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Relational dynamic Bayesian network modeling for uncertainty quantification and propagation in airline disruption management

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期112卷

作者： Ogunsina, Kolawole Papamichalis, Marios DeLaurentis, Daniel Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN USA Purdue Univ Dept Stat W Lafayette IN USA

Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, reactive airline disruption management during schedule execution, and proactive airline disruption management after schedule execution to enable the construction of quantitative tools that can allow an intelligent agent to rationalize complex interactions and procedures for robust airline disruption management. Specifically, we use historical scheduling and operations data from a major U.S. airline to facilitate the development and assessment of the UTFM, defined by hidden Markov models (a special class of probabilistic graphical models) that can efficiently perform pattern learning and inference on portions of large data *** employ the UTFM to assess two independent and separately disrupted flight legs from the airline route network. Assessment of a flight leg from Dallas to Houston, disrupted by air traffic control hold for bad weather at Dallas, revealed that proactive disruption management for turnaround in Dallas before schedule execution is impractical because of zero transition probability between turnaround and taxi-out. Assessment of another flight leg from Chicago to Boston, disrupted by air traffic control hold for bad weather at Boston, showed that proactive disruption management before schedule execution is possible because of non-zero state transition probabilities at all phases of flight operation.

关键词： Airline disruption management probabilistic graphical models Hidden Markov models Intelligent systems Explainable artificial intelligence Expert systems

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Asynchronous decoding of finger movements from ECoG signals using long-range dependencies conditional random fields

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JOURNAL OF NEURAL ENGINEERING 2016年第3期13卷 036017-036017页

作者： Delgado Saa, Jaime F. De Pesters, Adriana Cetin, Mujdat Univ Norte Biomed Signal Proc & Artificial Intelligence Lab Barranquilla Colombia Natl Ctr Adapt Neurotechnol Schalk Lab Albany NY USA Sabanci Univ Signal Proc & Informat Syst Lab Istanbul Turkey

Objective. In this work we propose the use of conditional random fields with long-range dependencies for the classification of finger movements from electrocorticographic recordings. Approach. The proposed method uses long-range dependencies taking into consideration time-lags between the brain activity and the execution of the motor task. In addition, the proposed method models the dynamics of the task executed by the subject and uses information about these dynamics as prior information during the classification stage. Main results. The results show that incorporating temporal information about the executed task as well as incorporating long-range dependencies between the brain signals and the labels effectively increases the system's classification performance compared to methods in the state of art. Significance. The method proposed in this work makes use of probabilistic graphical models to incorporate temporal information in the classification of finger movements from electrocorticographic recordings. The proposed method highlights the importance of including prior information about the task that the subjects execute. As the results show, the combination of these two features effectively produce a significant improvement of the system's classification performance.

关键词： Brain-computer interfaces ECoG synchronous classification temporal dynamics probabilistic graphical models

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Random Fields in Physics, Biology and Data Science

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FRONTIERS IN PHYSICS 2021年 9卷

作者： Hernandez-Lemus, Enrique Natl Inst Genom Med Computat Genom Div Arenal Tepepan Mexico Univ Nacl Autonoma Mexico Ctr Ciencias Complejidad Coyoacan Mexico

A random field is the representation of the joint probability distribution for a set of random variables. Markov fields, in particular, have a long standing tradition as the theoretical foundation of many applications in statistical physics and probability. For strictly positive probability densities, a Markov random field is also a Gibbs field, i.e., a random field supplemented with a measure that implies the existence of a regular conditional distribution. Markov random fields have been used in statistical physics, dating back as far as the Ehrenfests. However, their measure theoretical foundations were developed much later by Dobruschin, Lanford and Ruelle, as well as by Hammersley and Clifford. Aside from its enormous theoretical relevance, due to its generality and simplicity, Markov random fields have been used in a broad range of applications in equilibrium and non-equilibrium statistical physics, in non-linear dynamics and ergodic theory. Also in computational molecular biology, ecology, structural biology, computer vision, control theory, complex networks and data science, to name but a few. Often these applications have been inspired by the original statistical physics approaches. Here, we will briefly present a modern introduction to the theory of random fields, later we will explore and discuss some of the recent applications of random fields in physics, biology and data science. Our aim is to highlight the relevance of this powerful theoretical aspect of statistical physics and its relation to the broad success of its many interdisciplinary applications.

关键词： random fields probabilistic graphical models Gibbs fields Markov fields Gaussian random fields

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Word-level language modeling for P300 spellers based on discriminative graphical models

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JOURNAL OF NEURAL ENGINEERING 2015年第2期12卷 026007-026007页

作者： Saa, Jaime F. Delgado de Pesters, Adriana McFarland, Dennis Cetin, Mujdat Sabanci Univ Signal Proc Info Syst Lab Istanbul Turkey Univ Norte Robot & Intelligent Syst Lab Barranquilla Colombia Wadsworth Ctr Lab Injury & Repair Albany NY USA

Objective. In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. Approach. This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller. Main results. Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system. Significance. The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.

关键词： brain computer interfaces probabilistic graphical models language models P300 speller inference algorithms

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High-performance training of conditional random fields for large-scale applications of labeling sequence data

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2007年第1期E90D卷 13-21页

作者： Phan, Xuan-Hieu Nguyen, Le-Minh Inoguchi, Yasushi Horiguchi, Susumu Japan Adv Inst Sci & Technol Grad Sch Informat Sci Nomishi 9231211 Japan Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808579 Japan

Conditional random fields (CRFs) have been successfully applied to various applications of predicting and labeling structured data, such as natural language tagging & parsing, image segmentation & object recognition, and protein secondary structure prediction. The key advantages of CRFs are the ability to encode a variety of overlapping, nonindependent features from empirical data as well as the capability of reaching the global normalization and optimization. However, estimating parameters for CRFs is very time-consuming due to an intensive forward-backward computation needed to estimate the likelihood function and its gradient during training. This paper presents a high-performance training of CRFs on massively parallel processing systems that allows us to handle huge datasets with hundreds of thousand data sequences and millions of features. We performed the experiments on an important natural language processing task (text chunking) on large-scale corpora and achieved significant results in terms of both the reduction of computational time and the improvement of prediction accuracy.

关键词： parallel computing probabilistic graphical models conditional random.fields structured prediction text processing

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Learning causal Bayesian networks based on causality analysis for classification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期114卷

作者： Wang, Limin Zhou, Jiaping Wei, Junyang Pang, Meng Sun, Minghui Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Coll Software Changchun 130012 Peoples R China

Revealing causal information by analyzing purely observational data, known as causal discovery, has drawn much attention. To prove that the causal knowledge mined from data can be applied to facilitate various machine learning tasks (e.g., classification), we propose to measure, describe and evaluate the causalities in the framework of Bayesian network (BN) learning. In this paper, heuristic search strategy is applied to explore the causal interpretation in the form of directed acyclic graph (DAG) for classification. While adding directed edges to the DAG, we first introduce the log-likelihood equivalence assertion to make the learned joint probability encoded in BN approximates the true one, then introduce the causal dependence assertion to assess the rationality of the learned causal relationship. We perform a range of experiments on 35 datasets and empirically show that this novel algorithm demonstrates competitive classification performance and excellent causal interpretation compared to state-of-the-art Bayesian network classifiers (e.g. SKDB, WATAN, SLB, and TAODE).

关键词： Bayesian networks Causality probabilistic graphical models Bayesian learning

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Root cause analysis of failures and quality deviations in manufacturing using machine learning

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Procedia CIRP 2018年 72卷 1057-1062页

作者： Anna Lokrantz Emil Gustavsson Mats Jirstrand Systems and Data Analysis Fraunhofer-Chalmers Research Centre for Industrial Mathematics Chalmers Science Park SE-412 88 Gothenburg Sweden

Today root causes of failures and quality deviations in manufacturing are usually identified using existing on-site expert knowledge about causal relationships between process steps and the nature of failures and deviations. Automatization of identification and back tracking of root causes for said failures and deviations would benefit companies both in that knowledge can be transferred between factories and that knowledge will be preserved for future use. We propose a machine learning framework using Bayesian networks to model the causal relationships between manufacturing stages using expert knowledge, and demonstrate the usefulness of the framework on two simulated manufacturing processes.

关键词： Root cause analysis machine learning probabilistic graphical models Bayesian networks

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Estimation of Multi-components System's reliability: Comparison of two graphical Model Approaches

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IFAC Proceedings Volumes 2009年第4期42卷 480-485页

作者： Laurent Bouillaut Roland Donat Abdelmoez Neji Patrice Aknin French National Institute for Transportation and Safety Research Noisy le Grand F-93166 France (Tel: +33 1 45 92 56 53)

Reliability analysis is an integral part of system design and operating. Moreover, it can be an input to optimize maintenance policies. Recently, Bayesian Networks (BN) and Dynamic Bayesian Networks (DBN) have been proved relevant to represent complex systems and perform reliability studies. The major drawback of this approach comes from the constraint on the state sojourn times which are necessarily exponentially distributed, as in usual markovian approaches. Therefore, a new formalism was introduced to avoid this constraint: the graphical Duration models (GDM). This paper aims to quantify the reliability estimation error due to an exponential approximation when the system follows other kinds of sojourn time's distributions. Finally results obtained by DBN and GDM will be compared.

关键词： Reliability Markov models Semi-Markov process probabilistic graphical models Bayesian Network

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Using probabilistic models for Missing Data Prediction in Network Industries Performance Measurement Systems

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Procedia Engineering 2015年 100卷 1348-1353页

作者： Kristjan Kuhi Kati Kõrbe Kaare Ott Koppel Department of Logistics and Transport Tallinn University of Technology Akadeemia 15A 12616 Tallinn Estonia

The vast development of information and communication technologies has created new possibilities to acquire and analyze data to take performance measurement systems to next level. Most commonly performance measurement has been known as a financial management tool. Sophisticated new technologies have made it possible to collect continuous real-time data and enabled to start designing and implementing nonfinancial performance measurement systems. Most network industries are undertakings of dominant position and therefore subjects to strict supervision. For the authorities to fulfill their regulatory functions, precise monitoring and systemized feedback on the performance of network industries is essential. The problem lies in non-complete data in terms of missing, faulty or delayed values which might lead to incorrect management decisions. The objective of this paper is to explore the use of mathematical models for missing data prediction in performance measurement systems. Applying deterministic models hide the uncertainty of the value state therefore with higher likelihood false diagnoses occur. Authors propose probabilistic models because likelihood based methods for missing data calculation are able to take into account different parameters and time aspect in a single model to convey more trustworthy estimates in performance measurement systems than traditional methods.

关键词： industrial engineering performance measurement data collection probabilistic graphical models

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Context from within: Hierarchical context modeling for semantic segmentation

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PATTERN RECOGNITION 2020年 105卷 107358-107358页

作者： Nguyen, Kien Fookes, Clinton Sridharan, Sridha Queensland Univ Technol Image & Video Lab Brisbane Qld Australia

Conditional Random Fields (CRFs) have been widely adopted in conjunction with Fully Convolutional Networks (FCNs) to model and integrate contextual information in the semantic segmentation procedure. In contrast to existing approaches applying CRFs in parallel or in cascade with FCNs, we propose a new paradigm to incorporate CRFs deeper inside the architecture of FCNs to model the context exhibited within the middle layers of an FCN. We approximate the mean-field inference process of a dense CRF as a multi-dimensional Gated Recurrent Unit (GRU) layer, termed CRF-GRU layer, effectively extracting intermediate context within an FCN. More importantly, multiple CRF-GRU layers can be injected into an FCN to model hierarchical contexts presented in multiple middle layers, showing competitive results on the PASCAL VOC 2012 and PASCAL-Context datasets. Secondly, we contribute a new approach to automatically learn, from the training data, the optimal segmentation architecture of the FCN with multiple CRF-GRU layers injected. The proposed approach relies on Genetic Evolution Strategies to allow the existing architecture to iteratively evolve towards higher accuracy instances. The discovered network not only outperforms state-of-the-art segmentation techniques, but also provides exciting new insights into the design of the segmentation networks. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Semantic segmentation Context modeling Network evolution Conditional random field probabilistic graphical models

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