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2016 Industrial and systems Engineering Research Conference, ISERC 2016

作者： Lee, Dongjin Pan, Rong Arizona State University School of Computing Informatics Decision Systems Engineering 699 S.Mill Avenue TempeAZ85281 United States

Predictive Maintenance (PdM) can only be implemented when the online knowledge of system condition is available, and this has become available with deployment of on-equipment sensors. To date, most studies on predicting the remaining useful lifetime of a system have been focusing on either single-component systems or systems with deterministic reliability structures. This assumption is not applicable on some realistic problems, where there exist uncertainties in reliability structures of complex systems. In this paper, a PdM scheme is developed by employing a Discrete Time Markov Chain (DTMC) for forecasting the health of monitored components and a Bayesian Network (BN) for modeling the multi-component system reliability. Therefore, probabilistic inferences on both the system and its components' status can be made and PdM can be scheduled on both levels. © 2016 Proceedings of the 2016 Industrial and systems Engineering Research Conference, ISERC 2016. All rights reserved.

关键词： Bayesian networks

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A Restful Persistent Devs-Based Interaction Model For The Componentized Weap and Leap Restful Frameworks

A Restful Persistent Devs-Based Interaction Model For The Co...

引用

Simulation Winter Conference

作者： Mostafa D. Fard Hessam S. Sarjoughian Arizona Center for Integrative Modeling & Simulation School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

ISBN: (纸本)9781665433129

Modeling the interactions between separate models contributes to building flexible hybrid simulation frameworks. In earlier work, an Algorithmic Interaction Model was proposed and developed to integrate the componentized WEAP and LEAP RESTful frameworks for modeling and simulating water and energy systems. However, this approach does not separate modeling and simulation protocol from each other. It also does not support flexible, structured model hierarchies. To overcome these limitations, the parallel DEVS formalism is used to develop an Interaction Model for use with the DEVS-Suite simulator. The resulting DEVS Interaction Model (DEVS-IM) is supported with a RESTful framework and MongoDB for storing the interaction models for the coupled water and energy models. The DEVS-IM, grounded in system theory and component-based modeling, offers strong support for the model reusability, flexibility, and maintainability traits essential for developing realistic simulations of coupled energy and water systems.

关键词： Visualization Protocols Computational modeling Heuristic algorithms Buildings Computational efficiency Dynamical systems

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Generation of Dynamic Authorization Using Cryptography For Private-Keyless Data Sharing

SSRN

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SSRN 2023年

作者： Lu, Hai Zhu, Yan Yau, Stephen S. Zhu, Guizhen Lv, Kewei School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Data and Communication Science Technology Research Institute Beijing100191 China School of Cyber Security University of Chinese Academy of Sciences Beijing100093 China

Private-keyless access is emerging as an inevitable trend in the evolution of future information systems. It helps simplify key management, enhance security, and increase flexibility of access authorization. Hereby, we introduce a novel security model, Cryptographic Resource Enabled Infrastructure (CREI), to support private-keyless access and policy-based data custody. Firstly, in the data custody phase, data resource and its session key are uploaded in ciphertext form to CREI, and the access authorization complies with multiple policies designated by the provider. Secondly, in the dynamic authorization phase, traditional keys or keycards are replaced with real-time attribute credentials issued by multiple authorities. CREI makes dynamically cryptographic policy decision according to the credentials and the cryptographic policy (cryptopolicy). Technically, we design the Credentials-Driven Cryptosystem (CDC) over ideal lattice to build anti-quantum confidence for CREI. In CDC, preimage Gaussian sampler is adopted to sample short vector as real-time attribute credential instead of user's private key. Small Policy Matrix (SPM) is implemented to convert monotone policies into optimized cryptopolicies in order to reduce accumulated error. The security analysis confirms that the credentials, cryptopolicy and its decision result are existentially unforgeable, ensuring the semantic security of the whole CREI. Experimental results indicate CREI has lower storage and computational costs than existing schemes. Meanwhile, despite continuous improvements to the access policies as system evolves, CREI can simply select an appropriate policy among multiple candidates to make authorization decisions without having to re-encrypt data. © 2023, The Authors. All rights reserved.

关键词： Authorization

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Partitioning and Gaussian Processes for Accelerating Sampling in Monte Carlo Tree Search for Continuous decisions

Partitioning and Gaussian Processes for Accelerating Samplin...

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Simulation Winter Conference

作者： Menghan Liu Giulia Pedrielli Yumeng Cao Arizona State University School of Computing Informatics & Decision Systems Engineering Tempe USA Arizona State University School of Mathematical and Statistical Sciences Tempe USA

ISBN: (纸本)9781665433129

We propose Part-MCTS for sampling continuous decisions at each stage of a Monte Carlo Tree Search algorithm. At each MCTS stage, Part-MCTS sequentially partitions the decision space and keeps a collection of Gaussian processes to describe the landscape of the objective function. A classification criteria based on the estimation of the minimum allows us to focus the attention on regions with better predicted behavior, reducing the evaluation effort elsewhere. Within each subregion, we can use any sampling distribution, and we propose to sample using Bayesian optimization. We compare our approach to KR-UCT (Yee et al. 2016) as state of the art competitor. Part-MCTS achieves better accuracy over a set of nonlinear test functions, and it has the ability to identify multiple promising solutions in a single run. This can be important when multiple solutions from a stage can be preserved and expanded at subsequent stages.

关键词： Monte Carlo methods Stochastic systems Gaussian processes Prediction algorithms Linear programming Classification algorithms Partitioning algorithms

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STOCHASTIC GALERKIN METHODS FOR LINEAR STABILITY ANALYSIS OF systems WITH PARAMETRIC UNCERTAINTY

arXiv

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arXiv 2022年

作者： Sousedik, Bedřich Lee, Kookjin Department of Mathematics and Statistics University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States The School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

We present a method for linear stability analysis of systems with parametric uncertainty formulated in the stochastic Galerkin framework. Specifically, we assume that for a model partial differential equation, the parameter is given in the form of generalized polynomial chaos expansion. The stability analysis leads to the solution of a stochastic eigenvalue problem, and we wish to characterize the rightmost eigenvalue. We focus, in particular, on problems with nonsymmetric matrix operators, for which the eigenvalue of interest may be a complex conjugate pair, and we develop methods for their efficient solution. These methods are based on inexact, line-search Newton iteration, which entails use of preconditioned GMRES. The method is applied to linear stability analysis of Navier-Stokes equation with stochastic viscosity, its accuracy is compared to that of Monte Carlo and stochastic collocation, and the efficiency is illustrated by numerical *** Codes 35R60, 60H15, 65F15, 65L07, 65N22, 65N25 Copyright © 2022, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Reliability-based design optimization of load-sharing systems

Reliability-based design optimization of load-sharing system...

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2016 Industrial and systems Engineering Research Conference, ISERC 2016

作者： Subramaniyan, Arun Bala Pan, Rong Du, Xiaoping School of Computing Informatics and Decision Systems Engineering Arizona State University United States Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology United States

This research aims at developing computationally efficient method for the design optimization of load-sharing systems. In the case of designing a mechanical system with some reliability requirements, especially parallel or mixed systems, the current system reliability evaluation method assumes component's failure probability being constant. For real world applications, this may not be true, as the failure of one component will increase the failure probability for the remaining components when the system load gets redistributed. Primary applications include aerospace systems, bridges, automobiles, etc. In our approach, we consider the dynamic nature of component's failure probability in a load-sharing system. The failure of components are not independent to each other and the first failure may equal-likely happen on any component. The limit-state functions of components are built upon the Stress-Strength Interference theory. We use the Sequential Optimization and Reliability Assessment (SORA) method that decouples optimization from reliability analysis. The advantage of this method is that it allows flexibility in the use of optimization and reliability analysis methods. This methodology also allows the user to decide the optimal number of components required for the system with the given load characteristics. Finally, numerical examples are provided to verify the methodology. © 2016 Proceedings of the 2016 Industrial and systems Engineering Research Conference, ISERC 2016. All rights reserved.

关键词： Reliability analysis

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Managing technological vulnerability of urban dwellers: Analysis, trends, and solutions

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2020年第1期1卷 48-59页

作者： Robertson, Lindsay J. Munoz, Albert Michael, Katina SCIT University of Wollongong WollongongNSW2522 Australia Faculty of Business University of Wollongong WollongongNSW2522 Australia School for the Future of Innovation in Society School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287 United States

Urban dwellers are increasingly dependent on technological systems to supply goods and services essential for their way of life. Such dependence incurs vulnerability in situations when these goods and services are not available. The systems supplying these essentials are known to have many loci of failure. We consider the exposure of a technological system in terms of the number and type of loci of failure and present this as a metric of urban dweller vulnerability. Such a metric captures the vulnerability to the nonavailability of the specific goods or services provided by a particular technological system. By selecting from goods and services commonly required, this article identifies a representative range of essential goods and services provided to urban-dwelling individuals by technological systems, and examines the nature and extent of exposure associated with each system. Based upon the descriptions of these technological systems, this article classifies the contributors to each technological system’s exposure into a small number of categories. The analysis allows the inference of generalized approaches for reducing each category of exposure, and hence vulnerability. Thus, a theory of exposure can be used to inform engineering management approaches applicable to end-user vulnerability reduction, and to identify the feasibility of less exposed technological systems. © 2020 IEEE. All right reserved.

关键词： Risk assessment

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Light-Weight RetinaNet for Object Detection on Edge Devices

Light-Weight RetinaNet for Object Detection on Edge Devices

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Yixing Li Akshay Dua Fengbo Ren School of Computing Informatics and Decision Systems Engineering Arizona State University

ISBN: (数字)9781728155036

ISBN: (纸本)9781728155043

This paper aims at reducing computation for Retinanet, an mAP-30-tier network, to facilitate its practical deployment on edge devices for providing IoT-based object detection services. We first validate RetinaNet has the best FLOP-mAP trade-off among all mAP-30-tier network. Then, we propose a light-weight RetinaNet structure with effective computation- accuracy trade-off by only reducing FLOPs in computationally intensive layers. Compared with the most common way of trading off computation with accuracy-input image scaling, the proposed solution shows a consistently better FLOPs-mAP trade-off curve. Light-weight RetinaNet achieves a 0.3% mAP improvement at 1.8x FLOPs reduction point over the original RetinaNet, and gains 1.8x more energy-efficiency on an Intel Arria 10 FPGA accelerator in the context of edge computing. The proposed method potentially can help a wide range of the object detection applications to move closer to a preferred corner for a better runtime and accuracy, while enjoys more energy-efficient inference at the edge.

关键词：

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Boost-R: Gradient boosted trees for recurrence data

arXiv

引用

arXiv 2021年

作者： Liu, Xiao Pan, Rong Department of Industrial Engineering University of Arkansas University of Arkansas United States School of Computing Informatics Decision Systems Engineering Arizona State University United States

Recurrence data arise from multi-disciplinary domains spanning reliability, cyber security, healthcare, online retailing, etc. This paper investigates an additive-tree-based approach, known as Boost-R (Boosting for Recurrence Data), for recurrent event data with both static and dynamic features. Boost-R constructs an ensemble of gradient boosted additive trees to estimate the cumulative intensity function of the recurrent event process, where a new tree is added to the ensemble by minimizing the regularized L2 distance between the observed and predicted cumulative intensity. Unlike conventional regression trees, a time-dependent function is constructed by Boost-R on each tree leaf. The sum of these functions, from multiple trees, yields the ensemble estimator of the cumulative intensity. The divide-and-conquer nature of tree-based methods is appealing when hidden sub-populations exist within a heterogeneous population. The non-parametric nature of regression trees helps to avoid parametric assumptions on the complex interactions between event processes and features. Critical insights and advantages of Boost-R are investigated through comprehensive numerical examples. Datasets and computer code of Boost-R are made available on GitHub. To our best knowledge, Boost-R is the first gradient boosted additive-tree-based approach for modeling large-scale recurrent event data with both static and dynamic feature information. © 2021, CC BY.

关键词： Additives

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Determining optimal fuel strategies under uncertainty

Determining optimal fuel strategies under uncertainty

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2020 Institute of Industrial and systems Engineers Annual Conference and Expo, IISE 2020

作者： Edwards, Dominiqueca Davis, Lauren Mirchandani, Pitu Department of Industrial and Systems Engineering North Carolina A&T State University GreensboroNC United States School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ United States

ISBN: (纸本)9781713827818

In preparation for a hurricane, individuals affected often are required to evacuate. Evacuations can cause high increases in the demand for gasoline fuel. The purpose of this research is to determine alternate delivery locations and times for fuel vessels scheduled to arrive at a port that is impacted by an approaching hurricane. Motivated by the impact of Hurricane Irma in Florida, we develop a multi-period stochastic scheduling model that incorporates hurricane advisories, scheduled fuel deliveries, maritime port storage capacity and vessel capacity in a maritime port network. Our model determines the best delivery schedule based on multiple objectives. The first minimizes total unmet demand at each port. The second objective captures inequity in unmet demand across the port network. We conduct a numerical study based on fuel delivery data from maritime ports in Florida. The results show we are able to determine alternative delivery schedules for each vessel. One of the key findings is that the port availability is the driving factor in determining the vessel schedules. © Proceedings of the 2020 IISE Annual. All Rights Reserved.

关键词： Hurricanes

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