1,102 research outputs found
Parga, and the Ionian islands; comprehending a refutation of the mis-statements of the Quarterly Review and of Lieut.- Gen. Sir Thomas Maitland, on the subject; with a report of the trial between that officer and the author.
No full textPreface: Bosset, C.P.(de)Appendix.Dedication:Content description: TitleIllustration: (Maps ,)Pagination: PP26+530PVolumes: 1Text Genre:ProseIllustration: (χάρτες ,
Fault identification and diagnosis for telephone exchange building facilities
No full textThesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2005.Heating, ventilating and air conditioning (HVAC) systems consume 43 % of the
energy used by buildings. This percentage grows when the HVAC system operates
with malfunctions. Fault detection and diagnosis (FDD) methods are developed to
reduce abnormal events and down-times and to promote energy saving use of
equipment. Most FDD methodologies for HVAC systems found in the literature revolve around first principle models and mathematical models. This dissertation describes a FDD solution based on process history data and artificial neural network (ANN) models. ANN models, of HVAC components, are built from fault-free operation data. Faulty data are then used with the ANN models to build various residuals and statistical residual transformations. From these residuals, unique residual patterns are assigned to discern between a variety of malfunctions.
This FDD strategy is, firstly, applied to a static pressure control loop and secondly, applied to the overall power consumption of an HVAC system. In both studies, the FDD system successfully detected and classified unwanted anomalies - some deviating as little as 5% from normal operational standards. Finally, the FDD system is rated according to a common set of criteria reviewed in the literature study. This criterion shows the FDD strategy to be robust and adaptable, with low modelling and computational requirements.Master
Fault diagnostic system for predictive maintenance on a Brayton cycle power plant
No full textThesis (M.Ing. (Electronical Engineering))--North-West University, Potchefstroom Campus, 2004.Model-based fault detection and diagnostic systems have become an important solution (Munoz & Sanz-Bobi, 1998:178) in the industry for preventive maintenance. This not only increases plant safety, but also reduces down time and financial losses. This paper investigates a model-based fault detection and diagnostic system by using neural networks. To mimic process models, a normal feed-forward neural network with time delays is implemented by using the MATLAB@ neural network toolbox. By using these neural network models, residuals are generated. These residuals are then classified by using other neural networks. The main process in question is the Brayton cycle thermal process used in the pebble bed modular reactor. Flownet simulation software is used to generate the data, where practical data is absent. Various training algorithms were implemented and tested during the investigation of modelling and classification concepts on two benchmark processes. The training algorithm that performed best was finally implemented in an integrated concept.Master
Design of a state-based nonlinear controller
No full textThesis (M.Ing. (Computer and Electronical Engineering))--North-West University, Potchefstroom Campus, 2006.A developer of thermofluid simulation software requires algorithms which are used to design and implement PI controllers at some operating points of nonlinear industrial processes.
In general, the algorithm should be applicable to multivariable plant models which may be nonlinear. In some areas there is a hesitancy to use controllers for nonlinear processes which use neural networks or fuzzy logic or a combination thereof. PI controllers are also standard in
various SCADA systems. Since control normally takes place around an operating point, a linearised model is obtained. A
controller designed for a particular operating point, may not be suitable for other operating points. Since a multitude of variables are to be controlled in the plant, the problem becomes more acute. In this research, a methodology is derived for the design of multivariable control
using PI controllers. The parameters of the controllers depend on the operating point, and are therefore nonlinear. The behaviour is deterministic in a classical control sense around a range of operating points. This should remove concerns of non-deterministic behaviour as attached to neural networks due to the lack of stability tests for them which are industry accepted. A state-space approach leads to the development of a design methodology, which is then used
to implement these algorithms. The P- and PI-controllers will be designed using traditional methods, as well as by an optimal procedure which makes use of a genetic algorithm. The GA tuning algorithm yields superior performance when compared to other methods.Master
Neural network inference measurements applied to the pebble bed modular reactor
No full textThesis (Ph.D. (Electronical Engineering)--North-West University, Potchefstroom Campus, 2004.Inference measurements with time-delayed feed-forward neural networks facilitates the
inference of unknown variables from known variables in non-linear dynamic systems. This
is based only on the mapping data of the known variable and variable to be inferred. For successful inference, several constraints have to be overcome. This is, the neural network should have the correct topology, the training data set characteristics must have inherent attributes to ensure generalisation and the training algorithm must be capable of finding an acceptable local minimum on the error surface. At present, the neural network topology
is based on trial and error, while the generalisation capability of the trained neural network is tested by using test and validation sets. Due to the lack of design methods for the topology of neural networks and the need for independent testing and validation, this thesis endeavours to develop a generalised method to find the optimum topology for accurate inference measurements. The aim is further to develop a method for judging the training set that could lead to generalisation without using test sets or validation sets. For this to be done, the training algorithm should succeed in finding a small enough local minimum on the error surface. The developed methods are applied to a simulated model of the pebble bed modular reactor (PBMR).Doctora
La Cassazione si pronuncia sull'esatta portata della fattispecie di "denuncia di un sinistro non accaduto" di cui all'art. 642 c.p.
No full textNella prima parte della nota, l’Autore analizza la fattispecie di «denuncia di un sinistro non accaduto» di cui all’art. 642, comma 2, c.p., introdotta nel 2002 nell’ambito della riforma delle assicurazioni R.C. Auto, per, poi, affrontare la questione su cui si è pronunciata la Corte di cassazione, relativa al significato da attribuire al concetto di “sinistro”. Infine, l’Autore offre le proprie riflessioni sui rapporti intercorrenti tra la fattispecie esaminata e le altre ipotesi delittuose previste dall’art. 642 c.p. e sulla compatibilità di tali reati con il principio di offensività.In the first part of the note, the Author analyzes the crime of “denunciation of a false accident” that was introduced into the art. 642 c.p. in 2002, and, then, the question about the meaning of “sinistro” (accident) on which the Supreme Court stated. Finally, the Author offers his own reflections on the connection between the examinated crime and other felonies provided under art. 642 c.p., and, also, on compatibility of those crimes with the principle of seriousness of the offense
Book Review: Jesus in an age of enlightenment: Radical gospels from Thomas Hobbes to Thomas Jefferson. By Jonathan C.P Birch
No full textThis is a pre-copyedited, author-produced PDF of an article accepted for publication in [Literature and Theology] following peer review. The version of record [Greenaway, J. (2021). Jesus in an Age of Enlightenment: Radical gospels from Thomas Hobbes to Thomas Jefferson. By Jonathan C.P Birch. Literature and Theology, 35(1), 100–102] is available online at: [https://academic.oup.com/litthe/article/35/1/100/6130117?guestAccessKey=0523008b-46e6-4ed2-ab5d-001d93207bed].A review of Jesus in an Age of Enlightenment: Radical Gospels from Thomas Hobbes to Thomas Jefferson by Jonathan C.P Birc
On morphology, molecular composition and breakdown behaviour in semi-crystalline polymers
No full textState space model extraction of thermohydraulic systems
No full textThesis (Ph.D. (Computer and Electronical Engineering)--North-West University, Potchefstroom Campus, 2009.Many hours are spent by systemand control engineers deriving reduced order dynamicmodels portraying the dominant systemdynamics of thermohydraulic systems. A need therefore exists to develop a method that will automate the model derivation process. The model format
preferred for control system design and analysis during preliminary system design is the state space format. The aim of this study is therefore to develop an automated and generic state space model extraction method that can be applied to thermohydraulic systems. Well developed system identification methods exist for obtaining state space models from input-output data, but these models are not transparent, meaning the parameters do not have any physical meaning. For example one cannot identify system parameters such as heat or mass transfer coefficients. Another approach is needed to derive state space models automatically. Many commercial thermohydraulic simulation codes follow a network approach towards the representation of thermohydraulic systems. This approach is probably one of the most advanced approaches in terms of technical development. It would therefore be useful to develop a state space extraction algorithm that would be able to derive reduced order
state space models from network representations of thermohydraulic systems. In this regard a network approach is followed in the development of the state space extraction algorithm. The advantage of using a network-based extraction method is that the extracted state space model
is transparent and the algorithm can be embedded in existing simulation software that follow a network approach. In this study an existing state space extraction algorithm, used for electrical network analysis, is modified and applied in a new way to extract state space models of thermohydraulic systems. A thermohydraulic system is partitioned into its respective physical domains which, unlike electrical systems, have multiple variables. Network representations are derived for each
domain. The state space algorithm is applied to these network representations to extract
symbolic state spacemodels. The symbolic parametersmay then be substitutedwith numerical values. The state space extraction algorithm is applied to small scale thermohydraulic systems such as a U-tube and a heat exchanger, but also to a larger, more complex system such as the Pebble Bed Modular Reactor Power Conversion Unit (PBMR PCU). It is also shown that the algorithm can extract linear, nonlinear, time-varying and time-invariant state space
models. The extracted state space models are validated by solving the state space models and comparing the solutions with Flownex results. Flownex is an advanced and extensively validated thermo-fluid simulation code. The state space models compared well with Flownex results. The usefulness of the state space model extraction algorithm in model-based control system design is illustrated by extracting a linear time-invariant state space model of the PBMR PCU. This model is embedded in an optimal model-based control scheme called Model-Predictive Control (MPC). The controller is compared with standard optimised control schemes such as PID and Fuzzy PID control. The MPC controller shows superior performance compared to these control schemes. This study succeeded in developing an automated state space model extraction method that can be applied to thermohydraulic networks. Hours spent on writing down equations from first principles to derive reduced order models for control purposes can now be replaced with a click of a button. The need for an automated state space model extraction method for
thermohydraulic systems has therefore been resolved.Doctora
The identification of nonlinear dynamic systems around operating points using neural networks
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