A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science / This research report investigates the application of Charron-Bost's measure of currency m to Milner's Calculus of Communicating Systems (CCS). The aim of this is twofold: first to evaluate the measure m in terms of criteria gathered from the literature: and second to determine the feasiblllty of measuring concurrency in CCS and hence provide a new tool for understanding concurrency using CCS. The approach taken is to identify the differences hetween the message-passing formalism in which the measure m is defined, and CCS and to modify this formalism to-enable the mapping of CCS agents to it. A software tool, the Concurrency Measurement Tool, is developed to permit experimentation with chosen CCS agents. These experiments show that the measure m, although intuitively appealing, is defined by an algebraic expression that is ill-behaved. A new measure is defined and it is shown that it matches the evaluation criteria better than m, although it is still not ideal. This work demonstrates that it is feasible to measure concurrency in CCS and that a methodology has been developed for evaluating concurrency measures. / Andrew Chakane 2018

The focus of this thesis is formulation and development of a mathematical framework for the solution of the contingency constrained optimal power flow (OPF) based on sequential quadratic programming. The contingency constrained optimal power flow minimizes the total cost of a base case operating state as well as the expected cost of recovery from contingencies such as line or generation outages. The sequential quadratic programming (SCP) OPF formulation has been expanded in order to recognize contingency conditions and the problem is solved as a single entity by an efficient interior point method. The new formulation takes into account the system corrective capabilities in response to contingencies introduced through ramp-rate constraints. Contingency constrained OPF is a very challenging problem, because each contingency considered introduces a new problem as large as the base case problem. By proper system reduction and benefits of constraint relaxation (active set) methods, in which transmission constraints are not introduced until they are violated, the size of the system can be reduced significantly Therefore, restricting our attention to the active set constraint set makes this large problem significantly smaller and computationally feasible.

Orientadores: Stanley Robson de Medeiros Oliveira, Ana Maria Heuminski de Avila / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola / Made available in DSpace on 2018-08-22T14:12:46Z (GMT). No. of bitstreams: 1 Dourado_CamiladaSilva_M.pdf: 21521693 bytes, checksum: d0c749dfa3c77ac47a96acd234b8d3c3 (MD5) Previous issue date: 2013 / Resumo: O conhecimento das condições climáticas, identificando regiões com maiores riscos de ocorrências de eventos extremos, que possam impactar os diversos setores socioeconômicos e ambientais, tornou-se um grande desafio. No Brasil as maiores ocorrências de eventos extremos estão relacionadas aos fenômenos hidrológicos. Em particular, o estado da Bahia apresenta alta variabilidade temporal e espacial no clima, desde áreas consideradas áridas ou com risco de aridização (ao Norte) a regiões com clima úmido na faixa litorânea. O estado tem sido alvo nesses últimos anos de diferentes eventos extremos de chuva, com enchentes em algumas áreas e secas severas em outras. Neste contexto, o objetivo deste trabalho foi utilizar técnicas de mineração de dados para analisar a frequência das ocorrências dos eventos extremos de precipitação durante o período de 1981 a 2010 no estado da Bahia, com o propósito de subsidiar a tomada de decisão referente a ações preventivas e mitigadoras dos impactos socioeconômico e ambientais. Para isto, foram utilizados dados climáticos de precipitação fornecidos pelo Sistema de Informações Hidrológicas da Agência Nacional de Águas. Aplicando-se a tarefa de agrupamento (clusterização), por meio do algoritmo k-means, as séries históricas de dados climáticos foram agrupadas em cinco zonas pluviometricamente homogêneas. Posteriormente, foram realizadas análises em diferentes escalas temporais (anual, mensal e diária) identificando através da Técnica dos Quantis limiares superiores e inferiores de intensidade de chuva em cada região homogênea, para cada escala temporal. Na escala mensal, foram identificados padrões sequenciais das ocorrências dos eventos extremos positivos e negativos ao longo dos trinta anos. Os resultados reforçam a potencialidade da técnica de mineração de dados em agrupar zonas homogêneas por similaridade pluvial, com o uso do algoritmo k-means. Revelam ainda, para todas as escalas temporais utilizadas, uma alta variabilidade pluviométrica. Os anos registrados com maior ocorrência de eventos extremos negativos estão na década de 90 e os anos registrados com mais eventos extremos positivos na década de 2000 / Abstract: The knowledge of climate conditions, identifying areas with the greatest risk of occurrence of extreme events, that may impact the various socioeconomic and environmental sectors, has become a major challenge. In Brazil the largest occurrences of extreme events are related to hydrological phenomena. In particular, the state of Bahia presents a high temporal and spatial variability of climate, from areas considered arid or with risk to become arid - (in the North) to regions with humid along the coast. The state has been targeted of different extreme rainfall events recently, with floods in some areas and severe droughts in others. In this context, the aim of this study was to use data mining techniques to analyze the frequency of occurrences of extreme precipitation events during the period from 1981 to 2010 in the state of Bahia, in order to support decision making regarding the preventive and mitigative environmental and socioeconomic impacts. To accomplish that, it was used climate data of precipitation supplied by the Hydrological Information System of the National Water Agency. By applying the task of grouping (clustering) by means of the k-means algorithm, the time series of climate data were grouped into five homogeneous rainfall zones. Subsequently, analyzes were performed on different time scales (annually, monthly and daily) identifying by quantile methods the upper and lower thresholds of rainfall intensity in each homogeneous region, for each time scale. At the monthly scale, sequential patterns of occurrences of extreme positive and negative events were identified over the thirty years. The results reinforce the potential of the data mining technique to group homogeneous zones by similarity of rain, using the k-means algorithm. They also reveal, for all time scales used, high rainfall variability. The years with the highest recorded extreme negative events are in the 90's and those registered with more extreme positive events are in the 2000's / Mestrado / Planejamento e Desenvolvimento Rural Sustentável / Mestra em Engenharia Agrícola

Concurrency is generally considered to be difficult due to a lack of appropriate abstraction, rather than inherent complexity. Lock-based approaches to mutual exclusion are pervasive, despite the presence of models that are easier to understand, such as the message-passing model present in CSP (Communicating Sequential Processes). CSP provides a rich framework for building and reasoning about concurrent systems, but has historically required a change of programming language or paradigm in order to work with it. The Go programming language is a modern, imperative programming language that includes native support for processes and channels. The popularity of this language has grown and more and more people are being exposed to the fundamental ideas of CSP. There is a gap in the understanding of how a restrictive formal model can interact with and support the development of concurrent programs in a language such as Go. Through a series of case studies and analysis, we show how the CSP concurrency model can be used as the basis for the design of a concurrent system architecture without requiring the program to be written entirely as the composition of processes. It is also possible to use the CSP process algebra to build abstract models and use model-checking tools to verify properties of a concurrent system. These models can then be used to guide the decomposition of a system into a more fine-grained concurrent system. This thesis bridges the gap between the development of CSP-style concurrent software and the formal model of CSP. In particular, it shows how it is not necessary for a program or programming language to conform to rigid structure in order for CSP to be a useful tool for the development of reliable and easy to understand concurrent systems.

The Parameterised Model Checking Problem asks whether an implementation Impl(t) satisfies a specification Spec(t) for all instantiations of parameter t. In general, t can determine numerous entities: the number of processes used in a network, the type of data, the capacities of buffers, etc. The main theme of this thesis is automation of uniform verification of a subclass of PMCP with the parameter of the first kind, using techniques based on counter abstraction. Counter abstraction works by counting how many, rather than which, node processes are in a given state: for nodes with k local states, an abstract state (c(1), ..., c(k)) models a global state where c(i) processes are in the i-th state. We then use a threshold function z to cap the values of each counter. If for some i, counter c(i) reaches its threshold, z(i) , then this is interpreted as there being z(i) or more nodes in the i-th state. The addition of thresholds makes abstract models independent of the instantiation of the parameter. We adapt standard counter abstraction techniques to concurrent reactive systems modelled using the CSP process algebra. We demonstrate how to produce abstract models of systems that do not use node identifiers (i.e. where all nodes are indistinguishable). Every such abstraction is, by construction, refined by all instantiations of the implementation. If the abstract model satisfies the specification, then a positive answer to the particular uniform verification problem can be deduced. We show that by adding node identifiers we make the uniform verification problem undecidable. We demonstrate a sound abstraction method that extends standard counter abstraction techniques to systems that make full use of node identifiers (in specifications and implementations). However, on its own, the method is not enough to give the answer to verification problems for all parameter instantiations. This issue has led us to the development of a type reduction theory, which, for a given verification problem, establishes a function phi that maps all (sufficiently large) instantiations T of the parameter to some fixed type T and allows us to deduce that if Spec(T) is refined by phi(Impl(T)), then Spec(T) is refined by Impl(T). We can then combine this with our extended counter abstraction techniques and conclude that if the abstract model satisfies Spec(T), then the answer to the uniform verification problem is positive. We develop a symbolic operational semantics for CSP processes that satisfy certain normality requirements and we provide a set of translation rules that allow us to concretise symbolic transition graphs. The type reduction theory relies heavily on these results. One of the main advantages of our symbolic operational semantics and the type reduction theory is their generality, which makes them applicable in other settings and allows the theory to be combined with abstraction methods other than those used in this thesis. Finally, we present TomCAT, a tool that automates the construction of counter abstraction models and we demonstrate how our results apply in practice.

Model checking is an automatic formal verification technique for establishing correctness of systems. It has been widely used in industry for analysing and verifying complex safety-critical systems in application domains such as avionics, medicine and computer security, where manual testing is infeasible and even minor errors could have dire consequences. In our increasingly parallelised world, concurrency has become pivotal and seamlessly woven within programming paradigms, however, extremely challenging when it comes to modelling and establishing correctness of intended behaviour. Tools for model checking concurrent systems face severe limitations due to scalability problems arising from the need to examine all possible interleavings (schedules) of executions of parallel components. Moreover, concurrency poses additional challenges to model checking, giving rise to phenomena such as nondeterminism, deadlock, livelock, etc. In this thesis we focus on adapting and developing novel model-checking techniques for concurrent systems in the setting of the process algebra CSP and its primary model checker FDR. CSP allows for a compact modelling and precise analysis of event-based concurrency, grounded on synchronous message passing as a fundamental mechanism of inter-component communication. In particular, we investigate techniques based on symbolic model checking, static analysis and abstraction, all of them exploiting the compositionality inherent in CSP and targeting to increase the scale of systems that can be tractably analysed. Firstly, we investigate symbolic model-checking techniques based on Boolean satisfiability (SAT), which we adapt for the traces model of CSP. We tailor bounded model checking (BMC), that can be used for bug detection, and temporal k-induction, which aims at establishing inductiveness of properties and is capable of both bug finding and establishing the correctness of systems. Secondly, we propose a static analysis framework for establishing livelock freedom of CSP processes, with lessons for other concurrent formalisms. As opposed to traditional exhaustive state-space exploration, our framework employs a system of rules on the syntax of a process to calculate a sound approximation of its fair/co-fair sets of events. The rules either safely classify a process as livelock-free or report inconclusiveness, thereby trading accuracy for speed. Finally, we develop a series of abstraction/refinement schemes for the traces, stable-failures and failures-divergences models of CSP and embed them into a fully automated and compositional CEGAR framework. For each of those techniques we present an implementation and an experimental evaluation on a set of CSP benchmarks.

<p>Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third generation of artificial neural networks that can enable low-power event-driven data analytics. We propose ANN-SNN conversion using “soft re-set” spiking neuron model, referred to as Residual Membrane Potential (RMP) spiking neuron, which retains the “resid- ual” membrane potential above threshold at the firing instants. In addition, we propose a time-based coding scheme, named Temporal-Switch-Coding (TSC), and a corresponding TSC spiking neuron model. Each input image pixel is presented using two spikes with opposite polarity and the timing between the two spiking instants is proportional to the pixel intensity. We demonstrate near loss-less ANN-SNN conversion using RMP neurons for VGG-16, ResNet-20, and ResNet-34 SNNs on challenging datasets including CIFAR-10, CIFAR-100, and ImageNet. With the help of TSC coding, it achieves 7-14.5× less inference latency, and 30-60× fewer addition operations and memory accesses per inference across datasets compared to the state of the art (SOTA) SNN models. In the second part of the thesis, we propose a new type of recurrent neural network (RNN) architecture, named Os- cillatory Fourier Neural Network (O-FNN). We demonstrate that O-FNN is mathematically equivalent to a simplified form of Discrete Fourier Transform applied onto periodical activa- tion. In particular, the computationally intensive back-propagation through time in training is eliminated, leading to faster training while achieving the SOTA inference accuracy in a diverse group of sequential tasks. For instance, applying the proposed model to sentiment analysis on IMDB review dataset reaches 89.4% test accuracy within 5 epochs, accompanied by over 35x reduction in the model size compared to Long Short-Term Memory (LSTM). The proposed novel RNN architecture is well poised for intelligent sequential processing in resource constrained hardware.</p>

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