214 research outputs found
Parsing Science - Trusting Our Machines
No full textMight enabling computational aids to "self-correct" when they’re out of sync with people be a path toward their exhibition of recognizably intelligent behavior? In episode 46, Neera Jain from Purdue University discusses in her experiments into monitoring our trust in AI's abilities so as to drive us more safely, care for our grandparents, and do work that’s just too dangerous for humans. Her article "Computational Modeling of the Dynamics of Human Trust During Human–Machine Interactions" was published on October 23, 2018 in IEEE Transactions on Human-Machine Systems and was co-authored with Wan-Lin Hu, Kumar Akash, and Tahira Reid.https://www.parsingscience.org/2019/04/02/neera-jain/</div
sj-pdf-1-mde-10.1177_23821205231164022 - Supplemental material for Compassionate Off-Ramps: The Availability of Terminal Master's Degrees in US Medical Schools
No full textSupplemental material, sj-pdf-1-mde-10.1177_23821205231164022 for Compassionate Off-Ramps: The Availability of Terminal Master's Degrees in US Medical Schools by Kristina H Petersen, Neera R Jain, Ben Case, Sharad Jain, Sarah L Solomon and Lisa M Meeks in Journal of Medical Education and Curricular Development</p
Hohepa Wi Neera: Native Title and the Privy Council Challenge
No full textThe case of Hohepa Wi Neera illustrates an unprecedented clash of judicial approaches to native title claims. On the one hand, the New Zealand Court of Appeal was determined to continue the line of reasoning most notably enshrined in Wi Parata v Bishop of Wellington. On the other hand, the Privy Council, in Nireaha Tamaki v Baker had partially overturned Wi Parata by insisting that native title fell within the jurisdiction of the courts, at least when prerogative powers were not involved. The author argues that in Hohepa Wi Neera, the Court of Appeal quite deliberately tried to avoid the implications of the Privy Council's decision. In doing so, it exhibited a marked "colonial consciousness" which it was prepared to defend even to the extent of open breach with the Privy Council. The 1912 case of Tamihana Korokai v Solicitor-General, however, showed the extent to which the Court of Appeal was capable of shedding that "colonial consciousness" and embracing the earlier Privy Council ruling. The author demonstrates that this apparent irony sheds light on our understanding of the earlier cases
Dynamic Modeling and Validation of Power Generators
No full textA fundamental challenge with any modeling effort is characterizing the right amount of model fidelity needed for a particular engineering task. Across the spectrum of modeling approaches, ranging from purely physics-based to purely data-based, there are variations in computational complexity, development time, accuracy, and experimental data dependency. The intent of this project is to validate and improve the performance of an existing genset model developed by Cummins, specifically during transient operation. A key aspect of this project is first quantifying desired fidelity and then identifying quantitative metrics to verify whether or not the desired fidelity is achieved. The outcome of this research provides the genset emulator lab (GEL) at Cummins Power Systems with an improved model of the existing genset model.
Inferring Takeover and Trust in SAE Level 2 Automated Vehicles
No full textWith the rise of automation in today\u27s society, a growing amount of research is focused on improving human-automation interaction. This includes a particular emphasis on how humans trust automation, as human trust is a driving factor of human reliance on automation. Specifically, trust needs to be calibrated for successful interaction between humans and automation. To avoid trust miscalibration (i.e., over/under trust), there is a need to design human-aware systems that can predict human trust and adapt their behavior accordingly. However, current computational trust models often overlook aspects of trust that have been highlighted by qualitative modeling efforts. Specifically, it is not clear how trust develops over longer timescales (greater than minutes) or across a gap in interaction. In this thesis, a computational trust model aimed at capturing changes in trust behavior over multiple timescales and interaction gaps is developed. A Non-linear Autoregressive with Exogenous Inputs (NARX) model is chosen to predict human trust levels by leveraging behavioral, psychophysiological, and environmental data. Trust is studied in an SAE Level 2 context, using a medium-fidelity driving simulator. A unique experiment is designed such that trust dynamics can be studied across two distinct interactions, separated by a period of one week with no interaction. The data collected from this experiment are evaluated to determine which features of the data set best predicted trust. These features are then used to train multiple trust models, which are then analyzed and compared. While model analysis reveals that trust dynamics differ between interactions, it also indicates that the differences may be captured by a single model
Hybrid Zonotopes: A Mixed-Integer Set Representation for the Analysis of Hybrid Systems
No full textSet-based methods have been leveraged in many engineering applications from robust control and global optimization, to probabilistic planning and estimation. While useful, these methods have most widely been applied to analysis over sets that are convex, due to their ease in both representation and calculation. The representation and analysis of nonconvex sets is inherently complex. When nonconvexity arises in design and control applications, the nonconvex set is often over-approximated by a convex set to provide conservative results. However, the level of conservatism may be large and difficult to quantify, often leading to trivial results and requiring repetitive analysis by the engineer. Nonconvexity is inherent and unavoidable in many applications, such as the analysis of hybrid systems and robust safety constraints.
In this dissertation, I present a new nonconvex set representation named the hybrid zonotope. The hybrid zonotope builds upon a combination of recent advances in the compact representation of convex sets in the controls literature with methods leveraged in solving mixed-integer programming problems. It is shown that the hybrid zonotope is equivalent to the union of an exponential number of convex sets while using a linear number of continuous and binary variables in the set’s representation. I provide identities for, and derivations of, the set operations of hybrid zonotopes for linear mappings, Minkowski sums, generalized intersections, halfspace intersections, Cartesian products, unions, complements, point containment, set containment, support functions, and convex enclosures. I also provide methods for redundancy removal and order reduction to improve the compactness and computational efficiency of the represented sets. Therefore proving the hybrid zonotopes expressive power and applicability to many nonconvex set-theoretic methods. Beyond basic set operations, I specifically show how the exact forward and backward reachable sets of linear hybrid systems may be found using identities that are calculated algebraically and scale linearly. Numerical examples show the scalability of the proposed methods and how they may be used to verify the safety and performance of complex systems. These exact methods may also be used to evaluate the level of conservatism of the existing approximate methods provided in the literature. </p
Laboratory Load-Based Testing, Performance Mapping and Rating of Residential Cooling Equipment
No full textIn the U.S., unitary residential air conditioners are rated using standard AHRI 210/240 that is inadequate to credit equipment with advanced controls and variable-speed components since the ratings are based on results of steady-state laboratory tests. Contrarily, a load-based testing and rating approach is presented in this work that can capture equipment performance with its integrated controls and thermostat responses that is more representative of the field. In this approach, representative building sensible and latent loads are emulated in a psychrometric test facility at different indoor and outdoor test conditions utilizing a virtual building model. The indoor test room conditions are continuously adjusted to emulate the dynamic response of the virtual building to the test equipment sensible and latent cooling rates and the equipment dynamic response is measured. Meanwhile, the inlet temperatures to the test equipment thermostat are independently controlled to track the same virtual building response using a thermostat environment emulator that encloses the test thermostat, that provides typical flow conditions and of which the design and control are presented in this work. Climate-specific cooling seasonal performance ratings can be determined by propagating load-based test results through a temperature-bin method to estimate a seasonal coefficient of performance (SCOP). In addition, a next-generation rating approach is developed that extends load-based testing for performance mapping, such that the SCOP can be obtained using building simulations that incorporate specific building types, climates and an equipment-specific performance map. In this work, the proposed approaches were implemented to test and rate a variable-speed residential heat pump operating in cooling mode. Trained with results from only 12 load-based test intervals carried out using the test equipment, a quasi-steady-state mapping model was able to map the equipment performance across almost the entire operating envelope within ±10% errors and the R2values were very close to 1. Using the identified performance map, the next-generation SCOP was obtained based on an annual simulation deployed in EnergyPlus, where the map was coupled to a typical single-family building in Albuquerque,NM. Compared to the temperature-bin-based rating, this simulation-based rating is able to comprehensively and appropriately reflect equipment annual field performance associated with a specific building type and climate, as the rating is extended from automated laboratory load-based testing and performance mapping
Robust Iterative Learning Control for Linear Parameter-varying Systems with Time Delays
No full textThe work in this dissertation concerns the construction of a robust iterative learning control (ILC) algorithm for a class of systems characterized by measurement delays, parametric uncertainty, and linear parameter varying (LPV) dynamics. One example of such a system is the twin roll strip casting process, which provides a practical motivation for this research. I propose three ILC algorithms in this dissertation that advance the state of the art. The first algorithm compensates for measurement delays that are longer than a single iteration of a periodic process. I divide the delay into an iterative and residual component and show how each component effects the asymptotic stability properties of the ILC algorithm. The second algorithm is a coupled delay estimation and ILC algorithm that compensates for time-varying measurement delays. I use an adaptive delay estimation algorithm to force the delay estimate to converge to the true delay and provide stability conditions for the coupled delay estimation and ILC algorithm. The final algorithm is a norm optimal ILC algorithm that compensates for LPV dynamics as well as parametric uncertainty and time delay estimation error. I provide a tuning method for the cost function weight matrices based on a sufficient condition for robust convergence and an upper bound on the norm of the error signal. The functionality of all three algorithms is demonstrated through simulated case studies based on an identified system model of the the twin roll strip casting process. The simulation testing is also augmented with experimental testing of select algorithms through collaboration with an industrial sponsor
Reduced-Order Modeling and Design Optimization of Metal-PCM Composite Heat Exchangers
No full textThermal energy storage (TES) modules are specifically designed to respond to transient thermal loading. Their dynamic response depends on the overall structure of the module, including module geometry and dimensions, the internal spatial distribution of phase change material (PCM) and conductive heat-spreading elements, and the thermophysical properties of the different materials composing the module. However, due to the complexity of analyzing a system’s dynamic thermal response to transient input signals, optimal design of a TES module for a particular application is challenging. Conventional design approaches are limited by (1) the computational cost associated with high fidelity simulation of heat transfer in nonlinear systems undergoing a phase transition and (2) the lack of model integration with robust optimization tools. To overcome these challenges, I derive reduced-order dynamic models of two different metal-PCM composite TES modules and validate them against a high fidelity CFD model. Through simulation and validation of both turbulent and laminar flow cases, I demonstrate the accuracy of the reduced-order models in predicting, both spatially and temporally, the evolution of the dynamic model states and other system variables of interest, such as PCM melt fraction. The validated models are used to conduct univariate and bivariate parametric studies to understand the effects of various design parameters on different performance metrics. Finally, a case study is presented in which the models are used to conduct detailed design optimization for the two HX geometries
Development of a Framework for Projecting Line-Haul Truck Technology Adoption and Greenhouse Gas Emissions in the U.S. Using a System-of-Systems Methodology
No full textIn order to displace diesel fuel consumption and reduce greenhouse gas emissions in the line-haul freight transportation system, a strong uptake of low and zero emission vehicle technologies must be incentivized by manufacturers and policymakers alike. A simulation tool that can project a wide array of future scenarios and predict the effects of freight transportation system evolution on mixed technology adoption trajectories is needed. This tool can assist the system stakeholders identify the level of innovation and policies necessary to increase the economic attractiveness of cleaner technologies and therefore incentivize the market to reduce system-wide emissions.In this thesis I present a simulation framework for projecting adoption and utilization of emerging technologies and network-wide emissions in a line-haul freight transportation system network. A System-of-Systems engineering methodology is followed to realize the definition, abstraction and simulation of the system. This results in a framework capable of modeling the evolution of system factors with respect to time and their influence across a set of representative heterogeneous line-haul fleets operating on a regional network. A constrained mixed-integer linear program is used to represent the decision-making process for heterogeneous fleets selecting vehicles and allocating them on freight delivery routes to minimize total cost of ownership. The proposed model is parametrized and validated using a Design of Experiments (DOE) and historical adoption data. The results of this SoS model demonstrate 90% accuracy in prediction outcome when modeling historical technology adoption across a set of 12 heterogeneous representative fleets over an 11-year period. The formula-tion is then implemented to project alternative powertrain technology adoption and utilization trends for a set of line-haul fleets. Alternative powertrain technologies include compressed and liquefied natural gas engines, diesel-electric hybrid, battery electric, and hydrogen fuel cell. Future policies, economic factors, and availability of fueling and charging infrastructure are input assumptions to the proposed modeling framework. Three mixed-adoption scenarios, including BE, HFC, and CNG vehicle market penetration, are identified by the DOE study to demonstrate the potential to reduce cumulative CO2 emissions by more than 25% between 2018–2028. Next, the framework is exercised to project powertrain adoption, utilization, and emissions from 2019–2035 given a set of assumptions for the impact different levels of autonomy may have on purchase costs, vehicle efficiency, driver wages, vehicle reliability, and hours of service regulations. The proposed model formulation, which predicts both adoption and utilization, can enable stakeholders with a deeper understanding of how and why different levels of autonomy impact the broader freight transportation network. Finally, the framework is extended to predict adoption and utilization behaviors upon introduction of intra-fleet 2-vehicle platooning. A study on the effects of platooning fuel efficiency and freight demand on adoption, utilization, and resulting network emissions is presented
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