in Mauritius. A SMART Learning Environment making use of Artificial Neural Networks and Backpropagation Algorithm to personalise learning materials was eventually designed and implemented. Design Science Research Methodology (DSRM), Activity Theory, Bloom’s Taxonomy and the Technology Acceptance Model were used in this study. Due to the inherent limitations of the models mentioned, the researcher also proposed and evaluated an emergent conceptual model, called the SMART Learning model. The major findings of this research show that personalisation of learning materials through the use of a SMART Learning Environment can be used to effectively address the training needs of Cybersecurity professionals in Mauritius

Framework for sustainability assessments providing a basis for evidence-based and goal-oriented decision making support : based on the example of electric power systems

There is little doubt that electricity has become an indispensable ingredient for modern societies to thrive and to secure our individual well-being. Today, the vast majority of our daily activities are powered by this invisible yet powerful resource. Furthermore, electricity ensures that key institutions can deliver critical services to the public or manufacturing industries that produce and deliver goods to their ever-growing consumer base. In order to enable this energy supply, we build sophisticated power infrastructure and operate electrical devices that fundamentally alter natural energy and material flows on planet Earth. Against this backdrop, some scholars and political leaders are concerned that current power consumption patterns in industrialised countries may render future generations unable to meet their needs. This begs the question: How can we further improve the current level of well-being without eroding the ecological capital of planet Earth? There are many potential answers to this question. One response often suggested is that such a way should meet the requirements of sustainable development (SD). The guiding principles of SD are, however, considered to be too ambiguous to operationalise and have given rise to a wide range of interpretations. Sustainability assessments play a central role in the process of extracting and evaluating data against predefined sustainability objectives and thereby require some form of interpretation of SD and an understanding of the relevant aspects of the system under review. With this thesis, I strive to contribute a holistic and transparent framework for sustainability assessments that integrates normative features of SD, instrumental aspects of governance and functional components of the system. It strives to provide a comprehensive basis for evidence-based and goal-oriented decision making by determining relevant categories and enabling an evaluation of system data against predefined sustainability objectives. It assumes that more of the right data provides a better basis for informing decision making on the long-term development of key systems and, thus, may serve as a starting point for the design of policy instruments. My work aims to promote scientific discussion on appropriate methodologies for sustainability assessments. Exemplary results for power systems show that the framework is able to produce new meaningful criteria hitherto absent in sustainability assessments. Furthermore, for the first time ever it provides general goals to criteria that mirror the requirements of SD directly in sustainability assessments. The framework does not aim to provide a single best solution and rather seeks to balance the development of the system by considering negotiation and deliberation to agree on priorities. Accordingly, it is not meant as a management tool, but rather seeks to provide additional information on the system under review and potential sustainability goals for societal steering processes