Phd Dissertation

Development of Dielectric Elastomer Nanocomposites as Stretchable and Flexible Actuating Materials

Publication Date

June 30, 2015

Author(s)

Areas of Expertise

Abstract

Dielectric elastomers (DEs) are a new type of smart materials showing promising functionalities as energy harvesting materials as well as actuating materials for potential applications such as artificial muscles, implanted medical devices, robotics, loud speakers, micro-electro-mechanical systems (MEMS), tunable optics, transducers, sensors, and even generators due to their high electromechanical efficiency, stability, lightweight, low cost, and easy processing. Despite the advantages of DEs, technical challenges must be resolved for wider applications. A high electric field of at least 10-30 V/um is required for the actuation of DEs, which limits the practical applications especially in biomedical fields. We tackle this problem by introducing the multiwalled carbon nanotubes (MWNTs) in DEs to enhance their relative permittivity and to generate their high electromechanical responses with lower applied field level. This work presents the dielectric, mechanical and electromechanical properties of DEs filled with MWNTs. The micromechanics-based finite element models are employed to describe the dielectric, and mechanical behavior of the MWNT-filled DE nanocomposites. A sufficient number of models are computed to reach the acceptable prediction of the dielectric and mechanical responses. In addition, experimental results are analyzed along with simulation results. Finally, laser Doppler vibrometer is utilized to directly detect the enhancement of the actuation strains of DE nanocomposites filled with MWNTs. All the results demonstrate the effective improvement in the electromechanical properties of DE nanocomposites filled with MWNTs under the applied electric fields.

Suggested Citation
Yu Wang (2015) Development of Dielectric Elastomer Nanocomposites as Stretchable and Flexible Actuating Materials. PhD Dissertation. UC Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/1gpb62p/alma991016230969704701.

working paper

Uncovering the Distribution of Motorists' Preferences for Travel Time and Reliability: Implications for Road Pricing

Publication Date

August 1, 2002

Author(s)

Kenneth Small, Clifford Winston, Jia Yan

Working Paper

UCI-ITS-WP-02-4, UCTC 546

Areas of Expertise

Abstract

Recent econometric advances have made it possible to empirically identify the varied nature of consumers’ preferences. We apply these advances to study commuters’ preferences for speedy and reliable highway travel with the objective of exploring the efficiency and distributional effects of road pricing that accounts for users’ heterogeneity. Our analysis combines revealed and stated commuter choices of whether to pay a toll for congestion-free express travel or to travel free on regular congested roads. We find that highway users exhibit substantial heterogeneity in their values of travel time and reliability. Moreover, we show that road pricing policies that cater to varying preferences can substantially increase efficiency while maintaining the political feasibility exhibited by current experiments. By recognizing heterogeneity, policymakers may break the current impasse in efforts to relieve highway congestion.

Suggested Citation
Kenneth A. Small, Clifford Winston and Jia Yan (2002) Uncovering the Distribution of Motorists' Preferences for Travel Time and Reliability: Implications for Road Pricing. Working Paper UCI-ITS-WP-02-4, UCTC 546. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/0vs152zt.

published journal article

Automated detection of lane-blocking freeway incidents using artificial neural networks

Transportation Research Part C: Emerging Technologies

Publication Date

December 1, 1995
Suggested Citation
Ruey L. Cheu and Stephen G. Ritchie (1995) “Automated detection of lane-blocking freeway incidents using artificial neural networks”, Transportation Research Part C: Emerging Technologies, 3(6), pp. 371–388. Available at: 10.1016/0968-090x(95)00016-c.

published journal article

Unifiable multi-commodity kinematic wave model

Transportation Research Part B: Methodological

Publication Date

November 1, 2018

Author(s)

Suggested Citation
Wen-Long Jin (2018) “Unifiable multi-commodity kinematic wave model”, Transportation Research Part B: Methodological, 117, pp. 639–659. Available at: 10.1016/j.trb.2017.08.013.

working paper

Evaluation of a Shared-Use Electric Vehicle Program: Integrating a Web-Based Survey with In-Vehicle Tracking

Publication Date

June 1, 2001

Working Paper

UCI-ITS-WP-01-10, UCI-ITS-AS-WP-01-5

Areas of Expertise

Abstract

An experimental shared-use vehicle program in Irvine, California, is assigning 15 Toyota ecom electric vehicles to several public and private sector organizations who have identified a group of employees to utilize the vehicles in a shared-use mode. The primary goal of this experiment is to evaluate the potential of shared-use electric vehicles as a means of reducing urban traffic and vehicle emissions. The decision to travel with the shared-use vehicles can be understood only through examining the entire process of how participants schedule activites before, during and after shared-use vehicles become a travel option. To effectively evaluate performance of this prototype application, a novel data collection procedure is proposed that integrates GPS-based vehicle tracking and web-based travel survey technologies. The data collection process will occur in three stages: (1) before the vehicle-sharing program, (2) during the program and (3) after the program is completed. A computer-aided self-administered interview (CASI) program, REACT!, is being used to collect travel/activity schedules from participants for one week periods. The data derived from the first stage will depict participants’ typical weekly activity programs before the alternative of sharing vehicles is present. After a simple installation of REACT! on a home or work personal computer, each respondent completes a 45 minute self-administered inverview and summarizes known travel plans for the week. On each subsequent evening, REACT! prompts respondents to update what they actually did that day, as well as to update any plans for the remainder of the week. Data are sent to a project web site at the end of each REACT! session. At the end of the week, REACT! provides a summary of travel/activity for the entire week. In the second phase, when electric vehicles are in shared-use, REACT! will be used in conjunction with the GPS tracing instrument, TRACER, equipped within the study vehicles. TRACER will be used to track each venicle for the duration of the study, recording vehicle position and speed every second. At one of two intervals within the six month study period, respondents will repeat the REACT! study. These repeat applications will involve a brief initial self-administered interview and daily updates focused on the use of the shared-use vehicles. The actual tracings of the vehicles will be provided to each respondent both as a memory-jogger to help them complete the survey as well as to examine under what conditions shared-use vehicles were utilized. In the third stage, the stage 1 procedure will be repeated to test for residual effects on travel patterns.

Suggested Citation
Ming S. Lee, James E. Marca, Craig R. Rindt, Angela M. Koos and Michael G. McNally (2001) Evaluation of a Shared-Use Electric Vehicle Program: Integrating a Web-Based Survey with In-Vehicle Tracking. Working Paper UCI-ITS-WP-01-10, UCI-ITS-AS-WP-01-5. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/15x0v29d.

policy brief

How Risky Are Cyber Security Threats Against Autonomous Vehicles?

Abstract

To operate safely, autonomous vehicles (AVs) rely on external sensors such as cameras, light detection and ranging (LiDAR) technology, and radar. These sensors pair with machine learning-based perception modules that interpret the surrounding environment and enable the AV to act accordingly. Perception modules are the “eyes and ears” of the vehicle and are vulnerable to cybersecurity attacks. The most critical and practical threats, however, arise from physical attacks that do not require access to the AV’s internal systems. The risks of these types of attacks are still unknown. To advance the field in this area, we conducted the first ever quantitative risk assessment for physical adversarial attacks on AVs. First, we identified relevant attack vectors, or types of cyber security attacks, targeting AV perception modules. Next, we conducted an in-depth analysis of the stages of an attack. Finally, we used these exercises to identify risk metrics and perform a subsequent computation of risk scores for different attack vectors. Through this process, we were able to quantitatively rank the real-life risks posed by different attack vectors identified in existing research. This analysis provides a framework for comprehensive risk analysis to ensure the safety of AVs on our roadways.

Suggested Citation
Trishna Chakraborty and Alfred Chen (2024) How Risky Are Cyber Security Threats Against Autonomous Vehicles?. Policy Brief. UC ITS. Available at: https://doi.org/10.7922/g29p3004.

book/book chapter

Transit in American Cities

Publication Date

January 1, 1986
Suggested Citation
Gordon J Fielding (1986) “Transit in American Cities”, in The Geography of Urban Transportation. 2nd ed. New York: Guilford Press, pp. 229–246.

MS Thesis

A prototype real-time expert system for arterial street incident management

Publication Date

June 30, 1993

Author(s)

Abstract

TBD

Suggested Citation
Dean Deeter (1993) A prototype real-time expert system for arterial street incident management. MS Thesis. UC Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/17uq3m8/alma991033953149704701.

published journal article

What drives variations in public health and social services expenditures? the association between political fragmentation and local expenditure patterns

The European Journal of Health Economics

Publication Date

July 1, 2022

Author(s)

Yonsu Kim, Jae Hong Kim

Abstract

The US spends two times more than the OECD average in health expenditure but has a much smaller portion of public health spending to total health expenditure than other OECD countries. While it has been suggested that public health and social services spending is crucial to promoting health outcomes, less is known about what drives variations in public health expenditure across regions. This study aims to examine whether political fragmentation in local governance is associated with variations in public health and social services expenditures. Using the US Census of Governments, we constructed a panel dataset of political fragmentation and local government spending patterns (1997–2012) for 792 US counties (population > 60,882, top 25%) and employed Least Squares Dummy Variable (LSDV) and Generalized Estimating Equations (GEE) models. We found that per capita public health spending tended to be smaller in areas where the degree of political fragmentation was higher (Coef:  – 0.034; p < 0.01), particularly when general-purpose governments were more fragmented (Coef:  – 0.087; p < 0.001). The proportion of public health spending also decreased when local governments were more fragmented (Coef:  – 0.012; p < 0.001). Social services expenditures and their proportions to total government expenditure fell with an increase in the degree of political fragmentation. Our findings suggest that fragmented governance settings, in which localities are more likely to face competition with others, may lead to a reduction in public spending essential for population health and that political fragmentation can also have a deterrent effect on broader categories of health-related social services spending.

Suggested Citation
Yonsu Kim and Jae Hong Kim (2022) “What drives variations in public health and social services expenditures? the association between political fragmentation and local expenditure patterns”, The European Journal of Health Economics, 23(5), pp. 781–789. Available at: 10.1007/s10198-021-01394-x.