scholarly journals Assessment of the Robustness of a Fixtureless Inspection Method for Nonrigid Parts Based on a Verification and Validation Approach

2017 ◽  
Vol 2 (4) ◽  
Author(s):  
Sasan Sattarpanah Karganroudi ◽  
Jean-Christophe Cuillière ◽  
Vincent François ◽  
Souheil-Antoine Tahan
Keyword(s):  
Point Cloud ◽  
Hypothesis Test ◽  
Aerospace Industry ◽  
Maximum Amplitude ◽  
Statistical Hypothesis ◽  
Inspection Method ◽  
Statistical Hypothesis Test ◽  
Kolmogorov Smirnov ◽  
Computer Aided Inspection ◽  
Nonparametric Statistical

The increasing practical use of computer-aided inspection (CAI) methods requires assessment of their robustness in different contexts. This can be done by quantitatively comparing estimated CAI results with actual measurements. The objective is comparing the magnitude and dimensions of defects as estimated by CAI with those of the nominal defects. This assessment is referred to as setting up a validation metric. In this work, a new validation metric is proposed in the case of a fixtureless inspection method for nonrigid parts. It is based on using a nonparametric statistical hypothesis test, namely the Kolmogorov–Smirnov (K–S) test. This metric is applied to an automatic fixtureless CAI method for nonrigid parts developed by our team. This fixtureless CAI method is based on calculating and filtering sample points that are used in a finite element nonrigid registration (FENR). Robustness of our CAI method is validated for the assessment of maximum amplitude, area, and distance distribution of defects. Typical parts from the aerospace industry are used for this validation and various levels of synthetic measurement noise are added to the scanned point cloud of these parts to assess the effect of noise on inspection results.

Parameter Estimation for Industrial Servomotor by Using Statistical Hypothesis Test

2019 ◽  
Vol 19 (2) ◽  
pp. 134-140
Author(s):  
Baek-Ju Sung ◽  
Sung-kyu Lee ◽  
Mu-Seong Chang ◽  
Do-Sik Kim
Keyword(s):  
Parameter Estimation ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test

Item-specific overlap between hallucinatory experiences and cognition in the general population: A three-step multivariate analysis of international multi-site data

2021 ◽  
Author(s):  
Abhijit Mahesh Chinchani ◽  
Mahesh Menon ◽  
Meighen Roes ◽  
Heungsun Hwang ◽  
Paul Allen ◽  
...  
Keyword(s):  
Signal Detection ◽  
Source Monitoring ◽  
Hypothesis Test ◽  
The Other ◽  
Statistical Hypothesis ◽  
Auditory Signal ◽  
Attentional Processes ◽  
Hearing Voices ◽  
Statistical Hypothesis Test ◽  
Using Data

Cognitive mechanisms hypothesized to underlie hallucinatory experiences (HEs) include dysfunctional source monitoring, heightened signal detection, or impaired attentional processes. HEs can be very pronounced in psychosis, but similar experiences also occur in nonclinical populations. Using data from an international multisite study on nonclinical subjects (N = 419), we described the overlap between two sets of variables - one measuring cognition and the other HEs - at the level of individual items, allowing extraction of item-specific signal which might considered off-limits when summary scores are analyzed. This involved using a statistical hypothesis test at the multivariate level, and variance constraints, dimension reduction, and split-half reliability checks at the level of individual items. The results showed that (1) modality-general HEs involving sensory distortions (hearing voices/sounds, troubled by voices, everyday things look abnormal, sensations of presence/movement) were associated with more liberal auditory signal detection, and (2) HEs involving experiences of sensory overload and vivid images/imagery (viz., HEs for faces and intense daydreams) were associated with other-ear distraction and reduced laterality in dichotic listening. Based on these results, it is concluded that the overlap between HEs and cognition variables can be conceptualized as modality-general and bi-dimensional: one involving distortions, and the other involving overload or intensity.

scholarly journals Particulate Matter and COVID-19 Disease Diffusion in Emilia-Romagna (Italy). Already a Cold Case?

Computation ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 59 ◽  
Author(s):  
Giovanni Delnevo ◽  
Silvia Mirri ◽  
Marco Roccetti
Keyword(s):  
Granger Causality ◽  
Air Pollutants ◽  
Climatic Factors ◽  
Virus Disease ◽  
Hypothesis Test ◽  
Statistical Hypothesis ◽  
Clear Correlation ◽  
The Real ◽  
Statistical Hypothesis Test ◽  
Before And After

As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination of demographic, healthcare, political, business, organizational, and climatic factors that are out of our scientific scope. Nonetheless, looking at this problem from a patient’s perspective, it is indisputable that risk factors, considered as associated with the development of the virus disease, include older age, history of smoking, hypertension and heart disease. While several studies have already shown that many of these diseases can also be favored by a protracted exposure to air pollution, there has been recently an insurgence of negative commentary against authors who have correlated the fatal consequences of COVID-19 (also) to the exposition of specific air pollutants. Well aware that understanding the real connection between the spread of this fatal virus and air pollutants would require many other investigations at a level appropriate to the scale of this phenomenon (e.g., biological, chemical, and physical), we propose the results of a study, where a series of the measures of the daily values of PM2.5, PM10, and NO2 were considered over time, while the Granger causality statistical hypothesis test was used for determining the presence of a possible correlation with the series of the new daily COVID19 infections, in the period February–April 2020, in Emilia-Romagna. Results taken both before and after the governmental lockdown decisions show a clear correlation, although strictly seen from a Granger causality perspective. Moving beyond the relevance of our results towards the real extent of such a correlation, our scientific efforts aim at reinvigorating the debate on a relevant case, that should not remain unsolved or no longer investigated.

scholarly journals Local Variation as a Statistical Hypothesis Test

2015 ◽  
Vol 117 (2) ◽  
pp. 131-141 ◽  
Author(s):  
Michael Baltaxe ◽  
Peter Meer ◽  
Michael Lindenbaum
Keyword(s):  
Hypothesis Test ◽  
Local Variation ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test

Analysis of Microarray Data by Empirical Wavelet Transform for Cancer Classification Using Block by Block Method

2021 ◽  
Vol 11 (3) ◽  
pp. 697-702
Author(s):  
S. Jayanthi ◽  
C. R. Rene Robin
Keyword(s):  
Wavelet Transform ◽  
Microarray Data ◽  
Hypothesis Test ◽  
Cancer Classification ◽  
Statistical Hypothesis ◽  
Support Vector ◽  
Adaptive Wavelet ◽  
Statistical Hypothesis Test ◽  
Empirical Wavelet Transform ◽  
Powers Of 2

In this study, DNA microarray data is analyzed from a signal processing perspective for cancer classification. An adaptive wavelet transform named Empirical Wavelet Transform (EWT) is analyzed using block-by-block procedure to characterize microarray data. The EWT wavelet basis depends on the input data rather predetermined like in conventional wavelets. Thus, EWT gives more sparse representations than wavelets. The characterization of microarray data is made by block-by-block procedure with predefined block sizes in powers of 2 that starts from 128 to 2048. After characterization, a statistical hypothesis test is employed to select the informative EWT coefficients. Only the selected coefficients are used for Microarray Data Classification (MDC) by the Support Vector Machine (SVM). Computational experiments are employed on five microarray datasets; colon, breast, leukemia, CNS and ovarian to test the developed cancer classification system. The obtained results demonstrate that EWT coefficients with SVM emerged as an effective approach with no misclassification for MDC system.

Statistical Analysis of Binder Behavior during Debinding Step in Powder Injection Molding (PIM)

2014 ◽  
Vol 970 ◽  
pp. 172-176 ◽  
Author(s):  
Parinya Chakartnarodom ◽  
Nutthita Chuankrerkkul
Keyword(s):  
Linear Regression ◽  
Injection Molding ◽  
Hypothesis Test ◽  
Powder Injection Molding ◽  
Statistical Hypothesis ◽  
Powder Injection ◽  
Dissolution Behavior ◽  
Linear Regression Method ◽  
Statistical Hypothesis Testing ◽  
Statistical Hypothesis Test

The aim of this paper is to propose the approach for applying statistical methods (linear regression and statistical hypothesis testing) to study the behavior of binder during binder removing (debinding) step in powder injection molding (PIM) and also the parameters that affect the binder removing rate. In this work, the binder system under the investigation is the composite binder of 85wt% polyethylene glycol (PEG) and 15 wt% poly (methyl methacrylate) (PMMA) where PEG can be removed from the green product by using warm water while PMMA is removed later during sintering. At 0.05 level of significance, the linear regression method and the statistical hypothesis test prove that the dissolution behavior of PEG can be described using Avarami equation. Furthermore, the dissolution rates of PEG were independent of all parameters used in this study including binder contents in the green products, temperatures, and powder sizes.

Bootstrap test for process capability indices

2017 ◽  
Vol 34 (7) ◽  
pp. 925-939
Author(s):  
Bahram Sadeghpour Gildeh ◽  
Sedigheh Rahimpour ◽  
Fatemeh Ghanbarpour Gravi
Keyword(s):  
Hypothesis Test ◽  
Bootstrap Method ◽  
Process Capability ◽  
Statistical Hypothesis ◽  
Process Capability Indices ◽  
Content Type ◽  
Statistical Hypothesis Test ◽  
Test Of Hypotheses ◽  
Capability Indices ◽  
The Difference

Purpose The purpose of this paper is to construct a statistical hypotheses test for process capability indices and compare the pairs of them with a fixed sample size. Design/methodology/approach Since the sampling distribution of the estimators of pairs of two process capability indices (PCIs) is very complex, an exact statistical hypothesis test for them cannot be constructed. Therefore, the authors have proposed a bootstrap method to construct the hypothesis test for them on the basis of p-value. Findings The authors have shown that by increasing n, the bootstrap method has better output relative to other methods and it can be easily implemented. The authors have also demonstrated that sometimes an exact hypotheses test cannot be constructed and need some assumptions. Originality/value In the present paper, several methods to test of hypotheses about the difference between two process capability indices have been compared.

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