Learning and testing stochastic discrete event

Dissertação de mestrado em Engenharia de InformáticaSistemas de eventos discretos (DES) são uma importante subclasse de sistemas (à luz da teoria dos sistemas). Estes têm sido usados, particularmente na indústria para analisar e modelar um vasto conjunto de sistemas reais, tais como, sistemas de produção, sistemas de computador, sistemas de controlo de tráfego e sistemas híbridos. O nosso trabalho explora uma extensão de DES com ênfase nos processos estocásticos, comummente chamado como sistemas de eventos discretos estocásticos (SDES). Existe assim a necessidade de estabelecer uma abstração estocástica através do uso de processos semi-Markovianos generalizados (GSMP) para SDES. Assim, o objetivo do nosso trabalho é propor uma metodologia e um conjunto de algoritmos para aprendizagem de GSMP, usar técnicas de model-checking estatístico para a verificação e propor duas novas abordagens para teste de DES e SDES (respetivamente, não estocasticamente e estocasticamente). Este trabalho também introduz uma noção de modelação, analise e verificação de sistemas contínuos e modelos de perturbação no contexto da verificação por model-checking estatístico.Discrete event systems (DES) are an important subclass of systems (in systems theory). They have been used, particularly in industry, to analyze and model a wide variety of real systems, such as production systems, computer systems, traffic systems, and hybrid systems. Our work explores an extension of DES with an emphasis on stochastic processes, commonly called stochastic discrete event systems (SDES). There was a need to establish a stochastic abstraction for SDES through generalized semi-Markov processes (GSMP). Thus, the aim of our work is to propose a methodology and a set of algorithms for GSMP learning, using model checking techniques for verification, and to propose two new approaches for testing DES and SDES (non-stochastically and stochastically). This work also introduces a notion of modeling, analysis, and verification of continuous systems and disturbance models in the context of verifiable statistical model checking

Dynamic contracts for verification and enforcement of real-time systems properties

Programa de Doutoramento em Informática (MAP-i) das Universidades do Minho, de Aveiro e do PortoRuntime veri cation is an emerging discipline that investigates methods and tools to enable the veri cation of program properties during the execution of the application. The goal is to complement static analysis approaches, in particular when static veri cation leads to the explosion of states. Non-functional properties, such as the ones present in real-time systems are an ideal target for this kind of veri cation methodology, as are usually out of the range of the power and expressiveness of classic static analyses. Current real-time embedded systems development frameworks lack support for the veri - cation of properties using explicit time where counting time (i.e., durations) may play an important role in the development process. Temporal logics targeting real-time systems are traditionally undecidable. Based on a restricted fragment of Metric temporal logic with durations (MTL-R), we present the proposed synthesis mechanisms 1) for target systems as runtime monitors and 2) for SMT solvers as a way to get, respectively, a verdict at runtime and a schedulability problem to be solved before execution. The later is able to solve partially the schedulability analysis for periodic resource models and xed priority scheduler algorithms. A domain speci c language is also proposed in order to describe such schedulability analysis problems in a more high level way. Finally, we validate both approaches, the rst using empirical scheduling scenarios for unimulti- processor settings, and the second using the use case of the lightweight autopilot system Px4/Ardupilot widely used for industrial and entertainment purposes. The former also shows that certain classes of real-time scheduling problems can be solved, even though without scaling well. The later shows that for the cases where the former cannot be used, the proposed synthesis technique for monitors is well applicable in a real world scenario such as an embedded autopilot ight stack.A verificação do tempo de execução e uma disciplina emergente que investiga métodos e ferramentas para permitir a verificação de propriedades do programa durante a execução da aplicação. O objetivo é complementar abordagens de analise estática, em particular quando a verificação estática se traduz em explosão de estados. As propriedades não funcionais, como as que estão presentes em sistemas em tempo real, são um alvo ideal para este tipo de metodologia de verificação, como geralmente estão fora do alcance do poder e expressividade das análises estáticas clássicas. As atuais estruturas de desenvolvimento de sistemas embebidos para tempo real não possuem suporte para a verificação de propriedades usando o tempo explicito onde a contagem de tempo (ou seja, durações) pode desempenhar um papel importante no processo de desenvolvimento. As logicas temporais que visam sistemas de tempo real são tradicionalmente indecidíveis. Com base num fragmento restrito de MTL-R (metric temporal logic with durations), apresentaremos os mecanismos de síntese 1) para sistemas alvo como monitores de tempo de execução e 2) para solvers SMT como forma de obter, respetivamente, um veredicto em tempo de execução e um problema de escalonamento para ser resolvido antes da execução. O ultimo é capaz de resolver parcialmente a analise de escalonamento para modelos de recursos periódicos e ainda para algoritmos de escalonamento de prioridade fixa. Propomos também uma linguagem especifica de domínio para descrever esses mesmos problemas de analise de escalonamento de forma mais geral e sucinta. Finalmente, validamos ambas as abordagens, a primeira usando cenários de escalonamento empírico para sistemas uni- multi-processador e a segunda usando o caso de uso do sistema de piloto automático leve Px4/Ardupilot amplamente utilizado para fins industriais e de entretenimento. O primeiro mostra que certas classes de problemas de escalonamento em tempo real podem ser solucionadas, embora não seja escalável. O ultimo mostra que, para os casos em que a primeira opção não possa ser usada, a técnica de síntese proposta para monitores aplica-se num cenário real, como uma pilha de voo de um piloto automático embebido.This thesis was partially supported by National Funds through FCT/MEC (Portuguese Foundation for Science and Technology) and co- nanced by ERDF (European Regional Development Fund) under the PT2020 Partnership, within the CISTER Research Unit (CEC/04234); FCOMP-01-0124-FEDER-015006 (VIPCORE) and FCOMP-01-0124-FEDER- 020486 (AVIACC); also by FCT and EU ARTEMIS JU, within project ARTEMIS/0003/2012, JU grant nr. 333053 (CONCERTO); and by FCT/MEC and the EU ARTEMIS JU within project ARTEMIS/0001/2013 - JU grant nr. 621429 (EMC2)

Efeito de prática diferenciada (com e sem oposição) na aprendizagem de uma tarefa de largar e pontapear uma bola, sem ressalto, em precisão

O objetivo do presente estudo foi verificar se a prática diferenciada (com e sem oposição) influencia a aprendizagem e/ou retenção de uma tarefa de largar e pontapear uma bola, em precisão. Para a realização deste estudo, 15 crianças com 10.9 ± 0.5 anos de idade, foram divididas em dois grupos homogéneos, relativamente aos resultados obtidos na avaliação inicial. Posteriormente, cada grupo realizou treino de Tripela com, ou sem oposição. Os resultados, apesar de não obtidas diferenças estatisticamente significativas, demonstraram uma tendência de melhoria na performance (desvio ao alvo) do grupo que treinou com oposição, quer para a fase de aquisição, quer de retenção

Monitoring for a decidable fragment of MTL-∫

Temporal logics targeting real-time systems are traditionally undecidable. Based on a restricted fragment of MTL-R, we propose a new approach for the runtime verification of hard real-time systems. The novelty of our technique is that it is based on incremental evaluation, allowing us to e↵ectively treat duration properties (which play a crucial role in real-time systems). We describe the two levels of operation of our approach: offline simplification by quantifier removal techniques; and online evaluation of a three-valued interpretation for formulas of our fragment. Our experiments show the applicability of this mechanism as well as the validity of the provided complexity results

Real-time MTL with durations as SMT with applications to schedulability analysis

This paper introduces a synthesis procedure for the satisfiability problem of RMTL-D formulas as SAT solving modulo theories. RMTL-D is a real-time version of metric temporal logic (MTL) extended by a duration quantifier allowing to measure time durations. For any given formula, a SAT instance modulo the theory of arrays, uninterpreted functions with equality and non-linear real-arithmetic is synthesized and may then be further investigated using appropriate SMT solvers. We show the benefits of using RMTL-D with the given SMT encoding on a diversified set of examples that include in particular its application in the area of schedulability analysis. Therefore, we introduce a simple language for formalizing schedulability problems and show how to formulate timing constraints as RMTL-D formulas. Our practical evaluation based on our synthesis and Z3 as back-end SMT solver also shows the feasibility of the overall approach.This work was partially supported by BMVI project IHATEC / SecurePort; by National Funds through FCT/M- CTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UID/CEC/04234) and the INESC TEC (UIDB/50014/2020); also by the Norte Portugal Regional Operational Programme (NORTE 2020) under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and also by national funds through the FCT, within project NORTE-01-0145- FEDER-028550 (REASSURE)

Collision avoidance on unmanned aerial vehicles using neural network pipelines and flow clustering techniques

UIDB/04111/2020 PCIF/SSI/0102/2017 IF/00325/2015Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.publishersversionpublishe

Innovative Smart Grid Solutions for Network Planning and Access

Smart Grids are the cornerstone for Distribution System Operators transformation. Having new solutions to deal with historical and future problems is key to ensure a smooth transition to an advanced power system that not only integrate a large share of renewables and distributed energy resources (e.g. storage, electrical vehicles), but also requires efficient operation, better planning and exceptional customer service. EDP Distribuição is at the forefront of this transformation, as it is developing Inovgrid, a smart grid project in Évora city (Portugal), where a smart grid infrastructure was deployed, and new data is now available to incorporate in planning and access tools and procedures, hence contributing to a Smarter Grid. This paper discusses the results that EDP Distribuição has attained so far in these areas of the smart grid development, as well as the projected evolution of these innovative approaches to the future of the distribution grid, which are being developed in European projects like SuSTAINABLE (www.sustainableproject.eu)

Deep dense and convolutional autoencoders for machine acoustic anomaly detection

Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the two proposed deep autoencoders, when combined with a mel-spectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Overall, a high-quality class discrimination level was achieved, ranging from 72% to 92%.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) - Project n ∘ 039334; Funding Reference: POCI-01-0247-FEDER-039334

How to build a 2d and 3d aerial multispectral map?—all steps deeply explained

UIDB/04111/2020 PCIF/SSI/0102/2017 IF/00325/2015 UIDB/00066/2020The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets.publishersversionpublishe

A sequence to sequence long short-term memory network for footwear sales forecasting

Footwear sales forecasting is a critical task for supporting product managerial decisions, such as the management of footwear stocks and production levels. In this paper, we explore a recently proposed Sequence to Sequence (Seq2Seq) Long Short-Term Memory (LSTM) deep learning architecture for multi-step ahead footwear sales Time Series Forecasting (TSF). The analyzed Seq2Seq LSTM neural network is compared with two popular TSF methods, namely ARIMA and Prophet. Using real-world data from a Portuguese footwear company, several computational experiments were held. Focusing on daily sales, we analyze data recently collected during a 3-year period (2019-2021) and related with seven types of products (e.g., sandals). The evaluation assumed a robust and realistic rolling window scheme that considers 28 training and testing iterations, each related with one week of multi-step ahead predictions. Overall, competitive predictions were obtained by the proposed LSTM model, resulting in a weekly Normalized Mean Absolute Error (NMAE) that ranges from 5% to 11%.- This work was financed by the project "GreenShoes 4.0 Calcado, Marroquinaria e Tecnologias Avancadas de Materiais, Equipamentos e Software" (N. POCI-01-0247-FEDER-046082), supported by COMPETE 2020, under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)