Data collection is an integral part of hydrologic investigations; yet, hydrologic data collection is costly, particularly in subsurface environments. Consequently, it is critical to target data collection efforts toward prospective data sets that will best address the questions at hand, in the context of the study. Experimental and monitoring network designs that have been carefully planned with a specific objective in mind are likely to yield information-rich data that can address critical questions of concern. Conversely, data collection undertaken without careful planning may yield datasets that contain little information relevant to the questions of concern. This dissertation research develops and presents approaches that can be used to support careful planning of hydrologic experiments and monitoring networks. Specifically, three general types of problems are considered. Under the first problem type, the objective of the hydrologic investigation is to discriminate among rival conceptual models, or among rival predictive groupings. A Bayesian methodology is presented that can be used to rank prospective datasets during the planning phases of a hydrologic investigation. Under the second problem type, the objective is to quantify the impact of existing data on reductions in parameter uncertainty. An inverse modeling approach is presented to quantify the impact of existing data on parameter uncertainty when the hydrogeologic conceptual model is uncertain. The third and final problem type focuses on data collection in a water resource management context, with the specific goal to maximize profits without imposing adverse environmental impacts. A risk-based decision support framework is developed using detailed hydrologic simulation to evaluate probabilistic constraints. This enables direct calculation of the profit gains associated with prospective reductions in system parameter uncertainty, and the possible environmental impacts of unknown bias in the system parameters.

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The study of developmental metacognition was originally proposed as a way to better understand memory, by elucidating the processes that act upon and therefore affect it. Much research has been conducted to examine the nature of metacognitive processes, and the interaction between metacognitive judgments and learning behavior in adults. Developmental research has demonstrated that there is a strong developmental trend, such that metacognitive abilities emerge at age four years at the earliest and mature until adulthood. However, this estimate raises a potential paradox, given young children's excellent learning abilities, if monitoring and differentially responding to changes in internal states of knowledge is an important component of learning. This dissertation proposes that metacognitive processes, like memory-monitoring, rather than being distinct from and externally operating on core cognitive process, may be intrinsically linked to basic cognitive functions, arising naturally as a result of processing. By this account, metacognitive abilities emerge in implicit form early in development along with other developing cognitive functions like memory, and what is observed later as the emergence of metacognition may rather be the transition from an implicit and undifferentiated process to an explicit and more readily testable process. This dissertation presents six experiments exploring the relationship between memory-monitoring in non-human animals, preschool children, and adults, using a non-verbal paradigm adapted from comparative literature. Participants learned a set of visual paired-associates, and at test were given the option to selectively accept or decline a memory trial for each item. Accuracy for accepted items was significantly higher than for declined in children and there was a similar tendency with adults, suggesting implicit memory-monitoring skills. Additionally, a relationship between memory-monitoring assessments and other cognitive processes was found, suggesting that memory-monitoring does not function independently of other cognitions. The results suggest that children may have implicit access to internal knowledge states at very young ages, providing an explanation for how they are able to guide learning, even as infants. Further the results suggest that the relationship between metacognitive and other cognitive skills may be rather more dynamic and complex than has typically been described.

Flywheel energy storage units have become a viable alternative to electrochemical batteries in applications such as satellites, uninterrupted power supplies, and hybrid vehicles. However, this performance is contingent upon safe operation since these flywheels can release their stored energy almost instantaneously upon failure. The research presented here investigates a health monitoring technology that may give an early indication of degraded material properties in a concentric ring composite flywheel. The existence of degraded material properties is manifested as a change in mass eccentricity due to asymmetric growth of the outermost flywheel ring. A test rig concept to investigate the technology is developed in detail using a systems engineering design process. Successful detection of the change in mass eccentricity was verified analytically through dynamic modeling of the flywheel rotor and magnetic suspension system. During steady state operation detection was determined to be feasible via measurements of the magnetic bearing currents and shaft position provided by the magnetic suspension feedback sensors. A rotordynamic analysis was also conducted and predicts successful operation to the maximum operating speed of 50,000 Rpm.

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Efforts to alleviate poverty and conserve biodiversity require reliable methods to monitor and assess changes in conservation and development status. Projects intended to achieve biodiversity conservation and poverty alleviation objectives often fall short of their goal. Considering the investments made to support these efforts, this is a real concern to society. Evaluating the effectiveness of these efforts is crucial to receive ongoing support and to learn what’s working, what’s not and how it can be improved. This research examines current M&E and impact assessment practices and systems at conservation and development organizations garnered through a survey and interviews and documents opinions, experiences and lessons learned from key informants. Organizations are facing common barriers but share opportunities to improving M&E. Embracing a culture of learning, synthesizing a common vocabulary and implementing organizational change are important steps in improving M&E practice to provide information and data for better M&E and impact assessment.

This thesis evaluates different sites for a weather measurement system and a suitable PV- simulation for University of Surabaya (UBAYA) in Indonesia/Java. The weather station is able to monitor all common weather phenomena including solar insolation. It is planned to use the data for scientific and educational purposes in the renewable energy studies. During evaluation and installation it falls into place that official specifications from global meteorological organizations could not be meet for some sensors caused by the conditions of UBAYA campus. After arranging the hardware the weather at the site was monitored for period of time. A comparison with different official sources from ground based and satellite bases measurements showed differences in wind and solar radiation. In some cases the monthly average solar insolation was deviating 42 % for satellite-based measurements. For the ground based it was less than 10 %. The average wind speed has a difference of 33 % compared to a source, which evaluated the wind power in Surabaya. The wind direction shows instabilities towards east compared with data from local weather station at the airport. PSET has the chance to get some investments to investigate photovoltaic on there own roof. With several simulations a suitable roof direction and the yearly and monthly outputs are shown. With a 7.7 kWpeak PV installation with the latest crystalline technology on the market 8.82 MWh/year could be achieved with weather data from 2012. Thin film technology could increase the value up to 9.13 MWh/year. However, the roofs have enough area to install PV. Finally the low price of electricity in Indonesia makes it not worth to feed in the energy into the public grid.

Walking is an essential task important to recovery after stroke. However, there is a limited understanding regarding the characteristics of walking in in-patients with stroke. The objectives of this thesis were to: 1) develop an instrument capable of acquiring temporal characteristics of everyday walking; 2) investigate the quantity and control of everyday walking; and 3) profile the task-specific link between walking and cardiorespiratory response. In study 1 we developed and validated a wireless monitoring system (ABLE system). Study 2 revealed low quantities of everyday walking (4816 steps; SD 3247) characterized by short bout durations (59.8s; SD 23.4) and asymmetric walking. In study 3 we observed a modest task-related response in HR(19.4% HRR); however, the intensity and duration of everyday walking did not approach the guidelines for aerobic benefit. Monitoring in-patient walking can help guide clinical decision making in developing methods to maximize recovery after stroke.

The modern industry today is highly dependent on electric motors of differenttypes and sizes. Synchronous motors are used in applications where a fixedspeed is desired. These machines are often found in high power applicationswhere they are preferred over induction motors due to their higher efficiency.Synchronous motors represent large investments and typically drive processeswhere downtime results in significant capital losses. Thus, detecting faults atan early stage can help avoid catastrophic failures and be useful in thescheduling of maintenance. In order to detect faulty conditions before theyterminate in a failure, machine operators must perform some kind ofmonitoring on the machines. Typically, the more critical the machine is for aprocess, the more effort is put on monitoring it. Before building a monitoringsystem for a machine, one must first decide what parameters that should bemonitored. The obvious desire is to find a parameter that is easy and cheap tomeasure and at the same time can give detailed information about the workingstate of the machine.The aim of this thesis is to evaluate whether the exciter stator current is anadequate parameter to use within a monitoring system for synchronous motors.The evaluation has been made through simulations of two different setups;One using a synchronous motor in the 20 MW range fed by a synchronousmachine type exciter. And the other using the same motor but instead fed byan induction machine type exciter. It has been found that the exciter statorcurrent can be used for detection of faults associated to the rectifier and statorshort circuit of the main machine stator winding. It has not been possible todetect turn-to-turn faults in the main machine rotor.The work has been performed at ABB Corporate Research in Västerås fromJune until December 2013.

Fault detection and diagnosis is quite important in engineering systems, and deserves further attention in view of the increasing complexity of modern machinery. Traditional single-sensor methods of fault monitoring and diagnosis may find it difficult to meet modern industrial requirements because there is usually no direct way to measure and accurately correlate a machine fault to a single sensor output. Fusion of information from multiple sensors can overcome this shortcoming. In this thesis, a neural-fuzzy approach of multi-sensor fusion is developed for a network-enabled remote fault diagnosis system. The approach is validated by applying it to an industrial machine called the Iron Butcher, which is a machine used in the fish processing industry for the removal of the head in fish prior to further processing for canning. An important characteristic of the fault diagnosis approach developed in this thesis is to make an accurate decision of the machine condition by fusing information from different sensors. First, sound, vibration and vision signals are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Second, the sound and vibration signals are transformed into the frequency domain using fast Fourier transformation (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. Third, Sound, vibration and vision feature vectors are provided as inputs to a neuro-fuzzy network for fault detection and diagnosis. A four-layer neural network including a fuzzy hidden layer is developed in the thesis to analyze and diagnose existing faults. By training the neural network with sample data for typical faults, faults of five crucial components in the fish cutting machine are detected with high reliability and robustness. Alarms to warn about impending faults may be generated as well during the machine operation. A network-based remote monitoring architecture is developed as well in the thesis, which will facilitate engineers to monitor the machine condition in a more flexible manner from a remote site. Developed multi-sensor approaches are validated using computer simulations and physical experimentation with the industrial machine, and compared with a single-sensor approach.