Scent Lineups: Variables in Procedures and Statistical Verification

2016 ◽  
pp. 279-298 ◽  
Author(s):  
Tadeusz Jezierski
Keyword(s):  
Statistical Verification

scholarly journals Methodology for Assessing the Degree of Occupational Safety Specific to Hydrotechnical Construction Activities, in Order to Increase Their Sustainability

2021 ◽  
Vol 13 (3) ◽  
pp. 1105
Author(s):  
Gabriel Dragos Vasilescu ◽  
Codruț Dan Petrilean ◽  
Attila Kovacs ◽  
Gabriel Victor Vasilescu ◽  
Dragos Pasculescu ◽  
...  
Keyword(s):  
Occupational Safety ◽  
Security Risk ◽  
Internal Auditors ◽  
Construction Companies ◽  
Statistical Verification ◽  
Safety And Health ◽  
Human Material ◽  
Kolmogorov Smirnov ◽  
Verification Test ◽  
Risk Predictors

The effectiveness of the audit is conditioned by the systematic nature of the analyses and their periodicity. In this respect, the work of internal auditors must be coherent, continuous, and professional. These conditions can be met only if the activity is organized as a permanent system, which has its objectives and has resources (human, material, and financial), as well as adequate tools (audit method) corresponding to the activities carried out within the hydro-construction companies. The methodology for assessing the occupational safety status specific to companies in the field of hydrotechnical constructions was tested at the economic operators within SC HIDROCONSTRUCȚIA SA Bucharest. Thus, from the observation made based on the two calculation formulas’ application, it can be appreciated that the weighted amount more clearly and objectively reflects the existence of problems in achieving safety and health at work, thus constituting the result of a cautious approach, as opposed to the arithmetic mean formula, which leads to a result that tends to overestimate the value of the assessment. Additionally, the security risk determination in the field of occupational security at the economic operators was performed, according to the procedure of applying the method, based on the Gumbel probability function associated with insecurity, and the accuracy of various estimates on risk predictors was ensured by using the Kolmogorov–Smirnov statistical verification test in order to determine the confidence interval of the forecast results.

Failure-Adverse Closed-Loop Statistical Verification

2019 ◽  
Author(s):  
John F. Quindlen ◽  
Ufuk Topcu ◽  
Girish Chowdhary ◽  
Jonathan P. How
Keyword(s):  
Closed Loop ◽  
Statistical Verification

scholarly journals A New Empirical Approach to Calculating Flood Frequency in Ungauged Catchments: A Case Study of the Upper Vistula Basin, Poland

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 601 ◽  
Author(s):  
Dariusz Młyński ◽  
Andrzej Wałęga ◽  
Tomasz Stachura ◽  
Grzegorz Kaczor
Keyword(s):  
Empirical Model ◽  
Catchment Area ◽  
Flood Frequency ◽  
Ungauged Catchments ◽  
Volume Determination ◽  
Statistical Verification ◽  
Observation Series ◽  
Data Estimation

The aim of the work was to develop a new empirical model for calculating the peak annual flows of a given frequency of occurrence (QT) in the ungauged catchments of the upper Vistula basin in Poland. The approach to the regionalization of the catchment and the selection of the optimal form of the empirical model are indicated as a novelty of the proposed research. The research was carried out on the basis of observation series of peak annual flows (Qmax) for 41 catchments. The analysis was performed in the following steps: statistical verification of data; estimation of Qmax flows using kernel density estimation; determination of physiographic and meteorological characteristics affecting the Qmax flow volume; determination of the value of dimensionless quantiles for QT flow calculation in the upper Vistula basin; verification of the determined correlation for the calculation of QT flows in the upper Vistula basin. Based on the research we conducted, we found that the following factors have the greatest impact on the formation of flood flows in the upper Vistula basin: the size of catchment area; the height difference in the catchment area; the density of the river network; the soil imperviousness index; and the volume of normal annual precipitation. The verification procedure that we performed made it possible to conclude that the developed empirical model functions correctly.

scholarly journals Four-dimensional variational (4DVAR) performance test with assimilation satellite and radar data (case study of a heavy rainfall Bengkulu March 4, 2019)

2021 ◽  
Vol 893 (1) ◽  
pp. 012031
Author(s):  
D I Syafitri J ◽  
F P Sari
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Performance Test ◽  
Time Window ◽  
Time Integration ◽  
Model Performance ◽  
Temporal Analysis ◽  
Radar Data ◽  
Initial Time ◽  
Statistical Verification

Abstract Four-dimensional variational (4DVAR) is one of the assimilation techniques considering time integration to distribute observational data at time window intervals. In this study, we aim to evaluate the 4DVAR assimilation technique using satellite and radar data to simulate a heavy rainfall case in Bengkulu on March 4, 2019. The result shows that radar data assimilation (DA-RAD) can improve rainfall pattern over Bengkulu mainland areas, while the satellite data assimilation (DA-SAT) enhances rainfall over the ocean. In addition, for temporal analysis, the DA-RAD successfully correct the initial time of the event, producing the smallest error and the best correlation in statistical verification, also a small bias and higher accuracy for discrete verification. However, DA-SAT is more capable to improve rainfall accumulation with the lowest FAR value. In conclusion, compared to others, both satellite and radar can be used as assimilation data for 4DVAR methods as they have different roles in increasing the quality of model performance.

Statistical Verification of Process Model Conformance to Execution Log Considering Model Abstraction

2018 ◽  
Vol 27 (02) ◽  
pp. 1850002
Author(s):  
Sung-Hyun Sim ◽  
Hyerim Bae ◽  
Yulim Choi ◽  
Ling Liu
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Statistical Significance ◽  
Conformance Checking ◽  
Model Abstraction ◽  
Verification Method ◽  
Statistical Verification ◽  
New Concepts ◽  
Kolmogorov Smirnov ◽  
Process Execution

In Big data and IoT environments, process execution generates huge-sized data some of which is subsequently obtained by sensors. The main issue in such areas has been the necessity of analyzing data in order to suggest enhancements to processes. In this regard, evaluation of process model conformance to the execution log is of great importance. For this purpose, previous reports on process mining approaches have advocated conformance checking by fitness measure, which is a process that uses token replay and node-arc relations based on Petri net. However, fitness measure so far has not considered statistical significance, but just offers a numeric ratio. We herein propose a statistical verification method based on the Kolmogorov–Smirnov (K–S) test to judge whether two different log datasets follow the same process model. Our method can be easily extended to determinations that process execution actually follows a process model, by playing out the model and generating event log data from it. Additionally, in order to solve the problem of the trade-off between model abstraction and process conformance, we also propose the new concepts of Confidence Interval of Abstraction Value (CIAV) and Maximum Confidence Abstraction Value (MCAV). We showed that our method can be applied to any process mining algorithm (e.g. heuristic mining, fuzzy mining) that has parameters related to model abstraction. We expect that our method will come to be widely utilized in many applications dealing with business process enhancement involving process-model and execution-log analyses.

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