Cobweb/3: A portable implementation

An algorithm is examined for data clustering and incremental concept formation. An overview is given of the Cobweb/3 system and the algorithm on which it is based, as well as the practical details of obtaining and running the system code. The implementation features a flexible user interface which includes a graphical display of the concept hierarchies that the system constructs

METHODOLOGY FOR ON-LINE BATTERY HEALTH MONITORING

The growing demand for electric vehicles and renewable energy sources has increased the need for safe, reliable, and cost-effective energy-storage systems, many of which include batteries. The reliability and efficiency of these battery-based systems can be significantly improved using intelligent energy-management systems that effectively indicate battery health in real time. On-line monitoring can be difficult, however, because batteries are non-linear and time-varying systems whose characteristics depend on temperature, usage history, and other factors. The key metrics of interest in a battery are its remaining capacity and health. Most of the current methods require off-line measurement, and even the available on-line methods are only good in laboratory conditions. This thesis provides an enhanced streamlined framework for on-line monitoring. In this methodology, a non-intrusive test signal is superimposed upon a battery load which causes transient dynamics inside the battery. The resulting voltage and current are used as test data and the estimation is done in two parts. First, a non-linear least-squares routine is used to estimate the electrical parameters of a battery model. Second, a state-estimation algorithm is used to estimate the open-circuit voltage. Experimental results obtained at consistent temperatures demonstrate that the open-circuit voltage and parameter values together can combine to provide capacity and health measurements. This approach requires minimal hardware and could form the basis for a robust on-line monitoring system

Bayes approach to explore the mixture failure rate model

This thesis has two folds: Firstly, designing mixture failure rate functions by combing few other existing failure rate functions to obtain desirable mixture failure rate functions. The first proposed mixture failure rate is the non-linear failure rate. This failure rate is a mixture of the exponential and Weibull failure rate functions. It was designed for modeling data sets in which failures result from both random shock and wear out or modeling a series system with two components, where one component follows an exponential distribution and the other follows a Weibull distribution. The second proposed mixture failure rate is the additive Chen-Weibull failure rate. This failure rate is considered a mixture of the Chen and Weibull failure rates. It is decided for modeling lifetime data with flexible failure rate including bathtub-shaped failure rate. The final proposed mixture failure rate is the improvement of new modified Weibull failure rate. This failure rate is a mixture of the Weibull and modified Weibull failure rates. It is also decided for modeling lifetime data with flexible failure rate including bathtub-shaped failure rate. The superiority of the proposed models have been demonstrated by fitting to many well-known lifetime data sets. And secondly, applying modern statistical methods and techniques, such as the maximum likelihood estimation, Bayesian inference, cross-entropy method, adaptive Markov chain Monte Carlo, Hamiltonian Monte Carlo and bootstrapping, for analyzing failure time distributions which result from those mixture failure rate functions.Tato disertační práce byla vyvíjena dvěma směry: Za prvé, návrh směsových funkcí intenzity poruch, vycházející z kombinování několika existujících intenzit poruch s cílem získat požadovanou směsovou funkci intenzity poruch. První navržená směsová intenzita poruch je nelineární intenzita poruch. Tato funkce intenzity poruch je směsí exponenciální a Weibullovy funkce intenzity poruch. Byla navržena pro účely modelování datových souborů, ve kterých poruchy jsou výsledkem jak náhodných šoků, tak i procesu opotřebení, neboli modelování poruch lze popsat jako sériový systém se dvěma komponentami, kde jedna komponenta se řídí exponenciálním rozdělením a druhá Weibullovým. Druhá navrhovaná směsová funkce intenzity poruch je aditivní Chen-Weibullova intenzita poruch. Tato intenzita poruch je uvažována jako směs Chenovy a Weibullovy intenzity poruch. Je navržena pro účely modelování dat, popisujících životnost nějakých objektů, kdy intenzita poruch vykazuje velmi flexibilní chování, včetně průběhu ve tvaru vanové křivky. Poslední navržená směsová intenzita poruch představuje inovaci nově modifikované Weibullovy intenzity poruch, což je směs Weibullovy a nově modifikované Weibullovy intenzity poruch. Je to další alternativa pro modelování dat popisujících životnost, kdy intenzita poruch vykazuje velmi flexibilní chování, včetně průběhu ve tvaru vanové křivky. Vysoká kvalita navržených modelů byla demonstrována na mnoha známých datových souborech, vybraných ze světové literatury. Za druhé, byly aplikovány a programově implementovány moderní metody a techniky teorie odhadu, vycházející z Bayesova přístupu, používané pro analýzu takových pravděpodobnostních rozdělení doby do poruchy, která jsou založena na směsové funkci intenzity poruch.470 - Katedra aplikované matematikyvyhově

Closed-Loop Control of HCCI Engine Dynamics

The topic of the thesis is control of Homogeneous Charge Compression Ignition (HCCI) engine dynamics. HCCI offers a potential to combine high efficiency with very low emissions. In order to fulfill the potential benefits, closed-loop control is needed. The thesis discusses sensors, feedback signals and actuators for closed-loop control of the HCCI combustion. Closed-loop control of the HCCI combustion using ion current is demonstrated. Models of the HCCI dynamics suitable for purposes of control design are presented. It is shown that low-order models are sufficient to describe the HCCI dynamics. Models of HCCI combustion have been determined both by system identification and by physical modeling. Different methods for characterizing and controlling the HCCI combustion are outlined and demonstrated. In cases where the combustion phasing in a six-cylinder heavy-duty engine was controlled, either by a Variable Valve Actuation system using the inlet valve or a dual-fuel system, results are presented. Combustion phasing is a limiting factor of the load control and emission control performance. A system where control of HCCI on a cycle-to-cycle basis is outlined and cylinder individual cycle-to-cycle control on a six-cylinder heavy duty engine is presented. Various control strategies are compared. Model-based control, such as LQG and Model Predictive Control MPC, and PID control are shown to give satisfactory controller performance. An MPC controller is proposed as a solution to the problem of load-torque control with simultaneous minimization of the fuel consumption and emissions, while satisfying the constraints on cylinder pressure

Gravitational waves from binary neutron stars systems

The first observation of gravitational waves from a merger of binary neutron stars (BNS) along with measurements of electromagnetic counterpart has led the beginning of multi-messenger gravitational wave astronomy. In this thesis, we investigate various gravitational waveform models. These models are employed for extracting source properties from the gravitational wave signal from the BNS merger. We perform parameter estimation studies in order to deduce the systematics among these models. We employ different injection scenarios to understand the biases that occur due to differences in the physics included in different waveform models. We present the construction of hybrid waveforms and discuss their applications as a full waveform, e.g., for validation of other waveform models and to check the performance of the models by performing mismatch calculations and parameter estimation studies where hybrid waveforms used as a substitute for a real signal. Based on the systematics study, we show a few of the waveform models give biased esti- mates of the parameters for specific injection scenarios. We improve those models and present the results of the improved models. In the context of having an accurate yet fast-to-evaluate waveform model, we review reduced-order-modeling techniques and present its application for the multipolar TEOBResum model. Furthermore, to validate and tune analytical models, and to investigate the last few orbits near the merger and after the merger, numerical simulations are inevitable. We evaluate the performance of an initial data generating code, called new SGRID code for BNS systems. With the upcoming advance detectors, it is highly likely that events with extreme source properties will get observed. Therefore, in this thesis, we show preliminary results for numerical simulations of BNS mergers with high spins. We vary equation-of-states (EOSs) and spins to investigate the effects of spin and EOS on the dynamics and gravitational waves

On inter-satellite laser ranging, clock synchronization and gravitational wave data analysis

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Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives