Thematische Bibliographien / Environmental health Data processing

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Auswahl der wissenschaftlichen Literatur zum Thema „Environmental health Data processing“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 11. Februar 2022

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Zeitschriftenartikel zum Thema "Environmental health Data processing"

Ivanov, Alexander, und Alexander Platov. „Environmental monitoring based on data processing of Internet of Things“. E3S Web of Conferences 136 (2019): 01041. http://dx.doi.org/10.1051/e3sconf/201913601041.

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The concept of online monitoring of the urban environment is proposed. It is based on the online processing of hydrometeorological and traffic information received through the Internet of Things. The traditional approach of the Internet of things includes transfer and storage of huge arrays of measurements in digital form. This concept of online monitoring is primarily an analysis, evaluation of the results of processing information received from wireless networks. The concept was implemented at Nizhny Novgorod State University of Architecture and Civil Engineering in several services including Eco-routes, Quite-routes, in which the air pollution of the urban environment by vehicle emissions and the noise level from traffic flows are estimated in real time mode. The calculation is based on meteorological data and traffic flow velocity. The calculation and assessment of environment pollution is carried out at the request of the user via Internet. The concept includes micro weather and algal blooming monitoring of reservoirs and ponds. Developed services is the first to provide free short time health risk assessment for both decision makers and common internet users.

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Cummings, Stuart W. „Distributed Databases for Clinical Data Processing“. Drug Information Journal 27, Nr. 4 (Oktober 1993): 949–56. http://dx.doi.org/10.1177/009286159302700403.

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Pimazzoni, Monica. „Global Data Management: A Winning Approach to Clinical Data Processing“. Drug Information Journal 32, Nr. 2 (April 1998): 569–71. http://dx.doi.org/10.1177/009286159803200230.

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Woods, Valerie. „Musculoskeletal disorders and visual strain in intensive data processing workers“. Occupational Medicine 55, Nr. 2 (01.03.2005): 121–27. http://dx.doi.org/10.1093/occmed/kqi029.

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Jia, Xiao Yu, und Tao Li. „Data Processing in Environmental Performance of Building Systems Applied in Residential Design“. Advanced Materials Research 978 (Juni 2014): 145–48. http://dx.doi.org/10.4028/www.scientific.net/amr.978.145.

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In the face of the global energy and environment problems, our country carries out some measures toward energy-saving environmental protection route in the construction field, which requires architects must start from the overall design concept for the preliminary design, fully considering the benefits between the various elements of environmental performance of building system. Environmental performance of building systems involves building system, the indoor environment of regional environmental impact and influence on the earth's environment, is a complex system. This paper introduces in detail the effects of five important factors in residential design in order to save energy, the effective use of resources to build low environmental load safety, healthy, efficiency, comfortable living space, people, environment and the construction of coexistence, continuous development of new construction mode.

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Arisetty, Murty. „A Team-Based Approach to Clinical Data Processing“. Drug Information Journal 19, Nr. 1 (Januar 1985): 81–84. http://dx.doi.org/10.1177/009286158501900113.

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Leighton, Charles C. „Clinical Data Processing in Retrospect and in Prospect“. Drug Information Journal 20, Nr. 1 (Januar 1986): 7–15. http://dx.doi.org/10.1177/009286158602000103.

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Gillum, Terry L., Robert H. George und Jack E. Leitmeyer. „An Autoencoder for Clinical and Regulatory Data Processing“. Drug Information Journal 29, Nr. 1 (Januar 1995): 107–13. http://dx.doi.org/10.1177/009286159502900115.

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Hrzic, Rok, Timo Clemens, Daan Westra und Helmut Brand. „Comparability in Cross-National Health Research Using Insurance Claims Data: The Cases of Germany and The Netherlands“. Das Gesundheitswesen 82, S 01 (19.11.2019): S83—S90. http://dx.doi.org/10.1055/a-1005-6792.

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Abstract Objective Comparison is a key method in learning about what works in health and healthcare. We discuss the importance of comparability in cross-national health research using health insurance claims data, develop a framework to systematically asses these threats and apply it to the German (DaTraV) and Dutch (Vektis) national-level insurance claims datasets. Methods We propose a framework of threats to the comparability of health insurance claims databases, which includes three domains: (1) representation of populations compared, (2) data sources and data processing and (3) database contents and availability for research purposes. We apply the framework to analyze the comparability of DaTraV and Vektis databases using publicly available information (organization’s websites, scientific publications) and our experiences from an interregional project on rare diseases (EMRaDi). Results Both databases were created for the same purpose (morbidity-based risk adjustment) and use the same underlying sources of data. Differences in population representation and uncertainty about data processing procedures represent potential sources of incomparability. Access for research purposes is feasible in both databases but may be subject to long processing time. Conclusions We find important threats to the comparability of the Dutch and German national insurance claims databases and by extension to validity of any comparative health studies that rely on them. Standard adjustment techniques, making more information available about data collection and processing procedures and adding more diagnosis-related descriptors offer ways to overcome the identified threats to comparability.

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Kraft, Robin, Ferdinand Birk, Manfred Reichert, Aniruddha Deshpande, Winfried Schlee, Berthold Langguth, Harald Baumeister, Thomas Probst, Myra Spiliopoulou und Rüdiger Pryss. „Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform“. Sensors 20, Nr. 12 (18.06.2020): 3456. http://dx.doi.org/10.3390/s20123456.

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Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.

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Dissertationen zum Thema "Environmental health Data processing"

Wilmot, Peter Nicholas. „Modelling cooling tower risk for Legionnaires' Disease using Bayesian Networks and Geographic Information Systems“. Title page, contents and conclusion only, 1999. http://web4.library.adelaide.edu.au/theses/09SIS.M/09sismw744.pdf.

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Includes bibliographical references (leaves 115-120) Establishes a Bayesian Belief Network (BBN) to model uncertainty of aerosols released from cooling towers and Geographic Information Systems (GIS) to create a wind dispersal model and identify potential cooling towers as the source of infection. Demonstrates the use of GIS and BBN in environmental epidemiology and the power of spatial information in the area of health.

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Chitondo, Pepukayi David Junior. „Data policies for big health data and personal health data“. Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2479.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016.
Health information policies are constantly becoming a key feature in directing information usage in healthcare. After the passing of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 and the Affordable Care Act (ACA) passed in 2010, in the United States, there has been an increase in health systems innovations. Coupling this health systems hype is the current buzz concept in Information Technology, „Big data‟. The prospects of big data are full of potential, even more so in the healthcare field where the accuracy of data is life critical. How big health data can be used to achieve improved health is now the goal of the current health informatics practitioner. Even more exciting is the amount of health data being generated by patients via personal handheld devices and other forms of technology that exclude the healthcare practitioner. This patient-generated data is also known as Personal Health Records, PHR. To achieve meaningful use of PHRs and healthcare data in general through big data, a couple of hurdles have to be overcome. First and foremost is the issue of privacy and confidentiality of the patients whose data is in concern. Secondly is the perceived trustworthiness of PHRs by healthcare practitioners. Other issues to take into context are data rights and ownership, data suppression, IP protection, data anonymisation and reidentification, information flow and regulations as well as consent biases. This study sought to understand the role of data policies in the process of data utilisation in the healthcare sector with added interest on PHRs utilisation as part of big health data.

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Yang, Bin, und 杨彬. „A novel framework for binning environmental genomic fragments“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45789344.

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Gigandet, Katherine M. „Processing and Interpretation of Illinois Basin Seismic Reflection Data“. Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1401309913.

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Perovich, Laura J. (Laura Jones). „Data Experiences : novel interfaces for data engagement using environmental health data“. Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/95612.

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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 71-81).
For the past twenty years, the data visualization movement has reworked the way we engage with information. It has brought fresh excitement to researchers and reached broad audiences. But what comes next for data? I seek to create example "Data Experiences" that will contribute to developing new spaces of information engagement. Using data from Silent Spring Institute's environmental health studies as a test case, I explore Data Experiences that are immersive, interactive, and aesthetic. Environmental health datasets are ideal for this application as they are highly relevant to the general population and have appropriate complexity. Dressed in Data will focus on the experience of an individual with her/his own environmental health data while BigBarChart focuses on the experience of the community with the overall dataset. Both projects seek to present opportunities for nontraditional learning, community relevance, and social impact.
by Laura J. Perovich.
S.M.

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Ponsimaa, P. (Petteri). „Discovering value for health with grocery shopping data“. Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201605221849.

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Food retailers are taking more active role in the customer value creation process and shifting their attention from the sale of goods to support customer’s value-creation to discover more innovative service-based business models. From customer data consumers may develop more responsible consumption behaviour, make more economical choices, and raise awareness on food healthiness. This exploratory study sets out to answer the question what value if any does the use of grocery shopping data bring to the customers. Using design science research, the thesis makes use of grocery purchase data available to S-Group customers and presents ways of applying the data while making it meaningful for them. The aim was to construct visualization application prototypes for seeking value and benefits of purchase data experienced by the customers. To evaluate the application design, a study group of eight customers were invited to provide purchase data and feedback on the data visualizations. The focus was on building designs of the grocery consumption patterns based on customer interviews and then evaluating the impact on the study group via interviews and usage data. The visualization prototypes allowed the participants to discover something new of their shopping and food consumption behaviour, not known to them before the study and not visible from the mere purchase data. Interviews suggested that the visualizations of health data encourage reflection of consuming habits, and thus may be used as a tool for increasing awareness of one’s shopping behaviour. A number of limitations in the data utilization were met hindering inference-making and reflecting on the data. Lastly, the prototypes led the participants to envision new digital health services, some of which might have commercial value.

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Adu-Prah, Samuel. „GEOGRAPHIC DATA MINING AND GEOVISUALIZATION FOR UNDERSTANDING ENVIRONMENTAL AND PUBLIC HEALTH DATA“. OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/657.

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Within the theoretical framework of this study it is recognized that a very large amount of real-world facts and geospatial data are collected and stored. Decision makers cannot consider all the available disparate raw facts and data. Problem-specific variables, including complex geographic identifiers have to be selected from this data and be validated. The problems associated with environmental- and public-health data are that (1) geospatial components of the data are not considered in analysis and decision making process, (2) meaningful geospatial patterns and clusters are often overlooked, and (3) public health practitioners find it difficult to comprehend geospatial data. Inspired by the advent of geographic data mining and geovisualization in public and environmental health, the goal of this study is to unveil the spatiotemporal dynamics in the prevalence of overweight and obesity in United States youths at regional and local levels over a twelve-year study period. Specific objectives of this dissertation are to (1) apply regionalization algorithms effective for the identification of meaningful clusters that are in spatial uniformity to youth overweight and obesity, and (2) use Geographic Information System (GIS), spatial analysis techniques, and statistical methods to explore the data sets for health outcomes, and (3) explore geovisualization techniques to transform discovered patterns in the data sets for recognition, flexible interaction and improve interpretation. To achieve the goal and the specific objectives of this dissertation, we used data sets from the National Longitudinal Survey of Youth 1997 (NLSY'97) early release (1997-2004), NLSY'97 current release (2005 - 2008), census 2000 data and yearly population estimates from 2001 to 2008, and synthetic data sets. The NLSY97 Cohort database range varied from 6,923 to 8,565 individuals during the period. At the beginning of the cohort study the age of individuals participating in this study was between 12 and 17 years, and in 2008, they were between 24 and 28 years. For the data mining tool, we applied the Regionalization with Dynamically Constrained Agglomerative clustering and Partitioning (REDCAP) algorithms to identify hierarchical regions based on measures of weight metrics of the U.S. youths. The applied algorithms are the single linkage clustering (SLK), average linkage clustering (ALK), complete linkage clustering (CLK), and the Ward's method. Moreover, we used GIS, spatial analysis techniques, and statistical methods to analyze the spatial varying association of overweight and obesity prevalence in the youth and to geographically visualize the results. The methods used included the ordinary least square (OLS) model, the spatial generalized linear mixed model (GLMM), Kulldorff's Scan space-time analysis, and the spatial interpolation techniques (inverse distance weighting). The three main findings for this study are: first, among the four algorithms ALK, Ward and CLK identified regions effectively than SLK which performed very poorly. The ALK provided more promising regions than the rest of the algorithms by producing spatial uniformity effectively related to the weight variable (body mass index). The regionalization algorithm-ALK provided new insights about overweight and obesity, by detecting new spatial clusters with over 30% prevalence. New meaningful clusters were detected in 15 counties, including Yazoo, Holmes, Lincoln, and Attala, in Mississippi; Wise, Delta, Hunt, Liberty, and Hardin in Texas; St Charles, St James, and Calcasieu in Louisiana; Choctaw, Sumter, and Tuscaloosa in Alabama. Demographically, these counties have race/ethnic composition of about 75% White, 11.6% Black and 13.4% others. Second, results from this study indicated that there is an upward trend in the prevalence of overweight and obesity in United States youths both in males and in females. Male youth obesity increased from 10.3% (95% CI=9.0, 11.0) in 1999 to 27.0% (95% CI=26.0, 28.0) in 2008. Likewise, female obesity increased from 9.6% (95% CI=8.0, 11.0) in 1999 to 28.9% (95% CI=27.0, 30.0) during the same period. Youth obesity prevalence was higher among females than among males. Aging is a substantial factor that has statistically highly significant association (p < 0.001) with prevalence of overweight and obesity. Third, significant cluster years for high rates were detected in 2003-2008 (relative risk 1.92, 3.4 annual prevalence cases per 100000, p < 0.0001) and that of low rates in 1997-2002 (relative risk 0.39, annual prevalence cases per 100000, p < 0.0001). Three meaningful spatiotemporal clusters of obesity (p < 0.0001) were detected in counties located within the South, Lower North Eastern, and North Central regions. Counties identified as consistently experiencing high prevalence of obesity and with the potential of becoming an obesogenic environment in the future are Copiah, Holmes, and Hinds in Mississippi; Harris and Chamber, Texas; Oklahoma and McCain, Oklahoma; Jefferson, Louisiana; and Chicot and Jefferson, Arkansas. Surprisingly, there were mixed trends in youth obesity prevalence patterns in rural and urban areas. Finally, from a public health perspective, this research have shown that in-depth knowledge of whether and in what respect certain areas have worse health outcomes can be helpful in designing effective community interventions to promote healthy living. Furthermore, specific information obtained from this dissertation can help guide geographically-targeted programs, policies, and preventive initiatives for overweight and obesity prevalence in the United States.

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Kersten, Ellen Elisabeth. „Spatial Triage| Data, Methods, and Opportunities to Advance Health Equity“. Thesis, University of California, Berkeley, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3686356.

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This dissertation examines whether spatial measures of health determinants and health outcomes are being used appropriately and effectively to improve the health of marginalized populations in the United States. I concentrate on three spatial measures that have received significant policy and regulatory attention in California and nationally: access to healthful foods, climate change, and housing quality. I find that measures of these health determinants have both significant limitations and unrealized potential for addressing health disparities and promoting health equity.

I define spatial triage as a process of using spatial data to screen or select place-based communities for targeted investments, policy action, and/or regulatory attention. Chapter 1 describes the historical context of spatial triage and how it relates to ongoing health equity research and policy. In Chapter 2, I evaluate spatial measures of community nutrition environments by comparing data from in-person store surveys against data from a commercial database. I find that stores in neighborhoods with higher population density or higher percentage of people of color have lower availability of healthful foods and that inaccuracies in commercial databases may produce biased measures of healthful food availability.

Chapter 3 focuses on spatial measures of climate change vulnerability. I find that currently used spatial measures of "disadvantaged communities" ignore many important factors, such as community assets, region-specific risks, and occupation-based hazards that contribute to place-based vulnerability. I draw from examples of successful actions by community-based environmental justice organizations and reframe "disadvantaged" communities as sites of solutions where innovative programs are being used to simultaneously address climate mitigation, adaptation, and equity goals.

In Chapter 4, I combine electronic health records, public housing locations, and census data to evaluate patterns of healthcare utilization and health outcomes for low-income children in San Francisco. I find that children who live in redeveloped public housing are less likely to have more than one acute care hospital visit within a year than children who live in older, traditional public housing. These results demonstrate how integrating patient-level data across hospitals and with data from other sectors can identify new types of place-based health disparities. Chapter 5 details recommendations for analytic, participatory, and cross-sector approaches to guide the development and implementation of more effective health equity research and policy.

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Ling, Meng-Chun. „Senior health care system“. CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2785.

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Senior Health Care System (SHCS) is created for users to enter participants' conditions and store information in a central database. When users are ready for quarterly assessments the system generates a simple summary that can be reviewed, modified, and saved as part of the summary assessments, which are required by Federal and California law.

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Dulaney, D. R., Kurt J. Maier und Phillip R. Scheuerman. „Data Requirements for Developing Effective Pathogen TMDLs“. Digital Commons @ East Tennessee State University, 2005. https://dc.etsu.edu/etsu-works/2938.

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Bücher zum Thema "Environmental health Data processing"

Bonnyns, E. Enregistrement des résultats d'analyse des précipitations: Aspects informatiques. Bruxelles: Ministère de la santé publique et de la famille, Institut d'hygiène et dépidémiologie, 1985.

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Basher, Mian M. Abul, und Rajshahi University. Department of Statistics, Hrsg. International Conference on Statistical Data Mining for Bioinformatics, Health, Agriculture and Environment, 21-24 December, 2012: Proceedings. [Dhaka]: Higher Education Quality Enhancement Program, 2012.

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Office, General Accounting. International environment: U.S. funding of environmental programs and activities. Washington, D.C: The Office, 1996.

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Ifiyenia, Kececioglu, Murthy Jayathi und American Society of Mechanical Engineers. Heat Division., Hrsg. Adaptive computional methods in environmental transport processes: Presented at the Winter Annual Meeting of the American Society of Mechanical Engineers, Anaheim, California, November 8-13, 1992. New York, N.Y: American Society of Mechanical Engineers, 1992.

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E, Keller Paul, Hrsg. Applications of neural networks in evironment, energy, and health: Proceedings of the 1995 Workshop on Environmental and Energy Applications of Neural Networks, Richland, Washington, USA, 30-31 March 1995. Singapore: World Scientific, 1996.

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Workshop on Environmental and Energy Applications of Neural Networks (1995 Richland, Wash.). Applications of neural networks in evironment, energy, and health: Proceedings of the 1995 Workshop on Environmental and Energy Applicatins of Neural Networks, Pacific Northwest National Laboratory, Richland, Washington, USA, 30-31 March 1995. Singapore: World Scientific, 1996.

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Office, General Accounting. International environment: Strengthening the implementation of environmental agreements : report to Congressional requestors. Washington, D.C: The Office, 1992.

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Montana. Legislature. Office of the Legislative Auditor. Performance audit report: Air quality program, Department of Health and Environmental Sciences. Helena, Mont: The Office, 1994.

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United States. Congress. House. Committee on Commerce. Subcommittee on Health and the Environment. Y2K and medical devices: Screening for the Y2K bug : joint hearing before the Subcommittees on Health and Environment and Oversight and Investigations of the Committee on Commerce, House of Representatives, One Hundred Sixth Congress, first session, May 25, 1999. Washington: U.S. G.P.O., 1999.

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United States. Congress. House. Committee on Commerce. Subcommittee on Oversight and Investigations., Hrsg. Y2K and medical devices: Screening for the Y2K bug : joint hearing before the Subcommittees on Health and Environment and Oversight and Investigations of the Committee on Commerce, House of Representatives, One Hundred Sixth Congress, first session, May 25, 1999. Washington: U.S. G.P.O., 1999.

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Buchteile zum Thema "Environmental health Data processing"

Balter, Boris, M. Stal’naya und Victor Egorov. „Comparing Two Alternative Pollutant Dispersion Models and Actual Data within an Environmental Health Information Processing System (EHIPS)“. In Modelling of Environmental Chemical Exposure and Risk, 151–64. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0884-6_14.

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Awange, Joseph. „Data Processing and Adjustment“. In GNSS Environmental Sensing, 97–113. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58418-8_6.

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Awange, Joseph L. „Data Processing and Adjustment“. In Environmental Science and Engineering, 91–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-88256-5_6.

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Malley, Brian, Daniele Ramazzotti und Joy Tzung-yu Wu. „Data Pre-processing“. In Secondary Analysis of Electronic Health Records, 115–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43742-2_12.

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Jaafar, Amine, Bruno Sareni und Xavier Roboam. „Mission and Environmental Data Processing“. In Integrated Design by Optimization of Electrical Energy Systems, 1–43. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118561812.ch1.

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Bill, Ralf. „Spatial Data Processing in Environmental Information Systems“. In Environmental Informatics, 53–73. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-017-1443-3_4.

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Rao, P. Krishna, Susan J. Holmes, Ralph K. Anderson, Jay S. Winston und Paul E. Lehr. „Satellite Data Product Processing“. In Weather Satellites: Systems, Data, and Environmental Applications, 166–79. Boston, MA: American Meteorological Society, 1990. http://dx.doi.org/10.1007/978-1-944970-16-1_18.

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Zhang, Kuan, und Xuemin Shen. „Privacy-Preserving Health Data Processing“. In Wireless Networks, 81–98. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24717-5_5.

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Kang, Myeongsu, und Jing Tian. „Machine Learning: Data Pre-processing“. In Prognostics and Health Management of Electronics, 111–30. Chichester, UK: John Wiley and Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119515326.ch5.

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Schmidt, Henrik, A. B. Baggeroer, W. A. Kuperman und E. K. Scheer. „Robust Beamforming for Matched Field Processing Under Realistic Environmental Conditions“. In Underwater Acoustic Data Processing, 427–31. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_47.

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Konferenzberichte zum Thema "Environmental health Data processing"

Liu, Miao, Junsheng Yu, Zhijiao Chen, Jinglin Guo und Jun Zhao. „Processing Technology of Massive Human Health Data Based on Hadoop“. In 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/mmebc-16.2016.284.

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Fenton, Kevin, und Steven Simske. „Engineering of an artificial intelligence safety data sheet document processing system for environmental, health, and safety compliance“. In DocEng '21: ACM Symposium on Document Engineering 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3469096.3474933.

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Singh, Ajay, Vincent Koomson, Jaewook Yu und Goldie Nejat. „A Self-Powered Wireless Health and Environment Monitoring System“. In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-67051.

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The objective of our work is to develop a novel self-powered multi-modal wireless health monitoring sensory system architecture consisting of: (i) wearable devices to continuously monitor the vital signs of a person, and (ii) environmental sensory devices which can monitor the environment and also act as multi-hop routers providing data paths from the wearable devices to a main processing unit. Together these devices can provide effective remote health monitoring of a person and also inform the person of important information. In this paper, we address the significant issue of energy depletion for the devices, which can lead to critical interruptions in monitoring, by proposing a flexible unique vibration-based energy harvesting scheme to support our architecture. This active energy harvesting scheme will allow for continuous remote monitoring of the person and his/her environment in various situations. Experimental results demonstrate the potential utilization of electromagnetic and piezoelectric vibration-based harvesting techniques for the proposed application.

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Farreras-Alcover, Isaac, Jacob Egede Andersen und Preston Vineyard. „The Structural Health Monitoring System of the Governor Mario M. Cuomo Bridge“. In IABSE Conference, Copenhagen 2018: Engineering the Past, to Meet the Needs of the Future. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2018. http://dx.doi.org/10.2749/copenhagen.2018.439.

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The Governor Mario M. Cuomo Bridge, also known as the New NY Bridge is a twin cable-stayed bridge that replaces the Tappan Zee Bridge, in the USA. The bridge is equipped with a Structural Health Monitoring System (SHMS) consisting of more than 400 sensors deployed at relevant locations. The sensors capture environmental and operational conditions as well as the associated structural responses. The system is designed to process monitoring data to support data-driven management of the bridge. This is achieved through the system''s different functionalities, which include real-time data visualization via an on-line graphical user interface, customized data processing routines, alert notifications whenever data-based thresholds are exceeded, automatic reporting of pre-defined parameters and characterization of structural responses during extreme events. The present paper describes, as a case-study, the motivation, architecture, functionalities and installation aspects of the SHMS.

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Alexakis, Haris, Andrea Franza, Sinan Acikgoz und Matthew J. DeJong. „Structural Health Monitoring of a masonry viaduct with Fibre Bragg Grating sensors“. In IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/guimaraes.2019.1560.

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<p>The Marsh Lane viaduct is a masonry railway bridge constructed during the 19th century nearby Leeds Central Railway Station. The bridge appears significantly damaged due to the increase of the operational train loads over the last decades and due to environmental effects. Due to this degradation, extensive repair was conducted in 2015. After this repair work, an extensive fibre optic sensor network was installed below three spans of the bridge to monitor surface strains at 68 locations on the underside of the arch spans. The paper compares data collected from two monitoring periods, 16 months apart. Combining statistical analysis and signal processing techniques, the results show that local damage, as well as change in the global dynamic behaviour of the structure over time, can be effectively detected with the use of Fibre Bragg Grating sensors.</p>

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Гегерь, Эмилия, Emilia Geger, Александр Подвесовский, Aleksandr Podvesovskiy, Сергей Кузьмин, Sergey Kuzmin, Виктория Толстенок und Viktoriya Tolstenok. „Methods for the Intelligent Analysis of Biomedical Data“. In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-308-311.

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The paper discusses methodology of cleaning and analysis of small semi-structured samples of biomedical data. This methodology is aimed at statistical evaluation of harmful production factor correlation with workers’ laboratory test data. As a result of the analysis and interpretation of the data, a deviation from the norm is observed according to some indicators of a clinical blood test in individuals whose occupational activity is associated with harmful factors. Conclusions are drawn about the need for further research in the group of people whose work is related to harmful production factors. It is necessary to employ intelligent methods for analyzing possible health risks and their negative consequences in order to make management decisions. The presented assessment methodology can be used to create an occupational health and safety information system.

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Oesch, Christopher, Ajay Mahajan, Lucas Utterback, Haricharan Padmanaban, Sanjeevi Chitikeshi und Fernando Figueroa. „Intelligent Sensors for Integrated Health Management Systems“. In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-13576.

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This paper describes work being conducted on the development of intelligent sensors with learning capabilities as part of an integrated systems approach. The integrated systems approach treats the sensor as a complete system with its own sensing hardware (the traditional sensor), A/D converter, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow the system to learn its own behavior. The immediate application is the monitoring of rocket test stands, but the technology should be generally applicable to the Integrated Systems Health Monitoring (ISHM) vision. This paper outlines progress made in the development of intelligent sensors by describing the work done till date on Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS). The PIS as discussed here consists of a thermocouple used to read temperature in an analog form which is then converted into digital values. A microprocessor collects the sensor readings and runs numerous embedded event detection routines on the digital data. If any event, i.e. spike, drift, noise, is detected, it is reported, stored and sent to a remote system through an Ethernet connection. Hence the output of the PIS is data coupled with a confidence factor in the reliability of the data. The VIS discussed here is a virtual implantation of the PIS in the G2 software environment. The VIS is designed to mirror the operations of the PIS; however, the VIS works on a computer at which digital data is provided as the input. This work lays the foundation for the next generation of smart devices that have embedded intelligence for distributed decision making capabilities.

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Giantomassi, Andrea, Francesco Ferracuti, Alessandro Benini, Gianluca Ippoliti, Sauro Longhi und Antonio Petrucci. „Hidden Markov Model for Health Estimation and Prognosis of Turbofan Engines“. In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48174.

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Determining the residual life time of systems is a determinant factor for machinery and environment safety. In this paper the problem of estimate the residual useful life (RUL) of turbo-fan engines is addressed. The adopted approach is especially suitable for situations in which a large amount of data is available offline, by allowing the processing of such data for the determination of RUL. The procedure allows to calculate the RUL through the following steps: features extraction by Artificial Neural Networks (ANN) and determination of remaining life time by-prediction models based on a Hidden Markov Model (HMM). Simulations confirm the effectiveness of the proposed approach and the promising power of Bayesian methods.

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Suharto, K. S. „Structural Health Monitoring of An Offshore Platform Trend of Corrosion and Marine Growth With Predictive Maintenance“. In Digital Technical Conference. Indonesian Petroleum Association, 2020. http://dx.doi.org/10.29118/ipa20-se-424.

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Nowadays, there are a lot of oil and gas exploration activities all over the world, especially offshore. Oil and gas production itself has many supporting factors in the production process, of which one of them is the offshore platform. Over time, the offshore platform will experience a degradation in strength from the initial design, therefore offshore platforms need to carry out appropriate inspection, maintenance and repair (IMR) programs so that the offshore platform does not reach critical conditions and disrupt the oil and gas production process, resulting in significant losses and not achieving the planned production rates. The offshore platform design is a combination of steel structure and other materials, methods, and loads that are initially worked at the port, such as wave loads, currents, and several other parameters such as gravity, wind, and earthquake. Most of the offshore platforms that currently exist (worldwide) were made in the period of oil investment that developed between 1970 and 1980, thus, the platform's age has now reached 40 to 50 years (El-Reedy, 2012). This research data based on a platform in the Makassar strait between the islands of Kalimantan and Sulawesi. The method used in this research is the platform's design, data observation, data analysis, data processing, and statistical multilinear regression analysis. This research is investigating the degradation trend of the offshore platform and predicting the future of potential failure because of corrosion and marine growth. After the platform encounters degradation over several years, it will decrease the service life before the degradation starts to matter. The results show after data processing that, offshore platforms encounter degradation over several years, and it is presented by a graph containing the condition trend over a multi-year condition because of corrosion and marine growth, and its effect towards unity check (UC) that define structure health. Unity check value produced from allowable stress versus capacity. In this research, structural health monitoring is predicting the potential failure of the platform towards its UC effect of corrosion and marine growth, with a max of the UC value is 0.8 because its a critical value. The failure of the structure possibly would have an affect on the cost-effectiveness. Therefore it is necessary to predict when the structure will fail so that the costs used are effective. Unfortunately, there is no core data for the environmental changes every year at the platform, so the model's equation does not include environmental conditions.

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Tsai, Hanchung, Yung Y. Liu und James Shuler. „RFID Technology for Environmental Remediation and Radioactive Waste Management“. In ASME 2010 13th International Conference on Environmental Remediation and Radioactive Waste Management. ASMEDC, 2010. http://dx.doi.org/10.1115/icem2010-40218.

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An advanced Radio Frequency Identification (RFID) system capable of tracking and monitoring a wide range of materials and components—from fissionable stocks to radioactive wastes—has been developed. The system offers a number of advantages, including enhanced safety, security and safeguards, reduced personnel exposure to radiation, and improved inventory control and cost-effectiveness. Using sensors, RFID tags can monitor the state of health of the tracked items and trigger alarms instantly when the normal ranges are violated. Nonvolatile memories in the tags can store sensor data, event records, as well as a contents manifest. Gamma irradiation tests showed that the tag components possess significant radiation resistance. Long-life batteries and smart management circuitries permit the tags to operate for up to 10 years without battery replacement. The tags have a near universal form factor, i.e., they can fit different package types. The read range is up to >100 m with no line-of-sight required. With careful implementation, even a large-size processing or storage facility with a complex configuration can be monitored with a handful of readers in a network. In transportation, by incorporating Global Positioning System (GPS), satellite/cellular communication technology, and secure Internet, situation awareness is assured continuously. The RFID system, when integrated with Geographic Information System (GIS) technology, can promptly provide content- and event-specific information to first responders and emergency management teams in case of incidents. In stand-alone applications, the monitoring and tracking data are contained within the local computer. With a secure Internet, information can be shared within the complex or even globally in real time. As with the deployment of any new technology, overcoming the cultural resistance is part of the developmental process. With a strong institutional support and multiple successful live demonstrations, the cultural resistance has been mostly overcome. As a result, implementation of the RFID technology is taking place at several of U.S. Department of Energy sites and laboratories for processing, storage, and transportation applications.

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Berichte der Organisationen zum Thema "Environmental health Data processing"

Marter, W. L., und L. R. Bauer. Defense waste processing facility (DWPF) environmental dosimetry data. Office of Scientific and Technical Information (OSTI), April 1990. http://dx.doi.org/10.2172/6439530.

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Gautier, M. A. Health and environmental chemistry: analytical techniques, data management, and quality assurance. Volume 1. Office of Scientific and Technical Information (OSTI), Mai 1986. http://dx.doi.org/10.2172/5107848.

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Gautier, M. A. Health and environmental chemistry: Analytical techniques, data management, and quality assurance. Volume 1, Manual. Office of Scientific and Technical Information (OSTI), November 1993. http://dx.doi.org/10.2172/10136159.

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Lackland, D. T., J. B. Dunbar und R. M. Jones. Geo-coding of health and demographic data as a resource for environmental incidents preparedness and response. Office of Scientific and Technical Information (OSTI), Juli 1995. http://dx.doi.org/10.2172/88875.

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Mallon, B., D. Layton, R. Fish, P. Hsieh, L. Hall, L. Perry und G. Snyder. Conventional weapons demilitarization: A health and environmental effects data base assessment: Propellants and their co-contaminants. Office of Scientific and Technical Information (OSTI), August 1988. http://dx.doi.org/10.2172/5873712.

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Hunter, M. R. Construction project data sheet for the environmental, safety and health upgrades: Phase 3 Program FY 1991 line item. Office of Scientific and Technical Information (OSTI), Februar 1989. http://dx.doi.org/10.2172/115623.

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Shinn, J. H., S. A. Martins, P. L. Cederwall und L. B. Gratt. Smokes and obscurants: A health and environmental effects data base assessment: A first-order, environmental screening and ranking of Army smokes and obscurants: Phase 1 report. Office of Scientific and Technical Information (OSTI), März 1985. http://dx.doi.org/10.2172/6068996.

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Cook, R., S. Adams, J. Beauchamp, M. Bevelhimer, B. Blaylock, C. Brandt, C. Ford et al. Phase 1 data summary report for the Clinch River Remedial Investigation: Health risk and ecological risk screening assessment. Environmental Restoration Program. Office of Scientific and Technical Information (OSTI), Dezember 1992. http://dx.doi.org/10.2172/10117530.

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Rencz, A. N., und I. M. Kettles. Presentations and recommendations from the workshop on the role of geochemical data in environmental and human health risk assessment, Halifax, 2010. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2011. http://dx.doi.org/10.4095/287934.

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Smyre, J. L., M. E. Hodgson, B. W. Moll, A. L. King und Yang Cheng. Daytime multispectral scanner aerial surveys of the Oak Ridge Reservation, 1992--1994: Overview of data processing and analysis by the Environmental Restoration Remote Sensing Program, Fiscal year 1995. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/204019.

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