The utility of statistical moments in chromatography using trapezoidal and Simpson's rules of peak integration

2019 ◽  
Vol 42 (8) ◽  
pp. 1644-1657 ◽  
Author(s):  
Shobhit Misra ◽  
M. Farooq Wahab ◽  
Darshan C. Patel ◽  
Daniel W. Armstrong
Keyword(s):  
Statistical Moments ◽  
Peak Integration

scholarly journals Detecting Different Road Infrastructural Elements Based on the Stochastic Characterization of Speed Patterns

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mario Muñoz-Organero ◽  
Ramona Ruiz-Blázquez
Keyword(s):  
Total Variation ◽  
Traffic Congestion ◽  
Automatic Detection ◽  
Mass Function ◽  
Total Variation Distance ◽  
Statistical Moments ◽  
Probability Mass Function ◽  
Traffic Lights ◽  
Traffic Conditions ◽  
Variation Distance

The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75) improves the best results using previous approaches based on statistical moments based features (0.71). Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals) at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one). Finally, a k-NN approach is used for assigning a class to each unlabelled element.

scholarly journals Coupling Computational Fluid Dynamics Simulations and Statistical Moments for Designing Healthy Indoor Spaces

2019 ◽  
Vol 16 (5) ◽  
pp. 800 ◽  
Author(s):  
Shamia Hoque ◽  
Firoza Omar
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Air Flow ◽  
Particle Dispersion ◽  
Statistical Moments ◽  
Dispersion Pattern ◽  
Case Scenario ◽  
Worst Case ◽  
Site Specific ◽  
Indoor Spaces

Cross-contamination between occupants in an indoor space may occur due to transfer of infectious aerosols. Computational fluid dynamics (CFD) provides detailed insight into particle transport in indoor spaces. However, such simulations are site-specific. This study couples CFD with statistical moments and establishes a framework that transitions site-specific results to generating guidelines for designing “healthy” indoor spaces. Eighteen cases were simulated, and three parameters were assessed: inlet/outlet location, air changes per hour, and the presence/absence of desks. Aerosol release due to a simulated “sneeze” in a two-dimensional ventilated space was applied as a test case. Mean, standard deviation, and skewness of the velocity profiles and particle locations gave an overall picture of the spread and movement of the air flow in the domain. A parameter or configuration did not dominate the values, confirming the significance of considering the combined influence of multiple parameters for determining localized air-flow characteristics. Particle clustering occurred more when the inlet was positioned above the outlet. The particle dispersion pattern could be classified into two time zones: “near time”, <60 s, and “far time”, >120 s. Based on dosage, the 18 cases were classified into three groups ranging from worst case scenario to best case scenario.

The twentieth century contiguous US temperature changes indicated by daily data and higher statistical moments

2011 ◽  
Vol 109 (3-4) ◽  
pp. 287-317 ◽  
Author(s):  
Samuel S. P. Shen ◽  
Ai B. Gurung ◽  
Hee-S. Oh ◽  
T. Shu ◽  
David R. Easterling
Keyword(s):  
Twentieth Century ◽  
Statistical Moments ◽  
Temperature Changes ◽  
Daily Data

scholarly journals Controlling statistical moments of stochastic dynamical networks

2016 ◽  
Vol 94 (1) ◽  
Author(s):  
Dmytro Bielievtsov ◽  
Josef Ladenbauer ◽  
Klaus Obermayer
Keyword(s):  
Statistical Moments ◽  
Dynamical Networks
Sign in / Sign up

Export Citation Format

Share Document