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Discrete event systems (DESs) are systems which involve quantities that take a discrete set of values, called states, and which evolve according to the occurrence of certain discrete qualitative changes, called events. Examples of DESs include many man-made systems such as computer and communication networks, robotics and manufacturing systems, computer programs, and automated trac systems. Supervisory control and failure diagnosis are two important problems in the study of DESs. This dissertation presents a temporal logic approach to the control and failure diagnosis of DESs. For the control of DESs, full branching time temporal logic-CTL* is used to express control specifications. Control problem of DES in the temporal logic setting is formulated; and the controllability of DES is defined. By encoding the system with a CTL formula, the control problem of CTL* is reduced to the decision problem of CTL*. It is further shown that the control problem of CTL* (resp., CTL{computation tree logic) is complete for deterministic double (resp., single) exponential time. A sound and complete supervisor synthesis algorithm for the control of CTL* is provided. Special cases of the control of computation tree logic (CTL) and linear-time temporal logic (LTL) are also studied; and for which algorithms of better complexity are provided. For the failure diagnosis of DESs, LTL is used to express fault specifications. Failure diagnosis problem of DES in the temporal logic setting is formulated; and the diagnosability of DES is defined. The problem of testing the diagnosability is reduced to that of model checking. An algorithm for the test of diagnosability and the synthesis of a diagnoser is obtained. The algorithm has a polynomial complexity in the number of system states and the number of fault specifications. For the diagnosis of repeated failures in DESs, different notions of repeated failure diagnosability, K-diagnosability, [1,K]-diagnosability, and [1,1]-diagnosability, are introduced. Polynomial algorithms for checking these various notions of repeated failure diagnosability are given, and a procedure of polynomial complexity for the on-line diagnosis of repeated failures is also presented.

The increasingly popular cloud-computing paradigm provides on-demand access to computing and storage with the appearance of unlimited resources. Users are given access to a variety of data and software utilities to manage their work. Users rent virtual resources and pay for only what they use. In spite of the many benefits that cloud computing promises, the lack of dependability in shared virtualized infrastructures is a major obstacle for its wider adoption, especially for mission-critical applications. Virtualization and multi-tenancy increase system complexity and dynamicity. They introduce new sources of failure degrading the dependability of cloud computing systems. To assure cloud dependability, in my dissertation research, I develop autonomic failure identification and diagnosis techniques that are crucial for understanding emergent, cloud-wide phenomena and self-managing resource burdens for cloud availability and productivity enhancement. We study the runtime cloud performance data collected from a cloud test-bed and by using traces from production cloud systems. We define cloud signatures including those metrics that are most relevant to failure instances. We exploit profiled cloud performance data in both time and frequency domain to identify anomalous cloud behaviors and leverage cloud metric subspace analysis to automate the diagnosis of observed failures. We implement a prototype of the anomaly identification system and conduct the experiments in an on-campus cloud computing test-bed and by using the Google datacenter traces. Our experimental results show that our proposed anomaly detection mechanism can achieve 93% detection sensitivity while keeping the false positive rate as low as 6.1% and outperform other tested anomaly detection schemes. In addition, the anomaly detector adapts itself by recursively learning from these newly verified detection results to refine future detection.

O objetivo deste estudo foi avaliar o uso da cintilografia de perfusão miocárdica planar de repouso para o diagnóstico de insuficiência coronariana aguda em pacientes com dor torácica e eletrocardiograma sem alterações sugestivas de isquemia. Foram estudados 71 pacientes com idades entre 34 e 87 (média 58, desvio-padrão 12) anos; 44 (62%) eram do sexo masculino e 27 (38%) do feminino. Os pacientes com dor torácica foram avaliados na unidade de emergência com anamnese, exame físico e eletrocardroqrarna de 12 derivações. Pacientes com dor torácica de duração superior a 20 minutos, em vigência da dor ou sem dor, mas que sofreram dor torácica até seis horas anies do atendimento e com eletrocardioqrarna sem alterações sugestivas de isquemia miocárdica, foram submetidos à cintilografia planar de perfusão miocárdica de repouso quando solicitada pelo médico assistente. O tempo médio entre o início da dor toràcica e a Injeção do radiotraçador foi de três horas e seis minutos. Treze pacientes apresentavam dor torácica no momento da injeção. Foram colhidas amostras sanguineas para dosagens de atividade da creatinoquinase-MB (CK-MB), CK-MB massa, troponina I e mioglobina, seis horas após o início da dor torácica. O diagnóstico de insuficiência coronariana aguda foi feito em pacientes com angina de repouso, infarto agudo do miocárdio, pacientes submetidos à revascularização miocárdica, presença de lesões coronarianas significativas na angiografia (>- 70% estenose em artérias coronárias ou seus ramos ou .- 50% em tronco de artéria coronária esquerda) realizada durante a inernação e morte cardíaca foram considerados eventos cardíacos maiores. As cintilografias com defeito de captação foram consideradas sugestivas de isquemia miocárdica e foram comparadas com o diagnóstico clínico e com a ocorrência de eventos cardíacos maiores até três meses após a alta.Pacientes sem insuficiência coronariana aguda, dispensados da unidade de emergência, foram encaminhados para realização ambulatorial de cintilografia de perfusão miocárdica tomográfica de esforço ou com dipiridamol. Vinte e um pacientes (29,6%) tiveram o diagnóstico de insuficiência coronariana aguda e em 15 (21,1%) ocorreram eventos cardíacos maiores (oito com infarto agudo do miocárdio e sete foram submetidos à revascularização miocárdica). A cintilografia planar de perfusão miocárdica demonstrou defeitos de captação em 21 (29,6%) pacientes, dos quais 16 (76,2%) tiveram o diagnóstico de insuficiência coronariana aguda, 12 (80%) apresentaram eventos cardíacos maiores e 7 (87,5%) infarto agudo do miocárdio. O valor preditivo negativo da cintilografia planar de perfusão miocárdica foi de 90% para o diagnóstico de insuficiência coronariana aguda e de 94% para detecção de eventos cardíacos maiores. Portanto a cintilografia planar de perfusão miocárdica foi eficaz para o diagnóstico de insuficiência coronariana aguda em pacientes com dor torácica e electrocardiograma sem alterações sugestivas de isquemia / The objective of the study was to evaluate the usefulness of rest scintigraphic planar myocardial perfusion imaging in patients with acute chest pain suspected of myocardial ischemia and nondiagnostic ECG in the diagnosis of acute coronary syndromes and to predict adverse cardiac outcomes. Patients within 6 hours of chest pain onset and nondiagnostic ECGs underwent planar myocardial perfusion imaging with Technetium-99m sestamibi and measurements of serum creatine kinase-MB, creatine kinase-MB mass. troponin and myoglobin 6 hours after the onset of symptoms. Studies showing perfusion defects were considered suggestive of acute coronary syndromes and were compared to the diagnosis made by the attending cardiologist. Clinical diagnosis of acute coronary syndromes was made In patients with rest angina admitted to the hospital, acute myocardial Infarction, myocardial revascularization, demonstration of significant coronary artery disease on angiography or cardiac death. Acute myocardial revascularization and cardiac death were considered major cardiac events. Patients discharged without acute coronary syndromes were scheduled for outpatient stress myocardial perfusion imaging. A total of 71 patients underwent planar myocardial perfusion imaging. The mean age was 58 +- 12 years, 44 (62%) were male and 27 (38%) female. The mean time between chest pain onset and radiotracer injection was 3 hours and 6 minutes, thirteen patients had chest pain at the moment of iniection. Twenty-one (29,6%) patients had acute coronary syndromes, 15 (21,1 %) had major cardiac events (8 myocardial infarction and 7 underwent myocardial revascularization). Planar perfusion imaging demonstrated perfusion defects in 21 patients, 16 (76,2%) patients with acute coronary syndromes, 12 (80%) patients who had major cardiac events and in 7 (87,5%) patients with myocardial infarction. The negative predictive value of planar perfusion image was 90% for diagnosis of acute coronary syndromes and 94% for detecting major cardiac events. In conclusion, early planar perfusion imaging allowed for a rapid and accurate risk stratification of emergency departments patients with possible myocardial ischemia and nondiagnostic ECGs

O objetivo deste estudo foi avaliar o uso da cintilografia de perfusão miocárdica planar de repouso para o diagnóstico de insuficiência coronariana aguda em pacientes com dor torácica e eletrocardiograma sem alterações sugestivas de isquemia. Foram estudados 71 pacientes com idades entre 34 e 87 (média 58, desvio-padrão 12) anos; 44 (62%) eram do sexo masculino e 27 (38%) do feminino. Os pacientes com dor torácica foram avaliados na unidade de emergência com anamnese, exame físico e eletrocardroqrarna de 12 derivações. Pacientes com dor torácica de duração superior a 20 minutos, em vigência da dor ou sem dor, mas que sofreram dor torácica até seis horas anies do atendimento e com eletrocardioqrarna sem alterações sugestivas de isquemia miocárdica, foram submetidos à cintilografia planar de perfusão miocárdica de repouso quando solicitada pelo médico assistente. O tempo médio entre o início da dor toràcica e a Injeção do radiotraçador foi de três horas e seis minutos. Treze pacientes apresentavam dor torácica no momento da injeção. Foram colhidas amostras sanguineas para dosagens de atividade da creatinoquinase-MB (CK-MB), CK-MB massa, troponina I e mioglobina, seis horas após o início da dor torácica. O diagnóstico de insuficiência coronariana aguda foi feito em pacientes com angina de repouso, infarto agudo do miocárdio, pacientes submetidos à revascularização miocárdica, presença de lesões coronarianas significativas na angiografia (>- 70% estenose em artérias coronárias ou seus ramos ou .- 50% em tronco de artéria coronária esquerda) realizada durante a inernação e morte cardíaca foram considerados eventos cardíacos maiores. As cintilografias com defeito de captação foram consideradas sugestivas de isquemia miocárdica e foram comparadas com o diagnóstico clínico e com a ocorrência de eventos cardíacos maiores até três meses após a alta.Pacientes sem insuficiência coronariana aguda, dispensados da unidade de emergência, foram encaminhados para realização ambulatorial de cintilografia de perfusão miocárdica tomográfica de esforço ou com dipiridamol. Vinte e um pacientes (29,6%) tiveram o diagnóstico de insuficiência coronariana aguda e em 15 (21,1%) ocorreram eventos cardíacos maiores (oito com infarto agudo do miocárdio e sete foram submetidos à revascularização miocárdica). A cintilografia planar de perfusão miocárdica demonstrou defeitos de captação em 21 (29,6%) pacientes, dos quais 16 (76,2%) tiveram o diagnóstico de insuficiência coronariana aguda, 12 (80%) apresentaram eventos cardíacos maiores e 7 (87,5%) infarto agudo do miocárdio. O valor preditivo negativo da cintilografia planar de perfusão miocárdica foi de 90% para o diagnóstico de insuficiência coronariana aguda e de 94% para detecção de eventos cardíacos maiores. Portanto a cintilografia planar de perfusão miocárdica foi eficaz para o diagnóstico de insuficiência coronariana aguda em pacientes com dor torácica e electrocardiograma sem alterações sugestivas de isquemia / The objective of the study was to evaluate the usefulness of rest scintigraphic planar myocardial perfusion imaging in patients with acute chest pain suspected of myocardial ischemia and nondiagnostic ECG in the diagnosis of acute coronary syndromes and to predict adverse cardiac outcomes. Patients within 6 hours of chest pain onset and nondiagnostic ECGs underwent planar myocardial perfusion imaging with Technetium-99m sestamibi and measurements of serum creatine kinase-MB, creatine kinase-MB mass. troponin and myoglobin 6 hours after the onset of symptoms. Studies showing perfusion defects were considered suggestive of acute coronary syndromes and were compared to the diagnosis made by the attending cardiologist. Clinical diagnosis of acute coronary syndromes was made In patients with rest angina admitted to the hospital, acute myocardial Infarction, myocardial revascularization, demonstration of significant coronary artery disease on angiography or cardiac death. Acute myocardial revascularization and cardiac death were considered major cardiac events. Patients discharged without acute coronary syndromes were scheduled for outpatient stress myocardial perfusion imaging. A total of 71 patients underwent planar myocardial perfusion imaging. The mean age was 58 +- 12 years, 44 (62%) were male and 27 (38%) female. The mean time between chest pain onset and radiotracer injection was 3 hours and 6 minutes, thirteen patients had chest pain at the moment of iniection. Twenty-one (29,6%) patients had acute coronary syndromes, 15 (21,1 %) had major cardiac events (8 myocardial infarction and 7 underwent myocardial revascularization). Planar perfusion imaging demonstrated perfusion defects in 21 patients, 16 (76,2%) patients with acute coronary syndromes, 12 (80%) patients who had major cardiac events and in 7 (87,5%) patients with myocardial infarction. The negative predictive value of planar perfusion image was 90% for diagnosis of acute coronary syndromes and 94% for detecting major cardiac events. In conclusion, early planar perfusion imaging allowed for a rapid and accurate risk stratification of emergency departments patients with possible myocardial ischemia and nondiagnostic ECGs

Wang, Jing. / Thesis Ph.D. Chinese University of Hong Kong 2014. / Includes bibliographical references (leaves 123-151). / Abstracts also in Chinese. / Title from PDF title page (viewed on 19, September, 2016).

published_or_final_version / Community Medicine / Master / Master of Public Health

The size and complexity of cloud computing systems makes runtime errors inevitable. These errors could be caused by the system having insufficient resources or an unexpected failure in the system. In order to be able to provide highly available cloud computing services it is necessary to auto- mate the resource provisioning and failure diagnosing processes as much as possible. Log files are often a good source of information about the current status of the system. In this thesis methods for diagnosing failures and predicting system workload using log file analysis are presented and the performance of different machine learning algorithms using our proposed methods are compared. Our experimental results show that classification tree and random forest algorithms are both suitable for diagnosing failures and that Support Vector Regression outperforms linear regression and regression trees when predicting disk availability and memory usage. However, we conclude that predicting CPU utilization requires further studies.

Pinpointing the source of a software failure based on log files can be a time consuming process. Automated log analysis tools are meant to streamline such processes, and can be used for tasks like failure diagnosis. This thesis evaluates three supervised models for failure diagnosis of clustered log data. The goal of the thesis is to compare the performance of the models on industry data, as a way to investigate whether the chosen ML techniques are suitable in the context of automated log analysis. A Random Forest, an SVM and an MLP are generated from a dataset of 194 failed executions of tests on microservices, that each resulted in a large collection of logs. The models are tuned with random search and compared in terms of precision, recall, F1-score, hold-out accuracy and 5-fold cross-validation accuracy. The hold-out accuracy is calculated as a mean from 50 hold-out data splits, and the cross-validation accuracy is computed separately from a single set of folds. The results show that the Random Forest scores highest in terms of mean hold-out accuracy (90%), compared to the SVM (86%) and the Neural Network (85%). The mean cross-validation accuracy is the highest for the SVM (95%), closely followed by the Random Forest (94%), and lastly the Neural Network (85%). The precision, recall and F1-score are stable and consistent with the hold-out results, although the precision results are slightly higher than the other two measures. According to this evaluation, the Random Forest has the overall highest performance on the dataset when considering the hold-out- and cross-validation accuracies, and also the fact that it has the lowest complexity and thus the shortest training time, compared to the other considered solutions. All in all, the results of the thesis demonstrate that supervised learning is a promising approach to automatize log analysis. / Att identifiera orsaken till en misslyckad mjukvaruexekvering utifrån logg-filer kan vara en tidskrävande process. Verktyg för automatiserad logg-analysis är tänkta att effektivisera sådana processer, och kan bland annat användas för feldiagnos. Denna avhandling tillhandahåller tre övervakade modeller för feldiagnos av klustrad logg-data. Målet med avhandlingen är att jämföra modellernas prestanda på data från näringslivet, i syfte att utforska huruvida de valda maskininlärningsteknikerna är lämpliga för automatiserad logg-analys. En Random Forest, en SVM och en MLP genereras utifrån ett dataset bestående av 194 misslyckade exekveringar av tester på mikrotjänster, där varje exekvering resulterade i en stor uppsättning loggar. Modellerna finjusteras med hjälp av slumpmässig sökning och jämförs via precision, träffsäkerhet, F-poäng, noggrannhet och 5-faldig korsvalidering. Noggrannheten beräknas som medelvärdet av 50 datauppdelningar, och korsvalideringen tas fram separat från en enstaka uppsättning vikningar. Resultaten visar att Random Forest har högst medelvärde i noggrannhet (90%), jämfört med SVM (86%) och Neurala Nätverket (85%). Medelvärdet i korsvalidering är högst för SVM (95%), tätt följt av Random Forest (94%), och till sist, Neurala Nätverket (85%). Precisionen, träffsäkerheten och F-poängen är stabila och i enlighet med noggrannheten, även om precisionen är något högre än de andra två måtten. Enligt den här analysen har Random Forest överlag högst prestanda på datasetet, med hänsyn till noggrannheten och korsvalideringen, samt faktumet att denna modell har lägst komplexitet och därmed kortast träningstid, jämfört med de andra undersökta lösningarna. Sammantaget visar resultaten från denna avhandling att övervakad inlärning är ett lovande tillvägagångssätt för att automatisera logg-analys.

Falhas são eventos que não podem, pela sua própria natureza, serem totalmente eliminados num sistema de manufatura real. No entanto, a maioria das pesquisas e publicações técnicas nesta área consideram somente a descrição e otimização dos processos “normais” ou processos isolados de tratamento de falhas. Assim este trabalho é uma contribuição no desenvolvimento de uma metodologia de modelagem e análise que considera a detecção e o tratamento de falhas junto com os processos “normais”. A hipótese é que uma adequada abordagem de modelagem e análise de sistemas de manufatura considerando todas estas características é fundamental para melhorar a flexibilidade e autonomia do sistema. Tais sistemas podem ser abordados segundo a perspectiva de sistemas a eventos discretos (DEDS) e dentre as técnicas existentes de representação destes sistemas, destaca-se o potencial das rede de Petri (PN) como uma técnica uniforme de modelagem e análise, a qual permite o estudo e caracterização de diferentes propriedades de um sistema através de um mesmo modelo. Assim, este trabalho introduz uma metodologia, baseada no conceito de redes de Petri, que além da modelagem e a análise dos processos “normais” (de acordo com as especificações funcionais), permite a detecção e tratamento de falhas em sistemas de manufatura de uma forma hierárquica e modularizada utilizando supervisores distribuídos nos equipamentos do chão de fábrica. Esta metodologia considera a integração de três módulos referentes aos processos “normais”, aos processos de detecção de falhas e, aos processos de tratamento de falhas. Através das abordagens “top-down” e “bottom-up” a modelagem de um sistema é desenvolvida em níveis hierárquicos. Estudos de caso de sistemas com estas característica são considerados. Nos modelos desenvolvidos são realizados um estudo analítico e simulações para validar a metodologia proposta. / In a real manufacturing system, failures are events that should be considered. However in this area, most researches consider only the description and optimization of normal processes. This research is a contribution to develop a methodology for modeling and analyzing manufacturing system including normal processes, failure detection, and failure treatment. An approach considering those processes is basic for improving flexibility and autonomy of the systems. These systems can be observed from a point of view of discrete event dynamics systems (DEDS). From this point of view, Petri nets are a powerful tool for modeling and analyzing different characteristics of a system using the same model. In this research a methodology based on Petri nets considering normal process, detection, and treatment of failures in manufacturing systems is introduced. This methodology considers a hierarchical and modular structure. The modular characteristic permits integration of three types of processes: normal, failure detection, and failure treatment processes. The hierarchical characteristic permits to model a system by hierarchical levels (such as factory, manufacturing cell, and equipment) based on top-down and bottom-up approaches, and using distributed supervisors inside of machines on the workshop level. Case studies with these characteristics are considered. On the developed models, analytical and simulation analyses are executed to validate the proposed methodology.