Papers by Keyword: Statistical Model

Abstract: The purpose of this work is developing of the statistical model of hydrogen diffusion in the crystal lattice of BCC metals with an estimate of the contribution of quantum effects and deviations from the Arrhenius equation. The values of the statistical model calculations of H diffusion coefficients in Fe, V, Nb and Ta are in good agreement with the experimental data. The statistical model can also explain deviations from the Arrhenius equation at temperatures 300-500 K in Fe and Nb. The downward deviation of the diffusion coefficient at 300K can be explained by the fact that the statistical model does not consider the tunneling effect at temperatures below 300K. It was suggested that thermally activated fast tunnelling transition of hydrogen atoms through the potential barrier at temperatures below 500 K provides an almost free movement of H atoms in the α-Fe and V. Using the statistical model allows for the prediction of the diffusion coefficient for H in BCC metals at intermediate temperatures.

Abstract: Recent findings on the production of quantum dots from various carbon sources shed light on their advantages such as sustainability, low toxicity and cost, and one-step synthesis over their heavy-metal counterpart. This paper focused on developing and analyzing the production of carbon quantum dots from glycerol via hydrothermal carbonization and conjugated with Tetraethylenepentamine (TEPA). A 2³ full factorial experimental design was applied considering factors: the compositional ratio of TEPA (A), time of exposure (B), and temperature of reaction (C). Statistical analyses revealed experimental factors A and B; and interactions of AB and AC had statistically significant effects on the response variable, quantum yield (QY). Factor C as the main effect was not significant but was included in the statistical model to maintain hierarchy and integrity. Coded and actual statistical models were presented here.

Abstract: Metal cutting is the commonly used method in mechanical design, and the tool is the most important key factor in metal cutting. When the tool is severely worn, it will cause the tool to break. This article takes the current value of machining precision turret as an example to study the relationship between current value and tool wear. We used statistical mathematical models to predict tool life and used scatter diagrams to verify the timing of tool change and the actual degree of tool wear, to achieve accurate prediction and reduce tool waste. In our experiment, the core part of the indexing plate (turret) is machined by the horizontal machining center, The CCD image capture system was utilized to evaluate cutting tool wear. Three methods are analyzed to predict tool wear and current. The probability statistical mathematical model shows good match to predict the tool life. it is possible to find out the holes with poor quality caused by tool wear and calculate the exchange rate.

Abstract: In this work, we are interested in the faults diagnosis and the faults prognosis in discrete event systems described by sequences of generated events. Through this work, we aim the maximization of the efficiency of diagnosis/prognosis operations by combining two concepts. The first one is the approach already developed in one of our works which consider the k-last generated events to perform the diagnosis/prognosis. The second concept is the reliability that takes into consideration the life cycle of each component of the discrete event systems to give the failure probability. This combination will be made using some notions of fuzzy logic.

Abstract: This study evaluates the effect of process parameters on depth of penetration and surface roughness in abrasive waterjet (AWJ) cutting of copper. Full factorial experiments are carried out on trapezoidal blocks for each of the three abrasive particle sizes used. Experimental parameters - abrasive mass flow rate, water jet pressure and traverse speed are varied at three levels. Main effects and contributions of process parameters to depth of penetration and surface roughness is calculated. From the data, it is observed that, high abrasive mass flow rate, high water jet pressure and low traverse speed resulted in higher depth of penetration and a high abrasive mass flow rate, high water jet pressure and low traverse speed resulted in lesser R_a value. Using experimental data a statistical model for predicting depth of penetration & surface roughness is developed. Error between experimental and statistical values are compared to validate the statistical model. The maximum DOP of 49.32mm was observed at AMFR=405.4 g/min, P=300 MPa, TS=60 mm/min, MS=60 Mesh and minimum DOP of 4.27mm was observed at AMFR=200 g/min, P=100 MPa, TS=90 mm/min, MS=80 Mesh.

Abstract: This paper aimed to develop a statistical analysis and studies the behavior of the thermo-chemically nitrided superficial steel layer characteristics during a dry wear process. The purpose of this study was to prove the causal relation between some characteristics of the superficial layer subjected to a wear process through the correlations between the depth of the worn-out superficial layer (Uh) or the mass loss (Δm) and the applied loading task (Q), during and after three hours of the wear process, taking into account two sliding degrees (ξ). After an experimental program, the obtained values were used to determine a correlation between these variables through an empirical study, to establish a causal relationship between them, using a statistical method. These characteristics were promoted according to behavior of the nitrided layer during the process of wear. To achieve this purpose we used a statistical method. The results of this study allow the prediction of the worn-out layer depth and the mass loss as a function of the normal load value.

Abstract: Zhang presented a statistical model of real-time video moving target detection based on Bayesian statistical theory. This article discusses the algorithm parameter selection and detection efficiency of the model by using the experimental simulation method. This article generates a reference background based on unsupervised learning methods, and uses a color space that has a better environmental adaptability to represent the background, and uses dynamic threshold method to classify the results of background subtraction and frame difference. By comparing experimental of different methods, it shows that this algorithm has a greater advantage in terms of accuracy and timeliness.

Abstract: Video moving target detection is an important foundation issues in computer vision, based on the analysis of the advantages and disadvantages of each existing moving target detection model, using Bayesian statistical theory as a framework, proposes a statistical model that can detect moving objects in video in real-time. The model combines time, space and color and other relevant information of pixel, divides and extracts Video segmentation’s foreground. By selecting the appropriate reference background can improve the precision and accuracy of the detection.

Abstract: Laser polishing is a finishing process based on melting material, with the objective of improving surface topography. Some operating parameters must be taken into consideration, such as laser power, feed rate, offset, and overlapping. Moreover, because of its dependence on the primary process, the initial topography has also an impact on the final result. This study describes a quadratic model, conceived to optimize final topography according to the primary process and laser polishing. Based on an experimental matrix, the model takes into account both laser operating parameters and the initial topography, in order to predict polished surfaces and to determine optimal set of parameters. After the phase of experimentation and the creation of the quadratic model, an optimal final topography is introduced, taking into account the initial surface and the laser parameters.

Abstract: Based on the statistical pattern recognition theory, the AMRA timing analysis methods are used in the article, through the combination of long autoregressive model residuals method and the least squares method the model parameters are estimated, and a system model is established. By using mean control chart method the vibration information and feature of the pressure pipe are extracted and selected, so whether the pressure pipes is damaged can be judged effectively. The simulation results show that structural abnormalities test method of the mean value ,which is Based on the recognition theory of statistical pattern, can accurately diagnose structural damage detection state ,the injury degree and damage location, it has a very strong sensitivity