scholarly journals Rare event analysis using the Limited Relative Error algorithm for OMNeT++ simulations

10.29007/ch8v ◽  
2018 ◽  
Author(s):  
Sebastian Lindner ◽  
Raphael Elsner ◽  
Phuong Nga Tran ◽  
Andreas Timm-Giel
Keyword(s):  
Statistical Method ◽  
Relative Error ◽  
Rare Events ◽  
A Priori ◽  
Rare Event ◽  
Data Evaluation ◽  
Event Analysis ◽  
Result Analysis ◽  
Simulation Time

The Limited Relative Error algorithm is an alternative statistical method for data evaluation. Through online result analysis it continuously requests more samples until it deems the evaluation confident enough. With this it allows researchers to hand over the control of simulation time to the algorithm, and through a-priori configuration the target result resolution is set so that arbitrarily rare events can be investigated. We provide a new description of the method as well as a stand-alone implementation and an integration of the algorithm into the OMNeT++ simulator.

Rare Event Analysis Considering Data and Model Uncertainty

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Malak El-Gheriani ◽  
Faisal Khan ◽  
Ming J. Zuo
Keyword(s):  
Risk Analysis ◽  
Model Uncertainty ◽  
Rare Events ◽  
Rare Event ◽  
Abductive Reasoning ◽  
Data Uncertainty ◽  
Event Analysis ◽  
Aggregated Data ◽  
Hierarchical Bayesian Analysis ◽  
Informative Prior Distribution

In risk analysis of rare events, there is a need to adopt data from different sources with varying levels of detail (e.g., local, regional, categorical data). Therefore, it is very important to identify, understand, and incorporate the uncertainty that accompanies the data. Hierarchical Bayesian analysis (HBA) addresses uncertainty among the aggregated data for each event through generating an informative prior distribution for the event's parameter of interest. The Bayesian network (BN) approach is used to model accident causation. BN enables both inductive and abductive reasoning, which helps to better understand and minimize model uncertainty. In this work, the methodology is proposed to integrate BN with HBA to model rare events, considering both data and model uncertainty. HBA considers data uncertainty, while BN uses an adaptive model to better represent and manage model uncertainty. Application of the proposed methodology is demonstrated using three types of offshore accidents. The proposed methodology provides a way to develop a dynamic risk analysis approach to rare events.

scholarly journals Normative decision asymmetries with symmetric priors but asymmetric evidence

2020 ◽  
Author(s):  
Tahra Eissa ◽  
Joshua I Gold ◽  
Krešimir Josić ◽  
Zachary P Kilpatrick
Keyword(s):  
Rare Events ◽  
A Priori ◽  
Rare Event ◽  
Ideal Observer ◽  
Market Crashes ◽  
On Line ◽  
Reward Contingencies ◽  
Common Laboratory ◽  
Real World Problems ◽  
Inference Task

AbstractDecisions based on rare events are challenging because rare events alone can be both informative and unreliable as evidence. How humans should and do overcome this challenge is not well understood. Here we present results from a preregistered study of 200 on-line participants performing a simple inference task in which the evidence was rare and asymmetric but the priors were symmetric. Consistent with a Bayesian ideal observer, most participants exhibited choice asymmetries that reflected a tendency to rationally interpret a rare event as evidence for the alternative likely to produce slightly more events, even when the two alternatives were equally likely a priori. A subset of participants exhibited additional biases based on an under-weighing of rare events. The results provide new quantitative and theoretically grounded insights into rare-event inference, which is relevant to both real-world problems like predicting stock-market crashes and common laboratory tasks like predicting changes in reward contingencies.

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

Application of pyrometer and standard sample to determine surface temperature of studied materials

2019 ◽  
pp. 9-13
Author(s):  
V.Ya. Mendeleyev ◽  
V.A. Petrov ◽  
A.V. Yashin ◽  
A.I. Vangonen ◽  
O.K. Taganov
Keyword(s):  
Composite Material ◽  
Surface Temperature ◽  
Relative Error ◽  
Wavelength Range ◽  
Standard Sample ◽  
A Priori ◽  
Temperature Changes ◽  
Surface Temperatures ◽  
Relative Errors ◽  
Normal Emissivity

Determining the surface temperature of materials with unknown emissivity is studied. A method for determining the surface temperature using a standard sample of average spectral normal emissivity in the wavelength range of 1,65–1,80 μm and an industrially produced Metis M322 pyrometer operating in the same wavelength range. The surface temperature of studied samples of the composite material and platinum was determined experimentally from the temperature of a standard sample located on the studied surfaces. The relative error in determining the surface temperature of the studied materials, introduced by the proposed method, was calculated taking into account the temperatures of the platinum and the composite material, determined from the temperature of the standard sample located on the studied surfaces, and from the temperature of the studied surfaces in the absence of the standard sample. The relative errors thus obtained did not exceed 1,7 % for the composite material and 0,5% for the platinum at surface temperatures of about 973 K. It was also found that: the inaccuracy of a priori data on the emissivity of the standard sample in the range (–0,01; 0,01) relative to the average emissivity increases the relative error in determining the temperature of the composite material by 0,68 %, and the installation of a standard sample on the studied materials leads to temperature changes on the periphery of the surface not exceeding 0,47 % for composite material and 0,05 % for platinum.

scholarly journals Interspecies Chromosome Mapping in Caprimulgiformes, Piciformes, Suliformes, and Trogoniformes (Aves): Cytogenomic Insight into Microchromosome Organization and Karyotype Evolution in Birds

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 826
Author(s):  
Rafael Kretschmer ◽  
Marcelo Santos de Souza ◽  
Ivanete de Oliveira Furo ◽  
Michael N. Romanov ◽  
Ricardo José Gunski ◽  
...  
Keyword(s):  
Convergent Evolution ◽  
Karyotype Evolution ◽  
Rare Events ◽  
Rare Event ◽  
Chromosome Mapping ◽  
The Other ◽  
Other Hand ◽  
Interchromosomal Rearrangement ◽  
Insight Into ◽  
Phalacrocorax Brasilianus

Interchromosomal rearrangements involving microchromosomes are rare events in birds. To date, they have been found mostly in Psittaciformes, Falconiformes, and Cuculiformes, although only a few orders have been analyzed. Hence, cytogenomic studies focusing on microchromosomes in species belonging to different bird orders are essential to shed more light on the avian chromosome and karyotype evolution. Based on this, we performed a comparative chromosome mapping for chicken microchromosomes 10 to 28 using interspecies BAC-based FISH hybridization in five species, representing four Neoaves orders (Caprimulgiformes, Piciformes, Suliformes, and Trogoniformes). Our results suggest that the ancestral microchromosomal syntenies are conserved in Pteroglossus inscriptus (Piciformes), Ramphastos tucanus tucanus (Piciformes), and Trogon surrucura surrucura (Trogoniformes). On the other hand, chromosome reorganization in Phalacrocorax brasilianus (Suliformes) and Hydropsalis torquata (Caprimulgiformes) included fusions involving both macro- and microchromosomes. Fissions in macrochromosomes were observed in P. brasilianus and H. torquata. Relevant hypothetical Neognathae and Neoaves ancestral karyotypes were reconstructed to trace these rearrangements. We found no interchromosomal rearrangement involving microchromosomes to be shared between avian orders where rearrangements were detected. Our findings suggest that convergent evolution involving microchromosomal change is a rare event in birds and may be appropriate in cytotaxonomic inferences in orders where these rearrangements occurred.

scholarly journals Adaptive Multilevel Splitting for Rare Event Analysis

2007 ◽  
Vol 25 (2) ◽  
pp. 417-443 ◽  
Author(s):  
Frédéric Cérou ◽  
Arnaud Guyader
Keyword(s):  
Rare Event ◽  
Event Analysis

scholarly journals Optimized Polynomial Virtual Fields Method for Constitutive Parameters Identification of Orthotropic Bimaterials

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Xueyi Ma ◽  
Yu Wang ◽  
Jian Zhao
Keyword(s):  
Relative Error ◽  
Numerical Experiments ◽  
A Priori ◽  
Heterogeneous Materials ◽  
Image Correlation ◽  
Constitutive Parameter ◽  
Virtual Fields Method ◽  
Bending Load ◽  
Virtual Field ◽  
Constitutive Parameters

Heterogeneous materials are widely applied in many fields. Owing to the spatial variation of its constitutive parameters, the mechanical characterization of heterogeneous materials is very important. The virtual fields method has been used to identify the constitutive parameters of materials. However, there is a limitation: constitutive parameters of one material have to be a priori; then, constitutive parameters of the other one can be identified. Aiming at this limitation, this article presents a method to identify the constitutive parameters of heterogeneous orthotropic bimaterials under the condition that constitutive parameters of both materials are all unknown from a single test. A constitutive parameter identification method of orthotropic bimaterials based on optimized virtual field and digital image correlation is proposed. The feasibility of this method is verified by simulating the deformation fields of a two-layer material under three-point bending load. The results of numerical experiments with FEM simulations show that the weighted relative error of the constitutive parameter is less than 1%. The results suggest that the variation coefficient-to-noise ratio can perform a priori evaluation of a confidence interval on the identified stiffness components. The results of numerical experiments with DIC simulations show that the weighted relative error is 1.44%, which is due to the noise in the strain data calculated by DIC method.

Sequential Sampling Based Reliability Analysis for High Dimensional Rare Events With Confidence Intervals

2020 ◽  
Author(s):  
Yanwen Xu ◽  
Pingfeng Wang
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Computational Efficiency ◽  
Rare Events ◽  
Computational Cost ◽  
Rare Event ◽  
Probability Analysis ◽  
High Dimensional ◽  
Dimensional System ◽  
Study Results

Abstract Analysis of rare failure events accurately is often challenging with an affordable computational cost in many engineering applications, and this is especially true for problems with high dimensional system inputs. The extremely low probabilities of occurrences for those rare events often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. Generally, confidence intervals of an estimator can be established based on the central limit theorem, but one of the critical obstacles is the low computational efficiency, since the widely used Monte Carlo method often requires a large number of simulation samples to derive a reasonably narrow confidence interval. This paper develops a new probability analysis approach that can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high dimensional problems. The asymptotic behaviors of the developed estimator has also been proved theoretically without imposing strong assumptions. Further, an asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique is assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be built efficiently using the developed approach, and the true values have always been located within the estimation bounds, indicating that good estimation accuracy along with a significantly improved efficiency.

scholarly journals On computer-intensive simulation and estimation methods for rare-event analysis in epidemic models

2015 ◽  
Vol 34 (28) ◽  
pp. 3696-3713
Author(s):  
Stéphan Clémençon ◽  
Anthony Cousien ◽  
Miraine Dávila Felipe ◽  
Viet Chi Tran
Keyword(s):  
Rare Event ◽  
Epidemic Models ◽  
Estimation Methods ◽  
Event Analysis
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