PROBABILISTIC MODELS FOR LONG FATIGUE CRACK GROWTH RATES OF LZ50 AXLE STEEL

Experimental study is performed on the probabilistic models for the long fatigue crack growth rates （da/dN） of LZ50 axle steel. An equation for crack growth rate was derived to consider the trend of stress intensity factor range going down to the threshold and the average stress effect. The probabilistic models were presented on the equation. They consist of the probabilistic da/dN-△K relations, the confidence-based da/dN-△K relations, and the probabilistic- and confidence-based da/dN-△K relations. Efforts were made respectively to characterize the effects of probabilistic assessments due to the scattering regularity of test data, the number of sampling, and both of them. These relations can provide wide selections for practice. Analysis on the test data of LZ50 steel indicates that the present models are available and feasible.

A Probabilistic Model for Fatigue Crack Propagation Analysis

A simple probabilistic model for predicting crack growth behavior under random loading is presented. In the model, the parameters c and m in the Paris-Erdogan Equation are taken as random variables, and their stochastic characteristic values are obtained through fatigue crack propagation tests on an offshore structural steel under constant amplitude loading. Furthermore, by using the Monte Carlo simulation technique, the fatigue crack propagation life to reach a given crack length is predicted. The tests are conducted to verify the applicability of the theoretical prediction of the fatigue crack propagation.

Establishment of probabilistic model for Salmonella Enteritidis growth and inactivation under acid and osmotic pressure

The growth and survival characteristic of Salmonella Enteritidis under acidic and osmotic conditions were studied.Meanwhile,a probabilistic model based on the theory of cell division and mortality was established to predict the growth or inactivation of S.Enteritidis.The experimental results demonstrated that the growth curves of planktonic and detached cells showed a significant difference(p<0.05)under four conditions,including pH5.0+0.0%NaCl,pH7.0+4.0%NaCl,pH6.0+4.0%NaCl,and pH5.0+4.0%NaCl.And the established primary and secondary models could describe the growth of S.enteritis well by estimating four mathematics evaluation indexes,including determination coefficient(R2),root mean square error(RMSE),accuracy factor(Af)and bias factor(Bf).Moreover,sequential treatment of 15%NaCl stress followed by pH 4.5 stress was the best condition to inactivate S.Enteritidis in 10 h at 25◦C.The probabilistic model with Logistical or Weibullian form could also predict the inactivation of S.Enteritidis well,thus realize the unification of predictive model to some extent or generalization of inactivation model.Furthermore,the primary 4-parameter probabilistic model or generalized inactivation model had slightly higher applicability and reliability to describe the growth or inactivation of S.Enteritidis than Baranyi model or exponential inactivation model within the experimental range in this study.

Probabilistic Modeling of Oil Spills at the Exclusive Economic Zone of Cuba Using Petromar-3D Model

This article shows the probabilistic modeling of hydrocarbon spills on the surface of the sea, using climatology data of oil spill trajectories yielded by applying the lagrangian model PETROMAR-3D. To achieve this goal, several computing and statistical tools were used to develop the probabilistic modeling solution based in the methodology of Guo. Solution was implemented using a databases approach and SQL language. A case study is presented which is based on a hypothetical spill in a location inside the Exclusive Economic Zone of Cuba. Important outputs and products of probabilistic modeling were obtained, which are very useful for decision-makers and operators in charge to face oil spill accidents and prepare contingency plans to minimize its effects. In order to study the relationship between the initial trajectory and the arrival of hydrocarbons spills to the coast, a new approach is introduced as an incoming perspective for modeling. It consists in storage in databases the direction of movement of the oil slick at the first 24 hours. The probabilistic modeling solution presented is of great importance for hazard studies of oil spills in Cuban coastal areas.

A probabilistic model based on the peak-over-threshold approach for risk assessment of airport controllers’performance

Airport tower control plays an instrumental role in ensuring airport safety.However,obtaining objective,quantitative safety evaluations is challenging due to the unavailability of pertinent human operation data.This study introduces a probabilistic model that combines aircraft dynamics and the peak-over-threshold(POT)approach to assess the safety performance of airport controllers.We applied the POT approach to model reaction times extracted from a radiotelephony dataset via a voice event detection algorithm.The model couples the risks of tower control and aircraft operation to analyze the influence of human factors.Using data from radiotele-phony communications and the Base of Aircraft Data(BADA)database,we compared risk levels across scenarios.Our findings revealed heightened airport control risks under low demand(0.374)compared to typical conditions(0.197).Furthermore,the risks associated with coupling under low demand exceeded those under typical de-mand,with the final approach stage presenting the highest risk(4.929×107).Our model underscores the significance of human factors and the implications of mental disconnects between pilots and controllers for safety risks.Collectively,these consistent findings affirm the reliability of our probabilistic model as an evaluative tool for evaluating the safety performance of airport tower controllers.The results also illuminate the path toward quantitative real-time safety evaluations for airport controllers within the industry.We recommend that airport regulators focus on the performance of airport controllers,particularly during the final approach stage.

A Probabilistic Description of the Impact of Vaccine-Induced Immunity in the Dynamics of COVID-19 Transmission

The recent outbreak of COVID-19 has caused millions of deaths worldwide and a huge societal and economic impact in virtually all countries. A large variety of mathematical models to describe the dynamics of COVID-19 transmission have been reported. Among them, Bayesian probabilistic models of COVID-19 transmission dynamics have been very efficient in the interpretation of early data from the beginning of the pandemic, helping to estimate the impact of non-pharmacological measures in each country, and forecasting the evolution of the pandemic in different potential scenarios. These models use probability distribution curves to describe key dynamic aspects of the transmission, like the probability for every infected person of infecting other individuals, dying or recovering, with parameters obtained from experimental epidemiological data. However, the impact of vaccine-induced immunity, which has been key for controlling the public health emergency caused by the pandemic, has been more challenging to describe in these models, due to the complexity of experimental data. Here we report different probability distribution curves to model the acquisition and decay of immunity after vaccination. We discuss the mathematical background and how these models can be integrated in existing Bayesian probabilistic models to provide a good estimation of the dynamics of COVID-19 transmission during the entire pandemic period.

Unified Probabilistic Models for Face Recognition from a Single Example Image per Person

This paper presents a new technique of unified probabilistic models for facerecognition from only one single example image per person. The unified models, trained on anobtained training set with multiple samples per person, are used to recognize facial images fromanother disjoint database with a single sample per person. Variations between facial images aremodeled as two unified probabilistic models: within-class variations and between-class variations.Gaussian Mixture Models are used to approximate the distributions of the two variations and exploita classifier combination method to improve the performance. Extensive experimental results on theORL face database and the authors'' database (the ICT-JDL database) including totally 1,750 facialimages of 350 individuals demonstrate that the proposed technique, compared with traditionaleigenface method and some well-known traditional algorithms, is a significantly more effective androbust approach for face recognition.

Simultaneous image classification and annotation based on probabilistic model

The paper proposes a novel probabilistic generative model for simultaneous image classification and annotation. The model considers the fact that the category information can provide valuable information for image annotation. Once the category of an image is ascertained, the scope of annotation words can be narrowed, and the probability of generating irrelevant annotation words can be reduced. To this end, the idea that annotates images according to class is introduced in the model. Using variational methods, the approximate inference and parameters estimation algorithms of the model are derived, and efficient approximations for classifying and annotating new images are also given. The power of our model is demonstrated on two real world datasets： a 1 600-images LabelMe dataset and a 1 791-images UIUC-Sport dataset. The experiment results show that the classification performance is on par with several state-of-the-art classification models, while the annotation performance is better than that of several state-of-the-art annotation models.

Probabilistic Modeling and Optimization of Real-Time Protocol for Multifunction Vehicle Bus

In this paper, we present the modeling and optimization of a Real-Time Protocol（RTP） used in Train Communication Networks（TCN）. In the proposed RTP, message arbitration is represented by a probabilistic model and the number of arbitration checks is minimized by using the probability of device activity. Our optimized protocol is fully compatible with the original standard and can thus be implemented easily. The experimental results demonstrate that the proposed algorithm can reduce the number of checks by about 50%, thus significantly enhancing bandwidth.

STOCHASTIC TRUST-REGION METHODS WITH TRUST-REGION RADIUS DEPENDING ON PROBABILISTIC MODELS

We present a stochastic trust-region model-based framework in which its radius is related to the probabilistic models.Especially,we propose a specific algorithm termed STRME,in which the trust-region radius depends linearly on the gradient used to define the latest model.The complexity results of the STRME method in nonconvex,convex and strongly convex settings are presented,which match those of the existing algorithms based on probabilistic properties.In addition,several numerical experiments are carried out to reveal the benefits of the proposed methods compared to the existing stochastic trust-region methods and other relevant stochastic gradient methods.

A probabilistic model for assessing the reliability of wind farms in a power system

Modeling the generation of a wind farm and its effect on power system reliability is a challenging task,largely due to the random behavior of the output power.In this paper,we propose a new probabilistic model for assessing the reliability of wind farms in a power system at hierarchical level II(HLII),using a Monte Carlo simulation.The proposed model shows the effect of correlation between wind and load on reliability calculation.It can also be used for identifying the priority of various points of the network for installing new wind farms,to promote the reliability of the whole system.A simple grid at hierarchical level I(HLI) and a network in the north-eastern region of Iran are studied.Simulation results showed that the correlation between wind and load significantly affects the reliability.

Lane changing assistance strategy based on an improved probabilistic model of dynamic occupancy grids

Lane changing assistance in autonomous vehicles is a popular research topic. Scene modeling of the driving area is a prerequisite for lane changing decision problems. A road environment representation method based on a dynamic occupancy grid is proposed in this study. The model encapsulates the data such as vehicle speed, obstacles, lane lines, and traffic rules into a form of spatial drivability probability. This information is compiled into a hash table, and the grid map is mapped into a hash map by means of hash function. A vehicle behavior decision cost equation is established with the model to help drivers make accurate vehicle lane changing decisions based on the principle of least cost, while considering influencing factors such as vehicle drivability, safety, and power. The feasibility of the lane changing assistance strategy is verified through vehicle tests, and the results show that the lane changing assistance system based on a probabilistic model of dynamic occupancy grids can provide lane changing assistance to drivers taking into consideration the dynamics and safety.

Probabilistic model for vessel-bridge collisions in the Three Gorges Reservoir

Based on a field observation on vessel transit path of three bridges over the Yangtze River in the Three Gorges Reservoir,and an analysis of the geometric probabilistic model of transiting vessels in collision probability calculation,the aberrancy angle and vessel velocity probabilistic model related with impact force,a probabilistic model is established and also verified by goodness-of-fit test.The vessel transit path distribution can be expressed by the normal distribution model.For the Three Gorges Reservoir,the mean and standard deviation adopt 0.2w and 0.1w,respectively(w is the channel width).The aberrancy angle distribution of vessels accepts maximum I distribution model,and its distribution parameters can be taken as 0.314 and 4.354.The velocity distribution of up-bound and down-bound vessels can also be expressed by the normal distribution model.

Induced Earthquake Hazard by Geothermal Power Plants: Statistical Evaluation and Probabilistic Modeling

This study statistically evaluated the characteristics of induced earthquakes by geothermal power plants(GPPs)and generated a probabilistic model for simulating stochastic seismic events.Four well-known power plant zones were selected worldwide from the United States,Germany,France,and New Zealand.The operational condition information,as well as the corresponding earthquake catalogs recorded in the vicinity of GPPs,were gathered from their commencement date.The statistical properties of events were studied elaborately.By using this proposed database,a probabilistic model was developed capable of generating the number of induced seismic events per month,their magnitude,focal depth,and distance from the epicenter to the power plant,randomly.All of these parameters are simulated as a function of power plant injection rate.Generally speaking,the model,introduced in this study,is a tool for engineers and scientists interested in the seismic risk assessment of built environments prone to induced seismicity produced by GPPs operation.

Probabilistic Models for Estimation of Random and Pseudo-Random Test Length

A new probabilistic testability measure is presented to ease test length analyses of random testing and pseudorandom testing.The testability measure given in this paper is oriented to signal conflict of reconvergent fanouts.Test length analyses in this paper are based on a hard fault set,calculations of which are practicable and simple.Experimental results have been obtained to show the accuracy of this test length analyser in comparison with that of Savir,Chin and McCluskey,and Wunderlich by using a pseudorandom test generator combined with exhaustive fault simulation.

Probabilistic models of vision and max-margin methods

It is attractive to formulate problems in computer vision and related fields in term of probabilis- tic estimation where the probability models are defined over graphs, such as grammars. The graphical struc- tures, and the state variables defined over them, give a rich knowledge representation which can describe the complex structures of objects and images. The proba- bility distributions defined over the graphs capture the statistical variability of these structures. These proba- bility models can be learnt from training data with lim- ited amounts of supervision. But learning these models suffers from the difficulty of evaluating the normaliza- tion constant, or partition function, of the probability distributions which can be extremely computationally demanding. This paper shows that by placing bounds on the normalization constant we can obtain compu- rationally tractable approximations. Surprisingly, for certain choices of loss functions, we obtain many of the standard max-margin criteria used in support vector machines （SVMs） and hence we reduce the learning to standard machine learning methods. We show that many machine learning methods can be obtained in this way as approximations to probabilistic methods including multi-class max-margin, ordinal regression, max-margin Markov networks and parsers, multiple- instance learning, and latent SVM. We illustrate this work by computer vision applications including image labeling, object detection and localization, and motion estimation. We speculate that rained by using better bounds better results can be ob- and approximations.

Probabilistic modeling for an integrated temporary acquired immunity with norovirus epidemiological data

Integration of acquired immunity into microbial risk assessment for illness incidence is of no doubt essential for the study of susceptibility to illness.In this study,a probabilistic model was set up as dose response for infection and a mathematical derivation was carried out by integrating immunity to obtain probability of illness models.Temporary acquire immunity from epidemiology studies which includes six different Norovirus transmission scenarios such as symptomatic individuals infectious,pre-and post-symptomatic infectiousness(low and high),innate genetic resistance,genogroup 2 type 4 and those with no immune boosting by asymptomatic infection were evaluated.Simulated results on illness inflation factor as a function of dose and exposure indicated that high frequency exposures had immense immunity build up even at high dose levels;hence minimized the probability of illness.Using Norovirus transmission dynamics data,results showed,and immunity included models had a reduction of 2e6 logs of magnitude difference in disease burden for both population and individual probable illness incidence.Additionally,the magnitude order of illness for each dose response remained largely the same for all transmission scenarios;symptomatic infectiousness and no immune boosting after asymptomatic infectiousness also remained the same throughout.With integration of epidemiological data on acquired immunity into the risk assessment,more realistic results were achieved signifying an overestimation of probable risk of illness when epidemiological immunity data are not included.This finding supported the call for rigorous integration of temporary acquired immunity in dose-response in all microbial risk assessments.

Pavement performance model for road maintenance and repair planning: a review of predictive techniques

This paper provides a review of predictive analytics for roads,identifying gaps and limitations in current methodologies.It explores the implications of these limitations on accuracy and application,while also discussing how advanced predictive analytics can address these challenges.The article acknowledges the transformative shift brought about by technological advancements and increased computational capabilities.The degradation of pavement surfaces due to increased road users has resulted in safety and comfort issues.Researchers have conducted studies to assess pavement condition and predict future changes in pavement structure.Pavement Management Systems are crucial in developing prediction performance models that estimate pavement condition and degradation severity over time.Machine learning algorithms,artificial neural networks,and regression models have been used,with strengths and weaknesses.Researchers generally agree on their accuracy in estimating pavement condition considering factors like traffic,pavement age,and weather conditions.However,it is important to carefully select an appropriate prediction model to achieve a high-quality prediction performance system.Understanding the strengths and weaknesses of each model enables informed decisions for implementing prediction models that suit specific needs.The advancement of prediction models,coupled with innovative technologies,will contribute to improved pavement management and the overall safety and comfort of road users.

Assessing citizen science opportunities in forest monitoring using probabilistic topic modelling

Background： With mounting global environmental, social and economic pressures the resilience and stability of forests and thus the provisioning of vital ecosystem services is increasingly threatened. Intensified monitoring can help to detect ecological threats and changes earlier, but monitoring resources are limited. Participatory forest monitoring with the help of ＂citizen scientists＂ can provide additional resources for forest monitoring and at the same time help to communicate with stakeholders and the general public. Examples for citizen science projects in the forestry domain can be found but a solid, applicable larger framework to utilise public participation in the area of forest monitoring seems to be lacking. We propose that a better understanding of shared and related topics in citizen science and forest monitoring might be a first step towards such a framework. Methods： We conduct a systematic meta-analysis of 1015 publication abstracts addressing ＂forest monitoring＂ and ＂citizen science＂ in order to explore the combined topical landscape of these subjects. We employ ＇topic modelling an unsupervised probabilistic machine learning method, to identify latent shared topics in the analysed publications. Results： We find that large shared topics exist, but that these are primarily topics that would be expected in scientific publications in general. Common domain-specific topics are under-represented and indicate a topical separation of the two document sets on ＂forest monitoring＂ and ＂citizen science＂ and thus the represented domains. While topic modelling as a method proves to be a scalable and useful analytical tool, we propose that our approach could deliver even more useful data if a larger document set and full-text publications would be available for analysis. Conclusions： We propose that these results, together with the observation of non-shared but related topics, point at under-utilised opportunities for public participation in forest monitoring. Citizen science could be applied as a versatile tool in forest ecosystems monitoring, complementing traditional forest monitoring programmes, assisting early threat recognition and helping to connect forest management with the general public. We conclude that our presented approach should be pursued further as it may aid the understanding and setup of citizen science efforts in the forest monitoring domain.

VIDEO MULTI-TARGET TRACKING BASED ON PROBABILISTIC GRAPHICAL MODEL

In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.