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This thesis demonstrates the value of integrating electrical distribution engineering analysis with Geographic Information Systems (GIS). The 37-Node IEEE Feeder model was used as the base distribution system in this study. It was modeled separately, both in software capable of unbalanced load-flow and in an industry-standard GIS environment. Both tools utilized were commercially available, off-the shelf products indicative of those used in academia and in basic GIS installations. The foundational data necessary to build these models is representative of information required by a variety of utility departments for a multitude of applications. It is inherent to most systems within an enterprise-level, business-wide data model and therefore can be used to support a variety of applications. In this instance, infrastructure information is assumed to be managed and housed with the GIS. This data provides the required information as input for load-flow calculations. The engineering analysis is performed within DistributionSystem 4.01 and its output is passed back to the GIS in tabular format for incorporation. This thesis investigates the transfer of information between GIS and DistributionSystem 4.01 and demonstrates the extended display capabilities in the GIS environment. This research is implemented on a small scale, but is intended to highlight the need for standardization and automatic integration of these systems as well as others that are fundamental to the effective management of electrical distribution systems. / Master of Science

Given that achieving nominal (all dimensions are theoretically perfect) geometry is challenging during building construction, understanding and anticipating sources of geometric variation through tolerances modeling and allocation is critical. However, existing building modeling environments lack the ability to support coordinated, incremental and systematic specification of manufacturing and construction requirements. This issue becomes evident when adding multi-material systems produced off site by different vendors during building erection. Current practices to improve this situation include costly and time-consuming operations that challenge the relationship among the stakeholders of a project. As one means to overcome this issue, this research proposes the development of a knowledge-aided modeling framework that integrates a parametric CAD tool with a system modeling application to assess variability in building construction. The CAD tool provides robust geometric modeling capabilities, while System Modeling allows for the specification of feature-based manufacturing requirements aligned with construction standards and construction processes know-how. The system facilitates the identification of conflicting interactions between tolerances and manufacturing specifications of building material systems. The expected contributions of this project are the representation of manufacturing knowledge and tolerances interaction across off-site building subsystems to identify conflicting manufacturing requirements and minimize costly construction errors. The proposed approach will store and allocate manufacturing knowledge as Model-Based Systems Engineering (MBSE) design specifications for both single and multiple material systems. Also, as new techniques in building design and construction are beginning to overlap with engineering methods and standards (e.g. in-factory prefabrication), this project seeks to create collaborative scenarios between MBSE and Building Information Modeling (BIM) based on parametric, simultaneous, software integration to reduce human-to-data translation errors, improving model consistency among domains. Important sub-stages of this project include the comprehensive review of modeling and allocation of tolerances and geometric deviations in design, construction and engineering; an approach for model integration among System Engineering models, mathematical engines and BIM (CAD) models; and finally, a demonstration computational implementation of a System-level tolerances modeling and allocation approach.

Simulations are a good method for the verification of the correct operation of solar-powered sensor nodes over the desired lifetime. They do, however, require accurate models to capture the influences of the loads and solar energy harvesting system. Artificial neural networks promise a simplification and acceleration of the modeling process in comparison to state-of-the-art modeling methods. This work focuses on the influence of the modeling process's different configurations on the accuracy of the model. It was found that certain parameters, such as the network's number of neurons and layers, heavily influence the outcome, and that these factors need to be determined individually for each modeled harvesting system. But having found a good configuration for the neural network, the model can predict the supercapacitor's charge depending on the solar current fairly accurately. This is also true in comparison to the reference models in this work. Nonetheless, the results also show a crucial need for improvements regarding the acquisition and composition of the neural network's training set.

The electrical activity of skeletal muscle¡ªthe electromyogram (EMG)¡ªis of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. For many applications the EMG is related to muscular tension, joint torque and/or applied forces. In these cases, a goal is for an EMG-torque model to emulate the natural relationship between the central nervous system and peripheral joints and muscles. This thesis mainly describes an experimental study which relates the simultaneous biceps/triceps surface EMG of 12 subjects to elbow torque at seven joint angles (ranging from 45¡ÃƒÂ£to 135¡ÃƒÂ£) during constant-posture, quasi-constant-torque contractions. The contractions ranged between 50% maximum voluntary contractions (MVC) extension and 50% MVC flexion. Advanced EMG amplitude (EMG¦Ãƒâ€™) estimation processors were investigated, and three nonlinear EMG¦Ãƒâ€™-torque models were evaluated. Results show that advanced (i.e., whitened, multiple-channel) EMG¦Ãƒâ€™ processors lead to improved joint torque estimation, compared to unwhitened, single-channel EMG¦Ãƒâ€™ processors. Depending on the joint angle, use of the multiple-channel whitened EMG¦Ãƒâ€™ processor with higher polynomial degrees produced a median error that was 50%-66% that found when using the single-channel, unwhitened EMG¦Ãƒâ€™ processor with a polynomial degree of 1. The best angle-specific model achieved a minimum error of 3.39% MVCF90 (i.e., error referenced to MVC at 90¢X flexion), yet it does not allow interpolation across angles. The best model which parameterizes the angle dependence achieved an error of 3.55% MVCF90. This thesis also summarizes other collaborative research contributions performed as part of this thesis. (1) Decomposition of needle EMG data was performed as part of a study to characterize motor unit behavior in patients with amyotrophic lateral sclerosis (ALS) [with Spaulding Rehabilitation Hospital, Boston, MA]. (2) EMG-force modeling of force produced at the finger tips was studied with the purpose of assessing the ability to determine two or more independent, continuous degrees of freedom of control from the muscles of the forearm [with WPI and Sherbrooke University]. (3) Identification of a nonlinear, dynamic EMG-torque relationship about the elbow was studied [WPI]. (4) Signal whitening preprocessing for improved classification accuracies in myoelectric control of a prosthesis was studied [with WPI and the University of New Brunswick].

In the presented work I devote to creating a computer model of a professional service firm using the theory of business modeling. The whole model was programmed using the application Vensim. Another part of this work was to use this model to answer some key questions about the professional service firms. I dealt with the issue of optimal volume and optimal size of potential orders. I have also studied the influence of marketing and employee productivity on business performance. Another part of the work was to find patterns in employees career growth and to monitor sales and quality of projects delivery. I also determined the size and characteristics of critical mass. Such a model could then be used to better understand patterns of companies in the professional services industry and we can use it to manage the company more effectively in this environment.

We present an energy conscious location-aware computing system that provides relevant information about the users current location. The location-aware computing system is initialized with a map (in the form of a graph) as well as audio files associated with several locations in the map. The system consists of: GPS receiver module, Serial port, Compact flash module, Stereo codec, Power manager module implementing three sub modules namely, GPS-to-real-world position conversion module (implements algorithm to convert GPS co-ordinates to graph nodes), Nearest-location-search module (implements modified Dijkstras algorithm), and User speed estimation module. The location-aware computing system receives the GPS co-ordinates for the current location from GPS receiver through the serial port. The system converts the GPS co-ordinates to map co-ordinates stored in the Compact Flash card. If the current location matches the landmarks of interest in the site, then the relevant audio details of the current location is played out to the user. The power manager sets the GPS co-ordinates update frequency to avoid keeping the system component on throughout the entire course of travel. The power manager implements an algorithm that works as follows: at any given location, the algorithm predicts the user speed by exponential average approach. The attenuation factor of this approach can be varied to account for the user speed history. The estimated speed is used to predict the time (say T) required to reach the next nearest location determined by Nearest-location-search module implementing modified Dijkstras algorithm. The subsystems are shut-down or switched to low-power mode for time T. After time T, the system will wake up and re-execute the algorithm.

The current approach to performance measurement in British Columbia is to select and match performance measures with strategic goals and objectives so that health administrators and decision makers can evaluate the performance of different care sectors (e.g. primary, community and acute care) within the provincial health system. Although this approach offers basic understanding of system performance, it is static and considers the performance of organizational components in isolation from their interrelationships and external influences. The purpose of this research is to enhance the current performance measurement approach in BC by linking health system variables through causal relationships and feedback loops that can impact and lead to health system sustainability. The qualitative system dynamics method was applied to develop a conceptual performance measurement model. Fifteen interviews with stakeholders were conducted at the BC Ministry of Health to validate and improve the pre-validation model. A post-validation model was then created based on the feedback and comments from the 15 interview participants. As a product of this research, the post-validation model, Web of Measures 2.0, will explain how the identified cause and feedback mechanisms both internal and external to the BC health system may help determine policy levers for designing and developing quality improvement initiatives. Although quantitative analysis is out of scope for this research, potential benefits of inputting BC data into the proposed model are discussed at the end of this thesis. / Graduate / 0769 / 0790 / qi.william.yang@gmail.com

Manufacturer-specific models of wind turbines are favored for use in wind power interconnection studies. While they are detailed and accurate, their usages are limited to the terms of the non-disclosure agreement, thus stifling model sharing. The primary objective of the work proposed is to develop universal manufacturer-independent wind power plant models that can be shared, used, and improved without any restrictions by project developers, manufacturers, and engineers. Each of these models includes representations of general turbine aerodynamics, the mechanical drive-train, and the electrical characteristics of the generator and converter, as well as the control systems typically used. In order to determine how realistic model performance is, the performance of the one of the models (doubly fed induction generator model) has been validated using real-world wind power plant data. This work also documents selected applications of these models. / text

Dynamically determining input signals to a complex system, to increase performance and/or reduce cost, is a difficult task unless users are provided with feedback on the consequences of different input decisions. For example, users self-determine the set point schedule (i.e. temperature thresholds) of their HVAC system, without an ability to predict cost--they select only comfort. Users are unable to optimize the set point schedule with respect to cost because the cost feedback is provided at billing-cycle intervals. To provide rapid feedback (such as expected monthly/daily cost), mechanisms for system monitoring, data-driven modeling, simulation, and optimization are needed. Techniques from the literature require in-depth knowledge in the domain, and/or significant investment in infrastructure or equipment to measure state variables, making these solutions difficult to implement or to scale down in cost. This work introduces methods to approximate complex system behavior prediction and optimization, based on dynamic data obtained from inexpensive sensors. Unlike many existing approaches, we do not extract an exact model to capture every detail of the system; rather, we develop an approximated model with key predictive characteristics. Such a model makes estimation and prediction available to users who can then make informed decisions; alternatively, these estimates are made available as an input to an optimization tool to automatically provide pareto-optimized set points. Moreover, the approximation nature of this model makes the determination of the prediction and optimization parameters computationally inexpensive, adaptive to system or environment change, and suitable for embedded system implementation. Effectiveness of these methods is first demonstrated on an HVAC system methodology, and then extended to a variety of complex system applications.