scholarly journals Stochastic Modeling for Environmental Stress Screening

2014 ◽  
Vol 51 (02) ◽  
pp. 387-399 ◽  
Author(s):  
Ji Hwan Cha ◽  
Maxim Finkelstein
Keyword(s):  
Stochastic Model ◽  
Environmental Stress ◽  
Stochastic Modeling ◽  
Population Characteristics ◽  
Latent Defects

Environmental stress screening (ESS) of manufactured items is used to reduce the occurrence of future failures that are caused by latent defects by eliminating the items with these defects. Some practical descriptions of the relevant ESS procedures can be found in the literature; however, the appropriate stochastic modeling and the corresponding thorough analysis have not been reported. In this paper we develop a stochastic model for the ESS, analyze the effect of this operation on the population characteristics of the screened items, and also consider the relevant optimization issues.

scholarly journals Stochastic Modeling for Environmental Stress Screening

2014 ◽  
Vol 51 (2) ◽  
pp. 387-399 ◽  
Author(s):  
Ji Hwan Cha ◽  
Maxim Finkelstein
Keyword(s):  
Stochastic Model ◽  
Environmental Stress ◽  
Stochastic Modeling ◽  
Population Characteristics ◽  
Latent Defects

Environmental stress screening (ESS) of manufactured items is used to reduce the occurrence of future failures that are caused by latent defects by eliminating the items with these defects. Some practical descriptions of the relevant ESS procedures can be found in the literature; however, the appropriate stochastic modeling and the corresponding thorough analysis have not been reported. In this paper we develop a stochastic model for the ESS, analyze the effect of this operation on the population characteristics of the screened items, and also consider the relevant optimization issues.

Au sujet du débit maximal de la rivière Mistassibi durant la période printanière

2001 ◽  
Vol 28 (6) ◽  
pp. 1041-1045
Author(s):  
Mario Lefebvre
Keyword(s):  
Linear Regression ◽  
Stochastic Model ◽  
Stochastic Modeling ◽  
Gaussian Distribution ◽  
Maximal Flow ◽  
Correlation Peak ◽  
Hydrological Forecast ◽  
Regression Correlation

First a stochastic model is found for the maximal flow of the Mistassibi river, in Québec, during each of the months of April, May, and June, as well as for the maximal flow during the 3-month period. Next, the problem of forecasting the maximal flow in May, based on the maximal flow in April, is considered.Key words: stochastic modeling, hydrological forecast, Gaussian distribution, lognormal distributrion, linear regression, correlation, peak criterion.

Stochastic Modeling of Microgrinding Wheel Topography

2013 ◽  
Vol 1 (2) ◽  
Author(s):  
Jacob A. Kunz ◽  
J. Rhett Mayor
Keyword(s):  
Stochastic Model ◽  
Stochastic Modeling ◽  
Process Modeling ◽  
Grinding Wheel ◽  
Analytic Model ◽  
Geometric Accuracy ◽  
Simulation Techniques ◽  
Wheel Topography ◽  
A New Technique ◽  
The Individual

Superabrasive microgrinding wheels are used for jig grinding of microstructures using various grinding approaches. The desire for increased final geometric accuracy in microgrinding leads to the need for improved process modeling and understanding. An improved understanding of the source of wheel topography characteristics leads to better knowledge of the interaction between the individual grits on the wheel and the grinding workpiece. Analytic stochastic modeling of the abrasives in a general grinding wheel is presented as a method to stochastically predict the wheel topography. The approach predicts the probability of the number of grits within a grind wheel, the individual grit locations within a given wheel structure, and the static grit density within the wheel. The stochastic model is compared to numerical simulations that imitate both the assumptions of the analytic model where grits are allowed to overlap and the more realistic scenario of a grind wheel where grits cannot overlap. A new technique of grit relocation through collective rearrangement is used to limit grit overlap. The results show that the stochastic model can accurately predict the probability of the static grit density while providing results two orders of magnitude faster than the numerical simulation techniques. It is also seen that grit overlap does not significantly impact the static grit density allowing for the simpler, faster analytic model to be utilized without sacrificing accuracy.

Mathematical basis of stochastic modeling multicomponent risks in security systems

2021 ◽  
Vol 94 ◽  
pp. 125-143
Author(s):  
M. Yu. Prus ◽  
Keyword(s):  
Stochastic Model ◽  
Stochastic Modeling ◽  
Security Systems ◽  
Mathematical Basis ◽  
Current State ◽  
System Risk ◽  
Protected Object ◽  
Technical Systems ◽  
Labor Resources ◽  
Anthropogenic Risks

Introduction. It is shown that the development of methods for modeling multicomponent risks is a promising direction for improving information and analytical support for control in security systems. The purpose of the study is to develop new approaches to the study of natural, technogenic and anthropogenic risks based on stochastic modeling of the structure of multicomponent risks in socio-technical systems. Methods of stochastic modeling are based on a matrix representation of risk components, detailing the states of the protected object and the probabilistic characteristics of the functioning of security systems. Results and discussion. A method for analyzing multicomponent risks is presented, reflecting in-depth detailing of the states of the protected object and the probabilistic characteristics of the functioning of security systems. A stochastic model has been built that describes the structure of risk as a result of the interaction of two components, a multiplier and an accelerator, associated with various elements of the model, which, respectively, determine the possibility of occurrence of dangerous events, as well as the degree of vulnerability of protected objects. A connection is established between the indicators of expected losses in a certain territory with the presence of forces, means and systems of protection against the effects of hazardous factors and their current state. The procedures for determining the main parameters of the proposed stochastic model based on statistical and expert methods are discussed. A mathematical toolkit has been created for comparative analysis of the effectiveness of measures to reduce risks in socio-technical systems. The problem of multicriteria combinatorial optimization of planned costs and distribution of financial, material, technical and labor resources in territorial security systems is formulated. Conclusions. Methods for modeling multicomponent risks can be used to create effective algorithms for supporting risk-oriented management in security systems. Key words: stochastic modeling, multicomponent risk, socio-technical system, risk management, security system.

Effects of in-sewer processes: a stochastic model approach

2005 ◽  
Vol 52 (3) ◽  
pp. 171-180 ◽  
Author(s):  
J. Vollertsen ◽  
A.H. Nielsen ◽  
W. Yang ◽  
T. Hvitved-Jacobsen
Keyword(s):  
Organic Matter ◽  
Hydrogen Sulfide ◽  
Stochastic Model ◽  
Gas Phase ◽  
Stochastic Modeling ◽  
Transport Processes ◽  
Water Phase ◽  
Model Concept ◽  
Volatile Substances ◽  
Model Approach

Transformations of organic matter, nitrogen and sulfur in sewers can be simulated taking into account the relevant transformation and transport processes. One objective of such simulation is the assessment and management of hydrogen sulfide formation and corrosion. Sulfide is formed in the biofilms and sediments of the water phase, but corrosion occurs on the moist surfaces of the sewer gas phase. Consequently, both phases and the transport of volatile substances between these phases must be included. Furthermore, wastewater composition and transformations in sewers are complex and subject to high, natural variability. This paper presents the latest developments of the WATS model concept, allowing integrated aerobic, anoxic and anaerobic simulation of the water phase and of gas phase processes. The resulting model is complex and with high parameter variability. An example applying stochastic modeling shows how this complexity and variability can be taken into account.

scholarly journals Stochastic modeling of nanoparticle internalization and expulsion through receptor-mediated transcytosis

Nanoscale ◽  
2019 ◽  
Vol 11 (23) ◽  
pp. 11227-11235
Author(s):  
Hua Deng ◽  
Prashanta Dutta ◽  
Jin Liu
Keyword(s):  
Stochastic Model ◽  
Stochastic Modeling ◽  
Nanoparticle Transport

A stochastic model of clathrin-mediated endocytosis and actin-mediated exocytosis is developed for the study of transcellular nanoparticle transport.

R&M 2000 Environmental Stress Screening

1988 ◽  
Vol 31 (1) ◽  
pp. 17-23
Author(s):  
Carlos Talbott
Keyword(s):  
Environmental Stress ◽  
Air Force ◽  
Manufacturing Process ◽  
Random Vibration ◽  
Electronic Equipment ◽  
High Quality ◽  
Latent Defects ◽  
Equipment Manufacturing ◽  
Graphical Technique ◽  
Field Reliability

Air Force policy requires the implementation of Environmental Stress Screening (ESS) programs on electronic equipment procurements and the establishment of ESS for electronic inventory repair. The purpose of this policy is to move failures, due to weak parts and poor workmanship, out of the field and back into the factory. This procedure will improve field reliability and, thereby, increase combat capability. When properly applied, ESS quickly precipitates latent defects to failure without damaging equipment. Air Force ESS policy requires that electronic equipment manufacturing and repair processes begin with high-quality piece parts as measured by a defective rate of 100 parts/million or less. The policy calls for a minimum of thermal and random vibration screening during various stages of the manufacturing process according to a baseline ESS regimen. Alternative regimens are permissible provided they are as effective as the baseline regimen in reducing premature failures in the field. This paper discusses ESS theory as it relates to Air Force policy and also outlines a graphical technique for illustrating the effectiveness of ESS regimens based on field experience.

Stochastic Modeling on the Emotional Contagion

2014 ◽  
Author(s):  
Taide Tan ◽  
Zhao Liu
Keyword(s):  
Stochastic Model ◽  
Stochastic Modeling ◽  
Emotional Contagion ◽  
Advantages And Disadvantages ◽  
Modeling Methods ◽  
Ca Model ◽  
Multi Level ◽  
Initial Results ◽  
Working Efficiency ◽  
The Ideal

The effects of emotional contagion between leader and followers have been proven of great importance, especially on the outcomes and the working efficiency. The mechanisms of the multi level emotional infections have been analyzed. The stochastic modeling methods on emotional contagion have been reviewed. The advantages and disadvantages have been compared between these methods. A novel stochastic model based on cellular automaton (CA) has been presented. The initial results have been shown and the simulation demonstrated the CA model is one of the ideal tools for the estimation of emotional contagion and to evaluate the influence of positive and negative emotions from the leader group.

Stochastic Modeling of Maximum Still-Water Load Effects in Ship Structures

1990 ◽  
Vol 34 (03) ◽  
pp. 199-205
Author(s):  
C. Guedes Soares
Keyword(s):  
Statistical Analysis ◽  
Stochastic Model ◽  
Time Dependence ◽  
Stochastic Modeling ◽  
Random Variables ◽  
Ship Structures ◽  
Water Load ◽  
Load Duration ◽  
Probability Of Occurrence ◽  
Load Effects

A stochastic model of an alternating renewal pulse process is proposed to describe the time dependence of still-water load effects in ship structures, extending previous studies that dealt only with random variables. The model proposed is applied to different ship types and the probability of occurrence of annual maxima is also determined in those cases. The results of a brief statistical analysis of load duration are also included as a basis to describe the time dependence of the process. Applications of this model to the development of code requirements are also included.

scholarly journals LS-VCE Applied to Stochastic Modeling of GNSS Observation Noise and Process Noise

2022 ◽  
Vol 14 (2) ◽  
pp. 258
Author(s):  
Pengyu Hou ◽  
Jiuping Zha ◽  
Teng Liu ◽  
Baocheng Zhang
Keyword(s):  
Stochastic Model ◽  
Stochastic Modeling ◽  
Ambiguity Resolution ◽  
Sampling Rate ◽  
Dual Frequency ◽  
Process Noise ◽  
Dilution Of Precision ◽  
Observation Noise ◽  
The Impact ◽  
Better Than

Stochastic models play a crucial role in global navigation satellite systems (GNSS) data processing. Many studies contribute to the stochastic modeling of GNSS observation noise, whereas few studies focus on the stochastic modeling of process noise. This paper proposes a method that is able to jointly estimate the variances of observation noise and process noise. The method is flexible since it is based on the least-squares variance component estimation (LS-VCE), enabling users to estimate the variance components that they are specifically interested in. We apply the proposed method to estimate the variances for the dual-frequency GNSS observation noise and for the process noise of the receiver code bias (RCB). We also investigate the impact of the stochastic model upon parameter estimation, ambiguity resolution, and positioning. The results show that the precision of GNSS observations differs in systems and frequencies. Among the dual-frequency GPS, Galileo, and BDS code observations, the precision of the BDS B3 observations is highest (better than 0.2 m). The precision of the BDS phase observations is better than two millimeters, which is also higher than that of the GPS and Galileo observations. For all three systems, the RCB process noise ranges from 0.5 millimeters to 1 millimeter, with a data sampling rate of 30 s. An improper stochastic model of the observation noise results in an unreliable ambiguity dilution of precision (ADOP) and position dilution of precision (PDOP), thus adversely affecting the assessment of the ambiguity resolution and positioning performance. An inappropriate stochastic model of RCB process noise disturbs the estimation of the receiver clock and the ionosphere delays and is thus harmful for timing and ionosphere retrieval applications.

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