The interaction of an evanescent wave and plasmonic nanostructures are simulated in Finite Element Method. Specifically, the optical absorption cross section (Cabs) of a silver nanoparticle (AgNP) and a gold nanoparticle (AuNP) in the presence of metallic (gold) and dielectric (silicon) atomic force microscope (AFM) probes are numerically calculated in COMSOL. The system was illuminated by a transverse magnetic polarized, total internally reflected (TIR) waves or propagating surface plasmon (SP) wave. Both material nanoscale probes localize and enhance the field between the apex of the tip and the particle. Based on the absorption cross section equation the author was able to demonstrate the increment of absorption cross section when the Si tip was brought closer to the AuNP, or when the Si tip apex was made larger. However, the equation was not enough to predict the absorption modification under metallic tips, especially for a AgNP's Cabs; neither it was possible to estimate the optical absorption based on the localized enhanced field caused by a gold tip. With the help of the driven damped harmonic oscillator equation, the Cabs of nanoparticles was explained. In addition, this model was applicable for both TIR and Surface Plasmon Polaritons illuminations. Fitting the numerical absorption data to a driven damped harmonic oscillator (HO) model revealed that the AFM tip modifies both the driving force (F0), consisting of the free carrier charge and the driving field, and the overall damping of the oscillator beta. An increased F0 or a decreased beta will result in an increased Cabs and vice versa. Moreover, these effects of F0 and beta can be complementary or competing, and they combine to either enhance or suppress absorption. Hence, a significantly higher beta with a small increment in F0 will result in an absorption suppression. Therefore, under a Si tip, Cabs of a AuNP is enhanced while Cabs of a AgNP is suppressed. In contrast, a Au tip suppresses the Cabs for both Au and Ag NPs. As an extension of this absorption model, further investigation of the guided mode and a close by nanostructure is proposed, where the scattered wave off the structure attenuates the guided mode with destructive interference.

Distribution system voltage and VAR control (VVC) is a technique that combines conservation voltage reduction and reactive power compensation to operate a distribution system at its optimal conditions. Coordinated VVC can provide major economic benefits for distribution utilities. Incorporating distributed generation (DG) to VVC can improve the system efficiency and reliability. The first part of this dissertation introduces a direct optimization formulation for VVC with DG. The control is formulated as a mixed integer non-linear programming (MINLP) problem. The formulation is based on a three-phase power flow with accurate component models. The VVC problem is solved with a state of the art open-source academic solver utilizing an outer approximation algorithm. Applying the approach to several test feeders, including IEEE 13-node and 37-node radial test feeders, with variable load demand and DG generation, validates the proposed control. Incorporating renewable energy can provide major benefits for efficient operation of the distribution systems. However, when the number of renewables increases the system control becomes more complex. Renewable resources, particularly wind and solar, are often highly intermittent. The varying power output can cause significant fluctuations in feeder voltages. Traditional feeder controls are often too slow to react to these fast fluctuations. DG units providing reactive power compensation they can be utilized in supplying voltage support when fluctuations in generation occur. The second part of this dissertation focuses on two new approaches for dual-layer VVC. In these approaches the VVC is divided into two control layers, slow and fast. The slow control obtains optimal voltage profile and set points for the distribution control. The fast control layer is utilized to maintain the optimal voltage profile when the generation or loading suddenly changes. The MINLP based VVC formulation is utilized as the slow control. Both local reactive power control of DG and coordinated quadratic programming (QP) based reactive power control is considered as the fast control approaches. The effectiveness of these approaches is studied with test feeders, utility load data, and fast-varying solar irradiance data. The simulation results indicate that both methods achieve good results for VVC with DG.

Noble metal nanoparticles supporting localized surface plasmon resonances (LSPR) have been extensively investigated for label free detection of various biological and chemical interactions. When compared to other optical sensing techniques, LSPR sensors offer label-free detection of biomolecular interactions in localized sensing volume solutions. However, these sensors also suffer from a major disadvantage – LSPR sensors remain highly susceptible to interference because they respond to both solution refractive index change and non-specific binding as well as specific binding of the target analyte. These interactions can severely compromise the measurement of the target analyte in a complex unknown media and hence limit the applicability and impact of the sensor. In spite of the extensive amount of work done in this field, there has been a clear absence of efforts to make LSPR sensors immune to interfering effects. The work presented in this document investigates, both experimentally and numerically, dual- and tri-mode LSPR sensors that utilize the multiple surface plasmon modes of gold nanostructures to distinguish target analyte from interfering bulk and non-specific binding effects. Finally, a series of biosensing experiments are performed to examine various regeneration assays for LSPR sensors built on indium tin oxide coated glass substrate.

Submerged Arc Welding (SAW) for fillet joints is one of the major applications in the shipbuilding industry. Due to the requirement for the weld size, a sufficient amount of metal must be deposited. In conventional SAW process, the heat input is proportional to the amount of metal melted and is thus determined by the required weld size. To meet this requirement, an excessive amount of heat is applied causing large distortions on the welded structures whose follow-up straightening is highly costly. In order to reduce the needed heat input, Double-Electrode (DE) technology has been practiced creating the Double-Electrode SAW (DE-SAW) method for fillet joints. The reduction in the heat input, however, also reduces the penetration capability of the process, and the ability to produce required weld beads has to be compromised. To eliminate the unwanted side effect after using DE-SAW, a root opening between the panel and the tee has been proposed in this dissertation to form a modified fillet joint design. Experimental results verified that the use of root opening improves the ability of DE-SAW to produce the required weld beads at reduced heat input and penetration capability. Unfortunately, the use of root opening decreases the stability of the process significantly. To control the heat input at a minimally necessary level that guarantees the weld size and meanwhile the process stability, a feedback is needed to control the currents at their desired levels. To this end, the fillet DE-SAW process is modeled and a multivariable predictive control algorithm is developed based on the process model. Major parameters including the root opening size, travel speed and heat input level have been selected/optimized/minimized to produce required fillet weld beads with a minimized heat input based on qualitative and quantitative analyses. At the end of this dissertation, a series of experiments validated the feasibility and repeatability of the predictive control based DE-SAW process for fillet joints with root opening.

An innovative auxiliary control system is developed to cooperate with an unskilled welder in a manual GTAW in order to obtain a consistent welding performance. In the proposed system, a novel mobile sensing system is developed to non-intrusively monitor a manual GTAW by measuring three-dimensional (3D) weld pool surface. Specifically, a miniature structured-light laser amounted on torch projects a dot matrix pattern on weld pool surface during the process; Reflected by the weld pool surface, the laser pattern is intercepted by and imaged on the helmet glass, and recorded by a compact camera on it. Deformed reflection pattern contains the geometry information of weld pool, thus is utilized to reconstruct its $3$D surface. An innovative image processing algorithm and a reconstruction scheme have been developed for (3D) reconstruction. The real-time spatial relations of the torch and the helmet is formulated during welding. Two miniature wireless inertial measurement units (WIMU) are mounted on the torch and the helmet, respectively, to detect their rotation rates and accelerations. A quaternion based unscented Kalman filter (UKF) has been designed to estimate the helmet/torch orientations based on the data from the WIMUs. The distance between the torch and the helmet is measured using an extra structure-light low power laser pattern. Furthermore, human welder's behavior in welding performance has been studied, e.g., a welder`s adjustments on welding current were modeled as response to characteristic parameters of the three-dimensional weld pool surface. This response model as a controller is implemented both automatic and manual gas tungsten arc welding process to maintain a consistent full penetration.

Video surveillance is an important tool used in security and environmental monitoring, however, the widespread deployment of surveillance cameras has raised serious privacy concerns. Many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. To identify these individuals for protection, the most reliable approach is to use biometric signals as they are immutable and highly discriminative. If misused, these characteristics of biometrics can seriously defeat the goal of privacy protection. In this dissertation, an Anonymous Biometric Access Control (ABAC) procedure is proposed based on biometric signals for privacy-aware video surveillance. The ABAC procedure uses Secure Multi-party Computational (SMC) based protocols to verify membership of an incoming individual without knowing his/her true identity. To make SMC-based protocols scalable to large biometric databases, I introduce the k-Anonymous Quantization (kAQ) framework to provide an effective and secure tradeoff of privacy and complexity. kAQ limits systems knowledge of the incoming individual to k maximally dissimilar candidates in the database, where k is a design parameter that controls the amount of complexity-privacy tradeoff. The relationship between biometric similarity and privacy is experimentally validated using a twin iris database. The effectiveness of the entire system is demonstrated based on a public iris biometric database. To provide the protected subjects with full access to their privacy information in video surveillance system, I develop a novel privacy information management system that allows subjects to access their information via the same biometric signals used for ABAC. The system is composed of two encrypted-domain protocols: the privacy information encryption protocol encrypts the original video records using the iris pattern acquired during ABAC procedure; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of my framework.

Power outages usually lead to customer complaints and revenue losses. Consequently, fast and accurate fault location on electric lines is needed so that repair work can be carried out as fast as possible. Chapter 2 describes novel fault location algorithms for radial and non-radial ungrounded power distribution systems. For both types of systems, fault location approaches using line to neutral or line to line measurements are presented. It’s assumed that network structure and parameters are known, so that during-fault bus impedance matrix of the system can be derived. Functions of bus impedance matrix and available measurements at substation are formulated, from which the unknown fault location can be estimated. Evaluation studies on fault location accuracy and robustness of fault location methods to load variations and measurement errors has been performed. Most existing fault location methods rely on measurements obtained from meters installed in power systems. To get the most from a limited number of meters available, optimal meter placement methods are needed. Chapter 3 presents a novel optimal meter placement algorithm to keep the system observable in terms of fault location determination. The observability of a fault location in power systems is defined first. Then, fault location observability analysis of the whole system is performed to determine the least number of meters needed and their best locations to achieve fault location observability. Case studies on fault location observability with limited meters are presented. Optimal meter deployment results based on the studied system with equal and varying monitoring cost for meters are displayed. To enhance fault location accuracy, an optimal fault location estimator for power distribution systems with distributed generation (DG) is described in Chapter 4. Voltages and currents at locations with power generation are adopted to give the best estimation of variables including measurements, fault location and fault resistances. Chi-square test is employed to detect and identify bad measurement. Evaluation studies are carried out to validate the effectiveness of optimal fault location estimator. A set of measurements with one bad measurement is utilized to test if a bad data can be identified successfully by the presented method.

For present day global economies energy use is the single most important criteria determining a nation’s wealth and sustainability. The global energy needs are met primarily by fossil fuels which are of finite supply. One means to extend the life span of the fossil fuel reserves has been to improve the efficiency of existing systems including generation, storage, usage etc., as a means of conservation. This dissertation investigates a motor technology that promises to complement existing conservation techniques. Given advances in high voltage power semiconductors, manufacturing techniques and materials research this dissertation evaluates the potential of the switched impedance motor (SIM), a type of electrostatic motor (ESM), as an efficient alternative to induction machines. Starting with a broad look at how force can be created using electrical means, this dissertation reviews the contributions of prior art, details their inhibitors and develops analytic expressions that allow these inhibitors to be overcome. Using these analytic expressions as design tools allows future SIM designers to realize this motor topology’s potential as a highly efficient machine and a global tool for energy conservation.

Combining human welder (with intelligence and sensing versatility) and automated welding robots (with precision and consistency) can lead to next generation intelligent welding systems. In this dissertation intelligent welding robots are developed by process modeling / control method and learning the human welder behavior. Weld penetration and 3D weld pool surface are first accurately controlled for an automated Gas Tungsten Arc Welding (GTAW) machine. Closed-form model predictive control (MPC) algorithm is derived for real-time welding applications. Skilled welder response to 3D weld pool surface by adjusting the welding current is then modeled using Adaptive Neuro-Fuzzy Inference System (ANFIS), and compared to the novice welder. Automated welding experiments confirm the effectiveness of the proposed human response model. A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot. The learning of human welder movement (i.e., welding speed) is first realized with Virtual Reality (VR) enhancement using iterative K-means based local ANFIS modeling. As a separate effort, the learning is performed without VR enhancement utilizing a fuzzy classifier to rank the data and only preserve the high ranking “correct” response. The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined. A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed. The data fusion model can outperform individual machine-based control algorithm and welder intelligence-based models (with and without VR enhancement). Finally a data-driven approach is proposed to model human welder adjustments in 3D (including welding speed, arc length, and torch orientations). Teleoperated training experiments are conducted in which a human welder tries to adjust the torch movements in 3D based on his observation on the real-time weld pool image feedback. The data is off-line rated by the welder and a welder rating system is synthesized. ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements. A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots.

Power electronic systems have been widely used in the electrical power processing for applications with power levels ranging from less than one watt in battery-operated portable devices to more than megawatts in the converters, inverters and rectifiers of the utility power systems. These systems typically involve the passive elements such as inductors, capacitors, and resistors, the switching electronic components such as IGBTs, MOSFETS, and diodes, and other electronic circuits. Multifrequency averaging is one of the widely used modeling and simulation techniques today for the analysis and design of power electronic systems. This technique is capable of providing the average behavior as well as the ripple behavior of power electronic systems. This work begins with the extension of multifrequency averaging to represent uniformly sampled PWM converters. A new multifrequency averaging method of solving an observed issue with model stability is proposed and validated. Multifrequency averaging can also be applied to study the instability phenomenon in power electronic systems. In particular, a reduced-order multifrequency averaging method, along with a genetic algorithm based procedure, is proposed in this work to estimate the regions of attraction of power electronic converters. The performance of this method is shown by comparing the accuracy and efficiency with the existing methods. Finally, a new continuous-time multifrequency averaging method of representing discrete-time systems is proposed. The proposed method is applied to model digitally controlled PWM converters. Simulation and hardware results show that the proposed method is capable of predicting the average behavior as well as the ripple behavior of the closed-loop systems. Future research in the area of multifrequency averaging is proposed.