The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Thierry Van Cutsem - One of the best experts on this subject based on the ideXlab platform.
-
Hybrid processing of SCADA and synchronized phasor measurements for Tracking Network state
2015 IEEE Power & Energy Society General Meeting, 2015Co-Authors: Boris A. Alcaide-moreno, Mevludin Glavic, Claudio R. Fuerte-esquivel, Thierry Van CutsemAbstract:This paper proposes a new Tracking state estimator aimed at following some of the dynamics of the Network state (bus voltage phasors) by a hybrid processing of SCADA and synchronized phasor measurements. The latter are assumed to be available in limited number. To avoid time skew effects, only the SCADA measurements received since the last execution of the estimator are processed. To ensure observability, estimated SCADA measurements are used as pseudo-measurements. The procedure includes a prediction, an innovation analysis and a correction step. The latter consists of solving a constrained least-squares optimization. The simulation results refer to a test system undergoing large disturbances, evolving to long-term voltage instability or stabilized by emergency control. The proposed method appears to satisfactorily track the overall Network evolution, even during those severe conditions.
-
Tracking Network state from combined SCADA and synchronized phasor measurements
2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization Security and Control of the Emerging Power Grid, 2013Co-Authors: Mevludin Glavic, Thierry Van CutsemAbstract:Initiated by the authors' previous work on state reconstruction from a limited number of synchrophasors [1], this paper goes one step further by exploring the possibility to track the Network state using both SCADA and synchronized phasor measurements. When a SCADA measurement is received, it is used in the next state reconstruction; otherwise, it is replaced by a pseudo-measurement stemming from the previous state reconstruction. The approach resorts to a standard weighted least squares formulation and Hachtel's augmented matrix method. State reconstruction is intended to be used at a much higher rate than classical state estimation, for instance every second. It has been validated using simulated measurements obtained from detailed time simulation.
-
Reconstructing and Tracking Network state from a limited number of synchrophasor measurements
IEEE Transactions on Power Systems, 2013Co-Authors: Mevludin Glavic, Thierry Van CutsemAbstract:A method is proposed to reconstruct and track Network state from a limited number of phasor measurement unit (PMU) data. To deal with the resulting unobservability, the state with bus powers and generator voltages closest to previously estimated values is computed. Those values, treated as pseudo-measurements, are obtained from the last reconstructed state, in a recursive manner. The method involves solving an optimization problem with linear constraints. It is scalable insofar as it accommodates from a few PMUs up to configurations ensuring full Network observability. Reconstruction of only a region is possible. These and other features are demonstrated on the Nordic32 test system, with synchronized phasors obtained from detailed time simulation of a situation evolving towards instability. Suitable choices of PMU location and pseudo-measurements are also discussed.
Mevludin Glavic - One of the best experts on this subject based on the ideXlab platform.
-
Hybrid processing of SCADA and synchronized phasor measurements for Tracking Network state
2015 IEEE Power & Energy Society General Meeting, 2015Co-Authors: Boris A. Alcaide-moreno, Mevludin Glavic, Claudio R. Fuerte-esquivel, Thierry Van CutsemAbstract:This paper proposes a new Tracking state estimator aimed at following some of the dynamics of the Network state (bus voltage phasors) by a hybrid processing of SCADA and synchronized phasor measurements. The latter are assumed to be available in limited number. To avoid time skew effects, only the SCADA measurements received since the last execution of the estimator are processed. To ensure observability, estimated SCADA measurements are used as pseudo-measurements. The procedure includes a prediction, an innovation analysis and a correction step. The latter consists of solving a constrained least-squares optimization. The simulation results refer to a test system undergoing large disturbances, evolving to long-term voltage instability or stabilized by emergency control. The proposed method appears to satisfactorily track the overall Network evolution, even during those severe conditions.
-
Tracking Network state from combined SCADA and synchronized phasor measurements
2013 IREP Symposium Bulk Power System Dynamics and Control - IX Optimization Security and Control of the Emerging Power Grid, 2013Co-Authors: Mevludin Glavic, Thierry Van CutsemAbstract:Initiated by the authors' previous work on state reconstruction from a limited number of synchrophasors [1], this paper goes one step further by exploring the possibility to track the Network state using both SCADA and synchronized phasor measurements. When a SCADA measurement is received, it is used in the next state reconstruction; otherwise, it is replaced by a pseudo-measurement stemming from the previous state reconstruction. The approach resorts to a standard weighted least squares formulation and Hachtel's augmented matrix method. State reconstruction is intended to be used at a much higher rate than classical state estimation, for instance every second. It has been validated using simulated measurements obtained from detailed time simulation.
-
Reconstructing and Tracking Network state from a limited number of synchrophasor measurements
IEEE Transactions on Power Systems, 2013Co-Authors: Mevludin Glavic, Thierry Van CutsemAbstract:A method is proposed to reconstruct and track Network state from a limited number of phasor measurement unit (PMU) data. To deal with the resulting unobservability, the state with bus powers and generator voltages closest to previously estimated values is computed. Those values, treated as pseudo-measurements, are obtained from the last reconstructed state, in a recursive manner. The method involves solving an optimization problem with linear constraints. It is scalable insofar as it accommodates from a few PMUs up to configurations ensuring full Network observability. Reconstruction of only a region is possible. These and other features are demonstrated on the Nordic32 test system, with synchronized phasors obtained from detailed time simulation of a situation evolving towards instability. Suitable choices of PMU location and pseudo-measurements are also discussed.
Ambarish Vaidyanathan - One of the best experts on this subject based on the ideXlab platform.
-
Community drinking water data on the National Environmental Public Health Tracking Network: a surveillance summary of data from 2000 to 2010
Environmental Monitoring and Assessment, 2019Co-Authors: Michele M. Monti, Mikyong Shin, Felicita David, Ambarish VaidyanathanAbstract:This report describes the available drinking water quality monitoring data on the Centers for Disease Control and Prevention (CDC) National Environmental Public Health Tracking Network (Tracking Network). This surveillance summary serves to identify the degree to which ten drinking water contaminants are present in finished water delivered to populations served by community water systems (CWS) in 24 states from 2000 to 2010. For each state, data were collected from every CWS. CWS are sampled on a monitoring schedule established by the Environmental Protection Agency (EPA) for each contaminant monitored. Annual mean and maximum concentrations by CWS for ten water contaminants were summarized from 2000 to 2010 for 24 states. For each contaminant, we calculated the number and percent of CWS with mean and maximum concentrations above the maximum contaminant level (MCL) and the number and percent of population served by CWS with mean and maximum concentrations above the MCL by year and then calculated the median number of those exceedances for the 11-year period. We also summarized these measures by CWS size and by state and identified the source water used by those CWS with exceedances of the MCL. The contaminants that occur more frequently in CWS with annual mean and annual maximum concentrations greater than the MCL include the disinfection byproducts, total trihalomethanes (TTHM), and haloacetic acids (HAA5); arsenic; nitrate; radium and uranium. A very high proportion of exceedances based on MCLs occurred mostly in very small and small CWS, which serve a year-round population of 3,300 or less. Arsenic in New Mexico and disinfection byproducts HAA5 and TTHM, represent the greatest health risk in terms of exposure to regulated drinking water contaminants. Very small and small CWS are the systems’ greatest difficulty in achieving compliance.
-
Heat stress illness hospitalizations — Environmental Public Health Tracking Program, 20 States, 2001–2010
Morbidity and mortality weekly report. Surveillance summaries (Washington D.C. : 2002), 2014Co-Authors: Ekta Choudhary, Ambarish VaidyanathanAbstract:Problem/Condition: Heat stress illness (HSI), also known as heat-related illness, comprises mild heat edema, heat syncope, heat cramps, heat exhaustion (the most common type of HSI), and heat stroke (the most severe form). CDC's Environmental Public Health Tracking Program receives annual hospitalization discharge data from 23 states that are used to assess and monitor trends of HSI hospitalization over time. Reporting Period: May-September, 2001-2010. Description of System: The Environmental Public Health Tracking Program is a comprehensive surveillance system implemented in 25 states and one city health department. The core of the system is the Tracking Network, which collects data on environmental hazards, health effects, exposures, and population. The Tracking Network provides nationally consistent environmental and health outcome data that enable federal, state, and local public health agencies to assess trends, explore associations, and generate hypotheses using these data. For HSI surveillance, the Tracking Network uses state-based hospital discharge data. RESULTS: During 2001-2010, approximately 28,000 HSI hospitalizations occurred in 20 states participating in the Tracking Program. Data from three states were not included in this report because of missing data for ≥3 years. Two states joined the Tracking Program after the study period and also are not included in this report. The majority of HSI hospitalizations occurred among males and persons aged ≥65 years. The highest rates of hospitalizations were in the Midwest and the South. During this period, an overall 2%-5% increase in the rate of HSI hospitalizations occurred in all 20 states compared with the 2001 rate. The correlation between the average number of HSI hospitalizations and the average monthly maximum temperature/heat index was statistically significant (at pINTERPRETATION: Consistent with previous studies, age and sex were identified as major risk factors for HSI hospitalizations. Certain Tracking states that experienced high temperatures during summer months showed an increase in rate of HSI hospitalizations over the 10-year study period. Public Health Action: HSIs are preventable and an important focus of public health interventions at state and local health departments. Federal, state, and local public health agencies can use data on HSI hospitalizations for surveillance purposes to estimate trends over time and to design targeted intervention to reduce heat stress morbidity among at-risk populations. Language: en
-
Statistical air quality predictions for public health surveillance: evaluation and generation of county level metrics of PM2.5 for the environmental public health Tracking Network.
International journal of health geographics, 2013Co-Authors: Ambarish Vaidyanathan, William Fred Dimmick, Scott R. Kegler, Judith R. QualtersAbstract:Background The Centers for Disease Control and Prevention (CDC) developed county level metrics for the Environmental Public Health Tracking Network (Tracking Network) to characterize potential population exposure to airborne particles with an aerodynamic diameter of 2.5 μm or less (PM2.5). These metrics are based on Federal Reference Method (FRM) air monitor data in the Environmental Protection Agency (EPA) Air Quality System (AQS); however, monitor data are limited in space and time. In order to understand air quality in all areas and on days without monitor data, the CDC collaborated with the EPA in the development of hierarchical Bayesian (HB) based predictions of PM2.5 concentrations. This paper describes the generation and evaluation of HB-based county level estimates of PM2.5.
-
Statistical air quality predictions for public health surveillance: evaluation and generation of county level metrics of PM_2.5for the environmental public health Tracking Network
International Journal of Health Geographics, 2013Co-Authors: Ambarish Vaidyanathan, William Fred Dimmick, Scott R. Kegler, Judith R. QualtersAbstract:Background The Centers for Disease Control and Prevention (CDC) developed county level metrics for the Environmental Public Health Tracking Network (Tracking Network) to characterize potential population exposure to airborne particles with an aerodynamic diameter of 2.5 μm or less (PM_2.5). These metrics are based on Federal Reference Method (FRM) air monitor data in the Environmental Protection Agency (EPA) Air Quality System (AQS); however, monitor data are limited in space and time. In order to understand air quality in all areas and on days without monitor data, the CDC collaborated with the EPA in the development of hierarchical Bayesian (HB) based predictions of PM_2.5 concentrations. This paper describes the generation and evaluation of HB-based county level estimates of PM_2.5. Methods We used three geo-imputation approaches to convert grid-level predictions to county level estimates. We used Pearson ( r ) and Kendall Tau-B ( τ ) correlation coefficients to assess the consistency of the relationship, and examined the direct differences (by county) between HB-based estimates and AQS-based concentrations at the daily level. We further compared the annual averages using Tukey mean-difference plots. Results During the year 2005, fewer than 20% of the counties in the conterminous United States (U.S.) had PM_2.5 monitoring and 32% of the conterminous U.S. population resided in counties with no AQS monitors. County level estimates resulting from population-weighted centroid containment approach were correlated more strongly with monitor-based concentrations ( r = 0.9; τ = 0.8) than were estimates from other geo-imputation approaches. The median daily difference was −0.2 μg/m^3 with an interquartile range (IQR) of 1.9 μg/m^3 and the median relative daily difference was −2.2% with an IQR of 17.2%. Under-prediction was more prevalent at higher concentrations and for counties in the western U.S. Conclusions While the relationship between county level HB-based estimates and AQS-based concentrations is generally good, there are clear variations in the strength of this relationship for different regions of the U.S. and at various concentrations of PM_2.5. This evaluation suggests that population-weighted county centroid containment method is an appropriate geo-imputation approach, and using the HB-based PM_2.5 estimates to augment gaps in AQS data provides a more spatially and temporally consistent basis for calculating the metrics deployed on the Tracking Network.
Tao Yin - One of the best experts on this subject based on the ideXlab platform.
-
ICCS (2) - Modeling of Anti-Tracking Network Based on Convex-Polytope Topology
Lecture Notes in Computer Science, 2020Co-Authors: Changbo Tian, Yongzheng Zhang, Tao YinAbstract:Anti-Tracking Network plays an important role in protection of Network users’ identities and communication privacy. Confronted with the frequent Network attacks or infiltration to anti-Tracking Network, a robust and destroy-resistant Network topology is an important prerequisite to maintain the stability and security of anti-Tracking Network. From the aspects of Network stability, Network resilience and destroy-resistance, we propose the convex-polytope topology (CPT) applied in the anti-Tracking Network. CPT has three main advantages: (1) CPT can easily avoid the threat of key nodes and cut vertices to Network structure; (2) Even the nodes could randomly join in or quit the Network, CPT can easily keep the Network topology in stable structure without the global view of Network; (3) CPT can easily achieve the self-optimization of Network topology. Anti-Tracking Network based on CPT can achieve the self-maintenance and self-optimization of its Network topology. We compare CPT with other methods of topology optimization. From the experimental results, CPT has better robustness, resilience and destroy-resistance confronted with dynamically changed topology, and performs better in the efficiency of Network self-optimization.
-
CollaborateCom - A Smart Topology Construction Method for Anti-Tracking Network Based on the Neural Network
Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering, 2019Co-Authors: Changbo Tian, Yongzheng Zhang, Tao Yin, Yupeng Tuo, Ge RuihaiAbstract:Anti-Tracking Network is the effective method to protect the Network users’ privacy confronted with the increasingly rampant Network monitoring and Network tracing. But the architecture of the current anti-Tracking Network is easy to be attacked, traced and undermined. In this paper, We propose smart topology construction method (STon) to provide the self-management and self-optimization of topology for anti-Tracking Network. We firstly deploy the neural Network on each node of the anti-Tracking Network. Each node can collect its local Network state and calculate the Network state parameters by the neural Network to decide the link state with other nodes. At last, each node optimizes its local topology according to the link state. With the collaboration of all nodes in the Network, the Network can achieve the self-management and self-optimization of its own topology. The experimental results showes that STon has a better robustness, communication efficiency and anti-Tracking performance than the current popular P2P structures.
-
GLOBECOM - Achieving Dynamic Communication Path for Anti-Tracking Network
2019 IEEE Global Communications Conference (GLOBECOM), 2019Co-Authors: Changbo Tian, Yongzheng Zhang, Tao Yin, Yupeng Tuo, Ge RuihaiAbstract:The increasingly rampant Network monitoring and tracing bring the huge challenge on the protection of netizens' privacy. The anonymous Networks mitigate the threat of Network monitoring and tracing to a certain degree, but the static communication path has become the weakness. To address the problem, we propose a Tracking-resistant communication mechanism with dynamic paths(TresMep). Different with the stepping stone chain like Tor, TresMep provides a chain of node groups which include at least one honest node. The message is transferred between groups. Each group uses asynchronous DC-net to hide the exit node which deliver the message to the honest node of the next group, and each group randomly chooses the exit node in each round of transmission through lagrange interpolation. Then, the transmission path would be changed dynamically and randomly to provide stronger Tracking-resistance. The experimental results show that TresMep has a stronger performance of Trackingresistance than the stepping stones based anti-Tracking Network with static communication path. The communication efficiency of TresMep is also satisfactory. But when message load is big, the communication efficiency of TresMep becomes worse. TresMep takes a tradeoff between Tracking- resistance and communication efficiency.
Judith R. Qualters - One of the best experts on this subject based on the ideXlab platform.
-
data to action using environmental public health Tracking to inform decision making
Journal of Public Health Management and Practice, 2015Co-Authors: Judith R. Qualters, Heathe Strosnide, Rosaly EllAbstract:CONTEXT: Public health surveillance includes dissemination of data and information to those who need it to take action to prevent or control disease. The concept of data to action is explicit in the mission of the Centers for Disease Control and Prevention's (CDC's) National Environmental Public Health Tracking Program (Tracking Program). The CDC has built a National Environmental Public Health Tracking Network (Tracking Network) to integrate health and environmental data to drive public health action (PHA) to improve communities' health. OBJECTIVE: To assess the utility of the Tracking Program and Tracking Network in environmental public health practice and policy making. DESIGN: We analyzed information on how Tracking (all program components hereafter referred to generally as "Tracking") has been used to drive PHAs within funded states and cities (grantees). Two case studies are presented to highlight Tracking's utility. SETTING: Analyses included all grantees funded between 2005 and 2013. PARTICIPANTS: Twenty-seven states, 3 cities, and the District of Columbia ever received funding. MAIN OUTCOME MEASURES: We categorized each PHA reported to determine how grantees became involved, their role, the problems addressed, and the overall action. RESULTS: Tracking grantees reported 178 PHAs from 2006 to 2013. The most common overall action was "provided information in response to concern" (n = 42), followed by "improved a public health program, intervention, or response plan" (n = 35). Tracking's role was most often either to enhance surveillance (24%) or to analyze data (23%). In 47% of PHAs, the underlying problem was a concern about possible elevated rates of a health outcome, a potential exposure, or a potential association between a hazard and a health outcome. PHAs were started by a request for assistance (48%), in response to an emergency (8%), and though routine work by Tracking programs (43%). CONCLUSION: Our review shows that the data, expertise, technical infrastructure, and other resources of the Tracking Program and Tracking Network are driving state and local PHAs.
-
Statistical air quality predictions for public health surveillance: evaluation and generation of county level metrics of PM2.5 for the environmental public health Tracking Network.
International journal of health geographics, 2013Co-Authors: Ambarish Vaidyanathan, William Fred Dimmick, Scott R. Kegler, Judith R. QualtersAbstract:Background The Centers for Disease Control and Prevention (CDC) developed county level metrics for the Environmental Public Health Tracking Network (Tracking Network) to characterize potential population exposure to airborne particles with an aerodynamic diameter of 2.5 μm or less (PM2.5). These metrics are based on Federal Reference Method (FRM) air monitor data in the Environmental Protection Agency (EPA) Air Quality System (AQS); however, monitor data are limited in space and time. In order to understand air quality in all areas and on days without monitor data, the CDC collaborated with the EPA in the development of hierarchical Bayesian (HB) based predictions of PM2.5 concentrations. This paper describes the generation and evaluation of HB-based county level estimates of PM2.5.
-
Statistical air quality predictions for public health surveillance: evaluation and generation of county level metrics of PM_2.5for the environmental public health Tracking Network
International Journal of Health Geographics, 2013Co-Authors: Ambarish Vaidyanathan, William Fred Dimmick, Scott R. Kegler, Judith R. QualtersAbstract:Background The Centers for Disease Control and Prevention (CDC) developed county level metrics for the Environmental Public Health Tracking Network (Tracking Network) to characterize potential population exposure to airborne particles with an aerodynamic diameter of 2.5 μm or less (PM_2.5). These metrics are based on Federal Reference Method (FRM) air monitor data in the Environmental Protection Agency (EPA) Air Quality System (AQS); however, monitor data are limited in space and time. In order to understand air quality in all areas and on days without monitor data, the CDC collaborated with the EPA in the development of hierarchical Bayesian (HB) based predictions of PM_2.5 concentrations. This paper describes the generation and evaluation of HB-based county level estimates of PM_2.5. Methods We used three geo-imputation approaches to convert grid-level predictions to county level estimates. We used Pearson ( r ) and Kendall Tau-B ( τ ) correlation coefficients to assess the consistency of the relationship, and examined the direct differences (by county) between HB-based estimates and AQS-based concentrations at the daily level. We further compared the annual averages using Tukey mean-difference plots. Results During the year 2005, fewer than 20% of the counties in the conterminous United States (U.S.) had PM_2.5 monitoring and 32% of the conterminous U.S. population resided in counties with no AQS monitors. County level estimates resulting from population-weighted centroid containment approach were correlated more strongly with monitor-based concentrations ( r = 0.9; τ = 0.8) than were estimates from other geo-imputation approaches. The median daily difference was −0.2 μg/m^3 with an interquartile range (IQR) of 1.9 μg/m^3 and the median relative daily difference was −2.2% with an IQR of 17.2%. Under-prediction was more prevalent at higher concentrations and for counties in the western U.S. Conclusions While the relationship between county level HB-based estimates and AQS-based concentrations is generally good, there are clear variations in the strength of this relationship for different regions of the U.S. and at various concentrations of PM_2.5. This evaluation suggests that population-weighted county centroid containment method is an appropriate geo-imputation approach, and using the HB-based PM_2.5 estimates to augment gaps in AQS data provides a more spatially and temporally consistent basis for calculating the metrics deployed on the Tracking Network.
-
Exploiting Satellite Remote-Sensing Data in Fine Particulate Matter Characterization for Serving the Environmental Public Health Tracking Network (EPHTN): The HELIX-Atlanta Experience and NPOESS Implications
2008Co-Authors: Mohammad Z. Al-hamdan, Judith R. Qualters, William L. Crosson, Ashutosh Limaye, Douglas L. Rickman, Dale A. Quattrochi, Maurice G. Estes, Amber H. Sinclair, Dennis Tolsma, Kafayat A. AdeniyiAbstract:As part of the U.S. National Environmental Public Health Tracking Network (EPHTN), the National Center for Environmental Health (NCEH) at the U.S. Centers for Disease Control and Prevention (CDC) led a project in collaboration with the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) called Health and Environment Linked for Information Exchange (HELIX-Atlanta). Under HELIX-Atlanta, pilot projects were conducted to develop methods to better characterize exposure; link health and environmental datasets; and analyze spatial/temporal relationships. This paper describes and demonstrates different techniques for surfacing daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM(sub 2.5) for the purpose of integrating respiratory health and environmental data for the CDC's pilot study of HELIX-Atlanta. It describes a methodology for estimating ground-level continuous PM(sub 2.5) concentrations using spatial surfacing techniques and leveraging NASA Moderate Resolution Imaging Spectrometer (MODIS) data to complement the U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM(sub 2.5) from the EPA database for the year 2003 as well as PM(sub 2.5) estimates derived from NASA's MODIS data. Hazard data have been processed to derive the surrogate exposure PM(sub 2.5) estimates. The paper has shown that merging MODIS remote sensing data with surface observations of PM(sub 2.5), may provide a more complete daily representation of PM(sub 2.5), than either data set alone would allow, and can reduce the errors in the PM(sub 2.5) estimated surfaces. Future work in this area should focus on combining MODIS column measurements with profile information provided by satellites like the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Visible Infrared Imager/Radiometer Suite (VIIRS) and the Aerosol Polarimeter Sensor (APS) NPOESS sensors will provide first-order information on aerosol particle size and are anticipated to provide information on aerosol products at higher resolution and accuracy than MODIS. Use of the NPOESS remote sensing data should result in more robust remotely sensed data that can be coupled with the methods discussed in this paper to generate surface concentrations of PM(2.5) for linkage with health data in Environmental Public Health Tracking.