The resilience of manufacturing enterprises is an important research topic, since disruptions have severe effects on the normal operation of manufacturing enterprises, especially as manufacturing supply chains become global. Although many case studies have been carried out to address resilience in organizations, a systematic method to model and analyze the resilience dynamics in manufacturing enterprises is not well developed. This study is intended to conduct research on quantitative analysis and control for resilience. After reviewing the literature addressing resilience, a modeling framework is presented to characterize the resilience of a manufacturing enterprise responding to disruptive events, which includes inventory ow between enterprise nodes, different costs, resource, demand, etc. Each node within the network is represented as a dynamic model with associated costs of production and inventory. This mathematical model is the foundation of quantitative analysis and control. With this model, an optimal control problem is formulated, by which the control can be solved to achieve minimum cost. Several different types of systems are defined and analyzed in this work. We develop the approach of aggregation to simplify the network structures. The study is mainly focused on two categories of network systems: serial network systems and assembly tree network systems. The analysis on these two categories covers two conditions: in discrete time domain without considering capacities, and in continuous time domain with considering capacities. The methods to determining optimal operations are developed under different conditions. In the serial network systems analysis, a practical case study is introduced to show the corresponding method developed. Finally, the problems are discussed for future research. Based on the results of these analyses, we present optimal control policies for resilience. Our method can support the analysis of the impact of disruptions, and the development of control strategies that reduce the impact of the disruption.

Laser enhanced gas metal arc welding (GMAW) is a novel welding process where a laser is applied to provide an auxiliary detaching force to help detach the droplet such that welds may be made in gas tungsten arc welding high quality at GMAW high speeds. The current needed to generate the electromagnetic (detaching) force is thus reduced. The reduction in the current helps reduce the impact on the weld pool and over-heat fumes/smokes. However, in the previous studies, a continuous laser is applied. Since the auxiliary is only needed each time the droplet needs to be detached and the detachment time is relatively short in the transfer cycle, the laser energy is greatly wasted in the rest of the transfer cycle. In addition, the unnecessary application of the laser on the droplet causes additional over-heat fumes. Hence, this study proposes to use a pulsed laser such that the peak pulse is applied only when the droplet is ready to detach. To this end, the state of the droplet development needs to be closely monitored in real-time. Since the metal transfer is an ultra-high speed process and the most reliable method to monitor should be based on visual feedback, a high imaging system has been proposed to monitor the real-time development of the droplet. A high-speed image processing system has been developed to real-time extract the developing droplet. A closed-loop control system has been established to use the real-time imaging processing result on the monitoring of the developing droplet to determine if the laser peak pulse needs to be applied. Experiments verified the effectiveness of the proposed methods and established system. A controlled novel process – pulsed laser-enhanced GMAW - is thus established for possible applications in producing high-quality welds at GMAW speeds.

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module designs toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

The complex relationship between array gain patterns and microphone distributions limits the application of traditional optimization algorithms on irregular arrays, which show enhanced beamforming performance for human speech capture in immersive environments. This work analyzes the relationship between irregular microphone geometries and spatial filtering performance with statistical methods. Novel geometry descriptors are developed to capture the properties of irregular microphone distributions showing their impact on array performance. General guidelines and optimization methods for regular and irregular array design are proposed in immersive (near-field) environments to obtain superior beamforming ability for speech applications. Optimization times are greatly reduced through the objective function rules using performance-based geometric descriptions of microphone distributions that circumvent direct array gain computations over the space of interest. In addition, probabilistic descriptions of acoustic scenes are introduced to incorporate various levels of prior knowledge for the source distribution. To verify the effectiveness of the proposed optimization methods, simulated gain patterns and real SNR results of the optimized arrays are compared to corresponding traditional regular arrays and arrays obtained from direct exhaustive searching methods. Results show large SNR enhancements for the optimized arrays over arbitrary randomly generated arrays and regular arrays, especially at low microphone densities. The rapid convergence and acceptable processing times observed during the experiments establish the feasibility of proposed optimization methods for array geometry design in immersive environments where rapid deployment is required with limited knowledge of the acoustic scene, such as in mobile platforms and audio surveillance applications.

Heterojunctions of CuInSe2 (CIS) nanowires with cadmium sulfide (CdS) were fabricated demonstrating for the first time, vertically aligned nanowires of CIS in the conventional Mo/CIS/CdS stack. These devices were studied for their material and electrical characteristics to provide a better understanding of the transport phenomena governing the operation of heterojunctions involving CIS nanowires. Removal of several key bottlenecks was crucial in achieving this. For example, it was found that to fabricate alumina membranes on molybdenum substrates, a thin interlayer of tungsten had to be inserted. A qualitative model was proposed to explain the difficulty in fabricating anodized aluminum oxide (AAO) membranes directly on Mo. Experimental results were used to corroborate this model. Subsequently, a general procedure to use any material that can be deposited using sputtering or evaporation as a back contact for nanowires grown using AAO templates was developed. Experimental work to demonstrate this by transferring thin AAO templates onto flexible Polyimide (PI) substrates was performed. This pattern transfer approach opens doors for a wide variety of applications on almost any substrate. Any material that can be deposited by physical means can then be used as a back contact. Electron-beam induced deposition using a liquid precursor (LP-EBID) was used to selectively grow preconceived patterns of compound semiconductor (CdS) nanoparticles. Stoichiometric CdS nanoparticle patterns were grown successfully using this method. They were structurally and optically characterized indicating high purity deposits. This approach is promising because it marries the precision of e-beam lithography with the versatility of solution based deposition methods.

As electronics become smaller and more capable, it has become possible to conduct meaningful and sophisticated satellite missions in a small form factor. However, the capability of small satellites and the range of possible applications are limited by the capabilities of several technologies, including attitude determination and control systems. This dissertation evaluates the use of image-based visual attitude propagation as a compliment or alternative to other attitude determination technologies that are suitable for miniature satellites. The concept lies in using miniature cameras to track image features across frames and extracting the underlying rotation. The problem of visual attitude propagation as a small satellite attitude determination system is addressed from several aspects: related work, algorithm design, hardware and performance evaluation, possible applications, and on-orbit experimentation. These areas of consideration reflect the organization of this dissertation. A “stellar gyroscope” is developed, which is a visual star-based attitude propagator that uses relative motion of stars in an imager’s field of view to infer the attitude changes. The device generates spacecraft relative attitude estimates in three degrees of freedom. Algorithms to perform the star detection, correspondence, and attitude propagation are presented. The Random Sample Consensus (RANSAC) approach is applied to the correspondence problem to successfully pair stars across frames while mitigating false-positive and false-negative star detections. This approach provides tolerance to the noise levels expected in using miniature optics and no baffling, and the noise caused by radiation dose on orbit. The hardware design and algorithms are validated using test images of the night sky. The application of the stellar gyroscope as part of a CubeSat attitude determination and control system is described. The stellar gyroscope is used to augment a MEMS gyroscope attitude propagation algorithm to minimize drift in the absence of an absolute attitude sensor. The stellar gyroscope is a technology demonstration experiment on KySat-2, a 1-Unit CubeSat being developed in Kentucky that is in line to launch with the NASA ELaNa CubeSat Launch Initiative. It has also been adopted by industry as a sensor for CubeSat Attitude Determination and Control Systems (ADCS).

Compared with conventional measurements from supervisory control and data acquisition (SCADA) system, phasor measurement units (PMUs) provide time-synchronized and direct measurements of phasors. The availability of synchronized phasor measurements can significantly improve power system protection and analysis. This dissertation is specifically committed to using synchronized measurements for estimation of fault locations in transmission systems. Transmission lines are prone to various short-circuit faults. Accurate fault location is critical for rapid power recovery. Chapter 2 proposes a new fault location method based on sparse wide area measurements. One distinguishing feature of this method is its applicability to both transposed and untransposed transmission lines. In addition, the method is developed based on sparse-wide area measurement that may be taken far away from the faulted line. Shunt capacitances of transmission lines are also fully considered by the algorithm. Moreover, when synchronized measurements from multiple buses are available, an optimal estimator can be used to make the most use of measurements, and to detect and identify potential bad measurements. Most of the existing fault location literatures discuss common shunt faults, including single line-to-ground faults, line-to-line faults, line-to-line-to-ground faults, and three-phase faults. However, in addition to common shunt faults, some complex faults may also occur in power systems. Among these complex faults, evolving fault and inter-circuit fault are two typical examples. Chapter 3 extends the method developed in Chapter 2 to deal with evolving faults. The proposed wide-area fault location methods are immune to fault type evolution, and are applicable to both transposed and untransposed lines. Chapter 4 discusses location of inter-circuit faults. Inter-circuit fault is a type of simultaneous fault, and it is the most common simultaneous fault type. Inter-circuit faults between each circuit in a double-circuit line is the most common inter-circuit fault. A fault location method for inter-circuit faults on double-circuit lines are developed and evaluated in Chapter 4. Chapter 5 puts forward a fault location algorithm, which does not require line parameters information, for series-compensated transmission lines. Two-end synchronized voltage and current measurements are utilized. The proposed method is independent of source impedance and fully considers shunt capacitances of transmission lines.

Understanding, predicting, and controlling electromagnetic field interactions on and between complex RF platforms requires high fidelity computational electromagnetic (CEM) simulation. The primary CEM tool within NASA is GEMINI, an integral equation based method-of-moments (MoM) code for frequency domain electromagnetic modeling. However, GEMINI is currently limited in the size and complexity of problems that can be effectively handled. To extend GEMINI’S CEM capabilities beyond those currently available, primary research is devoted to integrating the MFDlib library developed at the University of Kentucky with GEMINI for efficient filling, factorization, and solution of large electromagnetic problems formulated using integral equation methods. A secondary research project involves the hybrid parallelization of GEMINI for the efficient speedup of the impedance matrix filling process. This thesis discusses the research, development, and testing of the secondary research project on the High Performance Computing DLX Linux supercomputer cluster. Initial testing of GEMINI’s existing MPI parallelization establishes the benchmark for speedup and reveals performance issues subsequently solved by the NASA CEM Lab. Implementation of hybrid parallelization incorporates GEMINI’s existing course level MPI parallelization with Open MP fine level parallel threading. Simple and nested Open MP threading are compared. Final testing documents the improvements realized by hybrid parallelization.

The rise of internet connected devices has enabled the home with a vast amount of enhancements to make life more convenient. These internet connected devices can be used to form a community of devices known as the internet of things (IoT). There is great value in IoT devices to promote healthy independent living for older adults. Fall-related injuries has been one of the leading causes of death in older adults. For example, every year more than a third of people over 65 in the U.S. experience a fall, of which up to 30 percent result in moderate to severe injury. Therefore, this thesis proposes an IoT-based fall detection system for smart home environments that not only to send out alerts, but also launches interaction models, such as voice assistance and camera monitoring. Such connectivity could allow older adults to interact with the system without concern of a learning curve. The proposed IoT-based fall detection system will enable family and caregivers to be immediately notified of the event and remotely monitor the individual. Integrated within a smart home environment, the proposed IoT-based fall detection system can improve the quality of life among older adults. Along with the physical concerns of health, psychological stress is also a great concern among older adults. Stress has been linked to emotional and physical conditions such as depression, anxiety, heart attacks, stroke, etc. Increased susceptibility to stress may accelerate cognitive decline resulting in conversion of cognitively normal older adults to MCI (Mild Cognitive Impairment), and MCI to dementia. Thus, if stress can be measured, there can be countermeasures put in place to reduce stress and its negative effects on the psychological and physical health of older adults. This thesis presents a framework that can be used to collect and pre-process physiological data for the purpose of validating galvanic skin response (GSR), heart rate (HR), and emotional valence (EV) measurements against the cortisol and self-reporting benchmarks for stress detection. The results of this framework can be used for feature extraction to feed into a regression model for validating each combination of physiological measurement. Also, the potential of this framework to automate stress protocols like the Trier Social Stress Test (TSST) could pave the way for an IoT-based platform for automated stress detection and management.

Time-Frequency (TF) masking is an audio processing technique useful for isolating an audio source from interfering sources. TF masking has been applied and studied in monaural and binaural applications, but has only recently been applied to distributed microphone arrays. This work focuses on evaluating the TF masking technique's ability to isolate human speech and improve speech intelligibility in an immersive "cocktail party" environment. In particular, an upper-bound on TF masking performance is established and compared to the traditional delay-sum and general sidelobe canceler (GSC) beamformers. Additionally, the novel technique of combining the GSC with TF masking is investigated and its performance evaluated. This work presents a resource-efficient method for studying the performance of these isolation techniques and evaluates their performance using both virtually simulated data and data recorded in a real-life acoustical environment. Further, methods are presented to analyze speech intelligibility post-processing, and automated objective intelligibility measurements are applied alongside informal subjective assessments to evaluate the performance of these processing techniques. Finally, the causes for subjective/objective intelligibility measurement disagreements are discussed, and it was shown that TF masking did enhance intelligibility beyond delay-sum beamforming and that the utilization of adaptive beamforming can be beneficial.