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Title: LOAD ANALYSIS BASED ON MACHINE LEARNING IN POWER SYSTEMS
Creator: Lu, Dan
Date: 2017, 2017-05
Description: The dissertation is composed by four parts, first, load sampling for SCUC based on Principal Component Analysis (PCA) and Kernel Density...
Show moreThe dissertation is composed by four parts, first, load sampling for SCUC based on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE); second, load forecasting based on PCA and Bayesian ridge regression; third, anomalies detection based on Machine Learning methodology; fourth the long-term planning of Battery-based Energy Storage Transportation (BEST) in power system. Mathematical models are constructed to fulfill the research of the three targets, and numerical examples are used to test the models. The first three parts are based on PCA, which reduced the load dimensions. In the first part, a robust power system Unit Commitment (UC) is the aim to fulfil the possible load. In the second part, a novel short-term nodal load forecasting is raised to give better prediction of the next day load to improve the next data UC scheduling. In the third part, anomalies are detected in the reduced power flow space based on the pattern identified in the lower dimensional space. The purpose of the fourth part is to find ways of better utilizing the existing resources from integrating the frontier technology, the mobility of more compact and higher capacity batteries. Mix-integer programming (MIP) is used in the formulation.
Ph.D. in Electrical Engineering, May 2017
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Title: METHODOLOGY FOR VEHICLE EMISSION IMPACTS ANALYSIS FROM SIGNAL TIMING OPTIMIZATION OF AN URBAN STREET NETWORK
Creator: Lu, Pu
Date: 2017, 2017-05
Description: The pace of urban street capacity expansion is much slower than the growth of vehicle travel, leading to several traffic congestions. To...
Show moreThe pace of urban street capacity expansion is much slower than the growth of vehicle travel, leading to several traffic congestions. To mitigate traffic congestion expanding capacity is not feasible for many cases due to the high cost and space restriction. Improving the efficient use of the available capacity becomes the solution. Traffic signal optimization is one of the most widely used ways of efficient capacity utilization. Concurrent to traffic signal optimization, more smooth traffic operations in term of reasonably higher speed and a reduced traffic delay will in turn change vehicle emissions. This research aims to quantify changes in vehicle emissions resulted from traffic signal optimization by introducing a new methodology for quantifying network wide vehicle emissions and real world application in of the Chicago urban network for validation. The proposed methodology considers undersaturation and oversaturation of traffic conditions and urban street segments with varying speeds for different types of vehicles and pollutants by hour of the day and location within the network. It begins with information collection and research through a review of existing methods for urban street network vehicle emission estimation, intersection vehicle emission evaluation, and the running vehicle emission modeling. The proposed methodology focuses on three elements: estimation of emissions from vehicles stopped at intersections and for vehicles cruising along segments, as well as analysis of network wide vehicle emissions and changes in overall network vehicle emissions by time of the day and by areas. Major steps of methodology application included the use of Chicago TRANSIMS model implementing optimized signal timing plans to obtain refined traffic volumes at intersections and on segments, increased vehicle operating speeds, changed green splits, and vehicle compositions for all intersections and segments in the urban street network, the application of an intersection vehicle emission model for stopped vehicles and a segment vehicle emission model for vehicles cruising on segments, and the network wide analysis of vehicle emission changes by vehicle type and pollutant type in a 24-hour period within an urban street network, respectively. The proposed methodology for intersection vehicle emission estimation was successfully applied to a dense urban street network in Chicago for each approach per cycle and then extended for intersections in hours of the day to analyze the impacts of traffic changes at intersections on exhaust changes. In order to develop the network vehicle emission analysis method, it is essential to evaluate the segment vehicle emissions. This is achieved by using the concept of vehicle specific power which is used to estimate emissions of cruising vehicles considered along with vehicle speeds and speed changes and hence analyzing changes in segment vehicle emissions affected by traffic volume changes derived from signal timing optimization. The decreased number of vehicles stopped at intersections by applying signal timing optimization will reduce intersection emissions, hence reducing overall network vehicle emissions. In addition to have vehicle emissions got reduced at intersections, the increasing vehicle speed for vehicles on segments could further reduce vehicle emissions on segments.
Ph.D. in Civil Engineering, May 2017
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Title: NEUROPSYCHOLOGICAL PROFILES IN ADULTS WITH SICKLE CELL DISEASE
Creator: Piper, Lauren E.
Date: 2014, 2014-12
Description: Cognitive impairment is documented in individuals with sickle cell disease (SCD). Studies investigating cognitive impairment in this...
Show moreCognitive impairment is documented in individuals with sickle cell disease (SCD). Studies investigating cognitive impairment in this population have primarily examined group differences in neuropsychological performance, which may have overlooked the heterogeneity of cognitive functioning. The objectives of this study were to determine whether distinct cognitive profiles occur in individuals with SCD and, if so, to examine potential differences in demographic, clinical, and psychosocial characteristics. Participants with SCD (n = 73) and similarly matched controls (n = 82), completed a brief neuropsychological protocol (Hachinski et al., 2006) and self-report measures of pain and mood-related symptoms. Cluster analysis was used to identify groups of participants based on their cumulative scores across the domains of executive functioning, language, memory, and visuospatial ability. Multivariate analysis of variance (MANOVA) was used to compare the cluster groups across the four cognitive domains. Analysis of variance (ANOVA) and chi-square tests were used to compare cluster groups on demographic, clinical, and psychosocial characteristics. Results indicated three distinct cognitive subtypes: (1) executive and memory impaired (56% of participants); (2) globally impaired (14%); and (3) cognitively intact (30%). The three cluster groups did not differ on most demographic factors, stroke history, or pain severity, but differed on level of education and current mood-related distress. Results demonstrated the presence of distinct cognitive profiles in adults with SCD, with a proportion of cognitively intact individuals. Implications for intervention and cognitive rehabilitation are discussed.
M.S. in Psychology, December 2014
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Title: THE SIMPLE EQUAL FLOW PROBLEM ON GENERALIZED NETWORKS
Creator: Fidler, Mary E.
Date: 2011-07, 2011-07
Description: We study algorithms for the simple equal ow problem on generalized networks. Network ows problems are concerned with optimization of the ow of...
Show moreWe study algorithms for the simple equal ow problem on generalized networks. Network ows problems are concerned with optimization of the ow of commodities over a network, a directed graph. In a network, the amount of ow that leaves a node equals the ow that arrives at the destination node. However, generalized networks have arc multipliers which change the rate of ow on each arc. A classical network ow problem is the min cost ow problem which asks for minimum cost required for the ow of a commodity that satis es individual commodity requirements of each node in a network. The simple equal ow problem considers the min cost ow problem with an additional non-network constraint that requires certain arcs to have equal ow. Ahuja et al. [2] developed a combinatorial parametric algorithm, binary search algorithm, and capacity scaling algorithm for the simple equal ow problem. In this thesis, we extend the rst two algorithms to generalized networks. To do so, we must rst reformulate the simple equal ow problem on generalized networks to parameterize the equal ow arcs. The resulting linear program creates a piecewise linear convex curve as a function of the parameter. Then, we exploit the simplex algorithm derived combinatorial basis of generalized networks to determine the distance between breakpoints of the piecewise parametric linear convex curve of optimal solutions, which helps to determine the appropriate termination condition for the algorithms. This allows us to formulate the modi ed combinatorial parametric algorithm and the modi ed binary search algorithm, and their running times.
M.S. in Applied Mathematics, July 2011
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Title: SKINNING AND STORAGE METHODS FOR STRUCTURAL AND FUNCTIONAL STUDIES OF MANDUCA SEXTA FLGHT MUSCLE
Creator: Zhang, Mengjie
Date: 2012-04-22, 2012-05
Description: Like mammalian cardiac muscle, the flight muscle of the hawk-moth Manduca sexta is synchronous. However, it also has significant structural...
Show moreLike mammalian cardiac muscle, the flight muscle of the hawk-moth Manduca sexta is synchronous. However, it also has significant structural similarities with asynchronous insect flight muscle systems, such as those of Drosophila and Lethocerus. Different physiological function depends on the underlying different molecular structures. Although Drosophila and Lethocerus have been well studied, Menduca sexta is still a newly developed research model. Many different skinning and storage methods are being used worldwide for in vitro studies of a wide variety of muscle systems. Here our goal is to develop better skinning solution and storage condition which will maintain muscle structure and function as well as intact muscle. To achieve this end, several kinds of skinning solutions and storage conditions were evaluated by laser and X-ray mechanical experiments. The solution from HAMM Lab (University of Washington) showed the best ability to destroy plasma membrane, without affecting muscle interior structure and function. For two storage conditions, 4°C without glycerol and -20°C with 50% glycerol, both laser and X-ray experiments show the latter one is better, which means muscles stored at -20°C with 50% glycerol have faster and stronger reaction for high calcium solution, and also have sharper peaks in X-ray diffraction patterns. The presence of protease inhibitors is also necessary to maintain the contraction ability of muscle. Furthermore, the X-ray experiments for muscle fibers stored at -20°C with 50% glycerol and -80°C with 75% glycerol, show the expected results, which the muscles stored at latter condition show far superior X-ray diffraction patterns, especially in meridional reflections and layer lines. The lower temperature can minimize protein degradation. Notably, incubating muscles in glycerol solutions at 4°C before storing them at -80°C, and washing muscles thoroughly after stored at 75% glycerol, are critical to ensure that all of the glycerol goes in and out of muscle fibers, respectively. The significance of these findings is that it now appears to be possible to store prepared Manduca sexta flight muscles for at least eight days at -80°C with good structural preservation enabling a large class of future experiments not requiring fresh deliveries of moth from the suppliers.
M.S. in Biology, May 2012
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Title: AN ACCELERATING COUETTE FLOW IN NEK5000: APPLICATIONS IN OCEANOGRAPHY AND MAGNETOHYDRODYNAMICS
Creator: Miksis, Zachary M.
Date: 2017, 2017-05
Description: Nek5000 is a highly scalable spectral element code used in a broad array of problems in computational fluid dynamics. In this thesis, we focus...
Show moreNek5000 is a highly scalable spectral element code used in a broad array of problems in computational fluid dynamics. In this thesis, we focus on applying the code to a model problem of an accelerating Couette flow, or a hydrodynamic flow between two plates, of which the top plate is accelerating and the bottom plate is stationary, and verifying the numerical methods as applied to this problem. We obtain an analytical solution to the hydrodynamic flow problem, and use this to analyze the effects of changing time step length, the size of the computational mesh, and the computational polynomial order on the accuracy and stability of Nek5000. Additionally, we discuss the addition of an applied magnetic field to the hydrodynamic Couette flow, and provide a formulation for an exact solution to this magnetohydrodynamic problem that can be used to further verify Nek5000 in a similar fashion to the hydrodynamic problem.
M.S. In Applied Mathematics, May 2017
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Title: TOPICS IN GRAPH FALL-COLORING
Creator: Mitillos, Christodoulos
Date: 2016, 2016-07
Description: Graph fall-coloring, also known as idomatic partitioning or independent domatic partitioning of graphs, was formally introduced by Dunbar,...
Show moreGraph fall-coloring, also known as idomatic partitioning or independent domatic partitioning of graphs, was formally introduced by Dunbar, Hedetniemi, Hedetniemi, Jacobs, Knisely, Laskar, and Rall in 2000 [1] as a simple extension of graph coloring and graph domination. It asks for a partition of the vertex set of a given graph into independent dominating sets. In this thesis, we will study a number of questions related to this concept. In the rst chapter we will give a brief background to graph theory, and introduce the topic of graph fall-coloring, after looking at the fundamental topics it builds on. In the second chapter, we identify the e ects on fall-colorability of various graphical operators, and look at the fall-colorability of certain families of graphs. In the third chapter we will explore certain constructions which create fall-colorable graphs given certain restrictions, and look at the interaction of fall-colorings and non-fall-colorings. Finally, in the fourth chapter, we lay the foundations to establish a connection between fall-coloring and certain existing open problems in graph theory, providing new possible avenues for exploring their solutions. We then provide two applied problems which can be solved with fall-coloring, and which motivate the notion of fall-nearcoloring. We also provide further questions in fall-coloring for future research. Keywords: Graph Fall-coloring, Idomatic Partition, Independent Dominating Sets, Chromatic number, Graph products.
Ph.D. in Applied Mechanics, July 2016
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Title: DYNAMIC RECONFIGURATION OF THE DISTRIBUTION NETWORK WITH UNCERTAINTIES CONSIDERING DISTRIBUTED GENERATOR
Creator: Zhang, Hao
Date: 2015, 2015-05
Description: The thesis focuses on the reconfiguration process of the electrical distribution system, which reduces the real power loss in the system...
Show moreThe thesis focuses on the reconfiguration process of the electrical distribution system, which reduces the real power loss in the system through optimizing the topology of the distribution system. The model selected for the reconfiguration process of the distribution system is an MILP (mixed-integer linear programming). In addition, an adjusted MILP model with distributed generator. DG is formulated, in which the DG can follow the requirement of real operation in an active way. The major contribution of this thesis is to add the time-dimension and uncertainties. In addition, compared to other methods, the addition of other features into the MILP model can be done easily since the linking constraints can be added into the whole model in a simple way. Through the linking constraints, the reconfiguration of distribution system can transfer from a static model of one time spot with constant supply and demand data to the dynamic optimization-based model that can deal with the entire time horizon and the uncertainty at specific hour. It makes the model closer to the reality. Finally the numerical result is used to verify the concept proposed in this thesis.
M.S. in Electrical Engineering, May 2015
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Title: HYBRID BATTERY-ULTRACAPACITOR ENERGY STORAGE SYSTEMS FOR NEXT GENERATION SHIPBOARD POWER SYSTEMS
Creator: Tang, Yichao
Date: 2011-04-22, 2011-05
Description: Batteries and ultracapacitors are likely to be candidates as excellent energy storage technologies for future shipboard power systems. This...
Show moreBatteries and ultracapacitors are likely to be candidates as excellent energy storage technologies for future shipboard power systems. This dissertation explores a hybrid Battery-Ultracapacitor Energy Storage System (BUCESS) for next generation shipboard application. Based on the power requirements of combat ships, a new configuration of the battery and ultracapacitor combined system is introduced for propulsion systems and pulse power loads. For one BUCESS unit, batteries and ultracapacitors are charged and discharged at high voltage level and high power level through a dual active bridge and a double-boost bidirectional converter. The converters are optimally designed to control the bidirectional power flow for batteries and ultracapacitors separately, and to ensure constant voltage regulation of ultracapacitors during charging and discharging. High-frequency switching devices are selected to achieve dc-dc conversion at high voltage and high power levels. The design guidelines and control schemes for simulations are provided for the proposed new topology. Finally, a double-input BUCESS unit is designed, analyzed and simulated to investigate different operation modes of the hybrid energy storage system.
M.S. in Electrical Engineering, May 2011
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Title: DEPTH MAP ENHANCEMENT FOR REAL-TIME 3D RECONSTRUCTION
Creator: Lee, Kitae
Date: 2015, 2015-07
Description: In this paper, we present a novel depth map enhancement for real-time 3D reconstruction by the Microsoft Kinect. The Kinect sensor is...
Show moreIn this paper, we present a novel depth map enhancement for real-time 3D reconstruction by the Microsoft Kinect. The Kinect sensor is relatively affordable and capable of generating high-resolution color image and depth maps of the scene at realtime rates. However, owning the low- cost, there are several artifacts. Generated depth map contains lots of holes, which they are missing information around object boundaries and mis-alignment with color image. The objective of 3D reconstruction is to recreate a real scene, as accurate as possible within a virtual three-dimensional space using a computer. The algorithm of 3D-recosntrution is highly based on the quality of the depth map. This poor depth map could not be applied in potential real-time 3D reconstruction. We present novel multi-step upsampling-based our novel anisotropic diffusion algorithms with generated depth map and color image by Kinect. This method has better performance than existed bilateral filtering and original anisotropic filtering in terms of filling holes, sharpening the boundaries of objects and alignment between depth map and color image. We compare the performance of these filters. It is difficult to do a meaningful comparison of two algorithms with using output of Kinect sensor directly; as for each observation of the same scene, we will get different sensed value. In order to circumvent this problem and to achieve an accurate comparison process, we used dataset from Computer Vision Group at Munchen Technology Universty(TUM). This dataset and the scripts is related to quantitative error metrics are avail at http://vision.in.tum.de/data/datasets/rgbd-‐dataset. We also contribute making our project parallel and GPU computing to satisfy real-time system condition.
M.S. in Electrical Engineering, July 2015
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Title: DISTRIBUTION SYSTEM STATE ESTIMATION
Creator: Li, Lingyan
Date: 2015, 2015-05
Description: This thesis provides a novel method to improve distribution system state estimation by an effective approach to processing bad data in...
Show moreThis thesis provides a novel method to improve distribution system state estimation by an effective approach to processing bad data in measurements. The first part of this research is focused on modeling distribution system state estimation with bad data rejection capability. We apply transmission level model to the distribution level system with specific properties, such as fewer real measurement data for state estimation in the distribution level system, three phase unbalance power flow and so on. For building a robust state estimation model, we optimize the system in the following ways: First, we optimize objective function. We use forecasted load as pseudo measurements. Then we apply different weights to distinguish the forecasted data and actual measurements in the state estimation. Second, we apply three phase power equation in the analysis. We add real power, reactive power, active line power, reactive line flow, voltage magnitude, phase angle and others as nonlinear constraints in the three phase model of state estimation. Third, we flexibly change objective function and constraints in the state estimation model. We can change objective function when state estimation method changes. Meanwhile, we can add power flow and bus limitations in the optimization to avoid state estimation results exceeding power system limitations. Finally, we conduct hybrid calculation. In the first optimization, we filter the bad data. Then, we add another weight to reduce the bad measurement weight and enlarge the good measurement weight. After this process, we get optimized state estimation results. The second part focuses on the implementation of the model. We explain how to preprocess testing case data in this part. The third part is case study. We use IEEE 34 node feeder to test this model. There are four test cases. One test case has no bad data. Other cases have bad data in different types of measurements. We compare these cases with conventional WLS approach. The results obtained from simulation indicate our model has better performance when there is bad data in measurements.
M.S. in Electrical Engineering, May 2015
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Title: CYBER ATTACKS AGAINST STATE ESTIMATION IN POWER SYSTEMS: VUNERABILITY ANALYSIS AND PROTECTION STRATEGIES
Creator: Liu, Xuan
Date: 2015, 2015-07
Description: Power grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. With the development of Smart...
Show morePower grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. With the development of Smart Grid, cyber security has become an area of growing concern. False data injection attack has recently attracted wide research interest. This thesis proposes a false data attack model with incomplete network information and develops optimal attack strategies for attacking load measurements and the real-time topology of a power grid. The impacts of false data on the economic and reliable operations of power systems are quantitatively analyzed in this thesis. To mitigate the risk of cyber attacks, a distributed protection strategies are also developed. It has been shown that an attacker can design false data to avoid being detected by the control center if the network information of a power grid is known to the attacker. In practice, however, it is very hard or even impossible for an attacker to obtain all network information of a power grid. In this thesis, we propose a local load redistribution attacking model based on incomplete network information and show that an attacker only needs to obtain the network information of the local attacking region to inject false data into smart meters in the local region without being detected by the state estimator. A heuristic algorithm is developed to determine a feasible attacking region by obtaining reduced network information. This thesis investigates the impacts of false data on the operations of power systems. It has been shown that false data can be designed by an attacker to: 1) mask the real-time topology of a power grid; 2) overload a transmission line; 3) disturb the line outage detection based on PMU data. To mitigate the risk of cyber attacks, this thesis proposes a new protection strategy, which intends to mitigate the most damaging effect of LR attacks on power system operation. The objective is to mitigate the damage effects of false data injection attacks by increasing the attacking cost of an attacker. This is achieved by protecting a small set of critical measurements. To further reduce the computation complexity, we also propose a mixed integer linear programming approach to separate the power grid into several subnetworks, then distributed protection strategy is applied to each subnetwork. The results of this thesis reveal the mechanism of local false data injection attacks and highlight the importance and complexity of defending power systems against false data injection attacks.
Ph.D. in Electrical and Computer Engineering, July 2015
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Title: COMPUTATIONAL COST OF SIMULATING MEAN EXIT TIME USING STOCHASTIC DIFFERENTIAL EQUATIONS
Creator: Liu, Fanjing
Date: 2016, 2016-05
Description: Stochastic di erential equations play an important role in modern science, including engineering, physics, computer science and nance. It has...
Show moreStochastic di erential equations play an important role in modern science, including engineering, physics, computer science and nance. It has been shown that numerically solving stochastic di erential equation is a productive methodto deal with such problems. In this work, we try to analyze the procedure of numerically computing the mean exit time of some stochastic processes from a given boundary using Monte Carlo simulations. The two methods, including the Euler-Maruyama Method and Milstein's higher order method, will be explained and used extensively when we simulate paths of the random process. The simulated processes generated through the methods will then be used to identify the exit times. Later we use the average of the exit times as a numerical solution of Mean Exit Time. We compare the e ciency of the above two methods by evaluating their computational complexity and CPU cost of reaching the same level of accuracy.
M.S. in Applied Mathematics, May 2016
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Title: STEREO-BASED DEPTH MAP PROCESSING: ESTIMATION AND REFINEMENT
Creator: Loghman, Maziar
Date: 2016, 2016-12
Description: During the past decade, research in 3D video has become a hot topic owing to advancements in both hardware and software. Amongst different...
Show moreDuring the past decade, research in 3D video has become a hot topic owing to advancements in both hardware and software. Amongst different methods proposed for representing 3D data, multi-view video plus depth (MVD) format has gained a lot of attention. Most of such 3D algorithms rely on a per-pixel depth representation of the scene called a depth map. Depth maps are very useful for rendering virtual views and have lead to advancements in 3D compression algorithms. Generating an accurate and dense depth map is one of the important prerequisite for many 3D video applications. In this thesis, we highlight the following major problems in MVD. * Depth map estimation * Depth map refinement * Depth map coding In order to generate an accurate depth map, we propose a method based on Census transform with adaptive window patterns and semi-global optimization. A modified cross-based cost aggregation technique is proposed which helps to calculate a more reliable depth map. In order to further enhance the quality of the generated depth map, a novel multi-resolution anisotropic diffusion based algorithm is presented. The proposed depth refinement algorithm computes a dense depth map in which the holes have been filled and the object boundaries are sharpened. The next part of the research is based on depth map coding. In depth map coding, a considerable amount of time is required to investigate the mode decision pro- cess for every block of depth pixels. However, in real-time purposes, we can partially skip the mode selection step. In this thesis, we propose a novel depth intra-coding scheme for 3D video coding based on HEVC standard. The core idea of the proposed method is motivated by the fact that depth maps have specific characteristics that distinguish them from those of color images. By analyzing the reference depth maps based on homogeneousness of different regions, for some particular blocks, the DMM full-RD search is skipped and the mode is selected based on the previous similar tree- blocks. By this means, the time complexity of the encoding process is significantly reduced.
Ph.D. in Electrical Engineering, December 2016
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Title: STATISTICAL LEARNING IN SOCIAL INTERACTIONS: ANTICIPATION OF CAREGIVER FEEDBACK TO COMMUNICATIVE BEHAVIOR IN PRELINGUISTIC INFANTS
Creator: Lossia, Amanda Kathryn
Date: 2014, 2014-05
Description: A growing body of literature has demonstrated that infants are able to detect patterns in structured external environmental stimuli through a...
Show moreA growing body of literature has demonstrated that infants are able to detect patterns in structured external environmental stimuli through a statistical learning mechanism. The present study examines whether statistical learning operates as a learning mechanism in social interactions as well. Prior research using an ABA experimental design demonstrated that infants modified their communicative behavior when the level of contingent caregiver feedback to infant gestures was altered (Miller & Lossia, 2013). These findings are extended in the present study by examining whether the infants developed modified expectations for caregiver feedback when the pattern of contingent feedback was altered, which might function as a possible mechanism for the changes seen in infant communicative behavior. Anticipatory looking to the caregiver was used as a measure of infants’ expectations for caregiver responsiveness. Results showed differences in anticipatory looking to the caregiver across periods. The pattern of anticipatory looking did not fully explain the changes seen in infant communicative behavior. However, the findings do suggest that infants detected the change in caregiver feedback and modified their expectations, providing support for the presence of a statistical learning mechanism in social interactions.
M.S. in Psychology, May 2014
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Title: THERMAL ANALYSIS OF PM MACHINES FOR HIGH PERFORMANCE ELECTRIC VEHICLES
Creator: La Marca, Frank
Date: 2016, 2016-07
Description: As demand grows for electrification of the automotive industry, the need for a traction motor becomes imperative. There has been considerable...
Show moreAs demand grows for electrification of the automotive industry, the need for a traction motor becomes imperative. There has been considerable effort by electric machine manufacturers to develop and build a traction motor that meets the speci- fications of the automobile industry. One of the limiting factors in the design of an electric automobile is the thermal performance of a traction motor. This thesis focuses on thermal analysis of an electric machine with a major focus on a machine that operates in an automotive environment. Thermal analysis of the electric machine will be focus on the analysis of an electric machine that is used in a FSAE electric racecar. Theory behind various thermal extraction methods are reviewed, including heat transfer and the fluid dynamics of an electric machine. Thermal modeling methods are also investigated, including analytical methods and numerical methods such as finite element analysis and computational fluid dynamics. The importance of thermal modeling of an electric machine is to understand the heat transfer occurring in the machine. In high performance electric machines the limiting factor of the machine the temperature rise when torque is applied. In- vestigation of the heat transfer of a machine can identify the hot spot of the machine and methods of reduction. This will allow for more current applied and more torque which will give a higher density machine.
M.S. in Electrical Engineering, July 2016
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Title: MODELS AND SIMULATIONS OF SPROUTING ANGIOGENESIS
Creator: Langman, Catherine
Date: 2016, 2016-05
Description: All living mammalian cells need to consume oxygen and nutrients for cellular processes and need a way to remove waste from those cellular...
Show moreAll living mammalian cells need to consume oxygen and nutrients for cellular processes and need a way to remove waste from those cellular processes. Capillary networks provide places for such exchanges to occur. The process of creating new capillaries from existing blood vessels is called angiogenesis. Understanding angiogenesis is critical to the advancement of knowledge in the life sciences, as well as in medical applications where blood vessels play an important role. Angiogenesis is a complex process composed of many subprocesses which are not yet fully understood and take place over varying temporal and spatial scales. Mathematically modeling and simulating angiogenesis, and evaluating the capillary networks that result from angiogenesis, can help further understanding of angiogenesis and improve therapeutic treatments. This thesis examines mathematical models and simulations of sprouting angiogenesis and proposes two generic models of sprouting angiogenesis based on descriptions found in educational and scientific literature. Future research opportunities for scientific study and educational study using these models as a starting place are discussed.
M.S. in Applied Mathematics, May 2016
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Title: MOTION OF BUBBLY FLUID IN A TANK
Creator: Langman, Michael
Date: 2014, 2014-07
Description: Computational uid dynamics is the numerical study of the motion of uids. In this thesis, an introduction to uid mechanics is presented and the...
Show moreComputational uid dynamics is the numerical study of the motion of uids. In this thesis, an introduction to uid mechanics is presented and the governing equations of uid mechanics are derived. The open-source computational uid dynamics library OpenFOAM is then used to simulate uid dynamics and to model the formation and movement of bubbles in a tank.
M.S. in Applied Mathematics, July 2014
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Title: MOBILITY IMPROVEMENT BENEFIT ANALYSIS OF SIGNAL TIMING OPTIMIZATION FOR URBAN STREET NETWORK
Creator: Zhang, Ji
Date: 2015, 2015-05
Description: The traffic congestion problem especially in urban areas is getting increasingly severe due to the ever-growing auto travel demand in the...
Show moreThe traffic congestion problem especially in urban areas is getting increasingly severe due to the ever-growing auto travel demand in the United States during the past few decades. In general, insufficient capacity can be solved by system expansion. However, expanding system is not feasible anymore because of the land scarcity in urban areas and its high cost. From this point of view, transportation operations that lead to the optimal system usage are more preferable thanks to their relatively low cost and remarkable consequences. Several performance indices were used in order to assess the effects of a given transportation operation. This study introduces a new method for evaluating the mobility performance of the transportation system before and after a transportation operation. And the mobility benefit is converted into monetary value. Further, a Life-Cycle Benefit Analysis is conducted to expand the evaluation process to the time dimension. An experimental study is performed to apply this method on the urban street network in Chicago downtown area that contains 917 intersections and 1675 roadway segments before and after a network-wide signal timing optimization treatment. Based on this application, the results indicate a few potential advantages and disadvantages of this system-wide signal timing optimization methodology.
M.S. in Civil, Architectural and Environmental Engineering, May 2015
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Title: MOBILITY IMPROVEMENT BENEFIT ANALYSIS OF SIGNAL TIMING OPTIMIZATION FOR URBAN STREET NETWORK
Creator: Zhang, Ji
Date: 2015, 2015-05
Description: The traffic congestion problem especially in urban areas is getting increasingly severe due to the ever-growing auto travel demand in the...
Show moreThe traffic congestion problem especially in urban areas is getting increasingly severe due to the ever-growing auto travel demand in the United States during the past few decades. In general, insufficient capacity can be solved by system expansion. However, expanding system is not feasible anymore because of the land scarcity in urban areas and its high cost. From this point of view, transportation operations that lead to the optimal system usage are more preferable thanks to their relatively low cost and remarkable consequences. Several performance indices were used in order to assess the effects of a given transportation operation. This study introduces a new method for evaluating the mobility performance of the transportation system before and after a transportation operation. And the mobility benefit is converted into monetary value. Further, a Life-Cycle Benefit Analysis is conducted to expand the evaluation process to the time dimension. An experimental study is performed to apply this method on the urban street network in Chicago downtown area that contains 917 intersections and 1675 roadway segments before and after a network-wide signal timing optimization treatment. Based on this application, the results indicate a few potential advantages and disadvantages of this system-wide signal timing optimization methodology.
M.S. in Civil Engineering, May 2015
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