Stochastic Approach to Aspect Tracking

2018 ◽  
pp. 217-229
Author(s):  
Maoto Inoue ◽  
Takao Miura
Keyword(s):  
Stochastic Approach

LIMITATIONS OF THE STOCHASTIC APPROACH IN TWO-PHASE TURBULENT FLOW CALCULATIONS

1996 ◽  
Vol 6 (2) ◽  
pp. 211-225 ◽  
Author(s):  
Keh-Chin Chang ◽  
Wen-Jing Wu ◽  
Muh-Rong Wang
Keyword(s):  
Turbulent Flow ◽  
Stochastic Approach ◽  
Two Phase

STOCHASTIC CALCULATION OF LIFETIME PREDICTION FOR COMPONENTS IN METAL STRUCTURES DUE TO THE GROWTH OF MICRODEFECTS

2020 ◽  
Vol 4 (97) ◽  
pp. 69-76
Author(s):  
IGOR N. SILVERSTOV
Keyword(s):  
Fatigue Strength ◽  
Fracture Mechanics ◽  
Stochastic Approach ◽  
Fatigue Curve ◽  
Probability Of Failure ◽  
Lifetime Prediction ◽  
Statistical Distributions ◽  
Metal Structures

A stochastic approach has been developed to evaluate fatigue strength using elements of the fracture mechanics. The article presents a method for determining the initial parameters of statistical distributions. It also considers the method for constructing a fatigue curve for a component of any size and configuration with any given probability of failure.

ESTIMATION OF STORM SURGE LOSS ALONG THE TOKYO BAY COAST BY STOCHASTIC APPROACH

2018 ◽  
Vol 74 (5) ◽  
pp. I_1393-I_1398
Author(s):  
Rikito HISAMATSU ◽  
Sooyoul KIM ◽  
Shigeru TABETA
Keyword(s):  
Storm Surge ◽  
Stochastic Approach ◽  
Tokyo Bay

A stochastic approach to approximate values in cooperative games

2019 ◽  
Vol 279 (1) ◽  
pp. 93-106 ◽  
Author(s):  
Stefano Benati ◽  
Fernando López-Blázquez ◽  
Justo Puerto
Keyword(s):  
Cooperative Games ◽  
Stochastic Approach

scholarly journals From Exploratory Tunnel to Base Tunnel: Hard Rock TBM Performance Prediction by Means of a Stochastic Approach

2020 ◽  
Vol 53 (12) ◽  
pp. 5473-5487 ◽  
Author(s):  
Andrea Rispoli ◽  
Anna Maria Ferrero ◽  
Marilena Cardu
Keyword(s):  
Performance Prediction ◽  
Early Stage ◽  
Tunnel Boring Machine ◽  
Stochastic Approach ◽  
Critical Issue ◽  
Utilization Factor ◽  
Tbm Performance ◽  
Factors Affecting ◽  
Tunnel Section ◽  
Input Variables

AbstractTunnel boring machine (TBM) performance prediction is often a critical issue in the early stage of a tunnelling project, mainly due to the unpredictable nature of some important factors affecting the machine performance. In this regard, deterministic approaches are normally employed, providing results in terms of average values expected for the TBM performance. Stochastic approaches would offer improvement over deterministic methods, taking into account the parameter variability; however, their use is limited, since the level of information required is often not available. In this study, the data provided by the excavation of the Maddalena exploratory tunnel were used to predict the net and overall TBM performance for a 2.96 km section of the Mont Cenis base tunnel by using a stochastic approach. The preliminary design of the TBM cutterhead was carried out. A prediction model based on field penetration index, machine operating level and utilization factor was adopted. The variability of the parameters involved was analysed. A procedure to take into account the correlation between the input variables was described. The probability of occurrence of the outcomes was evaluated, and the total excavation time expected for the tunnel section analysed was calculated.

A hybrid stochastic approach for offline train trajectory reconstruction

2020 ◽  
Author(s):  
Pier Giuseppe Sessa ◽  
Valerio De Martinis ◽  
Axel Bomhauer-Beins ◽  
Ulrich Alois Weidmann ◽  
Francesco Corman
Keyword(s):  
Stochastic Approach ◽  
Trajectory Reconstruction
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