Reliability analysis of multiplex control system of subsea blowout preventer based on stochastic Petri net

Višestruki (MUX − multiplex) upravljački sustav za sprečavanje podmorske erupcije bušotine (BOP − blowout preventer) ima bitnu ulogu u stvaranju sigurnih radnih uvjeta kod podmorskih aktivnosti bušenja. U skladu s radnim stanjima i kritičnim načinima kvara višestrukog upravljačkog sustava, u radu se predstavlja njegov stohastički model Petri mreža (SPN), uzimajući u obzir nesavršenu sposobnost otkrivanja greške. Predlaže se metoda numeričke analize temeljena na istolikom (izomorfnom) trajnom Markovljevom lancu modela. Istraživani su i uspoređivani pokazatelji pouzdanosti, odnosno pouzdanost, raspoloživost i MTTF višestrukog (MUX) upravljačkog sustava i probnog hidrauličkog upravljačkog sustava. Uz to, istraživani su učinci faktora prikrivenosti grešaka na vjerojatnosti stanja i dostupnost MUX upravljačkog sustava, a izvršena je i analiza nesigurnosti brzina paljenja u odnosu na MTTF.The multiplex (MUX) control system of subsea blowout preventer (BOP) plays a vital role in providing safe working conditions for the subsea drilling activities. According to the working states and critical failure modes of the MUX control system, this paper presents its stochastic Petri nets (SPN) model, taking into account the imperfect fault detection capacity. The numerical analysis method is proposed based on the isomorphic continuous-time Markov chain of the model. The reliability indexes, namely reliability, availability and MTTF of the MUX control system and pilot hydraulic control system are obtained and compared. In addition, the effects of fault coverage factor on state probabilities and availability of the MUX control system are researched and the uncertainty analysis of the firing rates related to MTTF is also performed

Quality of service modeling and analysis for carrier ethernet

Today, Ethernet is moving into the mainstream evolving into a carrier grade technology. Termed as Carrier Ethernet it is expected to overcome most of the\ud shortcomings of native Ethernet. It is envisioned to carry services end-to-end serving corporate data networking and broadband access demands as well as backhauling wireless traffic. As the penetration of Ethernet increases, the offered Quality of Service (QoS) will become increasingly important and a distinguishing factor between different service providers. The challenge is to meet the QoS requirements of end applications such as response times, throughput, delay and jitter by managing the network resources at hand. Since Ethernet was not designed to operate in large public networks it does not possess functionalities to address this issue. In this thesis we propose and analyze mechanisms which improve the QoS performance of Ethernet enabling it to meet the demands of the current and next generation services and applications.\u

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI

WICC 2017 : XIX Workshop de Investigadores en Ciencias de la Computación

Actas del XIX Workshop de Investigadores en Ciencias de la Computación (WICC 2017), realizado en el Instituto Tecnológico de Buenos Aires (ITBA), el 27 y 28 de abril de 2017.Red de Universidades con Carreras en Informática (RedUNCI