Robust semi-explicit model predictive control for hybrid automata

In this paper we propose an on-line design technique for the target control problem of hybrid automata. First, we compute on-line the shortest path, which has the minimum discrete cost, from an initial state to the given target set. Next, we derive a controller which successfully drives the system from the initial state to the target set while minimizing a cost function. The (robust) model predictive control (MPC) technique is used when the current state is not within a guard set, otherwise the (robust) mixed-integer predictive control (MIPC) technique is employed. An on-line, semi-explicit control algorithm is derived by combining the two techniques and applied on a high-speed and energy-saving control problem of the CPU processing

Mixed land use and travel behavior : a case study for incorporating land use patterns into travel demand models

textMetropolitan planning organizations (MPOs) have become increasingly interested in incorporating land use patterns and design ideas into transportation problems. Many design ideas under the umbrella of the New Urbanism; yet in practice they hardly get fully implemented in the standard transportation planning procedures. This research intends to contribute to the continuing debate on land use pattern-travel connection by adding further empirical evidence from the Austin, TX region. Also, it demonstrates ways to integrate land use patterns in transportation demand analysis. The study identifies 42 mixed use districts (MXD) in the Austin region and analyzes the following aspects of travel behavior in MXDs and non-MXDs: production trip rates, frequency of produced trips, network trip length, internal rate of capture, and person-miles of travel (PMT). The study contributes to transportation planning and policy making in Central Texas by providing local empirical evidence on urban form-travel connection. The study’s method and process can be of interest to a broad audience in academia and practice.Community and Regional Plannin

Quantitative Risk Assessment for Escherichia coli O157:H7 in Fresh-cut Lettuce

A farm-to-fork quantitative microbial risk assessment (QMRA) model was developed to estimate the risk of illnesses associated with Escherichia coli O157:H7 in fresh-cut lettuce, and to evaluate the effects of potential intervention strategies on reducing public health risks. Assuming a prevalence of 0.1% of lettuce entering the processing plant, the baseline model reflecting current industry practices predicted an average of 2,160 cases per year in the United States. For each of the additional intervention strategies evaluated, health risks were reduced by 11- to 18-fold. Treatment with ultrasound and organic acid combination was the most effective, reducing the mean number of cases by approximately 18-fold compared to baseline model. The developed risk model can be used to estimate the public health risk ofE. coli O157:H7 from fresh-cut lettuce and to evaluate different potential intervention strategies to mitigate such risk

Resilient nonlinear control for attacked cyber-physical systems

In this paper, the problem of resilient nonlinear control for cyber-physical systems (CPSs) over attacked networks is studied. The motivation for this paper comes from growing applications that demand the secure control of CPSs in industry 4.0. The nonlinear physical system considered can be attacked by changing the temporal characteristics of the network, causing fixed time or time-varying delays and changing the orders of received packets. The systems under attack can be destabilized if the controller is not designed to be robust with an adversarial attack. In order to cope with nonlinearity of the physical system, a nonlinear generalized minimum variance controller and a modified Kalman estimator are derived. A worst-case controller is presented for fixed-time delay. In the situations of time-varying delays and out-of-order transmissions, an opportunistic estimator and a resilient controller are designed through an on-line algorithm in the sense that it is calculated by using the information in the received packets immediately. The ability to use the received information immediately leads to the improvement of the controller's performance. Simulation results are provided to show the applicability and performance of control law developed

EVALUATION AND MODELING OF RISK FACTORS ASSOCIATED WITH MICROBIAL CONTAMINATION IN PRODUCE PRE-HARVEST ENVIRONMENT

Produce (fruits and vegetables) has been frequently linked to foodborne disease outbreaks in the United States and worldwide. Produce-related outbreaks have been traced back to contamination occurring at pre-harvest production level. The overall goal of this dissertation was to identify risk factors for produce pre-harvest contamination and develop models to predict the introduction, survival, and persistence of enteric foodborne bacteria in produce at the pre-harvest level. Produce from mixed farms, where vegetable crops and animals are grown at the same premise, is potentially at higher risk of microbial contamination due to its proximity to environmental reservoirs such animal enclosures and composting facilities. Such contamination can be affected by meteorological factors such as temperature, precipitation, and wind speed. By integrating microbial sampling and meteorological data, the effects of meteorological factors on prevalence and concentration of Listeria species and generic Escherichia coli in samples collected from a mixed produce and dairy farm were analyzed using logistic regression and tree-based methods. The developed models have robust predictive ability and can be used to estimate the risk of microbial contamination in mixed farms under different weather conditions. Survival and persistence of pathogens in field soil is a food safety concern as soil can serve as a source and route for microbial contamination in produce. Regression models were developed to evaluate the effects of meteorological factors, cover cropping, and farming system on the survival and persistence of generic E. coli and L. innocua in produce field soil. The models revealed that survival of E. coli and persistence of L. innocua were predominately influenced by temperature, precipitation, and relative humidity. Further, data from a large microbial sampling study were used to determine the effects of a variety of meteorological, environmental, and farm management factors on the presence and concentration of food safety and quality bacteria indicator in tomatoes and tomato environmental samples. The results suggest that microbial contamination in tomatoes and in tomato production environments can be significantly affected by certain meteorological conditions, environmental factors, and farm management practices. In conclusion, this study identified potential risk factors associated with the presence, concentration, survival, and persistence of enteric foodborne bacteria in produce and in produce production environments. The developed models can be used to predict the risk of microbial contamination in produce farms under different meteorological conditions, geographical regions, and farm management practices. Such information and tools will help growers to improve farm management practices to reduce potential contamination of produce

Indoor optical fiber eavesdropping approach and its avoidance

The optical fiber network has become a worldwide infrastructure. In addition to the basic functions in telecommunication, its sensing ability has attracted more and more attention. In this paper, we discuss the risk of household fiber being used for eavesdropping and demonstrate its performance in the lab. Using a 3-meter tail fiber in front of the household optical modem, voices of normal human speech can be eavesdropped by a laser interferometer and recovered 1.1 km away. The detection distance limit and system noise are analyzed quantitatively. We also give some practical ways to prevent eavesdropping through household fiber.Comment: 8 pages, 4 figures, submitted to Optics Expres