Confinement Effects of Solvation on a Molecule Physisorbed on a Metal Particle

We describe and present results of the implementation of the surface and volume polarization for electrostatics~(SVPE) and the iso-density surface solvation models. Unlike most other implementation of the solvation models where the solute and the solvent are described with multiple numerical representation, our implementation uses a multiresolution, adaptive multiwavelet basis to describe both solute and the solvent. This requires reformulation to use integral equations throughout as well as a conscious management of numerical properties of the basis. Likewise, we investigate the effects of solvation on the static properties of a molecule physisorbed on a spherical particle, modeled as a polarizable continuum colloid with a static dielectric constant. The effective polarizability of the physisorbed molecule is enhanced by a factor of 105 in vacuo and by only 102 when solvated. The variation of the polarizability of the molecules with respect to the changes in their environment illustrates the importance of electrostatic interaction in the enhancement of the effective polarizability. Finally, we investigated the optical properties of 1.4-phenylenedinitrene and 4,4\u27-stilbenedinitrene biraradical molecules. Using our computational model, we establish the structure property relationship in biradical organic compounds. The spin splitting is shown to be inversely proportional to the separation between the two spin carrying centers and is partly driven by the Coulombic interaction. The intense peaks on the absorption spectra is the result of the mixing of transitions from the spin carrying centers with those of pi origin

Instantaneous Harmonic Analysis and its Applications in Automatic Music Transcription

This thesis presents a novel short-time frequency analysis algorithm, namely Instantaneous Harmonic Analysis (IHA), using a decomposition scheme based on sinusoidals. An estimate for instantaneous amplitude and phase elements of the constituent components of real-valued signals with respect to a set of reference frequencies is provided. In the context of musical audio analysis, the instantaneous amplitude is interpreted as presence of the pitch in time. The thesis examines the potential of improving the automated music analysis process by utilizing the proposed algorithm. For that reason, it targets the following two areas: Multiple Fundamental Frequency Estimation (MFFE), and note on-set/off-set detection. The IHA algorithm uses constant-Q filtering by employing Windowed Sinc Filters (WSFs) and a novel phasor construct. An implementation of WSFs in the continuous model is used. A new relation between the Constant-Q Transform (CQT) and WSFs is presented. It is demonstrated that CQT can alternatively be implemented by applying a series of logarithmically scaled WSFs while its window function is adjusted, accordingly. The relation between the window functions is provided as well. A comparison of the proposed IHA algorithm with WSFs and CQT demonstrates that the IHA phasor construct delivers better estimates for instantaneous amplitude and phase lags of the signal components. The thesis also extends the IHA algorithm by employing a generalized kernel function, which in nature, yields a non-orthonormal basis. The kernel function represents the timbral information and is used in the MFFE process. An effective algorithm is proposed to overcome the non-orthonormality issue of the decomposition scheme. To examine the performance improvement of the note on-set/off-set detection process, the proposed algorithm is used in the context of Automatic Music Transcription (AMT). A prototype of an audioto-MIDI system is developed and applied on synthetic and real music signals. The results of the experiments on real and synthetic music signals are reported. Additionally, a multi-dimensional generalization of the IHA algorithm is presented. The IHA phasor construct is extended into the hyper-complex space, in order to deliver the instantaneous amplitude and multiple phase elements for each dimension

Polyelectrolyte nanostructures formed in the moving contact line: fabrication, characterization and application: Polyelectrolyte nanostructures formed in the moving contact line: fabrication, characterization and application

Having conducted the research described in this thesis I found that there exists a possibility to produce polyelectrolyte nanostructures on hydrophobic surfaces by application of the moving contact line approach. It was demonstrated that the morphology of nanostructures displays a range of structure variations from root-like to a single wire structure with a high anisotropy and aspect ratio (providing diameters of several nanometers and the length limited by the sample surface dimensions). Such nanostructures can be produced exactly on the spot of interest or can be transferred from the surface where they were produced to any other surfaces by the contact printing technique. A model describing the polymer deposition during the moving contact line processes on hydrophobic surfaces has been proposed. The application of this model provides the ground for an explanation of all the obtained experimental data. Utilizing moving contact line approach aligned one-dimensional polycation structures were fabricated and these structures were used as templates for assembling amphiphile molecules. Quasiperiodic aligned and oriented nanostructures of polyelectrolyte molecules formed in moving droplets were utilized for fabrication of electrically conductive one-dimensional nanowires

On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines. This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids. To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and appeals to a much wider variety of electric machines connected to power electronic converters or drivers. To sum up, this PhD research has a broad beneficial impact on a wide spectrum of power-converter-fed electric machines and is thus of practical importance

Automatic train control using neuro-fuzzy modeling and optimal control techniques

In rapid transit applications, it is often necessary to optimize the ride of the train for certain parameters based upon time of day, occupant density, and system-wide scheduling. Trade-offs have to be made between energy conservation, time minimization, and ride comfort. Typically the dynamics of the train are not well known (or not initially known at all), change over time, and are non-linear. In the past, a transit control engineer would typically use P-I control but could spend days or weeks on-site adjusting the P-I constants to obtain a ride that felt good and met the design constraints. This process was both time consuming and expensive. This paper presents a control scheme for a rapid transit train that uses optimal concepts coupled with fuzzy control and neuro-fuzzy modeling techniques. The optimal controller allows users to define different ride types by adjusting weights on the cost equation. The controller design is done almost automatically, with minimal control engineer effort needed, by post-processing data collected from the train. The post-processing process uses neuro-fuzzy modeling techniques to create a dynamic model for the train, which can be used with optimal techniques to obtain fuzzy control rules for controlling the train. Once the initial design is in place, the controller becomes adaptive and fine-tunes itself to match the dynamics of the particular train that it is on

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution