Sensor configuration selection for discrete-event systems under unreliable observations

Algorithms for counting the occurrences of special events in the framework of partially-observed discrete event dynamical systems (DEDS) were developed in previous work. Their performances typically become better as the sensors providing the observations become more costly or increase in number. This paper addresses the problem of finding a sensor configuration that achieves an optimal balance between cost and the performance of the special event counting algorithm, while satisfying given observability requirements and constraints. Since this problem is generally computational hard in the framework considered, a sensor optimization algorithm is developed using two greedy heuristics, one myopic and the other based on projected performances of candidate sensors. The two heuristics are sequentially executed in order to find best sensor configurations. The developed algorithm is then applied to a sensor optimization problem for a multiunit- operation system. Results show that improved sensor configurations can be found that may significantly reduce the sensor configuration cost but still yield acceptable performance for counting the occurrences of special events

Stochastic event counter for discrete-event systems under unreliable observations

This paper addresses the issues of counting the occurrence of special events in the framework of partiallyobserved discrete-event dynamical systems (DEDS). First, we develop a noble recursive procedure that updates active counter information state sequentially with available observations. In general, the cardinality of active counter information state is unbounded, which makes the exact recursion infeasible computationally. To overcome this difficulty, we develop an approximated recursive procedure that regulates and bounds the size of active counter information state. Using the approximated active counting information state, we give an approximated minimum mean square error (MMSE) counter. The developed algorithms are then applied to count special routing events in a material flow system

Intruder Activity Analysis under Unreliable Sensor Networks

This paper addresses the problem of counting intruder activities within a monitored domain by a sensor network. The deployed sensors are unreliable. We characterize imperfect sensors with misdetection and false-alarm probabilities. We model intruder activities with Markov Chains. A set of Hidden Markov Models (HMM) models the imperfect sensors and intruder activities to be monitored. A novel sequential change detection/isolation algorithm is developed to detect and isolate a change from an HMM representing no intruder activity to another HMM representing some intruder activities. Procedures for estimating the entry time and the trace of intruder activities are developed. A domain monitoring example is given to illustrate the presented concepts and computational procedures

Diagnosability of stochastic discreteevent systems,”

Abstract-We investigate diagnosability of stochastic discrete-event systems where the observation of certain events is unreliable, that is, there are non-zero probabilities of the misdetection and misclassification of events based on faulty sensor readings. Such sensor unreliability is unavoidable in applications such as nuclear energy generation. We propose the notions of uA-and uAA-diagnosability for stochastic automata and demonstrate their relationship with the concepts of A-and AA-diagnosabilty defined in [1]. We extend the concept of the stochastic diagnoser to the unreliable observation paradigm and find conditions for uA-and uAA-diagnosability

Sequential window diagnoser for discrete-event systems under unreliable observations

This paper addresses the issue of counting the occurrence of special events in the framework of partiallyobserved discrete-event dynamical systems (DEDS). Developed diagnosers referred to as sequential window diagnosers (SWDs) utilize the stochastic diagnoser probability transition matrices developed in [9] along with a resetting mechanism that allows on-line monitoring of special event occurrences. To illustrate their performance, the SWDs are applied to detect and count the occurrence of special events in a particular DEDS. Results show that SWDs are able to accurately track the number of times special events occur

Resilient plant monitoring system: Design, analysis, and performance evaluation

Resilient monitoring systems are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this paper is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of four layers: data quality assessment, process variable assessment, plant condition assessment, and sensor network adaptation. Each of these layers is analyzed by either analytical or numerical tools, and the performance of the overall system is evaluated using simulations. The measure of resiliency of the resulting system is evaluated using Kullback Leibler divergence, and is shown to be sufficiently high in all scenarios considered

Air Pollution and Acute Respiratory Response in a Panel of Asthmatic Children along the U.S.–Mexico Border

Background: Concerns regarding the health impact of urban air pollution on asthmatic children are pronounced along the U.S.–Mexico border because of rapid population growth near busy border highways and roads. Objectives: We conducted the first binational study of the impacts of air pollution on asthmatic children in Ciudad Juarez, Mexico, and El Paso, Texas, USA, and compared different exposure metrics to assess acute respiratory response. Methods: We recruited 58 asthmatic children from two schools in Ciudad Juarez and two schools in El Paso. A marker of airway inflammation [exhaled nitric oxide (eNO)], respiratory symptom surveys, and pollutant measurements (indoor and outdoor 48-hr size-fractionated particulate matter, 48-hr black carbon, and 96-hr nitrogen dioxide) were collected at each school for 16 weeks. We examined associations between the pollutants and respiratory response using generalized linear mixed models. Results: We observed small but consistent associations between eNO and numerous pollutant metrics, with estimated increases in eNO ranging from 1% to 3% per interquartile range increase in pollutant concentrations. Effect estimates from models using school-based concentrations were generally stronger than corresponding estimates based on concentrations from ambient air monitors. Both traffic-related and non–traffic-related particles were typically more robust predictors of eNO than was nitrogen dioxide, for which associations were highly sensitive to model specification. Associations differed significantly across the four school-based cohorts, consistent with heterogeneity in pollutant concentrations and cohort characteristics. Models examining respiratory symptoms were consistent with the null. Conclusions: The results indicate adverse effects of air pollution on the subclinical respiratory health of asthmatic children in this region and provide preliminary support for the use of air pollution monitors close to schools to track exposure and potential health risk in this population

Human Factors Issues For Multi-Modular Reactor Units

Smaller and multi-modular reactor (MMR) will be highly technologically-advanced systems allowing more system flexibility to reactors configurations (e.g., addition/deletion of reactor units). While the technical and financial advantages of systems may be numerous, MMR presents many human factors challenges that may pose vulnerability to plant safety. An important human factors challenge in MMR operation and performance is the monitoring of data from multiple plants from centralized control rooms where human operators are responsible for interpreting, assessing, and responding to different system’s states and failures (e.g., simultaneously monitoring refueling at one plant while keeping an eye on another plant’s normal operating state). Furthermore, the operational, safety, and performance requirements for MMR can seriously change current staffing models and roles, the mode in which information is displayed, procedures and training to support and guide operators, and risk analysis. For these reasons, addressing human factors concerns in MMR are essential in reducing plant risk

Demographic and Clinical Characteristics of Completed Suicides in Mexico City 2014–2015

Objective: To analyze sex differences in demographic and clinical characteristics of individuals who died by suicide in Mexico City.Method: Statistical analysis of residents of Mexico City whose cause of death was suicide, during two years period from January 2014 to December 2015, with a coroner's report. Suicide mortality rates were calculated by age, sex, and location within the city. The Chi-squared test was used to assess statistical differences.Results: From January 2014 to December 2015, 990 residents of Mexico City died by suicide (men: 78.28%, women: 21.72%). Among males, the highest mortality rates were among the groups of 20–24 and 75–79 years old, whereas in women, the group with the highest mortality rate was 15 to 19 years old. 74% of the sample used hanging as suicide method. However, men had higher rates of a positive result in the toxicology test (40%) (p < 0.05). There was no concordance between male and female suicide by city jurisdictions.Conclusion: Our results provide evidence that the characteristics of Mexico City's residents who committed suicide had significant sex-related differences, including where they used to live. Understanding the contributory factors associated with completed suicide is essential for the development of effective preventive strategies

Development of Protective Autoimmunity by Immunization with a Neural-Derived Peptide Is Ineffective in Severe Spinal Cord Injury

Protective autoimmunity (PA) is a physiological response to central nervous system trauma that has demonstrated to promote neuroprotection after spinal cord injury (SCI). To reach its beneficial effect, PA should be boosted by immunizing with neural constituents or neural-derived peptides such as A91. Immunizing with A91 has shown to promote neuroprotection after SCI and its use has proven to be feasible in a clinical setting. The broad applications of neural-derived peptides make it important to determine the main features of this anti-A91 response. For this purpose, adult Sprague-Dawley rats were subjected to a spinal cord contusion (SCC; moderate or severe) or a spinal cord transection (SCT; complete or incomplete). Immediately after injury, animals were immunized with PBS or A91. Motor recovery, T cell-specific response against A91 and the levels of IL-4, IFN-γ and brain-derived neurotrophic factor (BDNF) released by A91-specific T (TA91) cells were evaluated. Rats with moderate SCC, presented a better motor recovery after A91 immunization. Animals with moderate SCC or incomplete SCT showed significant T cell proliferation against A91 that was characterized chiefly by the predominant production of IL-4 and the release of BDNF. In contrast, immunization with A91 did not promote a better motor recovery in animals with severe SCC or complete SCT. In fact, T cell proliferation against A91 was diminished in these animals. The present results suggest that the effective development of PA and, consequently, the beneficial effects of immunizing with A91 significantly depend on the severity of SCI. This could mainly be attributed to the lack of TA91 cells which predominantly showed to have a Th2 phenotype capable of producing BDNF, further promoting neuroprotection