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probability modeling and material microstructure applied to corrosion and fatigue of aluminum and steel alloys

ENGINEERING FRACTURE MECHANICS 2009年第5期76卷 695-708页

作者： Harlow, D. Gary Wei, Robert P. Lehigh Univ Bethlehem PA 18015 USA

Engineering prognosis and life cycle management have increasingly become dependent on probabilistic methods and material microstructure. Due to variability in microstructural properties produced by differences in alloys and processing conditions, scientifically based probability modeling for life prediction and reliability is a challenging problem. For several years, probabilistic and statistical methodologies have been available to address prognosis issues, and there has been significant progress in computational methods for engineering applications. Their utility has been greatly limited by the lack of well developed physically and mechanistically based models for many of the complex applications. Without a reasonably accurate description of the damage process at a scale that is pertinent to the desired application, probabilistic computations have minimal value for prognosis and life cycle assessment. In this paper, the major methods in computational probability are reviewed, and their efficacy is illustrated through two engineering examples. One example is corrosion and corrosion fatigue crack growth for 7075-T6 aluminum alloy, which is used commonly for structural components in commercial and military aircraft. The other example is very high cycle S-N behavior of SUJ2 bearing steel for which multiple modes of failure are manifest in the high cycle regime. Their application to other critical engineering problems is discussed. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： probability modeling Corrosion and corrosion fatigue Very high cycle fatigue Simulation Scientifically based modeling

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probability modeling of accesses to the web parts of portal

Probability modeling of accesses to the web parts of portal

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1st World Conference on Information Technology (WCIT)

作者： Munk, Michal Vrabelova, Marta Kapusta, Jozef Constantine Philosopher Univ Nitra Dept Informat Tr A Hlinku 1 Nitra 94974 Slovakia

The analysis of behavior of portal visitors is one of the most important parts of web portal optimization. The results of the analysis are important for the further correction and improvement of web part organization. The aim of the paper is modeling of probabilities' accesses to the categories of web parts of portal. We deal with the access probabilities to the individual categories of faculty portal content depending on the day's hour and the week's day. The probabilities are estimated using multinomial logit model for employees and students separately. In logit models, in case of students and employees, the week's days present statistically significant signs, representing dummy variables (MON, TUE,.) in the model. On the other hand, day's hours representing with variables HOUR_DAY and their square HOUR_DAY_Q, are shown as statistically significant signs only in the case of students. These results correspond with the computing probabilities wherein the probabilities of access to web parts of the portal are more stable in the case of employees than of students during the day. The analysis provided us several interesting and surprising results. For instance, from the analysis, results follow that the part study is the most visited part by students in the evening and night hours. The analysis results confirmed general trends, for example the part announcements is the most visited part in morning's hours, at the beginning of the week especially. All of the analysis results will help us to further optimize our web portal. This is especially point in level of portal adaptivity on the basis user and access hour on portal. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.

关键词： web log mining user behavior web parts probability modeling multinomial logit model

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Improved probability modeling for Lossless Image Coding Using Example Search and Adaptive Prediction

Improved Probability Modeling for Lossless Image Coding Usin...

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International Workshop on Advanced Imaging Technology (IWAIT)

作者： Kojima, Hiroki Kita, Yasuyo Matsuda, Ichiro Itoh, Susumu Kameda, Yusuke Unno, Kyohei Kawamura, Kei Tokyo Univ Sci Dept Elect Engn 2641 Yamazaki Noda Chiba 2788510 Japan Sophia Univ Tokyo Japan KDDI Res Inc Fujimino Saitama Japan

ISBN: (数字)9781510653320

ISBN: (纸本)9781510653320;9781510653313

We previously proposed a novel lossless image coding method that utilizes example search and adaptive prediction within a framework of probability model optimization. In this paper, the definition of the probability model as well as its optimization procedure is modified to reduce the encoding complexity. In addition, affine predictors used in the adaptive prediction are refined for accurate probability modeling. Simulation results indicate that our modification contributes not only to encoding time reduction, but also to coding efficiency improvement for all of the tested images.

关键词： Lossless image coding example search adaptive prediction probability modeling quasi-Newton method

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Lossless Image Coding Based on probability modeling Using Template Matching and Linear Prediction

Lossless Image Coding Based on Probability Modeling Using Te...

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10th Anniversary International Workshop on Smart Info-Media Systems in Asia (SISA)

作者： Sumi, Toru Inamura, Yuta Kameda, Yusuke Ishikawa, Tomokazu Matsuda, Ichiro Itoh, Susumu Tokyo Univ Sci Noda Chiba 2788510 Japan

We previously proposed a lossless image coding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.

关键词： lossless coding still-image coding template matching probability modeling linear prediction

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probability modeling of accesses to the web parts of portal

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Procedia Computer Science 2011年 3卷 677-683页

作者： Michal Munk Marta Vrábelová Jozef Kapusta Constantine the Philosopher University in Nitra Department of Informatics Tr. A. Hlinku 1 949 74 Nitra Slovakia

The analysis of behavior of portal visitors is one of the most important parts of web portal optimization. The results of the analysis are important for the further correction and improvement of web part organization. The aim of the paper is modeling of probabilities‘ accesses to the categories of web parts of portal. We deal with the access probabilities to the individual categories of faculty portal content depending on the day’s hour and the week’s day. The probabilities are estimated using multinomial logit model for employees and students separately. In logit models, in case of students and employees, the week’s days present statistically significant signs, representing dummy variables (MON, TUE,…) in the model. On the other hand, day’s hours representing with variables HOUR_DAY and their square HOUR_DAY_Q, are shown as statistically significant signs only in the case of students. These results correspond with the computing probabilities wherein the probabilities of access to web parts of the portal are more stable in the case of employees than of students during the day. The analysis provided us several interesting and surprising results. For instance, from the analysis, results follow that the part study is the most visited part by students in the evening and night hours. The analysis results confirmed general trends, for example the part announcements is the most visited part in morning’s hours, at the beginning of the week especially. All of the analysis results will help us to further optimize our web portal. This is especially point in level of portal adaptivity on the basis user and access hour on portal.

关键词： Web log mining User behavior Web parts probability modeling Multinomial logit model

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Dynamic probability modeling-based aircraft structural health monitoring framework under time-varying conditions: Validation in an in-flight test simulated on ground

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AEROSPACE SCIENCE AND TECHNOLOGY 2019年 95卷 105467-000页

作者： Fang, Fang Qiu, Lei Yuan, Shenfang Ren, Yuanqiang Nanjing Univ Aeronaut & Astronaut Res Ctr Struct Hlth Monitoring & Prognosis State Key Lab Mech & Control Mech Struct Nanjing Jiangsu Peoples R China

This paper proposes a dynamic probability modeling-based aircraft Structural Health Monitoring (SHM) framework which provides a modular hierarchical SHM architecture for the development of applicable aircraft SHM techniques. The problem of reliable damage monitoring under time-varying conditions, which is the main application obstacle, is fully considered in the framework by the double probability models combined with the short term and long term dynamic update of the models. To realize the SHM capability of the framework, an adaptive constructing method of Gaussian mixture model is proposed for stable and efficient probability modeling and the probability similarity between dynamically updated models is measured for normalized damage detection. The framework is realized by combining with the guided wave SHM technique and validated in a full-scale aircraft fatigue test which is an in-flight test simulated on ground. The cracks of the right landing gear spar and the left wing panel on the aircraft structure are monitored reliably under the fatigue load conditions. (C) 2019 Elsevier Masson SAS. All rights reserved.

关键词： Aircraft structural health monitoring framework Time-varying condition probability modeling Dynamic update Full-scale aircraft fatigue test Guided wave

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probability output modeling for support vector machines

Probability output modeling for support vector machines

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Conference on Pattern Recognition and Computer Vision

作者： Zhang, Xiang Xiao, Xiaoling Tian, Jinwen Liu, Jian Yangtze Univ Jinzhou 434023 Hubei Peoples R China Minist Educ Key Lab Explorat Technol Oil & Gas Resources Jinzhou 434023 Hubei Peoples R China Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9780819469526

In this paper we propose an approach to model the posterior probability output of multi-class SVMs. The sigmoid function is used to estimate the posterior probability output in binary classification. This approach modeling the posterior probability output of multi-class SVMs is achieved by directly solving the equations that are based on the combination of the probability outputs of binary classifiers using the Bayes's rule. The differences and different weights among these two-class SVM classifiers, based on the posterior probability, are considered and given for the combination of the probability outputs among these two-class SVM classifiers in this method. The comparative experiment results show that our method achieves the better classification precision and the better probability distribution of the posterior probability than the pairwise couping method and the Hastie's optimization method.

关键词： support vector machine probability modeling multi-class classification

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CLGSDN: Contrastive-Learning-Based Graph Structure Denoising Network for Traffic Prediction

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IEEE INTERNET OF THINGS JOURNAL 2025年第7期12卷 8638-8652页

作者： Peng, Peng Chen, Xuewen Zhang, Xudong Tang, Haina Shen, Hanji Li, Jun Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China

The graph neural network-based prediction models have demonstrated remarkable utility in traffic prediction, and their efficacy is highly determined by the quality of the provided graphs. Consequently, there is an increasing demand for employing graph structure learning (GSL) techniques to optimize or generate the graphs. However, existing GSL techniques for traffic prediction encounter various issues, including the absence of temporal dynamicity, noisy connections, and insufficient supervisory information. To address these limitations, this article proposes a novel two-stage graph generation framework called contrastive learning-based graph structure denoising network (CLGSDN). This framework formulates the graph generation task as a probabilistic observation-inference process: using the self-learning adjacency matrix and time delayed self-attention (TDSA) methods to generate a series of graph observations, then inferring the optimal graph based on observations. The self-learning adjacency matrix is responsible for learning all potential connections in the graph, while TDSA enables the graph to change with traffic flow. In addition, CLGSDN identifies and eliminates noisy connections by modeling negative samples of the graph (edges), and defines virtual labels to achieve spatiotemporal graph contrastive learning (ST-GCL) in traffic prediction. The experimental results show that CLGSDN significantly enhances current mainstream traffic prediction models by providing reliable and efficient graphs. As such, it has significant implications for a wide range of applications, including traffic management, logistics, and smart transportation systems.

关键词： Probabilistic logic Predictive models Noise measurement Contrastive learning Optimization Noise reduction Adaptation models Spatiotemporal phenomena Noise Kernel Graph contrastive learning (GCL) graph generation probability modeling traffic prediction

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probability VERSUS STATISTICAL modeling: EXAMPLES FROM FATIGUE LIFE PREDICTION

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INTERNATIONAL JOURNAL OF RELIABILITY QUALITY & SAFETY ENGINEERING 2005年第6期12卷 535-550页

作者： Harlow, D. Gary Lehigh Univ Mech Engn & Mech 19 Mem Dr West Bethlehem PA 18015 USA

probability analyses are increasingly being used for reliability and durability assessments for life prediction of engineered components and systems. Nevertheless, many of the current analyses are predominately statistical rather than probabilistic. Fatigue life prediction has historically been based on the safe-life or the crack growth approaches, both of which are empirically based. Consequently, they do not adequately reflect long-term operating conditions, or identify the sources and extent of their contributions to variability. A comparison between probability and statistical approaches for fatigue life prediction is developed herein. Using simple crack growth models, the variability inherent in S-N response can be related to key random variables that are readily identified in the models. The identification and quantification of these variables are paramount for predicting fatigue lives. The effectiveness of probability modeling compared to statistical methodologies is shown through the analysis of an extensive set of S-N data for 2024-T4 aluminum alloy. Subsequently, the probability approach is demonstrated with S-N data for SUJ2 steel, in which two distinct failure modes are operative. Variability associated with manufacturing and material variables are considered. The adoption of this technique to put life prediction on a sound scientific and probabilistic basis is recommended.

关键词： probability modeling very high cycle fatigue S-N response fatigue crack growth surface and internal nucleation

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modeling the probability of freeway lane-changing collision occurrence considering intervehicle interaction

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TRAFFIC INJURY PREVENTION 2016年第2期17卷 181-187页

作者： Wang, Jiangfeng Wang, Yinhai Bi, Chaofan Weng, Jinxian Yan, Xuedong Beijing Jiaotong Univ MOE Key Lab Transportat Complex Syst Theory & Tec Beijing Peoples R China Univ Washington Dept Civil & Environm Engn Seattle WA 98105 USA Shanghai Maritime Univ Coll Transport & Commun Shanghai Peoples R China

Objective: To better capture the relationships between lane-changing collisions and explanatory variables, a microscopic model is developed for freeway lane-changing collisions based on the interactions between lane-changing vehicles. Methods: The model applies an intervehicle interaction structure to account for the occurrence mechanism of lane-changing collisions. The occurrence mechanism can be described as the failure of a vehicle driver of an adjacent lane in avoiding the lane-changing vehicle, which disturbs the smooth movement of the adjacent lane vehicle and requires the driver's brake action to avoid an angle collision. This model is examined using data collected from freeways in Washington State during 2010 to 2011 and validated using lane-changing collision data for the SR 520 freeway. Results: The findings of this study show that generalized truck percentage has a significant decreasing effect on lane-changing collision risk, whereas average spacing and several roadway characteristics have significant increasing effects. The frequency of slight collisions during peak hours is higher than that during off-peak hours. Young female drivers are more likely to be involved in collisions during lane-changing than young male drivers, but the result for senior drivers is opposite, with older male drivers having a higher probability of lane-changing collisions than female drivers in the same age group. Conclusion: The process of lane-changing collisions is a complicated maneuver. Truck percentage, average spacing, and good roadway characteristics, such as straight and level segment, in the target lane have a significant effect on the occurrence of lane-changing collisions. Age and gender are also 2 important factors contributing to the relationship between lane-changing collisions and explanatory variables.

关键词： traffic safety probability modeling vehicle interaction lane-changing collision

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