984 resultados para dynamic monitoring
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In future power systems, in the smart grid and microgrids operation paradigms, consumers can be seen as an energy resource with decentralized and autonomous decisions in the energy management. It is expected that each consumer will manage not only the loads, but also small generation units, heating systems, storage systems, and electric vehicles. Each consumer can participate in different demand response events promoted by system operators or aggregation entities. This paper proposes an innovative method to manage the appliances on a house during a demand response event. The main contribution of this work is to include time constraints in resources management, and the context evaluation in order to ensure the required comfort levels. The dynamic resources management methodology allows a better resources’ management in a demand response event, mainly the ones of long duration, by changing the priorities of loads during the event. A case study with two scenarios is presented considering a demand response with 30 min duration, and another with 240 min (4 h). In both simulations, the demand response event proposes the power consumption reduction during the event. A total of 18 loads are used, including real and virtual ones, controlled by the presented house management system.
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In this paper, a damage-detection approach using the Mahalanobis distance with structural forced dynamic response data, in the form of transmissibility, is proposed. Transmissibility, as a damage-sensitive feature, varies in accordance with the damage level. Besides, Mahalanobis distance can distinguish the damaged structural state condition from the undamaged one by condensing the baseline data. For comparison reasons, the Mahalanobis distance results using transmissibility are compared with those using frequency response functions. The experiment results reveal quite a significant capacity for damage detection, and the comparison between the use of transmissibility and frequency response functions shows that, in both cases, the different damage scenarios could be well detected. Copyright (c) 2015 John Wiley & Sons, Ltd.
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Sandwich structures with soft cores are widely used in applications where a high bending stiffness is required without compromising the global weight of the structure, as well as in situations where good thermal and damping properties are important parameters to observe. As equivalent single layer approaches are not the more adequate to describe realistically the kinematics and the stresses distributions as well as the dynamic behaviour of this type of sandwiches, where shear deformations and the extensibility of the core can be very significant, layerwise models may provide better solutions. Additionally and in connection with this multilayer approach, the selection of different shear deformation theories according to the nature of the material that constitutes the core and the outer skins can predict more accurately the sandwich behaviour. In the present work the authors consider the use of different shear deformation theories to formulate different layerwise models, implemented through kriging-based finite elements. The viscoelastic material behaviour, associated to the sandwich core, is modelled using the complex approach and the dynamic problem is solved in the frequency domain. The outer elastic layers considered in this work may also be made from different nanocomposites. The performance of the models developed is illustrated through a set of test cases. (C) 2015 Elsevier Ltd. All rights reserved.
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We consider an optimal control problem with a deterministic finite horizon and state variable dynamics given by a Markov-switching jump–diffusion stochastic differential equation. Our main results extend the dynamic programming technique to this larger family of stochastic optimal control problems. More specifically, we provide a detailed proof of Bellman’s optimality principle (or dynamic programming principle) and obtain the corresponding Hamilton–Jacobi–Belman equation, which turns out to be a partial integro-differential equation due to the extra terms arising from the Lévy process and the Markov process. As an application of our results, we study a finite horizon consumption– investment problem for a jump–diffusion financial market consisting of one risk-free asset and one risky asset whose coefficients are assumed to depend on the state of a continuous time finite state Markov process. We provide a detailed study of the optimal strategies for this problem, for the economically relevant families of power utilities and logarithmic utilities.
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Monitoring systems have traditionally been developed with rigid objectives and functionalities, and tied to specific languages, libraries and run-time environments. There is a need for more flexible monitoring systems which can be easily adapted to distinct requirements. On-line monitoring has been considered as increasingly important for observation and control of a distributed application. In this paper we discuss monitoring interfaces and architectures which support more extensible monitoring and control services. We describe our work on the development of a distributed monitoring infrastructure, and illustrate how it eases the implementation of a complex distributed debugging architecture. We also discuss several issues concerning support for tool interoperability and illustrate how the cooperation among multiple concurrent tools can ease the task of distributed debugging.
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Thesis to obtain the Master Degree in Electronics and Telecommunications Engineering
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Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática
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Partial dynamic reconfiguration of FPGAs can be used to implement complex applications using the concept of virtual hardware. In this work we have used partial dynamic reconfiguration to implement a JPEG decoder with reduced area. The image decoding process was adapted to be implemented on the FPGA fabric using this technique. The architecture was tested in a low cost ZYNQ-7020 FPGA that supports dynamic reconfiguration. The results show that the proposed solution needs only 40% of the resources utilized by a static implementation. The performance of the dynamic solution is about 9X slower than the static solution by trading-off internal resources of the FPGA. A throughput of 7 images per second is achievable with the proposed partial dynamic reconfiguration solution.
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Trabalho Final de Mestrado elaborado no Laboratório Nacional de Engenharia Civil (LNEC) para a obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo entre o ISEL e o LNEC
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Safety is one of the major concerns of process safety engineers in most industrial facilities all over the world. To this scope, some events play an important role once the effect of their consequences can be assumed as totally undesirable. One of these events refers to the occurrence of a fire. Such event can result in catastrophic consequences for life, equipment, and continuity of activities or even leading to environmental damage. A fire protection equipment with low reliability means that this equipment are often unavailable and thus the risk of a fire increases. Maintenance of fire protection equipment is very important because this kind of systems is mostly in a dormant mode, which gives uncertainty about their operability when demanded in a real situation of fire. This article outlines the importance of tests, inspection, and maintenance operations in the context of a fire sprinkler system and proposes a methodology based on international standards and supported by test/inspection reports to correct the frequency of these actions according to the level of degradation of the components and regarding safety purposes. © 2015 American Institute of Chemical Engineers.
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Summary form only given. Bacterial infections and the fight against them have been one of the major concerns of mankind since the dawn of time. During the `golden years' of antibiotic discovery, during the 1940-90s, it was thought that the war against infectious diseases had been won. However currently, due to the drug resistance increase, associated with the inefficiency of discovering new antibiotic classes, infectious diseases are again a major public health concern. A potential alternative to antibiotic treatments may be the antimicrobial photodynamic inactivation (PDI) therapy. To date no indication of antimicrobial PDI resistance development has been reported. However the PDI protocol depends on the bacteria species [1], and in some cases on the bacteria strains, for instance Staphylococcus aureus [2]. Therefore the development of PDI monitoring techniques for diverse bacteria strains is critical in pursuing further understanding of such promising alternative therapy. The present works aims to evaluate Fourier-Transformed-Infra-Red (FT-IR) spectroscopy to monitor the PDI of two model bacteria, a gram-negative (Escherichia coli) and a gram-positive (S. aureus) bacteria. For that a high-throughput FTIR spectroscopic method was implemented as generally described in Scholz et al. [3], using short incubation periods and microliter quantities of the incubation mixture containing the bacteria and the PDI-drug model the known bactericidal tetracationic porphyrin 5,10,15,20-tetrakis (4-N, N, Ntrimethylammoniumphenyl)-porphyrin p-tosylate (TTAP4+). In both bacteria models it was possible to detect, by FTIR-spectroscopy, the drugs effect on the cellular composition either directly on the spectra or on score plots of principal component analysis. Furthermore the technique enabled to infer the effect of PDI on the major cellular biomolecules and metabolic status, for example the turn-over metabolism. In summary bacteria PDI was monitored in an economic, rapid (in minutes- , high-throughput (using microplates with 96 wells) and highly sensitive mode resourcing to FTIR spectroscopy, which could serve has a technological basis for the evaluation of antimicrobial PDI therapies efficiency.
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Trabalho Final de Mestrado elaborado no Laboratório Nacional de Engenharia Civil (LNEC) para a obtenção do grau de Mestre em Engenharia Civil pelo Instituto Superior de Engenharia de Lisboa no âmbito do protocolo de cooperação entre o ISEL e o LNEC
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BACKGROUNDWhile the pharmaceutical industry keeps an eye on plasmid DNA production for new generation gene therapies, real-time monitoring techniques for plasmid bioproduction are as yet unavailable. This work shows the possibility of in situ monitoring of plasmid production in Escherichia coli cultures using a near infrared (NIR) fiber optic probe. RESULTSPartial least squares (PLS) regression models based on the NIR spectra were developed for predicting bioprocess critical variables such as the concentrations of biomass, plasmid, carbon sources (glucose and glycerol) and acetate. In order to achieve robust models able to predict the performance of plasmid production processes, independently of the composition of the cultivation medium, cultivation strategy (batch versus fed-batch) and E. coli strain used, three strategies were adopted, using: (i) E. coliDH5 cultures conducted under different media compositions and culture strategies (batch and fed-batch); (ii) engineered E. coli strains, MG1655endArecApgi and MG1655endArecA, grown on the same medium and culture strategy; (iii) diverse E. coli strains, over batch and fed-batch cultivations and using different media compositions. PLS models showed high accuracy for predicting all variables in the three groups of cultures. CONCLUSIONNIR spectroscopy combined with PLS modeling provides a fast, inexpensive and contamination-free technique to accurately monitoring plasmid bioprocesses in real time, independently of the medium composition, cultivation strategy and the E. coli strain used.
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Human mesenchymal stem/stromal cells (MSCs) have received considerable attention in the field of cell-based therapies due to their high differentiation potential and ability to modulate immune responses. However, since these cells can only be isolated in very low quantities, successful realization of these therapies requires MSCs ex-vivo expansion to achieve relevant cell doses. The metabolic activity is one of the parameters often monitored during MSCs cultivation by using expensive multi-analytical methods, some of them time-consuming. The present work evaluates the use of mid-infrared (MIR) spectroscopy, through rapid and economic high-throughput analyses associated to multivariate data analysis, to monitor three different MSCs cultivation runs conducted in spinner flasks, under xeno-free culture conditions, which differ in the type of microcarriers used and the culture feeding strategy applied. After evaluating diverse spectral preprocessing techniques, the optimized partial least square (PLS) regression models based on the MIR spectra to estimate the glucose, lactate and ammonia concentrations yielded high coefficients of determination (R2 ≥ 0.98, ≥0.98, and ≥0.94, respectively) and low prediction errors (RMSECV ≤ 4.7%, ≤4.4% and ≤5.7%, respectively). Besides PLS models valid for specific expansion protocols, a robust model simultaneously valid for the three processes was also built for predicting glucose, lactate and ammonia, yielding a R2 of 0.95, 0.97 and 0.86, and a RMSECV of 0.33, 0.57, and 0.09 mM, respectively. Therefore, MIR spectroscopy combined with multivariate data analysis represents a promising tool for both optimization and control of MSCs expansion processes.
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This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.