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作者： Pan, Rong Saleh, Moein School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States PayPal San Jose United States

Dynamic response systems are often found in science, engineering, and medical applications, but the discussion on experimental design for such a system is relatively rare in literature. For an experimenter, designing such experiments requires making decisions on (1) when or where to take response measurements along the dynamic variable and (2) how to choose the combination of experimental factors and their levels. The first consideration is unique for such experiments, especially when the measurement cost is high. In this paper, we present a design approach through the mixed-effect linear model, which is based on a hierarchical B-spline function for the dynamic response. We develop several theorems that can assist in finding a statistically efficient sampling plan and propose an algorithm for searching the D-optimal design of a dynamic response system. © 2019 John Wiley & Sons, Ltd.

关键词： Dynamic response

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Coupling Weap and Leap Models Using Interaction Modeling

Coupling Weap and Leap Models Using Interaction Modeling

引用

2020 Spring Simulation Conference, SpringSim 2020

作者： Fard, Mostafa D. Sarjoughian, Hessam S. Arizona State University Arizona Center for Integrative Modeling Simulation School of Computing Informatics and Decision Systems Engineering TempeAZ United States

ISBN: (纸本)9781565553705

It is useful to model the interactions between the water and energy models separately. A coupled water and energy model using an interaction model lends itself to developing flexible, yet rigorous predictive simulation studies. A WEAP-LEAP interaction model is developed based on the Knowledge Interchange Broker modeling approach. A RESTful simulation framework is developed for coupling the Componentized-WEAP and Componentized-LEAP systems. Every interaction model is defined to have a set of modules where each module has its input and output ports with data transformation schemes. The input and output ports of the Componentized-WEAP and Componentized-LEAP models are coupled with those of the interaction model. A cyclic, synchronous execution protocol is defined for concurrent, bidirectional data transformations. The computational efficiency of this loosely coupled water-energy framework is acceptable compared with the tightly WEAP-LEAP system. This proposed framework shows understanding the water-energy system nexus is advantageous using interaction modeling. © 2020 SCS.

关键词： Computational efficiency

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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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ENHANCING 3T RETINOTOPIC MAPS USING DIFFEOMORPHIC REGISTRATION

arXiv

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arXiv 2024年

作者： Jalili-Mallak, Negar Tu, Yanshuai Lu, Zhong-Lin Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Division of Arts and Sciences NYU Shanghai Shanghai China Center for Neural Science Department of Psychology New York University New York United States NYU-ECNU Institute of Brain and Cognitive Science NYU Shanghai Shanghai China

Retinotopic mapping aims to uncover the relationship between visual stimuli on the retina and neural responses on the visual cortical surface. This study advances retinotopic mapping by applying diffeomorphic registration to the 3T NYU retinotopy dataset, encompassing analyze-PRF and mrVista data. Diffeomorphic Registration for Retinotopic Maps (DRRM) quantifies the diffeomorphic condition, ensuring accurate alignment of retinotopic maps without topological violations. Leveraging the Beltrami coefficient and topological condition, DRRM significantly enhances retinotopic map accuracy. Evaluation against existing methods demonstrates DRRM’s superiority on various datasets, including 3T and 7T retinotopy data. The application of diffeomorphic registration improves the interpretability of low-quality retinotopic maps, holding promise for clinical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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RECONSTRUCTING RETINAL VISUAL IMAGES FROM 3T FMRI DATA ENHANCED BY UNSUPERVISED LEARNING

arXiv

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arXiv 2024年

作者： Xiong, Yujian Zhu, Wenhui Lu, Zhong-Lin Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Division of Arts and Sciences NYU Shanghai Shanghai China Center for Neural Science Department of Psychology New York University New York United States NYU-ECNU Institute of Brain and Cognitive Science NYU Shanghai Shanghai China

The reconstruction of human visual inputs from brain activity, particularly through functional Magnetic Resonance Imaging (fMRI), holds promising avenues for unraveling the mechanisms of the human visual system. Despite the significant strides made by deep learning methods in improving the quality and interpretability of visual reconstruction, there remains a substantial demand for high-quality, long-duration, subject-specific 7-Tesla fMRI experiments. The challenge arises in integrating diverse smaller 3-Tesla datasets or accommodating new subjects with brief and low-quality fMRI scans. In response to these constraints, we propose a novel framework that generates enhanced 3T fMRI data through an unsupervised Generative Adversarial Network (GAN), leveraging unpaired training across two distinct fMRI datasets in 7T and 3T, respectively. This approach aims to overcome the limitations of the scarcity of high-quality 7-Tesla data and the challenges associated with brief and low-quality scans in 3-Tesla experiments. In this paper, we demonstrate the reconstruction capabilities of the enhanced 3T fMRI data, highlighting its proficiency in generating superior input visual images compared to data-intensive methods trained and tested on a single subject. © 2024, CC BY.

关键词： Generative adversarial networks

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Evaluating reliability of complex systems for Predictive maintenance

Evaluating reliability of complex systems for Predictive mai...

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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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- D3WA+ - A case study of XAIP in a model acquisition task for dialogue planning 30

- D3WA+ - A case study of XAIP in a model acquisition task f...

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30th International Conference on Automated Planning and Scheduling, ICAPS 2020

作者： Sreedharan, Sarath Chakraborti, Tathagata Muise, Christian Khazaeni, Yasaman Kambhampati, Subbarao School of Computing Informatics and Decision Systems Engineering Arizona State University United States IBM Research AI School of Computing Queen's University

Recently, the D3WA system was proposed as a paradigm shift in how complex goal-oriented dialogue agents can be specified by taking a declarative view of design. However, it turns out actual users of the system have a hard time evolving their mental model and grasping the imperative consequences of declarative design. In this paper, we adopt ideas from existing works in the field of Explainable AI Planning (XAIP) to provide guidance to the dialogue designer during the model acquisition process. We will highlight in the course of this discussion how the setting presents unique challenges to the XAIP setting, including having to deal with the user persona of a domain modeler rather than the end-user of the system, and consequently having to deal with the unsolvability of models in addition to explaining generated plans. Quickview http://***/d3wa-xaip. Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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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...

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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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Determining optimal fuel strategies under uncertainty

Determining optimal fuel strategies under uncertainty

引用

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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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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