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Browsing by Subject "Quality control"

Now showing 1 - 2 of 2
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    Implementation of Traffic Data Quality Verification for WIM Sites
    (Center for Transportation Studies University of Minnesota, 2015-05) Liao, Chen-Fu; Chatterjee, Indrajit; Davis, Gary A.
    Weigh-In-Motion (WIM) system tends to go out of calibration from time to time, as a result generate biased and inaccurate measurements. Several external factors such as vehicle speed, weather, pavement conditions, etc. can be attributed to such anomaly. To overcome this problem, a statistical quality control technique is warranted that would provide the WIM operator with some guidelines whenever the system tends to go out of calibration. A mixture modeling technique using Expectation Maximization (EM) algorithm was implemented to divide the Gross Vehicle Weight (GVW) measurements of vehicle class 9 into three components, (unloaded, partially loaded, and fully loaded). Cumulative Sum (CUSUM) statistical process technique was used to identify any abrupt change in mean level of GVW measurements. Special attention was given to the presence of auto-correlation in the data by fitting an auto-regressive time series model and then performing CUSUM analysis on the fitted residuals. A data analysis software tool was developed to perform EM Fitting and CUSUM analyses. The EM analysis takes monthly WIM raw data and estimates the mean and deviations of GVW of class 9 fully loaded trucks. Results of the EM analyses are stored in a file directory for CUSUM analysis. Output from the CUSUM analysis will indicate whether there is any sensor drift during the analysis period. Results from the analysis suggest that the proposed methodology is able to estimate a shift in the WIM sensor accurately and also indicate the time point when the WIM system went out-of-calibration. A data analysis software tool, WIM Data Analyst, was developed using the Microsoft Visual Studio software development package based on the Microsoft Windows .NET framework. An open source software tool called R.NET was integrated into the Microsoft .NET framework to interface with the R software which is another open source software package for statistical computing and analysis.
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    Standard Operating Procedures (SOP): Aquatic Field Collection Guidelines Habitat Characterization Benthic Sample Processing
    (University of Minnesota Duluth, 1999) Breneman, Dan
    Assessing biological condition of aquatic habitats involves multiple phases and a variety of methodologies and techniques. Monitoring, as one form of assessing biological condition includes, but is not limited to, field observations, data and sample collection, laboratory processing, and data interpretation. This document is limited to aquatic biota sampling, characterizing habitat structure, assessing aquatic macrophytes and adjacent riparian condition, and evaluating water quality parameters. Both quantitative and qualitative methods are used to collect biological samples and evaluate habitat characteristics for interpreting, or continually monitoring, biological condition. Guidelines are provided to establish sample collection protocols once an experimental design is formulated. Descriptions that follow will be limited to habitat characteristic evaluation, monitoring water quality parameters, and fish, macroinvertebrate, and periphyton community sampling.