Matching Items (117)
Description
The choices of an operator under heavy cognitive load are potentially critical to overall safety and performance. Such conditions are common when technological failures arise, and the operator is forced into multi-task situations. Task switching choice was examined in an effort to both validate previous work concerning a model of task overload management and address unresolved matters related to visual sampling. Using the Multi-Attribute Task Battery and eye tracking, the experiment studied any influence of task priority and difficulty. Continuous visual attention measurements captured attentional switches that do not manifest into behaviors but may provide insight into task switching choice. Difficulty was found to have an influence on task switching behavior; however, priority was not. Instead, priority may affect time spent on a task rather than strictly choice. Eye measures revealed some moderate connections between time spent dwelling on a task and subjective interest. The implication of this, as well as eye tracking used to validate a model of task overload management as a whole, is discussed.
ContributorsZabala, Garrett (Author) / Gutzwiller, Robert S (Thesis advisor) / Cooke, Nancy J. (Committee member) / Gray, Rob (Committee member) / Arizona State University (Publisher)
Created2020
Description
Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant CIS O&M risks. However, challenges remain for reliable prediction of communication errors to ensure CIS O&M safety and efficiency. For example, existing studies lack a systematic summarization of risky contexts and features of communication processes for predicting communication errors. Limited studies examined quantitative methods for incorporating expert opinions as constraints for reliable communication error prediction. How to examine mitigation strategies (e.g., adjustments of communication protocols) for reducing communication-related CIS O&M risks is also challenging. The main reason is the lack of causal analysis about how various factors influence the occurrences and impacts of communication errors so that engineers lack the basis for intervention.
This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
ContributorsSun, Zhe (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven K (Committee member) / Cooke, Nancy J. (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2020
Description
Power management integrated circuit (PMIC) design is a key module in almost all electronics around us such as Phones, Tablets, Computers, Laptop, Electric vehicles, etc. The on-chip loads such as microprocessors cores, memories, Analog/RF, etc. requires multiple supply voltage domains. Providing these supply voltages from off-chip voltage regulators will increase the overall system cost and limits the performance due to the board and package parasitics. Therefore, an on-chip fully integrated voltage regulator (FIVR) is required.
The dissertation presents a topology for a fully integrated power stage in a DC-DC buck converter achieving a high-power density and a time-domain hysteresis based highly integrated buck converter. A multi-phase time-domain comparator is proposed in this work for implementing the hysteresis control, thereby achieving a process scaling friendly highly digital design. A higher-order LC notch filter along with a flying capacitor which couples the input and output voltage ripple is implemented. The power stage operates at 500 MHz and can deliver a maximum power of 1.0 W and load current of 1.67 A, while occupying 1.21 mm2 active die area. Thus achieving a power density of 0.867 W/mm2 and current density of 1.377 A/mm2. The peak efficiency obtained is 71% at 780 mA of load current. The power stage with the additional off-chip LC is utilized to design a highly integrated current mode hysteretic buck converter operating at 180 MHz. It achieves 20 ns of settling and 2-5 ns of rise/fall time for reference tracking.
The second part of the dissertation discusses an integrated low voltage switched-capacitor based power sensor, to measure the output power of a DC-DC boost converter. This approach results in a lower complexity, area, power consumption, and a lower component count for the overall PV MPPT system. Designed in a 180 nm CMOS process, the circuit can operate with a supply voltage of 1.8 V. It achieves a power sense accuracy of 7.6%, occupies a die area of 0.0519 mm2, and consumes 0.748 mW of power.
The dissertation presents a topology for a fully integrated power stage in a DC-DC buck converter achieving a high-power density and a time-domain hysteresis based highly integrated buck converter. A multi-phase time-domain comparator is proposed in this work for implementing the hysteresis control, thereby achieving a process scaling friendly highly digital design. A higher-order LC notch filter along with a flying capacitor which couples the input and output voltage ripple is implemented. The power stage operates at 500 MHz and can deliver a maximum power of 1.0 W and load current of 1.67 A, while occupying 1.21 mm2 active die area. Thus achieving a power density of 0.867 W/mm2 and current density of 1.377 A/mm2. The peak efficiency obtained is 71% at 780 mA of load current. The power stage with the additional off-chip LC is utilized to design a highly integrated current mode hysteretic buck converter operating at 180 MHz. It achieves 20 ns of settling and 2-5 ns of rise/fall time for reference tracking.
The second part of the dissertation discusses an integrated low voltage switched-capacitor based power sensor, to measure the output power of a DC-DC boost converter. This approach results in a lower complexity, area, power consumption, and a lower component count for the overall PV MPPT system. Designed in a 180 nm CMOS process, the circuit can operate with a supply voltage of 1.8 V. It achieves a power sense accuracy of 7.6%, occupies a die area of 0.0519 mm2, and consumes 0.748 mW of power.
ContributorsSingh, Shrikant (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Thesis advisor) / Kitchen, Jennifer (Committee member) / Song, Hongjiang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Communications between air traffic controllers and pilots are critical to national airspace traffic management. Measuring communications in real time made by pilots and air traffic controllers has the potential to predict human error. In this thesis a measure for Deviations from Closed Loop Communications is defined and tested to predict a human error event, Loss of Separation (LOS). Six retired air traffic controllers were recruited and tested in three conditions of varying workload in an Terminal Radar Approach Control Facility (TRACON) arrival radar simulation. Communication transcripts from simulated trials were transcribed and coding schemes for Closed Loop Communication Deviations (CLCD) were applied. Results of the study demonstrated a positive correlation between CLCD and LOS, indicating that CLCD could be a variable used to predict LOS. However, more research is required to determine if CLCD can be used to predict LOS independent of other predictor variables, and if CLCD can be used in a model that considers many different predictor variables to predict LOS.
ContributorsLieber, Christopher Shane (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S (Committee member) / Niemczyk, Mary (Committee member) / Arizona State University (Publisher)
Created2020
Description
The Luminosity Lab, located at Arizona State University, is a prototype for a novel model of interdisciplinary, student-led innovation. The model’s design was informed by the following desired outcomes: i) the model would be well-suited for the 21st century, ii) it would attract, motivate, and retain the university’s strongest student talent, iii) it would operate without the oversight of faculty, and iv) it would work towards the conceptualization, design, development, and deployment of solutions that would positively impact society. This model of interdisciplinary research was tested at Arizona State University across four academic years with participation of over 200 students, who represented more than 20 academic disciplines. The results have shown successful integration of interdisciplinary expertise to identify unmet needs, design innovative concepts, and develop research-informed solutions. This dissertation analyzes Luminosity’s model to determine the following: i) Can a collegiate, student-driven interdisciplinary model of innovation designed for the 21st century perform without faculty management? ii) What are the motivators and culture that enable student success within this model? and iii) How does Luminosity differ from traditional research opportunities and learning experiences?
Through a qualitative, grounded theory analysis, this dissertation examines the phenomena of the students engaging in Luminosity’s model, who have demonstrated their ability to serve as the principal investigators and innovators in conducting substantial discovery, research, and innovation work through full project life cycles. This study supports a theory that highly talented students often feel limited by the pace and scope of their college educations, and yearn for experiences that motivate them with agency, achievement, mastery, affinity for colleagues, and a desire to impact society. Through the cumulative effect of these motivators and an organizational design that facilitates a bottom-up approach to student-driven innovation, Luminosity has established itself as a novel model of research and development in the collegiate space.
Through a qualitative, grounded theory analysis, this dissertation examines the phenomena of the students engaging in Luminosity’s model, who have demonstrated their ability to serve as the principal investigators and innovators in conducting substantial discovery, research, and innovation work through full project life cycles. This study supports a theory that highly talented students often feel limited by the pace and scope of their college educations, and yearn for experiences that motivate them with agency, achievement, mastery, affinity for colleagues, and a desire to impact society. Through the cumulative effect of these motivators and an organizational design that facilitates a bottom-up approach to student-driven innovation, Luminosity has established itself as a novel model of research and development in the collegiate space.
ContributorsNaufel, Mark Naufel (Author) / Becker, David V (Thesis advisor) / Cooke, Nancy J. (Committee member) / Anderson, Derrick (Committee member) / Arizona State University (Publisher)
Created2020
Description
There is an increasing demand for fully integrated point-of-load (POL) isolated DC-DC converters that can provide an isolation barrier between the primary and the secondary side, while delivering a low ripple, low noise regulated voltage at their isolated sides to a high dynamic range, sensitive mixed signal devices, such as sensors, current-shunt-monitors and ADCs. For these applications, smaller system size and integration level is important because the whole system may need to fit to limited space. Traditional methods for providing isolated power are discrete solutions using bulky transformers. Miniaturization of isolated POL regulators is becoming highly desirable for low power applications.
A fully integrated, low noise isolated point-of-load DC-DC converter for supply regulation of high dynamic range analog and mixed signal sensor signal-chains is presented. The isolated DC-DC converter utilizes an integrated planar air-core micro-transformer as a coupled resonator and isolation barrier and enables direct connection of low-voltage mixed signal circuits to higher supply rails. The air core transformer is driven at its primary resonant frequency of 100 MHz to achieve maximum power transfer. A mixed-signal perturb-and-observe based frequency search algorithm is developed to improve maximum power transfer efficiency by 60% across the isolation barrier compared to fixed driving frequency method. The isolated converter’s output ripple is reduced by utilizing spread spectrum clocking in the driver. An isolated PMOS LDO in the secondary side is used to suppress switching noise and ripple by 21dB. Conducted and radiated EMI distribution on the IC is measured by a set of integrated ring oscillator based noise sensors with -68dBm noise sensitivity. The proposed isolated converter achieves highest level of integration with respect to earlier reported integrated isolated converters, while providing 50V on-chip junction isolation without the need for extra silicon post-processing steps.
A fully integrated, low noise isolated point-of-load DC-DC converter for supply regulation of high dynamic range analog and mixed signal sensor signal-chains is presented. The isolated DC-DC converter utilizes an integrated planar air-core micro-transformer as a coupled resonator and isolation barrier and enables direct connection of low-voltage mixed signal circuits to higher supply rails. The air core transformer is driven at its primary resonant frequency of 100 MHz to achieve maximum power transfer. A mixed-signal perturb-and-observe based frequency search algorithm is developed to improve maximum power transfer efficiency by 60% across the isolation barrier compared to fixed driving frequency method. The isolated converter’s output ripple is reduced by utilizing spread spectrum clocking in the driver. An isolated PMOS LDO in the secondary side is used to suppress switching noise and ripple by 21dB. Conducted and radiated EMI distribution on the IC is measured by a set of integrated ring oscillator based noise sensors with -68dBm noise sensitivity. The proposed isolated converter achieves highest level of integration with respect to earlier reported integrated isolated converters, while providing 50V on-chip junction isolation without the need for extra silicon post-processing steps.
ContributorsLiu, Chengxi (Author) / Bakkaloglu, Bertan (Thesis advisor) / Kiaei, Sayfe (Committee member) / Song, Hongjiang (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / McKenna, Anna (Committee member) / Cooke, Nancy J. (Committee member) / Arizona State University (Publisher)
Created2015