Analysis of Filtration Process Influence on the Results of Thermal Imaging Control of the Subsurface Defect’s Geometry in Solving Control Problems of Collaborative Robotic Systems

The paper proposes and experimentally verifies an approach for estimating the thermal imaging error depending from the method of the subsurface defect’s linear dimensions filtering for forming the appropriate commands of the control program in a collaborative robotic system. The results are presented for the absolute error estimation depending on the filtration method with a confidence probability p=0,9972.

The dependence of coordinate measurements error of the geometric elements shape characteristics of products on the control points number

The issues of estimating the error of coordinate measurements of the shape characteristics of geometric elements of products depending on the number of control points, taking into account a given confidence probability, are considered. Analytical models are proposed for estimating the error based on a priori data, similar to estimating uncertainty by type B. The correspondence of model and experimental results is verified by the Monte Carlo method using a specially developed program in VBA and the library functions of the Statistical Analysis package of the Microsoft Excel program. Such a characteristic of the shape of the part as roundness is investigated. The influence of the parameters of regular structures associated with the features of the technological processes of manufacturing the part on the parameters of the distribution function of the coordinates of the control points is revealed. Fourier analysis is used to identify and quantify regular structures on the surface of the part. The sources of error that have a significant impact on the results of measurements of the shape characteristics of geometric elements of products are given. Based on the results of statistical calculations, the dependence of the measurement error of the shape characteristics on the number of control points is analyzed, the scope of application of analytical formulas for estimating the error of single measurements for a given confidence probability is determined. The article is intended for specialists in the field of practical coordinate metrology and related fields.

Confidence set for connected stocks of stock market

The problem of analysis of pairwise connections between stocks of financial market by observations on stock returns is considered. Such problem arise in stock market network analysis. It is assumed that joint distribution of stock returns belongs to the wide class of elliptical distributions. Classical Pearson correlation, Fechner correlation and Kendall correlation are used as measure of dependence. The construction problems of sets of stocks with strong connections between its returns are investigated. The construction problems of sets of stocks with strong connections between its returns are investigated. To construct such sets the multiple hypotheses testing procedures on values of correlations are used. The properties of these statistical procedures are investigated by simulations. The simulation results show that procedures based on individual Fechner and Kendall tests lead to such sets of stocks with given confidence probability unlike procedure based on Pearson individual tests which do not control the confidence probability. At the same time it is emphasized that for Student distribution the constructed set is nearly the same to the confidence set. The procedure of consistency testing with elliptical model is proposed and exemplified. The peculiarities of the model are discussed.

Оценка вероятности выявления эксплуатационных дефектов в деталях авиационной техники из жаропрочных сплавов с использованием дефектоскопических жидкостей отечественного и зарубежного производства

Work was carried out to evaluate the effectiveness of the identification of operational defects during capillary testing of parts of aircraft from heat-resistant alloys using flaw detection fluids of domestic and foreign production, revealing defects with an opening size of 1 μm or less. Two complementary approaches to a qualitative assessment of the effectiveness of various sets of flaw detection materials recommended for use in conducting capillary control in the production and operation of parts of aircraft equipment are considered with the aim of choosing a kit that identifies defects of a certain type with the smallest dimensions. The first approach is based on the assessment of the main defectoscopic properties of penetrating multicomponent liquids, which ensure their penetration into the cavities of surface defects, and the second is based on the experimental determination of defect detection parameters with a probability of 50 and 90 % with a confidence probability of 50 % and with a probability of 90 % with a confidence probability of 95 %.

Bivariate confidence probability plots as a method to test the accuracy and variability of microbiological measures

IntroductionIn an interlaboratory calibration analysis to validate a methodology that will be proposed as a European standard for domestic laundry disinfection, tests were carried out to detect if there are different behaviors in the measurements regarding accuracies and variabilities. Interlaboratory tests using different doses of disinfectant and microorganisms were carried out. ISO 5725-2 and ISO 13528 form the basis of validations of quantitative methods, providing validation specifications for interlaboratory studies. However, a need for a simple graphical method to detect interlaboratory differences in accuracy and variability was observed.ObjectivesThe general goal of this work is to present a new exploratory methodology, graphical and easy to interpret, that can determine the accuracy and variability (precision) of a variable, and compare it to the methodology applied in ISO 5725-2 and ISO 13528.MethodsWe used confidence probability plots of the multivariate Student’s t-distribution to observe the accuracy and variability of microbiological measures carried out by different laboratories during a ring trial exercise. A function in R was built for this purpose: Miriam.analysis.ellipse(Y, factor_a, eel.plot = “ t-Student”). The different observations of accuracy and variability are represented in the ellipses. If any of the points are outside the ellipse with 95% confidence, we can assume a deviation in accuracy and / or variability.ResultsTwo examples are provided with real microbiological data (logarithmic unit reductions (LR) for Pseudomonas aeruginosa, Escherichia coli, Staphilococcus aureus, Enterococcus hirae, Candida albicans and microbial counts in water (WW)). The proposed new method allowed us to detect possible deviations in the WWMEA variable and we believe it has future application for the rapid control of microbiological measures.

Multi-scales Image Denoising Method Based on Joint Confidence Probability of Coefficients

Background: It is a classic problem that we estimate the original coefficient from the known coefficient disturbed with noise. Methods: This paper proposes an image denoising method which combines the dual-tree complex wavelet with good direction selection and translation invariance. Firstly, we determine the expression of probability density function through estimating the parameters by the variance and the fourth-order moment. Secondly, we propose two assumptions and calculate the joint confidence probability of original coefficient under the situation that the disturbed parental and present coefficients from neighborhood scale are known. Finally, we set the joint confidence probability as shrinkage function of coefficient for implementing the image denoising. Results: The simulation experiment results show that, compared to these traditional methods, this new method can reserve more detail information. Conclusion: Compared to the current methods, our novel algorithm can remove the most noise and reserve the detail texture in denoising results, which can make better visualization. In addition, our algorithm also shows advantage in PSNR.

A quantitative confidence signal detection model: 2. Confidence analysis

Decision making is a fundamental subfield within neuroscience. While recent findings have yielded major advances in our understanding of decision making, confidence in such decisions remains poorly understood. In this paper, we present a confidence signal detection (CSD) model that combines a standard signal detection model yielding a noisy decision variable with a model of confidence. The CSD model requires quantitative measures of confidence obtained by recording confidence probability judgments. Specifically, we model confidence probability judgments for binary direction recognition (e.g., did I move left or right) decisions. We use our CSD model to study both confidence calibration (i.e., how does confidence compare with performance) and the distributions of confidence probability judgments. We evaluate two variants of our CSD model: a conventional model with two free parameters (CSD2) that assumes that confidence is well calibrated and our new model with three free parameters (CSD3) that includes an additional confidence scaling factor. On average, our CSD2 and CSD3 models explain 73 and 82%, respectively, of the variance found in our empirical data set. Furthermore, for our large data sets consisting of 3,600 trials per subject, correlation and residual analyses suggest that the CSD3 model better explains the predominant aspects of the empirical data than the CSD2 model, especially for subjects whose confidence is not well calibrated. Moreover, simulations show that asymmetric confidence distributions can lead traditional confidence calibration analyses to suggest “underconfidence” even when confidence is perfectly calibrated. These findings show that this CSD model can be used to help improve our understanding of confidence and decision making. NEW & NOTEWORTHY We make life-or-death decisions each day; our actions depend on our “confidence.” Though confidence, accuracy, and response time are the three pillars of decision making, we know little about confidence. In a previous paper, we presented a new model — dependent on a single scaling parameter — that transforms decision variables to confidence. Here we show that this model explains the empirical human confidence distributions obtained during a vestibular direction recognition task better than standard signal detection models.

The Standardization of Aronia melanocarpa Fruits Growing on the Territory of Orenburg Region

For standardization of Aronia melanocarpa fruits the method of direct spectrophotometry at analytical wavelength 534 nm was used1 . The optimal extraction conditions of flavonoids from fruits of Aronia melanocarpa Elliot. - extractant is 96% ethyl alcohol; the ratio of "raw-extractant" - 1:30; extraction time -30 min1 . The relative degree of the determination of the total flavonoids in fruits of Aronia melanocarpa Elliot. in used method with confidence probability 0,95 is no more than ±3,64%. The content of total flavonoids in fruits of Aronia melanocarpa Elliot. varied from 1,6% to 7,5% (calculated on cyanidin-3-O-glikozid).

The Standardization of Crataegus sanguinea Fruits Growing on the Territory of Orenburg Region

For standardization of Crataegus sanguinea fruits the method of differential spectrophotometry at analytical wavelength 412 nm was used1. The optimal extraction conditions of flavonoids from fruits of Crataegus sanguinea Pall. – extractant is 70% ethyl alcohol; the ratio of “raw-extractant” – 1:30; extraction time -60 min1. The relative degree of the determination of the total flavonoids in fruits of Crataegus sanguinea Pall. in used method with confidence probability 0,95 is no more than ±3,5%. The content of total flavonoids in fruits of Crataegus sanguinea Pall. varied from 0,18±0,001% to 0,22±0,002% (calculated on hyperoside).