scholarly journals Extreme Value Charts & Analysis of Means Based on the New Weighted Exponential Distribution

2021 ◽  
Vol 23 (11) ◽  
pp. 825-836
Author(s):  
Anil Arepalli ◽  
◽  
B. Srinivasa Rao ◽  
Keyword(s):  
Probability Distribution ◽  
Exponential Distribution ◽  
Control Charts ◽  
Extreme Values ◽  
Quality Characteristics ◽  
Extreme Value ◽  
Analysis Method ◽  
Time Data ◽  
Analysis Of Means ◽  
Real Time Data

It is assumed that the probabilistic model of the quality characteristics follows the new weighted exponential distribution. Control charts based on each subgroup’s extreme values are established. The constants in the control chart are determined by the probability distribution of the extreme value order statistics of the sub-group and the sub-group size. The proposed chart is thus referred to as an extreme values chart. A biased overall mean analysis method (ANOM for truncated population) is used for the new weighted exponential distribution. Examples based on real time data are used to explain the findings.

Extreme Values of Waves and Ship Responses Subject to the Markov Chain Condition

1979 ◽  
Vol 23 (03) ◽  
pp. 188-197
Author(s):  
Michel K. Ochi
Keyword(s):  
Distribution Function ◽  
Markov Chain ◽  
Probability Distribution ◽  
Random Process ◽  
Probability Distribution Function ◽  
Extreme Values ◽  
Random Processes ◽  
Chain Condition ◽  
Extreme Value ◽  
Spectral Bandwidth

This paper discusses the effect of statistical dependence of the maxima (peak values) of a stationary random process on the magnitude of the extreme values. A theoretical analysis of the extreme values of a stationary normal random process is made, assuming the maxima are subject to the Markov chain condition. For this, the probability distribution function of maxima as well as the joint probability distribution function of two successive maxima of a normal process having an arbitrary spectral bandwidth are applied to Epstein's theorem for evaluating the extreme values in a given sample under the Markov chain condition. A numerical evaluation of the extreme values is then carried out for a total of 14 random processes, including nine ocean wave records, with various spectral bandwidth parameters ranging from 0.11 to 0.78. From the results of the computations, it is concluded that the Markov concept is applicable to the maxima of random processes whose spectral bandwidth parameter, ɛ, is less than 0.5, and that the extreme values with and without the Markov concept are constant irrespective of the e-value, and the former is approximately 10 percent greater than the latter. It is also found that the sample size for which the extreme value reaches a certain level with the Markov concept is much less than that without the Markov concept. For example, the extreme value will reach a level of 4.0 (nondimensional value) in 1100 observations of the maxima with the Markov concept, while the extreme value will reach the same level in 3200 observations of the maxima without the Markov concept.

scholarly journals Automated observatory in Antarctica: real-time data transfer on constrained networks in practice

2017 ◽  
Author(s):  
Stephan Bracke ◽  
Alexandre Gonsette ◽  
Jean Rasson ◽  
Antoine Poncelet ◽  
Olivier Hendrickx
Keyword(s):  
Real Time ◽  
Data Transfer ◽  
Extreme Values ◽  
Magnetic Data ◽  
Magnetic Observatory ◽  
File Transfer ◽  
Time Data ◽  
Magnetic Inclination ◽  
Real Time Data ◽  
The One

Abstract. In 2013 the scientists from the geophysical centre at Dourbes started a project to install a fully automated magnetic observatory in Antarctica. This isolated place comes with specific requirements: unmanned station during six months, low temperatures with extreme values up to –50°, minimize power consumption, satellite bandwidth limited to 56 Kbit/sec. The ultimate aim is to collect real-time magnetic data every second in Belgium: vector data from a lemi-25, absolute F measurements from a gems proton magnetometer and absolute magnetic inclination and declination measurements (approximately 5 times a day) with an automated DI-flux. To achieve real-time data transfer with traditional file transfer protocols (for instance sftp, mail, rsync), you fight against their limitations in terms of real-time. After evaluation of pro and cons of the on that moment available real-time internet of things (IoT) protocols and seismic software solutions known to UGCS, we chose to use message queuing telemetry transport (MQTT) and receive the one second data with a negligible latency cost and no loss of data. Each individual instrument sends his data towards Belgium immediately after capturing it and arrives at approximately 300 milliseconds after sending which corresponds with the normal satellite latency.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

Sign in / Sign up

Export Citation Format

Share Document