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Conference on Technologies for Sustainability (Sustech)

作者： Sohini Roy Harish Chandrasekaran Anamitra Pal Arunabha Sen School of Computing Informatics and Decision System Engineering School of Electrical Computer and Energy Engineering Arizona State University Arizona USA

ISBN: (数字)9781728131085

ISBN: (纸本)9781728131092

The reliable and resilient operation of the smart grid necessitates a clear understanding of the intra-and-inter dependencies of its power and communication systems. This understanding can only be achieved by accurately depicting the interactions between the different components of these two systems. This paper presents a model, called modified implicative interdependency model (MIIM), for capturing these interactions. Data obtained from a power utility in the U.S. Southwest is used to ensure the validity of the model. The performance of the model for a specific power system application namely, state estimation, is demonstrated using the IEEE 118-bus system. The results indicate that the proposed model is more accurate than its predecessor, the implicative interdependency model (IIM) [1], in predicting the system state in case of failures in the power and/or communication systems.

关键词： Substations Phasor measurement units Logic gates Bandwidth Servers Smart grids SONET

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Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

Copula Guided Parallel Gibbs Sampling for Nonparametric and ...

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International Conference on Data engineering

作者： Lihui Lin Yanghui Rao Haoran Xie Raymond Y. K. Lau Jian Yin Fu Lee Wang Qing Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Lingnan University New Territories Hong Kong SAR Department of Information Systems City University of Hong Kong Kowloon Hong Kong SAR Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangzhou China School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong SAR

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.

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User-centered requirements for patient fluid intake monitoring support 64th

User-centered requirements for patient fluid intake monitori...

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64th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2020

作者： Bass, Ellen J. Abbate, Andrew J. Noaiseh, Yaman Dimaria-Ghalili, Rose Ann College of Computing & Informatics United States College of Nursing & Health Professions United States School of Biomedical Engineering Science & Health Systems Drexel University PhiladelphiaPA United States

There is a need to support patients with monitoring liquid intake. This work addresses development of requirements for real-time and historical displays and reports with respect to fluid consumption as well as alerts based on critical clinical thresholds. We conducted focus groups with registered nurses and registered dietitians in order to identify the information needs and alerting criteria to support fluid consumption measurement. This paper presents results of the focus group data analysis and the related requirements resulting from the analysis. © 2020 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Ergonomics

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Modulated Predictive Current Control of NPC Converter-Based PMSG Wind Energy System 12

Modulated Predictive Current Control of NPC Converter-Based ...

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12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020

作者： Yaramasu, Venkata Milev, Kristiyan Dekka, Apparao Rodriguez, Jose Northern Arizona University School of Informatics Computing and Cyber Systems Flagstaff United States Lakehead University Department of Electrical Engineering Thunder Bay Canada Universidad Andres Bello Faculty of Engineering Santiago Chile

ISBN: (纸本)9781728158266

In this paper, an efficient modulated model predictive current control of back-to-back connected neutral-point clamped (NPC) converter in direct-driven surface-mounted permanent magnet synchronous generator-based variable-speed wind energy conversion system is presented. The proposed control scheme fulfils the wind energy requirements such as maximum power point tracking, control of neutral-point voltage and grid reactive power regulation through the control of generator and grid currents with fast transient response, smooth steady-state and fixed switching frequency operation simultaneously. The three voltage vectors corresponding to the optimal cost functions of NPC rectifier and inverter are synthesized by the space vector modulation with active control of dwell times to balance the DC capacitors voltage. To evaluate the proposed control scheme, dynamic simulation results are presented for a 3 MW and 3000 V wind turbine under varying wind speed conditions. © 2020 IEEE.

关键词： Model predictive control

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Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts

arXiv

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arXiv 2020年

作者： Campbell, Joseph Yamane, Katsu School of Computing Informatics and Decision Systems Engineering Arizona State University Honda Research Institute United States Honda Research Institute United States

This paper presents a learning-from-demonstration (LfD) framework for teaching human-robot social interactions that involve whole-body haptic interaction, i.e. direct human-robot contact over the full robot body. The performance of existing LfD frameworks suffers in such interactions due to the high dimensionality and spatiotemporal sparsity of the demonstration data. We show that by leveraging this sparsity, we can reduce the data dimensionality without incurring a significant accuracy penalty, and introduce three strategies for doing so. By combining these techniques with an LfD framework for learning multimodal human-robot interactions, we can model the spatiotemporal relationship between the tactile and kinesthetic information during whole-body haptic interactions. Using a teleoperated bimanual robot equipped with 61 force sensors, we experimentally demonstrate that a model trained with 121 sample hugs from 4 participants generalizes well to unseen inputs and human partners. Copyright © 2020, The Authors. All rights reserved.

关键词： Human robot interaction

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Leveraging GPT-4 for Food Effect Summarization to Enhance Product-Specific Guidance Development via Iterative Prompting

arXiv

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arXiv 2023年

作者： Shi, Yiwen Ren, Ping Wang, Jing Han, Biao ValizadehAslani, Taha Agbavor, Felix Zhang, Yi Hu, Meng Zhao, Liang Liang, Hualou College of Computing and Informatics Drexel University PhiladelphiaPA United States Office of Research and Standards Office of Generic Drugs Center for Drug Evaluation and Research United States Food and Drug Administration Silver SpringMD United States Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States School of Biomedical Engineering Science and Health Systems Drexel University PhiladelphiaPA United States

Food effect summarization from New Drug Application (NDA) is an essential component of product-specific guidance (PSG) development and assessment, which provides the basis of recommendations for fasting and fed bioequivalence studies to guide the pharmaceutical industry for developing generic drug products. However, manual summarization of food effect from extensive drug application review documents is time-consuming. Therefore, there is a need to develop automated methods to generate food effect summary. Recent advances in natural language processing (NLP), particularly large language models (LLMs) such as ChatGPT and GPT-4, have demonstrated great potential in improving the effectiveness of automated text summarization, but its ability with regard to the accuracy in summarizing food effect for PSG assessment remains unclear. In this study, we introduce a simple yet effective approach, iterative prompting, which allows one to interact with ChatGPT or GPT-4 more effectively and efficiently through multi-turn interaction. Specifically, we propose a three-turn iterative prompting approach to food effect summarization in which the keyword-focused and length-controlled prompts are respectively provided in consecutive turns to refine the quality of the generated summary. We conduct a series of extensive evaluations, ranging from automated metrics to FDA professionals and even evaluation by GPT-4, on 100 NDA review documents selected over the past five years. We observe that the summary quality is progressively improved throughout the iterative prompting process. Moreover, we find that GPT-4 performs better than ChatGPT, as evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%). Importantly, all the FDA professionals unanimously rated that 85% of the summaries generated by GPT-4 are factually consistent with the golden reference summary, a finding further supported by GPT-4 rating of 72% consistency. Taken together, these results strongly suggest a great pot

关键词： Iterative methods

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Learning hierarchical behavior and motion planning for autonomous driving

arXiv

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arXiv 2020年

作者： Wang, Jingke Wang, Yue Zhang, Dongkun Yang, Yezhou Xiong, Rong State Key Laboratory of Industrial Control and Technology Zhejiang University Hangzhou China School of Computing Informatics and Decision Systems Engineering Arizona State University

Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learningbased solution. Due to the coupled action space of behavior and motion, it is challenging to solve HBMP problem using reinforcement learning (RL) for long-horizon driving tasks. We transform HBMP problem by integrating a classical samplingbased motion planner, of which the optimal cost is regarded as the rewards for high-level behavior learning. As a result, this formulation reduces action space and diversifies the rewards without losing the optimality of HBMP. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment, so that models trained in a fast event-based simulator, SUMO, can be used to initialize and accelerate the RL training in a dynamics based simulator, CARLA. Experimental results demonstrate the effectiveness of the method. Besides, the model is successfully transferred to the real-world, validating the generalization capability. Copyright © 2020, The Authors. All rights reserved.

关键词： Motion planning

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Supplementary Document for Fast Proximal Gradient Descent for A Class of Non-convex and Non-smooth Sparse Learning Problems 35

Supplementary Document for Fast Proximal Gradient Descent fo...

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35th Conference on Uncertainty in Artificial Intelligence, UAI 2019

作者： Yang, Yingzhen Yu, Jiahui School of Computing Informatics and Decision Systems Engineering Arizona State University United States Beckman Institute University of Illinois at Urbana-Champaign United States

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Gemnet: Group Equivariant Mesh Convolutional Neural Network

SSRN

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SSRN 2022年

作者： Afzal, Muhammad Kamran Zang, Yu Zeb, Adnan Afzal, H.M. Rehan Liu, Weiquan Wang, Cheng Li, Jonathan Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University 422 Siming South Road Xiamen361005 China College of Engineering Southern University of Science and Technology Shenzhen China School of Electrical Engineering and Computing University of Newcastle CallaghanNSW2308 Australia Departments of Geography and Environmental Management and Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Recent convolutional neural networks (CNNs) in geometric deep-learning for 3D meshes are inadequate in a natural generalisation of CNNs that reduces sample complexity by exploiting symmetries. We propose a novel Group Equivariant Mesh Convolutional Neural Network (GEMnet) using group convolutional layers that exploit a higher degree of weight sharing than the traditional convolution layers, enhancing the expressive ability of the network without fostering additional parameters. Implementing GEMnet layers is easy, with nominal computational overhead for discrete groups produced by translations, rotations, and reflections. Our novel approach operates directly on 3D mesh data for more discriminant feature maps, exhibiting notable improvements across prior stateof-the-art methods. Employing a variety of benchmark datasets, i.e., SHREC with Split 16 & 10 reached 99.83% & 99.33%, Cubes gained 99.17%, and ModelNet40 datasets achieved 95.50% accuracies, respectively, our method surpasses traditional methods for triangular meshes in a classification task. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Critical avalanches of susceptible-infected-susceptible dynamics in finite networks

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Physical Review E 2023年第2期107卷 024310-024310页

作者： Daniele Notarmuzi Alessandro Flammini Claudio Castellano Filippo Radicchi Institut für Theoretische Physik TU Wien Wiedner Hauptstraβe 8-10 A-1040 Wien Austria Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA Istituto dei Sistemi Complessi (ISC-CNR) Via dei Taurini 19 I-00185 Rome Italy Centro Ricerche Enrico Fermi Piazza del Viminale 1 00184 Rome Italy

We investigate the avalanche temporal statistics of the susceptible-infected-susceptible (SIS) model when the dynamics is critical and takes place on finite random networks. By considering numerical simulations on annealed topologies we show that the survival probability always exhibits three distinct dynamical regimes. Size-dependent crossover timescales separating them scale differently for homogeneous and for heterogeneous networks. The phenomenology can be qualitatively understood based on known features of the SIS dynamics on networks. A fully quantitative approach based on Langevin theory is shown to perfectly reproduce the results for homogeneous networks, while failing in the heterogeneous case. The analysis is extended to quenched random networks, which behave in agreement with the annealed case for strongly homogeneous and strongly heterogeneous networks.

关键词： Continuous phase transition Epidemic Network phase transitions Spreading

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