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arXiv 2022年

作者： Rastegar, Reza Center of Excellence for Data Science and Modeling Occidental Petroleum Corporation HoustonTX77046 United States Departments of Mathematics and Engineering University of Tulsa OK74104 United States

In this note, we present several inequalities in the context of pattern containment, utilizing elementary applications of the Fortuin-Kasteleyn-Ginibre (FKG) inequality and Shearer’s *** Codes 05A05, 05D40 © 202...

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Usage Coordination Utilizing Flexible Contracts in Free-floating Car Sharing

Usage Coordination Utilizing Flexible Contracts in Free-floa...

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IEEE International Conference on Agents (ICA)

作者： Yuto Iwata Shigeo Matsubara Kyoto University Kyoto Japan Center for Mathematical Modeling and Data Science Osaka University Toyonaka Japan

ISBN: (纸本)9781665407175

We propose a novel distributed user-car matching method based on a contract between users to mitigate the imbalance problem between vehicle distribution and demand in free-floating car sharing. The previous contract-based regulation method assumes that contracts are binding. However, car-sharing systems users are in dynamic environments, and such binding fails to accommodate future events. Therefore, to overcome this drawback, we introduce a leveled-commitment contract into contract-based coordination among drop-off and pick-up users. In our method, an auction is conducted for drop-off users’ intended drop-off locations, and users are allowed to decommit from contracts by paying penalties. The amount of penalty is determined by considering the trade-off between the sunk cost due to movement and social surplus. We thoroughly evaluated the proposed method with a baseline method on a free-floating car-sharing simulator. The results show that it achieved a higher social surplus than the existing method when demand frequently occurred.

关键词： Costs Conferences Regulation Automobiles Vehicle dynamics Contracts

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Adaptive Budget Allocation for Cooperative Task Solving in Crowdsourcing

Adaptive Budget Allocation for Cooperative Task Solving in C...

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IEEE International Conference on Big data

作者： Yuya Itoh Shigeo Matsubara Kyoto University Kyoto Japan Center for Mathematical Modeling and Data Science Osaka University Toyonaka Japan

ISBN: (纸本)9781665445993

This paper proposes a new budget allocation method for crowdsourced sequential tasks. Complex tasks are decomposed into interdependent subtasks that can be executed cooperatively by individual workers. In the case of sequential tasks, the output of a task becomes the input to another task, and the quality of the final artifact thus depends on the qualities of the preceding tasks. In crowdsourcing, the abilities of workers are often difficult to learn in advance. Thus, a fixed budget allocation for the component subtasks cannot deal with a dynamic situation. Also, it is often difficult for a requester to accurately evaluate the quality of intermediate artifacts, which can result in budget misallocation and waste. To overcome these difficulties, we formalize the budget allocation problem as a partially observable Markov decision process (POMDP) by introducing quality evaluation actions and developing a contingent budget allocation method, which generates a conditional plan given uncertainty about the intermediate states and action effects. Experimental simulation results show that the proposed method can find a solution in a reasonable time and improve the quality of the final artifact.

关键词： Crowdsourcing Uncertainty Simulation Conferences Big data Markov processes Dynamic scheduling

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Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems

arXiv

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arXiv 2023年

作者： Gosea, Ion Victor Gugercin, Serkan Werner, Steffen W.R. Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstr. 1 Magdeburg39106 Germany Department of Mathematics and Division of Computational Modeling and Data Analytics Academy of Data Science Virginia Tech BlacksburgVA24061 United States Courant Institute of Mathematical Sciences New York University New YorkNY10012 United States

An essential tool in data-driven modeling of dynamical systems from frequency response measurements is the barycentric form of the underlying rational transfer function. In this work, we propose structured barycentric forms for modeling dynamical systems with second-order time derivatives using their frequency domain input-output data. By imposing a set of interpolation conditions, the systems’ transfer functions are rewritten in different barycentric forms using different parametrizations. Loewner-like algorithms are developed for the explicit computation of second-order systems from data based on the developed barycentric forms. Numerical experiments show the performance of these new structured data driven modeling methods compared to other interpolation-based data-driven modeling techniques from the *** Codes 41A20, 65D05, 93B15, 93C05, 93C80 Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Structured vector fitting framework for mechanical systems

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IFAC-PapersOnLine 2022年第20期55卷 163-168页

作者： Steffen W.R. Werner Ion Victor Gosea Serkan Gugercin Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstr. 1 39106 Magdeburg Germany Department of Mathematics and Division of Computational Modeling and Data Analytics Academy of Data Science Virginia Tech Blacksburg VA 24061 USA

We develop a structure-preserving formulation of the data-driven vector fitting algorithm for modally damped mechanical systems. Using the structured pole-residue form of the transfer function of modally damped second-order systems, we propose two potential, structured extensions of the barycentric formula for system transfer functions. Integrating these new forms within the classical vector fitting algorithm leads to the formulation of two new algorithms. These allow the computation of modally damped mechanical systems from data in a least-squares fashion, where the learned model is guaranteed to have the desired structure. We test the proposed algorithms on two benchmark models.

关键词： data-driven modeling mechanical systems reduced-order modeling vector fitting least-squares fit barycentric forms

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The 30m-NDVI-based Alpine Grassland Changes and Climate Impacts in the Three-River Headwaters Region on the Qinghai-Tibet Plateau from 1990 to 2018

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Journal of Resources and Ecology 2022年第2期13卷 186-195页

作者： SUN Ziyu WANG Junbang National Ecosystem Science Data Center Key Laboratory of Ecosystem Network Observation and ModelingInstitute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

The response of long-term vegetation changes and climate change has been a hot topic in recent ***,a Landsat-based fusion model was developed and used to produce a dataset of normalized vegetation index(NDVI)for the Three-River Headwater region on the Qinghai-Tibet Plateau with a spatial resolution of 30 m and the time spanning the nearly 30 years from 1990 to *** this study,the NDVI was applied to an analysis of the spatial and temporal changes in the alpine grassland and the impacts from climate change using the Theil-Sen Median method and linear *** results showed that:(1)The regional mean NDVI was 0.39and showed a spatial pattern of decreasing from the southeast to the northwest in the recent three *** the three parks,the Lancang River Park had the highest NDVI(0.43),followed by the Yellow River Park(0.38)and Yangtze River Park(0.23).(2)An upward trending was found in the NDVI time series at a rate of 0.0031 yr^(-1)(R^(2)=0.62,P<0.01)over the whole period of 1990–*** increasing rate(0.00649 yr^(-1),R^(2)=0.71,P<0.01)in the latter period of 2005–2018 was nearly 2.3 times of that(0.00284 yr^(-1),R^(2)=0.31,P<0.01)in the previous period of1990–*** the latest periods,the three parks experienced rates that were 2.3 to 63 times the corresponding values in the early period.(3)The NDVI is correlated more positively with temperature than *** impacts of climate change decreased along with the coverage fraction from the higher,median and then lower *** climate change can explain 34%of the variability in the NDVI time series of the areas with a higher fraction of grassland coverage,while it was 31%for the median fraction and 20%for the lower *** study is the first to use the 30 m NDVI dataset spanning nearly 30 years to analyze the spatial and temporal variability and climate impacts in the alpine grasslands of the Three-River Headwater region of the Qinghai-Tibet *** results provide a

关键词： Normalized Difference Vegetation Index(NDVI) the Three-River Headwater region alpine grassland climate change

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Accelerating Fractional PINNs using Operational Matrices of Derivative

arXiv

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arXiv 2024年

作者： Taheri, Tayebeh Aghaei, Alireza Afzal Parand, Kourosh Department of Computer and Data Sciences Faculty of Mathematical Sciences Shahid Beheshti University G.C. Tehran Iran Department of Cognitive Modeling Institute for Cognitive and Brain Sciences Shahid Beheshti University G.C. Tehran Iran Department of Statistics and Actuarial Science University of Waterloo Waterloo Canada

This paper presents a novel operational matrix method to accelerate the training of fractional Physics-Informed Neural Networks (fPINNs). Our approach involves a non-uniform discretization of the fractional Caputo operator, facilitating swift computation of fractional derivatives within Caputo-type fractional differential problems with 0 © 2024, CC BY.

关键词： Differential equations

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IoT-Enabled Smart Manhole Management System for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Md Asif Imran S M Masfequier Rahman Swapno Gunjan Chhabra Keshav KaushiK Md. Arif Anzum Mahi Md Babul Islam Rezaul Haque Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring System improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city.

关键词： Surveillance Urban areas Robot vision systems Transforms Cameras Real-time systems Maintenance

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Contrastive Metric Learning for Lithium Super-ionic Conductor Screening

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SN Computer science 2022年第6期3卷 465页

作者： Zhang, Boyu Wang, Shuo Gao, Fuchang Institute for Modeling Collaboration and Innovation University of Idaho 875 Perimeter Dr MS 1122 Moscow 83844-1122 ID United States Institute for Interdisciplinary Data Sciences University of Idaho 875 Perimeter Dr MS 1122 Moscow 83844-1122 ID United States Department of Materials Science and Engineering University of Maryland College Park 20742 MD United States Department of Mathematics and Statistical Science University of Idaho 875 Perimeter Drive MS 1103 Moscow 83844-1103 ID United States

High-performance Li-ion battery significantly impacts modern society, and materials with high conductivity play critical roles in battery development. Machine learning (ML) technologies have rapidly changed the field in recent years. However, it is still challenging to predict the high conductors directly due to the lack of validated conductor samples. This paper presents a succinct but effective metric-learning framework for high conductor screening. The material structures are mapped to an optimized feature space using a Siamese network, and an instance-based method is used to classify the input sample. The experiments demonstrate that the proposed method could effectively extract knowledge from imbalanced data and has good performance and generalization ability. © 2022, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

关键词： Conductor screening Contrastive learning Graph neural network Material science Metric learning

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Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning

arXiv

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arXiv 2024年

作者： Li, Hanzhe Wang, Xiangxiang Feng, Yuan Qi, Yaqian Tian, Jingxiao Computer Engineering New York University NY United States Computer Science University of Texas at Arlington ArlingtonTX United States Interdisciplinary Data Science Duke University NC United States Quantitative Methods and Modeling Baruch Collegue CUNY 55 Lexington Ave NY United States Electrical and Computer Engineering San Diego State University SD United States

This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. However, because the cloud computing model is limited by distance, it can be problematic in environments where the quality of the Internet connection is not ideal for critical operations. Therefore, edge computing, as a distributed computing architecture, moves the location of processing applications, data and services from the central node of the network to the logical edge node of the network to reduce the dependence on cloud processing and analysis of data, and achieve near-end data processing and analysis. The combination of iot and edge computing can reduce latency, improve efficiency, and enhance security, thereby driving the development of intelligent systems. The paper also introduces the development of iot monitoring and control technology, the application of edge computing in iot monitoring and control, and the role of machine learning in data analysis and fault detection. Finally, the application and effect of intelligent Internet of Things monitoring and control system in industry, agriculture, medical and other fields are demonstrated through practical cases and experimental studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Edge computing

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