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arXiv 2021年

作者： Zhang, Junyu Wang, Mengdi Hong, Mingyi Zhang, Shuzhong Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Electrical and Computer Engineering Princeton University United States Department of Electrical and Computer Engineering University of Minnesota United States Department of Industrial and Systems Engineering University of Minnesota United States

We consider the convex-concave saddle point problem minx maxy Φ(x, y), where the decision variables x and/or y are subject to certain multi-block structure and affine coupling constraints, and Φ(x, y) possesses certain separable structure. Although the minimization counterpart of this problem has been widely studied under the topics of ADMM, this minimax problem is rarely investigated. In this paper, a convenient notion of ∈-saddle point is proposed, under which the convergence rate of several proposed algorithms are analyzed. When only one of x and y has multiple blocks and affine constraint, several natural extensions of ADMM are proposed to solve the problem. Depending on the number of blocks and the level of smoothness, O(1/T) or O(1/√T) convergence rates are derived for our algorithms. When both x and y have multiple blocks and affine constraints, a new algorithm called Extra-Gradient Method of Multipliers (EGMM) is proposed. Under desirable smoothness conditions, an O(1/T) rate of convergence can be guaranteed regardless of the number of blocks in x and y. An in-depth comparison between EGMM (fully primal-dual method) and ADMM (approximate dual method) is made over the multi-block optimization problems to illustrate the advantage of the EGMM. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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Impact of Aviation Electrification on Airports: Flight Scheduling and Charging

SSRN

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SSRN 2022年

作者： Hou, Boya Bose, Subhonmesh Marla, Lavanya Haran, Kiruba College of Engineering Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Industrial and Enterprise Systems Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Electrification can help to reduce the carbon footprint of aviation. The transition away from the jet fuel-powered conventional airplane towards battery-powered electrified aircraft will impose extra charging requirements on airports. In this paper, we first quantify the increase in energy demands at several airports across the United States (US), when commercial airline carriers partially deploy hybrid electric aircraft (HEA). We then illustrate that smart charging and minor modifications to flight schedules can substantially reduce peak power demands, and in turn the needs for grid infrastructure upgrade. Motivated by our data analysis, we then formulate an optimization problem for flight rescheduling that incorporates HEA charging considerations. This problem jointly decides flight schedules and charging profiles to manage airport congestion and peak power demands. We further consider mechanisms via which airlines and airports can negotiate HEA assignments using said optimization problem. Finally, we illustrate the efficacy of our formulation through a case study on the John F. Kennedy International Airport. © 2022, The Authors. All rights reserved.

关键词： Electric utilities

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Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

arXiv

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arXiv 2022年

作者： Huo, Zepeng Qian, Xiaoning Huang, Shuai Wang, Zhangyang Mortazavi, Bobak J. Computer Science & Engineering Department Texas A&M University College StationTX United States Electrical & Computer Engineering Department Texas A&M University College StationTX United States Industrial & Systems Engineering Department University of Washington SeattleWA United States Electrical & Computer Engineering Department University of Texas AustinTX United States

Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive. Training models with this imbalance rate (class density discrepancy) may lead to suboptimal prediction. Traditionally this problem is addressed through ad-hoc methods such as resampling or reweighting but performance in many cases is still limited. We propose a framework for training models for this imbalance issue: 1) we first decouple the feature extraction and classification process, adjusting training batches separately for each component to mitigate bias caused by class density discrepancy;2) we train the network with both a density-aware loss and a learnable cost matrix for misclassifications. We demonstrate our model's improved performance in real-world medical datasets (TOPCAT and MIMIC-III) to show improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselines in the domain. © 2022, CC BY.

关键词： Medical computing

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Integrated Modeling and Optimal Operation of Multi-Energy System for Coastal Community

Integrated Modeling and Optimal Operation of Multi-Energy Sy...

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IEEE International Conference on Electro-Information Technology

作者： Yang Chen Jun Chen Chenang Liu Guodong Liu Maximiliano Ferrari Aditya Sundararajan Department of Industrial and Systems Engineering North Carolina Agricultural and Technical State University Greensboro USA Department of Electrical and Computer Engineering Oakland University Rochester MI USA School of Industrial Engineeering & Management Oklahoma State University Stillwater USA Electrification and Energy Infrastructures Division Oak Ridge National Laboratory Oak Ridge TN USA

Focusing on remote, isolated, and underserved communities, a multi-energy system is designed in this research which is capable of utilizing different energy sources in a more coordinated and energy-efficient way to support various demands, such as fresh water, electricity, hydrogen, thermal demand, etc. The energy sources considered are renewables (wind, solar, marine) and natural gas. The energy conversion process includes water desalination, gas combustion, water electrolyzation, and different types of storage (hydrogen tank, electricity, thermal, etc.) are designed to serve as buffers in supply-demand balancing. Sets of experiments are designed to demonstrate the effectiveness of the proposed operating model and investigate the impact of uncertainties from renewable generations and demands.

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Correction: Sustainable regional rail system pricing using a machine learning-based optimization approach

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Annals of Operations Research 2023年第1期332卷 1315-1316页

作者： Gokasar, Ilgin Karakurt, Ahmet Kuvvetli, Yusuf Deveci, Muhammet Delen, Dursun Pamucar, Dragan Department of Civil Engineering Bogazici University Bebek Turkey Department of Industrial Engineering Cukurova University Adana Turkey Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Turkey Earth Science and Engineering Royal School of Mines Imperial College London London UK Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Management Science and Information Systems Spears School of Business Oklahoma State University Stillwater USA Department of Industrial Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer Turkey Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia College of Engineering Yuan Ze University Taoyuan City Taiwan (R.O.C.) School of Engineering and Technology Sunway University Selangor Malaysia

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Content Moderation and the Formation of Online Communities: A Theoretical Framework

arXiv

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arXiv 2023年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: it restricts speech on a platform. However, when user participation decisions are taken into account, its effects can be more subtle - and counter-intuitive. For example, our model can straightforwardly demonstrate how moderation policies may increase participation and diversify content available on the platform. In our analysis, we explore a rich set of interconnected phenomena related to content moderation in online communities. We first characterize the effectiveness of a natural class of moderation policies for creating and sustaining communities. Building on this, we explore how resource-limited or ideological platforms might set policies, how communities are affected by differing levels of personalization, and competition between platforms. Our model provides a vocabulary and mathematically tractable framework for analyzing platform decisions about content moderation. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

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RETRACTED ARTICLE: Sustainable regional rail system pricing using a machine learning-based optimization approach

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Annals of Operations Research 2023年第1 SUPPLEMENT期337卷 1-2页

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A Multi-Cut L-Shaped Decomposition Algorithm for Stochastic Security Constrained Unit Commitment with Renewable Generation

SSRN

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SSRN 2022年

作者： Tolooie, Ali Wu, Hongyu Palani, Ananth Sinha, Ashesh Liu, Xuebo Industrial and Manufacturing Systems Engineering Kansas State University ManhattanKS66506 United States Mike Wiegers Department of Electrical and Computer Engineering Kansas State University ManhattanKS66506 United States

Two-stage stochastic security-constrained unit commitment (S-SCUC) has been used by independent system operators to manage the uncertainty attributed to an increasing penetration level of renewable energy. However, computational complexity has been widely regarded as a grand challenge in solving large-scale S-SCUC problems. This paper applies an L-shaped decomposition algorithm to solve the problem by incorporating two types of cuts, i.e., optimality and feasibility cuts. We mathematically prove that the S-SCUC problem has relatively complete recourse by leveraging the structural characteristics of the problem and the domain knowledge. A computationally efficient initial solution is further proposed to accelerate the proposed algorithm. Numerical tests are conducted on an IEEE 118-bus system with variable wind generation. Numerical results for single-cut and multi-cut approaches based on two different cases, i.e., with and without the proposed initial solution, are compared. The results show that, by using an initial solution in the proposed decomposition algorithm, a significant number of cuts required to add to the master problem based on the decomposition algorithm can be eliminated. The improved algorithm can reduce a significant number of linear programming problems that have to be solved in the second stage for generating Bender cuts, which leads to improved computational efficiency. © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

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Hey robot, which way are you going? Nonverbal motion legibility cues for human-robot spatial interaction

arXiv

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arXiv 2021年

作者： Hetherington, Nicholas J. Croft, Elizabeth A. van der Loos, H.F. Machiel The Department of Mechanical Engineering University of British Columbia Canada The Departments of Mechanical and Aerospace Engineering and Electrical and Computer Systems Engineering Monash University Australia

Mobile robots have recently been deployed in public spaces such as shopping malls, airports, and urban sidewalks. Most of these robots are designed with human-aware motion planning capabilities but are not designed to communicate with pedestrians. Pedestrians that encounter these robots without prior understanding of the robots' behaviour can experience discomfort, confusion, and delayed social acceptance. In this work we designed and evaluated nonverbal robot motion legibility cues, which communicate a mobile robot's motion intention to pedestrians. We compared a motion legibility cue using Projected Arrows to one using Flashing Lights. We designed the cues to communicate path information, goal information, or both, and explored different Robot Movement Scenarios. We conducted an online user study with 229 participants using videos of the motion legibility cues. Our results show that the absence of cues was not socially acceptable, and that Projected Arrows were the more socially acceptable cue in most experimental conditions. We conclude that the presence and choice of motion legibility cues can positively influence robots' acceptance and successful deployment in public spaces. © 2021, CC BY-NC-ND.

关键词： Robot programming

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Cnn-Based Framework for In-Situ Root Length, Diameter, and Color Estimation in Minirhizotron Images

SSRN

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SSRN 2024年

作者： Khoroshevsky, Faina Zhou, Kaining Bar-Hillel, Aharon Hadar, Ofer Rachmilevith, Shimon Ephrath, Jhonathan E. Lazarovitch, Naftali Edan, Yael Department of Industrial Engineering and Management Beer Sheva Israel The Jacob Blaustein Center for Scientific Cooperation The Jacob Blaustein Institutes for Desert Research Sede Boqer Israel Department of Communication Systems Engineering School of Electrical and Computer Engineering Beer Sheva Israel French Associates Institute for Agriculture and Biotechnology of Drylands The Jacob Blaustein Institutes for Desert Research Sede Boqer Israel Ben-Gurion University of the Negev Israel

A framework to estimate root phenotypes from minirhizotron (MR) imagery, specifically, length, diameter, and color based on convolutional neural networks (CNN), is presented. The proposed framework uses a set of reusable sub-network modules to compose different networks for both object (root) detection and attribute (phenotype) estimation for per-root and per-image root phenotyping tasks. The suggested framework provides a solution without requiring root segmentation. In per-root phenotyping, the first step involves detecting the roots in the image, and subsequently, the phenotypes of each detected root are estimated. In per-image root phenotyping, an aggregated per-image phenotype value is estimated, including total root length (TRL), mean root diameter (RD) and white root percentage. In this paper we demonstrate the use of both regression-based and objects' points-detection-based variations for both per-root and per-image root phenotype estimation, resulting in five network architectures, aimed to provide automatic solutions for non-destructive root analysis. These networks include two architectures previously used for TRL estimation (now used for mean root diameter and white root percentage estimation) and three new network architectures. The proposed framework is demonstrated on an annotated grapevine root dataset, made publicly available as part of this paper, containing 531 images. All images acquired in situ, using an MR system, annotated with Rootfly software for these tasks. For the detected individual root, regression-based modules yield errors of 8.8%, 15.5%, and 23.5% for color, length, and diameter respectively. The points-detection-based modules result in errors of 9.1%, 14.9%, and 25.0% for the same parameters. The obtained image level estimates result with errors of 11.5%-16.5% for white root color percentage, 13.7%- 16.0% for TRL, and 17.6%-22.1% for mean RD. We demonstrate that the per-root estimations of diameter and color, obtained with the new s

关键词： Color

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