Statistical distribution model of wsn node spacing

2014 ◽  
Author(s):  
Junhui Mei ◽  
Xidong Zhang ◽  
Heng Zhang ◽  
Guojun Lai ◽  
Guixia Kang
Keyword(s):  
Statistical Distribution ◽  
Distribution Model

scholarly journals Results of the Verification of the Statistical Distribution Model of Microseismicity Emission Characteristics

2016 ◽  
Vol 61 (3) ◽  
pp. 489-496
Author(s):  
Aleksander Cianciara
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Statistical Distribution ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Emission Characteristics ◽  
Goodness Of Fit Test ◽  
Empirical Cumulative Distribution Function

Abstract The paper presents the results of research aimed at verifying the hypothesis that the Weibull distribution is an appropriate statistical distribution model of microseismicity emission characteristics, namely: energy of phenomena and inter-event time. It is understood that the emission under consideration is induced by the natural rock mass fracturing. Because the recorded emission contain noise, therefore, it is subjected to an appropriate filtering. The study has been conducted using the method of statistical verification of null hypothesis that the Weibull distribution fits the empirical cumulative distribution function. As the model describing the cumulative distribution function is given in an analytical form, its verification may be performed using the Kolmogorov-Smirnov goodness-of-fit test. Interpretations by means of probabilistic methods require specifying the correct model describing the statistical distribution of data. Because in these methods measurement data are not used directly, but their statistical distributions, e.g., in the method based on the hazard analysis, or in that that uses maximum value statistics.

scholarly journals The Statistical Distributions of PM2.5 in Rayong and Chonburi Provinces, Thailand

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Vanida Pongsakchat ◽  
Pattaraporn Kidpholjaroen
Keyword(s):  
Fine Particulate Matter ◽  
Statistical Distribution ◽  
Distribution Model ◽  
Statistical Distributions ◽  
Average Data ◽  
Fine Particulate ◽  
Pearson Type ◽  
Anderson Darling ◽  
Log Normal ◽  
Log Normal Distribution

The fine particulate matter (PM2.5) concentrations is one of the most important issues that are often discussed since it has a greater impact on human health. Statistical distribution modeling plays an important role in predicting PM2.5 concentrations. This research aims to find the optimum statistical distribution model of PM2.5 in Rayong Province and Chonburi Province. The daily average data from 2014 – 2019 for Rayong and from 2015 – 2019 for Chonburi were using. Five statistical distributions were compared. A proper statistical distribution that represents PM2.5 concentrations has been chosen based on three criteria include Anderson-Darling statistic and RMSE. The results show that Pearson type VI distribution performs better compared to other distributions for PM2.5 concentrations in Rayong. For Chonburi, the proper statistical distribution is Log normal distribution.  

Determining fertilizer-induced NO emission ratio from soils by a statistical distribution model

2003 ◽  
Vol 39 (1) ◽  
pp. 45-50 ◽  
Author(s):  
Xiaoyuan Yan ◽  
Kunio Shimizu ◽  
Hajime Akimoto ◽  
Toshimasa Ohara
Keyword(s):  
Statistical Distribution ◽  
Distribution Model ◽  
No Emission ◽  
Emission Ratio

Finding the Best Statistical Distribution Model in PM10 Concentration Modeling by using Lognormal Distribution

2013 ◽  
Vol 13 (2) ◽  
pp. 294-300 ◽  
Author(s):  
Hazrul Abdul Hamid ◽  
Ahmad Shukri Yahaya ◽  
Nor Azam Ramli ◽  
Ahmad Zia Ul-Sau
Keyword(s):  
Lognormal Distribution ◽  
Statistical Distribution ◽  
Pm10 Concentration ◽  
Distribution Model

scholarly journals Risk Analysis on the Growth Rate of Covid-19 Cases in Indonesia Using Statistical Distribution Model

2021 ◽  
Vol 1 (01) ◽  
pp. 13-19
Author(s):  
Dina Tri Utari ◽  
Andrie Pasca Hendradewa
Keyword(s):  
Risk Analysis ◽  
Geometric Distribution ◽  
Daily Basis ◽  
Statistical Distribution ◽  
Mitigation Measures ◽  
Distribution Model ◽  
Rapid Rate ◽  
Limited Capacity ◽  
Distribution Fitting ◽  
Comprehensive Information

Coronavirus or Covid-19 outbreak has been declared as a pandemic and many countries were not ready to deal with such an eventuality. The highly rapid rate of transmission is one reason for the need to take mitigation measures, since healthcare system has limited capacity. Indonesia is one of the countries that has lost medical resources to the pandemic. In order to provide more comprehensive information about the characteristics of Covid-19 in Indonesia, risk analysis of the occurrence of new cases was needed. This study proposes a related overview about risk occurrence of new Covid-19 cases per daily basis by performing distribution fitting technique to form a statistical distribution model. Among the available alternative models, Geometric distribution is the most suitable to describe the growth of new cases in Indonesia. Received February 12, 2021Revised March 25, 2021Accepted April 15, 2021

scholarly journals Health index prediction of dissolved gases in transformer oil based on statistical distribution model

2021 ◽  
Vol 2087 (1) ◽  
pp. 012085
Author(s):  
Xiaogang Li ◽  
Jiajia Liu ◽  
Xiaoguang Wang ◽  
Zhongyuan Wu ◽  
Chenyao Liu
Keyword(s):  
Gas Analysis ◽  
Statistical Distribution ◽  
Transformer Oil ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Health Index ◽  
Dissolved Gas ◽  
Scoring Method ◽  
Predicted Values ◽  
The Individual

Abstract In this paper, dissolved gas analysis (DGA) and statistical distribution model (SDM) were used to predict the health index (HI) of dissolved gas in transformer oil. First, the individual DGA data are classified according to transformer ages ranging from 1 to 4 years. Then, representative fitting models were selected and extrapolated from 5 to 25 years. The inverse cumulative distribution function (ICDF) of the selected distribution model was used to calculate the single conditional parameter data from 5 to 25 years. Finally, the traditional scoring method is used to estimate the future HI value. The results show that DGA parameters can be expressed by exponential equation based on statistical model. The predicted values of DGA health index of transformer oil from 1 to 7 years were basically consistent with the calculated values, and the DGA score was 100 points. By the 20th year, the DGA score had dropped to 75, requiring timely monitoring. The research results can provide powerful data support and theoretical reference for transformer life prediction.

scholarly journals Application of Statistical Distribution Models to Predict Health Index for Condition-Based Management of Transformers

2021 ◽  
Vol 11 (6) ◽  
pp. 2728
Author(s):  
Amran Mohd Selva ◽  
Norhafiz Azis ◽  
Nor Shafiqin Shariffudin ◽  
Mohd Zainal Abidin Ab Kadir ◽  
Jasronita Jasni ◽  
...  
Keyword(s):  
Statistical Distribution ◽  
Cumulative Distribution ◽  
Quality Analysis ◽  
Statistical Hypothesis ◽  
Distribution Model ◽  
Health Index ◽  
Distribution Models ◽  
Distribution Parameters ◽  
The Individual ◽  
Individual Condition

In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition parameters data were categorized based on transformer age from year 1 to 15. Next, the individual condition parameters data for every age were fitted while using a probability plot to find the representative distribution models. The distribution parameters were calculated based on 95% confidence level and extrapolated from year 16 to 25 through representative fitting models. The individual condition parameters data within the period were later calculated based on the estimated distribution parameters through the inverse cumulative distribution function (ICDF) of the selected distribution models. The predicted HI was then determined based on the conventional scoring method. The Chi-square test for statistical hypothesis reveals that the predicted HI for the transformer data is quite close to the calculated HI. The average percentage of absolute error is 2.7%. The HI that is predicted based on SDM yields 97.83% accuracy for the transformer data.

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