scholarly journals DISCRETE NONLINEAR-PROBABILISTIC MODEL OF THE EQUIVALENT ELECTRICAL RESISTANCE OF A LAYER OF METAL GRANULES

2021 ◽  
Vol 2021 (2) ◽  
pp. 3-12
Author(s):  
N.A. Shidlovska ◽  
◽  
S.M. Zakharchenko ◽  
Keyword(s):  
Method Of Moments ◽  
Discharge Current ◽  
Probabilistic Model ◽  
Electrical Resistance ◽  
Probabilistic Models ◽  
Tap Water ◽  
Plasma Erosion ◽  
Spark Erosion ◽  
Trailing Edges ◽  
Normal Law

Two strategies for constructing nonlinear-probabilistic models of the equivalent electrical resistance of a layer of metal granules at their spark-erosion and plasma-erosion treatment and algorithms for their implementation are presented. A method for taking into account the parametric properties of such loads in their nonlinear and nonlinear-probabilistic models is described. Based on the data of direct experiments, the distributions of the electrical resistance of a layer of aluminum granules in tap water were obtained for eleven fixed values of the discharge current in it for both the leading and trailing edges of its pulses. The features of these distributions are described for different edges of the discharge current pulses. It has been proved that the obtained distributions can be adequately described by the normal law. The parameters of the normal law for each distribution obtained as a result of direct experiments are founded by the method of moments. A nonlinear-probabilistic model of the equivalent electrical resistance of a layer of aluminum granules in tap water is created, taking into account the main hysteresis of the dependence of resistance on current. It is shown that at large values of discharge currents, the nonlinear-probabilistic model of the equivalent electrical resistance of the layers of metal granules tends to degenerate into a nonlinear model. References 27, figures 4

scholarly journals DISCREPANCY PARAMETERS OF APPROXIMATIONS OF DISCRETELY SPECIFIED DEPENDENCIES BY ANALYTICAL FUNCTIONS AND SEARCH CRITERIA FOR OPTIMAL VALUES OF THEIR COEFFICIENTS

2021 ◽  
Vol 2021 (59) ◽  
pp. 11-19
Author(s):  
S.M. Zakharchenko ◽  
◽  
N.A. Shydlovska ◽  
I.L. Mazurenko ◽  
◽  
...  
Keyword(s):  
Power Function ◽  
Mathematical Expectation ◽  
Discharge Current ◽  
Electrical Resistance ◽  
Approximation Error ◽  
Analytical Functions ◽  
Spark Erosion ◽  
Instantaneous Values ◽  
Optimal Values

Universal discrepancy parameters of approximations of discretely specified dependencies by analytical functions and search criteria for optimal values of their coefficients, as well as analysis of features of their application are described. Discrepancy parameters of approximations, which do not depend on the ranges of variation of the values of functions and the number of points of a discretely specified dependence, are proposed. They can be effective for objectively comparing the quality of approximations of any dependencies by any functions. Approximations of a discretely specified dependence of the mathematical expectation of the equivalent electrical resistance of a layer of aluminum granules during spark-erosion dispersion in water on the instantaneous values of the discharge current are carried out. As approximating functions, we chose a power function with an exponent factor –1 and a function based on exponential. Using the criteria of the least approximation error, the optimal values of the coefficients of both approximating functions are founded. It is shown in which cases it is advisable to use the combined search criteria for the optimal values of the coefficients of the approximating functions, and in which are enough simple one-component ones. Ref. 27, fig. 2, tables 2.

scholarly journals ANALYTICAL NONLINEAR-PROBABILISTIC MODEL OF THE EQUIVALENT ELECTRICAL RESISTANCE OF A LAYER OF METAL GRANULES

2021 ◽  
Vol 2021 (5) ◽  
pp. 3-10
Author(s):  
S.M. Zakharchenko ◽  
◽  
N.A. Shydlovska ◽  
Keyword(s):  
Mathematical Expectation ◽  
Discharge Current ◽  
Probabilistic Model ◽  
Electrical Resistance ◽  
Nonlinear Models ◽  
Correction Coefficient ◽  
Analytical Functions ◽  
Instantaneous Values ◽  
Optimal Values ◽  
Working Liquid

As a result of processing the experimental data, an analytical continuous nonlinear-probabilistic model of the equivalent electrical resistance of a layer of metal granules in the working liquid was created. It is described by four equations: the modified Gaussian distribution and the dependences on the instantaneous values of the discharge current in the layer of metal granules of the mathematical expectation, dispersion and correction coefficient of the range of its equivalent electrical resistance. Based on the form of the dependences obtained during the experiments and the physics of the processes that occur in this case, two main groups of analytical functions are considered that approximate the obtained dependences. Criteria and methods for finding the optimal values of their coefficients are described. The adequacy of the approximation of each of the three obtained dependences by several analytical functions was investigated, the optimal values of the coefficients of which were found by the described method. Analytical functions was compared, which approximate the dependence of the mathematical expectation of the equivalent electrical resistance of a layer of metal granules on the instantaneous values of the discharge current in it with the known nonlinear models of the resistance of such a medium. References 33, figures 3, tables 3.

Statistical Method to Requalify Steel Grades During Conversion of Tankers to FPSO

2016 ◽  
Author(s):  
Philippe Cambos ◽  
Guy Parmentier
Keyword(s):  
Confidence Level ◽  
Probabilistic Model ◽  
Probabilistic Models ◽  
Empirical Distribution ◽  
Probability Of Failure ◽  
Operating Conditions ◽  
Existing Structures ◽  
Steel Grades ◽  
Determination Of Parameters ◽  
Acceptable Probability

During ship life, operating conditions may change, tanker may be converted into FPSO, and flag requirements may be modified. Generally these modifications have few impacts on existing structures; flag requirements only rarely are to be applied retroactively. Nevertheless in some cases modifications of operating condition may induce considerable consequences, making in the worst cases impossible any reengineering. For example converting a common tanker, built with plain steel of grade A into an Offshore Floating Unit able operating in cold region, may require a grade change corresponding to a grade B. It is obviously meaningless to replace all material just because material certificates. Steels used by shipyards have to fulfill Classification society’s requirements dealing with mechanical strength; generally shipbuilding corresponds to a small part of steelmaker’s production. For this reason steelmakers are reluctant to produce steels with mechanical properties corresponding exactly to the minima required. They generally deliver steels already in stock, with higher mechanical characteristics than required. In this case it can be taken advantage of this common practice. In order to demonstrate that the material fulfill the requirements of grade B it has been decided to adopt a statistic approach. At this stage there are two main issues, the first one is that it is needed to provide evidences that the actual material Charpy V characteristics fulfill the requirements of grade B; the second one is to provide these evidences with a minimum testing. To assess this assumption a random check has been carried out. Different probabilistic model have been tested in order to check common approaches and probabilistic model based on physical considerations. In the paper the main assumptions for estimating the minimum Charpy value main assumption in the probabilistic models are recalled, the behavior of empirical sample is examined, the parameters of probability laws fitting the empirical distribution and definitely as accuracy of probability law parameters determination is not perfect with a finite number of specimens the uncertainty in the determination of parameters is taken into account with confidence limits. According to the selected probabilistic model the minimum value corresponds to an acceptable probability of failure, taking into account the target confidence level, or is independent of any acceptable probability of failure and is defined with the same confidence level. At the end it is concluded that a random check with a data treatment assuming a random distribution of Charpy V test results distributed according to a Weibull probability law of the minimum allows providing evidences that with a sufficient confidence level the steel used for the considered structure fulfill the requirements of the new operating conditions.

How Prior Reward Experience Biases Exploratory Movements: A Probabilistic Model

2007 ◽  
Vol 97 (3) ◽  
pp. 2083-2093 ◽  
Author(s):  
Paul W. German ◽  
Howard L. Fields
Keyword(s):  
Probability Distribution ◽  
Computer Simulations ◽  
Probabilistic Model ◽  
Probabilistic Models ◽  
Neural Mechanisms ◽  
Behavioral Strategy ◽  
Line Configuration ◽  
Before And After ◽  
Exit Probabilities ◽  
Exploratory Movements

Animals return to rewarded locations. An example of this is conditioned place preference (CPP), which is widely used in studies of drug reward. Although CPP is expressed as increased time spent in a previously rewarded location, the behavioral strategy underlying this change is unknown. We continuously monitored rats ( n = 22) in a three-room in-line configuration, before and after morphine conditioning in one end room. Although sequential room visit durations were variable, their probability distribution was exponential, indicating that the processes controlling visit durations can be modeled by instantaneous room exit probabilities. Further analysis of room transitions and computer simulations of probabilistic models revealed that the exploratory bias toward the morphine room is best explained by an increase in the probability of a subset of rapid, direct transitions from the saline- to the morphine-paired room by the central room. This finding sharply delineates and constrains possible neural mechanisms for a class of self-initiated, goal-directed behaviors toward previously rewarded locations.

scholarly journals Probabilistic Model for Resource Demand Prediction in Cloud

2021 ◽  
Vol 12 (6) ◽  
pp. 1766-1771
Author(s):  
Niraja Jain, Dr B Raghu, Dr V Khanaa
Keyword(s):  
Probabilistic Model ◽  
Probabilistic Models ◽  
Probabilistic Method ◽  
Theoretical Computer Science ◽  
Cloud Services ◽  
Cloud Infrastructure ◽  
Shared Resources ◽  
Resource Demand ◽  
Demand Prediction ◽  
Degree Of Certainty

Dynamic cloud infrastructure provisioning is possible with the virtualization technology. Cost, agility and time to market are the key elements of the cloud services. Virtualization is the software layer responsible for interaction with multiple servers, bringing entire IT resources together and provide standardized Virtual compute centers that drives the entire infrastructure. The increased pooling of shared resources helps in improving self-provisioning and automation of service delivery. Probabilistic model proposed in this article is based on the hypothesis that the accurate resource demand predictions can benefit in improving the virtualization layer efficiency. The probabilistic method, uses the laws of combinatorics. The probability space gives an idea about both the partial certainty and randomness of the variable. The method is popular in theoretical computer science. The probabilistic models provide the predictions considering the randomness of the variables. In the cloud environment there are multiple factors dynamically affecting the resource demand needs. The resource demand has a certain degree of certainty but the randomness of requirements. This further leads to decrease in risk related to leveraging cloud services. It accelerates development and implementation of cloud services that overall improves the services pertaining to SLA.

scholarly journals Wave Probabilistic Model of Binary Time Series

10.14311/1055 ◽  
2008 ◽  
Vol 48 (5) ◽  
Author(s):  
M. Svítek
Keyword(s):  
Time Series ◽  
Probabilistic Model ◽  
Probabilistic Models ◽  
Product Rule ◽  
Binary Time Series

This paper presents the theory of wave probabilistic models, together with important features, such as the inclusion-exclusion rule, the product rule, the complementary principle and entanglement. These features are mathematically described, and an illustrative example of binary time series is shown to demonstrate possible applications of the theory. 

scholarly journals Quantitative Evaluations with 2D Electrical Resistance Tomography in the Low Conductivity Solutions Using 3D Printed Phantoms for Crystallization Process Monitoring

2020 ◽  
Author(s):  
Guruprasad Rao ◽  
Muhammad Awais Sattar ◽  
Radosław Wajman ◽  
Lidia Jackowska Strumiłło
Keyword(s):  
Electrical Resistance ◽  
Sucrose Solution ◽  
Tap Water ◽  
Imaging Modality ◽  
Spatial Accuracy ◽  
Reconstruction Algorithms ◽  
Electrical Resistance Tomography ◽  
Demineralized Water ◽  
Industrial Grade ◽  
Quantitative Evaluations

Crystallization is a significant procedure in the manufacturing of many pharmaceutical and solid food products. In-situ Electrical Resistance Tomography (ERT) is a novel Process Analytical Tool (PAT) to provide a cheap and quick way to test, visualize, and evaluate the progress of crystallization processes. In this work, the spatial accuracy of the non-conductive phantoms in low conductivity solutions was evaluated. Gauss-Newton, Linear Back Projection, and iterative Total Variation reconstruction algorithms were used to compare the phantom reconstructions for tap water, industrial-grade saturated sucrose solution, and demineralized water. Cylindrical phantom measuring 10 mm in diameter and a cross-section area of 1.5 % of the total beaker area was detected at the center of the beaker. Two phantoms with a 10 mm diameter were visualized separately in non-central locations. The quantitative evaluations were done for the phantoms with radii ranging from 10 mm to 50 mm in demineralized water. Multiple factors such as ERT device and sensor development, FEM mesh density and simulations, image reconstruction algorithms, number of iterations, segmentation methods, and morphological image processing methods were discussed and analyzed to achieve spatial accuracy. The development of ERT imaging modality for the purpose of monitoring crystallization in low conductivity solutions was performed satisfactorily.

scholarly journals Model-based kernel sum rule: kernel Bayesian inference with probabilistic models

2020 ◽  
Vol 109 (5) ◽  
pp. 939-972
Author(s):  
Yu Nishiyama ◽  
Motonobu Kanagawa ◽  
Arthur Gretton ◽  
Kenji Fukumizu
Keyword(s):  
Bayesian Inference ◽  
Probabilistic Model ◽  
Probabilistic Models ◽  
State Transition ◽  
Hybrid Approach ◽  
Transition Process ◽  
The State ◽  
Nonparametric Inference ◽  
Sum Rule ◽  
Model Based

AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.

Modeling and Spark Erosion Drilling Process Optimization of Inconel 718 Using RSM Technique

2019 ◽  
Vol 18 (01) ◽  
pp. 57-83 ◽  
Author(s):  
Rajeev Kumar ◽  
Somnath Chattopadhyaya ◽  
G. K. Singh ◽  
Umesh Kumar Vates
Keyword(s):  
Surface Roughness ◽  
Wear Rate ◽  
Tool Wear ◽  
Discharge Current ◽  
Inconel 718 ◽  
Removal Rate ◽  
Tool Wear Rate ◽  
Spark Erosion ◽  
Pulse On Time ◽  
Pulse Off Time

Electrical discharge machining with rotary tool is known as electric discharge drilling (EDD) which is being widely used for machining the difficult-to-cut materials like super alloy, ceramics and composite materials. Present research work has been introduced to find the impact of four influencing input factors discharge current (C), pulse off time ([Formula: see text]), pulse-on time ([Formula: see text]) and drill speed (S) on the response, tool wear rate (TWR), metal removal rate (MRR) and Centre line average value of surface roughness (Ra). The spark erosion drilling was performed on the Inconel 718 with rotating copper electrode. The major performances characteristics material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) are to be evaluated with consultation of Response Surface Methodology (RSM) techniques. The central composite rotatable design (CCRD) has been reported to plan the experimental design and developing the model for prediction of data within the range of investigation. ANOVA test was also carried out to check the adequacy for development of models. It has been evaluated that discharge current, [Formula: see text], and [Formula: see text] have been found as most significant factors that effects on the performance measures. The models have 86.02, 84.29, and 83.15% values of correlation coefficient (R2) for MRR, TWR and Ra whereas the adjusted R2 (R2 adj) are 73.80%, 70.55%, and 68.41% for MRR, TWR and SR, respectively.

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