RULES BASED MODELING OF DISCRETE EVENT SYSTEMS WITH FAULTS AND THEIR DIAGNOSIS

Failure diagnosis in large and complex systems is a critical task. In the realm of discrete event systems, Sampath et al. proposed a language based failure diagnosis approach. They introduced the diagnosability for discrete event systems and gave a method for testing the diagnosability by first constructing a diagnoser for the system. The complexity of this method of testing diagnosability is exponential in the number of states of the system and doubly exponential in the number of failure types. In this thesis, we give an algorithm for testing diagnosability that does not construct a diagnoser for the system, and its complexity is of 4th order in the number of states of the system and linear in the number of the failure types. In this dissertation we also study diagnosis of discrete event systems (DESs) modeled in the rule-based modeling formalism introduced in [12] to model failure-prone systems. The results have been represented in [43]. An attractive feature of rule-based model is it\u27s compactness (size is polynomial in number of signals). A motivation for the work presented is to develop failure diagnosis techniques that are able to exploit this compactness. In this regard, we develop symbolic techniques for testing diagnosability and computing a diagnoser. Diagnosability test is shown to be an instance of 1st order temporal logic model-checking. An on-line algorithm for diagnosersynthesis is obtained by using predicates and predicate transformers. We demonstrate our approach by applying it to modeling and diagnosis of a part of the assembly-line. When the system is found to be not diagnosable, we use sensor refinement and sensor augmentation to make the system diagnosable. In this dissertation, a controller is also extracted from the maximally permissive supervisor for the purpose of implementing the control by selecting, when possible, only one controllable event from among the ones allowed by the supervisor for the assembly line in automaton models

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

New Directions in Model Checking Dynamic Epistemic Logic

Dynamic Epistemic Logic (DEL) can model complex information scenarios in a way that appeals to logicians. However, its existing implementations are based on explicit model checking which can only deal with small models, so we do not know how DEL performs for larger and real-world problems. For temporal logics, in contrast, symbolic model checking has been developed and successfully applied, for example in protocol and hardware verification. Symbolic model checkers for temporal logics are very efficient and can deal with very large models. In this thesis we build a bridge: new faithful representations of DEL models as so-called knowledge and belief structures that allow for symbolic model checking. For complex epistemic and factual change we introduce transformers, a symbolic replacement for action models. Besides a detailed explanation of the theory, we present SMCDEL: a Haskell implementation of symbolic model checking for DEL using Binary Decision Diagrams. Our new methods can solve well-known benchmark problems in epistemic scenarios much faster than existing methods for DEL. We also compare its performance to to existing model checkers for temporal logics and show that DEL can compete with established frameworks. We zoom in on two specific variants of DEL for concrete applications. First, we introduce Public Inspection Logic, a new framework for the knowledge of variables and its dynamics. Second, we study the dynamic gossip problem and how it can be analyzed with epistemic logic. We show that existing gossip protocols can be improved, but that no perfect strengthening of "Learn New Secrets" exists

Understanding user behavior aspects on emergency mobile applications during emergency communications using NLP and text mining techniques

Abstract. The use of mobile devices has been skyrocketing in our society. Users can access and share any type of information in a timely manner through these devices using different social media applications. This enabled users to increase their awareness of ongoing events such as election campaigns, sports updates, movie releases, disaster occurrences, and studies. The attractiveness, affordability, and two-way communication capabilities empowered these mobile devices that support various social media platforms to be central to emergency communication as well. This makes a mobile-based emergency application an attractive communication tool during emergencies. The emergence of mobile-based emergency communication has intrigued us to learn about the user behavior related to the usage of these applications. Our study was mainly conducted on emergency apps in Nordic countries such as Finland, Sweden, and Norway. To understand the user objects regarding the usage of emergency mobile applications we leveraged various Natural Language Processing and Text Mining techniques. VADER sentiment tool was used to predict and track users’ review polarity of a particular application over time. Lately, to identify factors that affect users’ sentiments, we employed topic modeling techniques such as the Latent Dirichlet Allocation (LDA) model. This model identifies various themes discussed in the user reviews and the result of each theme will be represented by the weighted sum of words in the corpus. Even though LDA succeeds in highlighting the user-related factors, it fails to identify the aspects of the user, and the topic definition from the LDA model is vague. Hence we leveraged Aspect Based Sentiment Analysis (ABSA) methods to extract the user aspects from the user reviews. To perform this task we consider fine-tuning DeBERTa (a variant of the BERT model). BERT is a Bidirectional Encoder Representation of transformer architecture which allows the model to learn the context in the text. Following this, we performed a sentence pair sentiment classification task using different variants of BERT. Later, we dwell on different sentiments to highlight the factors and the categories that impact user behavior most by leveraging the Empath categorization technique. Finally, we construct a word association by considering different Ontological vocabularies related to mobile applications and emergency response and management systems. The insights from the study can be used to identify the user aspect terms, predict the sentiment of the aspect term in the review provided, and find how the aspect term impacts the user perspective on the usage of mobile emergency applications

Constructive formal methods and protocol standardization

This research is part of the NWO project "Improving the Quality of Protocol Standards". In this project we have cooperated with industrial standardization committees that are developing protocol standards. Thus we have contributed to these international standards, and we have generated relevant research questions in the field of formal methods. The first part of this thesis is related to the ISO/IEEE 1073.2 standard, which addresses medical device communication. The protocols in this standard were developed from a couple of MSC scenarios that describe typical intended behavior. Upon synthesizing a protocol from such scenarios, interference between these scenarios may be introduced, which leads to undesired behaviors. This is called the realizability problem. To address the realizability problem, we have introduced a formal framework that is based on partial orders. In this way the problem that causes the interference can be clearly pointed out. We have provided a complete characterization of realizability criteria that can be used to determine whether interference problems are to be expected. Moreover, we have provided a new constructive approach to solve the undesired interference in practical situations. These techniques have been used to improve the protocol standard under consideration. The second part of this thesis is related to the IEEE 1394.1-2004 standard, which addresses High Performance Serial Bus Bridges. This is an extension of the IEEE 1394-1995 standard, also known as FireWire. The development of the distributed spanning tree algorithm turned out to be a serious problem. To address this problem, we have first developed and proposed a much simpler algorithm. We have also studied the algorithm proposed by the developers of the standard, namely by formally reconstructing a version of it, starting from the specification. Such a constructive approach to verification and analysis uses mathematical techniques, or formal methods, to reveal the essential mechanisms that play a role in the algorithm. We have shown the need for different levels of abstraction, and we have illustrated that the algorithm is in fact distributed at two levels. These techniques are usually applied manually, but we have also developed an approach to automate parts of it using state-of-the-art theorem provers

Biomolecular Event Extraction using Natural Language Processing

Biomedical research and discoveries are communicated through scholarly publications and this literature is voluminous, rich in scientific text and growing exponentially by the day. Biomedical journals publish nearly three thousand research articles daily, making literature search a challenging proposition for researchers. Biomolecular events involve genes, proteins, metabolites, and enzymes that provide invaluable insights into biological processes and explain the physiological functional mechanisms. Text mining (TM) or extraction of such events automatically from big data is the only quick and viable solution to gather any useful information. Such events extracted from biological literature have a broad range of applications like database curation, ontology construction, semantic web search and interactive systems. However, automatic extraction has its challenges on account of ambiguity and the diverse nature of natural language and associated linguistic occurrences like speculations, negations etc., which commonly exist in biomedical texts and lead to erroneous elucidation. In the last decade, many strategies have been proposed in this field, using different paradigms like Biomedical natural language processing (BioNLP), machine learning and deep learning. Also, new parallel computing architectures like graphical processing units (GPU) have emerged as possible candidates to accelerate the event extraction pipeline. This paper reviews and provides a summarization of the key approaches in complex biomolecular big data event extraction tasks and recommends a balanced architecture in terms of accuracy, speed, computational cost, and memory usage towards developing a robust GPU-accelerated BioNLP system

Investigation of a GaN-Based Power Supply Topology Utilizing Solid State Transformer for Low Power Applications

Gallium nitride (GaN) power devices exhibit a much lower gate capacitance for a similar on-resistance than its silicon counterparts, making it highly desirable for high-frequency operation in switching converters, which leads to their significant benefits on power density, cost, and system volume. High-density switching converters are being realized with GaN power devices due to their high switching speeds that reduce the size of energy-storage circuit components. The purpose of this dissertation research is to investigate a new isolated GaN AC/DC switching converter based on solid-state transformer configuration with a totem-pole power factor corrector (PFC) front-end, a half-bridge series-resonant converter (SRC) for power conversion, and a current-doubler rectifier (CDR) at its output. A new equivalent circuit model for the converter is constructed consisting of a loss-free resistor model for the PFC rectifier with first harmonic approximation model for the SRC and the CDR. Then, state-space analysis is performed to derive the converter transfer function in order to design the controllers to yield sufficient phase margins. The converter offers the advantages of voltage regulation feature of the solid-state transformer, low harmonics and close-to-unity power factor of the PFC rectifier, soft-switching of the half-bridge SRC, reduced size of the high-frequency transformer, and smaller leakage inductance of the CDR which is used for low-voltage high-current applications as the CDR draws half of the load current in the transformer secondary side yielding less copper losses. A high-frequency nanocrystalline toroid transformer, based on a modified equation to determine its leakage inductance, is designed and fabricated to satisfy the performance specifications of the converter. A meticulously planned gate driving strategy together with a Kelvin-source return circuitry is used to mitigate Miller effects, minimize gate ringing, and minimize the parasitics of the pull-down and pull-up loops of the converter. A new programming method that combines MATLAB Simulink embedded coder with code composer studio for the TMS320F28335 digital signal processor (DSP) controller is developed and demonstrated. Finally, the GaN-based AC/DC converter is experimentally verified for a 120Vac to 48Vdc/60Vdc conversion operating at 100 kHz for various loadings