Applicability of the Statistical Pattern Recognition Paradigm for Structural Health Monitoring of Bridges

In the last decades, health monitoring systems have gained an increasing importance in our society. The main purpose of these systems is to support the engineers to get more insight into the behavior of structures under service conditions, so they can optimize and improve maintenance programs and, hopefully, to avoid structural failures or disasters. It is possible to integrate these systems in any type of civil or mechanical infrastructure. However, in this dissertation, the preferential targets are the civil infrastructures with major strategically importance in the social environment, such as bridges and viaducts. Therefore, the goal of this dissertation is (i) to review the most recent bridge collapses in order to unveil the main causes and challenges posed by those catastrophic events; (ii) to review the concept and need of Structural Health Monitoring (SHM) of bridges as well as its associated potential for significant life-safety and economic benefits; and (iii) to study the applicability of the SHM concepts. Due to recent promising research developments, the SHM process is posed in the context of the Statistical Pattern Recognition (SPR) paradigm, which tries to implement a damage identification strategy based on the comparison of different state conditions. The applicability of the SHM-SPR paradigm is studied by applying its concepts in two separate cases: firstly on data sets from a base-excited three-story frame structure, created and tested in a laboratory environment at Los Alamos National Laboratory; secondly, on data sets from a real-world bridge, namely the Z24 Bridge in Switzerland. The major contributions of this dissertation are the extension of previous results obtained by Figueiredo et al. from the three-story frame structure and the development and application of an algorithm that uses a Gaussian mixture model as a way of improving the feature classification performance under varying operational and environmental conditions.Nas últimas décadas, os sistemas de monitorização estrutural ganharam uma crescente importância na nossa sociedade. O principal objetivo destes sistemas é de ajudar os engenheiros a aprofundar o conhecimento relativo ao comportamento das estruturas sob condições de serviço para que possam otimizar e melhorar os programas de manutenção e, em último caso, evitar desastres ou falhas estruturais. É possível integrar estes sistemas em qualquer tipo de infra-estrutura civil ou mecânica. No entanto, nesta dissertação, os alvos preferenciais são as infra-estruturas com elevada importância estratégica no seio da engenharia civil, tais como as pontes e os viadutos. Portanto, o objetivo desta dissertação é (i) rever os recentes colapsos de pontes, de forma a desvendar as causas que os originaram assim como os desafios colocados por estes eventos; (ii) rever o conceito e a necessidade de sistemas de monitorização da integridade estrutural (SHM) de pontes, bem como o seu potencial associado aos benefícios ao nível da segurança e do ponto de vista económico; e (iii) estudar a aplicabilidade dos conceitos da SHM. Devido a recentes desenvolvimentos promissores, o processo de SHM pode ser colocado no contexto de um paradigma de reconhecimento de padrões (SPR), o qual tenta implementar uma estratégia de identificação de dano com base na comparação de diferentes estados de condição da estrutura. A aplicabilidade do paradigma SHM-SPR é estudada através da aplicação dos seus conceitos em dois casos distintos: em primeiro lugar, em conjuntos de dados recolhidos de uma estrutura de três pisos, criada e testada em ambiente laboratorial no Los Alamos National Laboratory; em segundo lugar, em conjuntos de dados de uma ponte real, mais especificamente, a Ponte Z24, na Suíça. As contribuições originais desta dissertação são a extensão dos resultados anteriormente obtidos por Figueiredo et al. relativos à estrutura de três pisos, e o desenvolvimento e aplicação de um algoritmo, que utiliza como base um modelo de mistura Gaussiana, de forma a melhorar o desempenho da classificação de características sob condições operacionais e ambientais variáveis

Analysis of Lisbon startups’ business intelligence capabilities

“Business intelligence (BI) is a broad category of applications, technologies, and processes for gathering, storing, accessing, and analysing data to help business users make better decisions.”1 Organisations pursue these activities most often to build competitive advantage or improve the customer experience and it’s, undeniably, expected to have a positive impact on company revenues, margins, and organizational efficiency.2 This work project, Analysis of Lisbon Startups’ Business Intelligence Capabilities, intends to clarify the relationship between the usage of current Business Intelligence & Analytics tools and the success of business enterprises, using three different startups based in Lisbon as case studies to illustrate that: Aptoide, Misk, and Tiger Time

Kidney Care - A Personal Assistant Assessment

A cognitive disability is a medical condition that, despite all technological progress, still does not have a cure, i.e., there are cases where the physician may use medication, but the only purpose is to decrease the progression of the disease, not its cure. This is the case in many situations, and in particular in kidney illnesses, which have a dominant impact on a person well being, i.e., the assistance to an individual to whom was diagnosed cognitive disabilities is essential, where the location of the individual is not decisive or important. Hence, the presence of a Personal Assistance Service can become a cornerstone in achieving independence and quality of life. Therefore, the objective of this work is to present an intelligent system aimed at an endless individuals monitoring and alerting system, based on a Logical Programming approach to Knowledge Representation and Reasoning, and centre on RapidMiner, a software platform that provides an integrated environment for machine learning, predictive analysis or application development and deployment. It undergoes a Case Based approach to computing that tracks patient’s performance, learn and deliver content when it is needed, and assures that patient’s key information is changed into the indispensable ongoing knowledge

CNC Machines Integration in Smart Factories using OPC UA

This work was partially developed under the project TOOLING4G (POCI-01-0247-FEDER-024516) and the project S4PLAST - Sustainable Plastics Advanced Solutions (POCI-01-0247-FEDER-046089), supported by Programa Operacional Competitividade e Internacionalização (POCI), Programa Operacional Regional de Lisboa, Portugal 2020 and Fundo Europeu de Desenvolvimento Regional (FEDER). This project was also partially financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within projects UIDB/00308/2020 and LA/P/0063/2020, and under the Scientific Employment Stimulus - Institutional Call CEECINST/00051/2018. Special thanks to the Technological University of the Shannon: Midlands Midwest, a RUN-EU partner who also supported this workThis paper examines the idea of Industry 4.0 from the perspective of the molds industry, a vital industry in today’s industrial panorama. Several technologies, particularly in the area of machining equipment, have been introduced as a result of the industry’s constant modernization. This technological diversity makes automatic interconnection with production management software extremely difficult, as each brand and model requires different, mostly proprietary, interfaces and communication protocols. In the methodology presented in this paper, a development of monitoring solutions for machining devices is defined supporting the leading equipment and operations used by molds industry companies. OPC UA is employed for high-level communication between the various systems for a standardized approach. The approach combines various machine interfaces on a single system to cover a significant subset of machining equipment currently used by the molds industry, as a key result of this paper and given the variety of monitoring systems and communication protocols. This type of all-in-one approach will provide production managers with the information they need to monitor and improve the complete manufacturing process.info:eu-repo/semantics/publishedVersio

multidisciplinary approaches to the experiences and landscapes of seclusion and solitude

UIDB/00749/2020 UIDP/00749/2020publishersversionpublishe

Introduction

UIDB/00749/2020 UIDP/00749/2020publishersversionpublishe

Developing an OPC UA Server for CNC Machines

Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing.Acknowledgements This work was developed under the project TOOLING4G (POCI-01-0247-FEDER-024516), supported by Programa Operacional Competitividade e Internacionalizac¸ao (POCI), Portugal 2020 and Fundo Europeu de Desenvolvimento Regional (FEDER). This project was also financed by National Funds through the Portuguese funding agency, FCT - Fundac¸ao para a Ci ˜ encia e a Tecnologia, within projects UIDB/00308/2020 and UIDB/50014/2020.This paper addresses the concept of Industry 4.0 from the perspective of the molds industry, a key industry in today’s industrial panorama. With its constant modernization, several technologies have been introduced, in particular regarding machining equipment. With each brand and model requiring different (proprietary) interfaces and communication protocols, this technological diversity renders the automatic interconnection with production management software extremely challenging. In this paper a methodology to build monitoring solutions for machining devices is defined, based on the main equipment and operations used by molds industry companies. For a standardized approach, OPC UA is used for high-level communication between the various systems. As a key result of this paper, and given the variety of monitoring systems and communication protocols, the developed approach combines various different machine interfaces on a single system, in order to cover a relevant subset of machining equipment currently in use by the molds industry. This kind of all-in-one approach will give production managers access to the information needed for a continuous monitoring and improvement of the entire production process.info:eu-repo/semantics/publishedVersio

A case-based reasoning view of thrombophilia risk

Thrombophilia stands for a genetic or an acquired tendency to hypercoagulable states that increase the risk of venous and arterial thromboses. Indeed, venous thromboembolism is often a chronic illness, mainly in deep venous thrombosis and pulmonary embolism, requiring lifelong prevention strategies. Therefore, it is crucial to identify the cause of the disease, the most appropriate treatment, the length of treatment or prevent a thrombotic recurrence. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a logic programming approach to knowledge representation and reasoning, complemented with a case-based approach to computing. The proposed model has been quite accurate in the assessment of thrombophilia predisposition risk, since the overall accuracy is higher than 90% and sensitivity ranging in the interval [86.5%, 88.1%]. The main strength of the proposed solution is the ability to deal explicitly with incomplete, unknown, or even self-contradictory information

Genetic and genomic diversity in a Tarwi (Lupinus mutabilis Sweet) germplasm collection and adaptability to Mediterranean climate conditions

Lupinus mutabilis (tarwi) is a species of Andean origin with high protein and oil content and regarded as a potential crop in Europe. The success in the introduction of this crop depends in part on in depth knowledge of the intra-specific genetic variability of the collections, enabling the establishment of breeding and conservation programs. In this study, we used morphological traits, Inter-Simple Sequence Repeat markers and genome size to assess genetic and genomic diversity of 23 tarwi accessions under Mediterranean conditions. Phenotypic analyses and yield component studies point out accession LM268 as that achieving the highest seed production, producing large seeds and e ciently using primary branches as an important component of total yield, similar to the L. albus cultivars used as controls. By contrast, accession JKI-L295 presents high yield concentrated on the main stem, suggesting a semi-determinate development pattern. Genetic and genomic analyses revealed important levels of diversity, however not relatable to phenotypic diversity, reflecting the recent domestication of this crop. This is the first study of genome size diversity within L. mutabilis, revealing an average size of 2.05 pg/2C (2001 Mbp) with 9.2% variation (1897–2003 Mbp), prompting further studies for the exploitation of this diversityinfo:eu-repo/semantics/publishedVersio

An Integrated Soft Computing Approach to Hughes Syndrome Risk Assessment

The AntiPhospholipid Syndrome (APS) is an acquired autoimmune disorder induced by high levels of antiphospholipid antibodies that cause arterial and veins thrombosis, as well as pregnancy-related complications and morbidity, as clinical manifestations. This autoimmune hypercoagulable state, usually known as Hughes syndrome, has severe consequences for the patients, being one of the main causes of thrombotic disorders and death. Therefore, it is required to be preventive; being aware of how probable is to have that kind of syndrome. Despite the updated of antiphospholipid syndrome classification, the diagnosis remains difficult to establish. Additional research on clinically relevant antibodies and standardization of their quantification are required in order to improve the antiphospholipid syndrome risk assessment. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a Logic Programming approach to knowledge representation and reasoning, complemented with a computational framework based on Artificial Neural Networks. The proposed model allows for improving the diagnosis, classifying properly the patients that really presented this pathology (sensitivity higher than 85%), as well as classifying the absence of APS (specificity close to 95%)